Miracle-dz/UniFAR-FTUs huggingface dataset:
A 10:1 unequal gysel power divider/combiner

This article introduces a modified Gysel power divider, providing high power division ratios, alongside the advantage of high power handling. To overcome fabrication limitations of high characteristic impedance line, caused by the anticipated high power division ratio, a coupled line structure with two shorts is used. This proposed structure makes closed‐form equations available for design and leads to a simple microstrip fabrication. A 10:1 unequal Gysel power divider is presented and fabricated as a design example and a satisfactory agreement between measurements and simulation results is verified. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2689–2692, 2016
Analysis of Moral Judgment on Reddit

Moral outrage has become synonymous with social media in recent years. However, the preponderance of academic analysis on social media websites has focused on hate speech and misinformation. This article focuses on analyzing moral judgments rendered on social media by capturing the moral judgments that are passed in the subreddit /r/AmITheAsshole on Reddit. Using the labels associated with each judgment, we train a classifier that can take a comment and determine whether it judges the user who made the original post to have positive or negative moral valence. Then, we employ human annotators to verify the performance of this classifier and use it to investigate an assortment of website traits surrounding moral judgments in ten other subreddits. Our analysis looks to answer three questions related to moral judgments and how these apply to different aspects of Reddit. We seek to determine whether moral valence impacts post scores, in which subreddit communities contain users with more negative moral valence, and whether gender and age play a role in moral judgments. Findings from our experiments show that users upvote posts more often when posts contain positive moral valence. We also find that certain subreddits, such as /r/confessions, attract users who tend to be judged more negatively. Finally, we found that men and older age were judged negatively more often.
Enteroendocrine System and Gut Barrier in Metabolic Disorders

With the continuous rise in the worldwide prevalence of obesity and type 2 diabetes, developing therapies regulating body weight and glycemia has become a matter of great concern. Among the current treatments, evidence now shows that the use of intestinal hormone analogs (e.g., GLP1 analogs and others) helps to control glycemia and reduces body weight. Indeed, intestinal endocrine cells produce a large variety of hormones regulating metabolism, including appetite, digestion, and glucose homeostasis. Herein, we discuss how the enteroendocrine system is affected by local environmental and metabolic signals. These signals include those arising from unbalanced diet, gut microbiota, and the host metabolic organs and their complex cross-talk with the intestinal barrier integrity.
Electrical Stability and Piezoresistive Sensing Performance of High Strain-Range Ultra-Stretchable CNT-Embedded Sensors

Highly flexible and stretchable sensors are becoming increasingly widespread due to their versatile applicability in human/robot monitoring sensors. Conductive polymeric composites have been regarded as potential candidates for such sensors, and carbon nanotubes (CNTs) are widely used to fabricate such composites. In the present study, CNT-embedded high flexible sensors were fabricated using a facile three-roll milling method, which mitigates the drawbacks of the conventional fabrication methods. CNTs content varied between 0.5 and 4.0 wt. %, and the percolation threshold range was obtained via conductivity/resistivity values of the fabricated sensors. Following this, the electrical stability of the sensors was examined against the various DC and AC signals. Furthermore, the fabricated sensors were stretched up to 500% strain, and their sensitivity against varying strain amplitudes was investigated in terms of the change in resistance and gauge factors. Lastly, the fabricated sensors were applied to human fingers for monitoring finger bending and releasing motions to validate their potential applications. The experimental results indicated that these sensors have a percolation threshold of around 2% CNTs content, and the sensors fabricated with 2 to 4% CNTs content showed measurable resistance changes against the applied strain amplitudes of 50–500%. Among these sensors, the sensor with 2% CNTs content showed the highest sensitivity in the studied strain range, exhibiting a resistance change and gauge factor of about 90% and 1.79 against 50% strain amplitude and about 18,500% and 37.07 against 500% strain amplitude, respectively. All these sensors also showed high sensitivity for finger motion detection, showing a resistance change of between 22 and 69%.
Inverse estimation method for internal defects based on surface stress of carbon-fiber-reinforced plastics using machine learning

Carbon-fiber-reinforced plastic (CFRP) is a composite material whose base material is plastic and reinforcement material is carbon fibers. CFRP is widely used in various fields for laminating prepregs. The laminated plate tends to sustain damage, such as delamination, fiber breakage, and base material breakage; hence, we must conduct high-precision and efficient nondestructive testing (NDT). Examples of NDT are ultrasonic examination, X-ray tomography, and infrared stress analysis. With most NDT methods, it is difficult to easily obtain detailed correct information of defects, such as their depth, position, and size. To solve this problem, we develop a machine-learning-aided inverse analysis model that predicts the spatial information of defects from the sum of the principal stresses on the surface calculated from the temperature change measured by infrared analysis, and we propose it as an alternative method to the existing damage analysis. Applying the proposed method to the simulated stress distributions of quasi-isotropic CFRP laminates with defects, the results showed over 99% success to recognize the detail information of defects. Additionally, we examine the properties of the dataset using a forward analysis model and a variational autoencoder. Our method with a convolutional neural network enables us to successfully estimate the information of defects at high speed. Experimental data can be applicable as well as the simulation results to our proposed method, and we believe our method will be a powerful supporting tool for the current NDT for CFRPs.
Radiomic texture feature descriptor to distinguish recurrent brain tumor from radiation necrosis using multimodal MRI

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can accurately distinguish rBT from RN. In this work, sophisticated radiomic texture features are used to distinguish rBT from RN on multimodal MRI for disease characterization. First, stochastic multiresolution radiomic descriptor that captures voxel-level textural and structural heterogeneity as well as intensity and histogram features are extracted. Subsequently, these features are used in a machine learning setting to characterize the rBT from RN from four sequences of the MRI with 155 imaging slices for 30 GBM cases (12 RN, 18 rBT). To reduce the bias in accuracy estimation our model is implemented using Leave-one-out crossvalidation (LOOCV) and stratified 5-fold cross-validation with a Random Forest classifier. Our model offers mean accuracy of 0.967 ± 0.180 for LOOCV and 0.933 ± 0.082 for stratified 5-fold cross-validation using multiresolution texture features for discrimination of rBT from RN in this study. Our findings suggest that sophisticated texture feature may offer better discrimination between rBT and RN in MRI compared to other works in the literature.
Toward Data Interoperability of Enterprise and Control Applications via The Industry 4.0 Asset Administration Shell

In a cyber-physical system, enterprise applications, control functions, and physical manufacturing assets are interconnected to seamlessly exchange information. However, the software and hardware often differ in terms of their data interfaces, data formats and semantics. Conventional enterprise applications usually employ file-based interfaces (e.g., Excel); control applications always support Ethernet-based protocols (e.g., OPC UA). Thus, data interoperability is currently lacking. The asset administration shell (AAS) is a new approach for achieving data interoperability across the automation pyramid as the AASX data format represents AAS information and supports data communication via OPC UA. In this study, we first provide insights into the AASX format and give exemplary applications on AASX; we then present an AASX-based solution for bidirectional data exchange between enterprise and control applications. In this solution, the conversion between AASX and Excel spreadsheets is completed to enable interoperable data exchange. Finally, a data exchange scenario for motor control is constructed to validate the effectiveness of the solution. The result shows no data were lost during the two-way conversions.
Facile Amidation of Non-Protected Hydroxycinnamic Acids for the Synthesis of Natural Phenol Amides

Phenol amides are bioactive compounds naturally present in many plants. This class of compounds is known for antioxidant, anti-inflammatory, and anticancer activities. To better understand the reactivity and structure–bioactivity relationships of phenol amides, a large set of structurally diverse pure compounds are needed, however purification from plants is inefficient and laborious. Existing syntheses require multiple steps, including protection of functional groups and are generally overly complicated and only suitable for specific combinations of hydroxycinnamic acid and amine. Thus, to facilitate further studies on these promising compounds, we aimed to develop a facile general synthetic route to obtain phenol amides with a wide structural diversity. The result is a protocol for straightforward one-pot synthesis of phenol amides at room temperature within 25 h using equimolar amounts of N,N′-dicyclohexylcarbodiimide (DCC), amine, hydroxycinnamic acid, and sodium bicarbonate in aqueous acetone. Eight structurally diverse phenol amides were synthesized and fully chemically characterized. The facile synthetic route described in this work is suitable for a wide variety of biologically relevant phenol amides, consisting of different hydroxycinnamic acid subunits (coumaric acid, ferulic acid, and sinapic acid) and amine subunits (agmatine, anthranilic acid, putrescine, serotonin, tyramine, and tryptamine) with yields ranging between 14% and 24%.
Vitamin B12 Alleviates Methotrexate-Induced Lung Injury In Rat: A Histopathological, Immunohistochemical, And Biochemical Study

ÖZET Amaç: Metotreksat (MTX) önemli bir kemoterapötik olup kanser hastalarında kullanılan önemli bir antikanserojen olmakla birlikte diğer dokularda yan etkilere yol açmaktadır. Bu yan etkiler arasında akciğer toksisitesi de yer almaktadır. B12 Vitamini güçlü bir antioksidandır ve reaktif oksijen türlerinin azalmasını sağlar. Bu çalışma, B12 vitamininin sıçan akciğerinde metotreksat kaynaklı hasara karşı koruma sağlayıp sağlayamayacağını belirlemek için tasarlanmıştır.
Exploring the Ability to Perform Activities of Daily Living and Cognitive Status after Hospitalization with COVID-19: A Multiple Case Study

Background Multidisciplinary rehabilitation including occupational therapy after COVID-19 is recommended. However, evidence on how COVID-19 affects the ability to perform activities of daily living (ADL) is sparse. Objective The aim of this study was to explore the ability to perform ADL and cognitive status in patients with COVID-19 at time of discharge and three months postdischarge. Methods This prospective multiple case study included adults with COVID-19, who at time of discharge had decreased ADL performance compared to habitual functional level. Data collection included Assessment of Motor and Process Skills (AMPS) and Montreal Cognitive Assessment (MoCA) at discharge and three-month follow-up. Exploratory analysis was used to identify similarities and trends within and across cases. Results Eleven patients were included. 75% had a significant increase in motor ability measures, and 27% had a significant increase in process ability measures at follow-up. 67% of follow-up cases showed mild cognitive impairment, where executive functioning and memory were most predominant. Conclusions The ability to perform ADL was affected at discharge and at three-month follow-up. Furthermore, mild cognitive impairment was present at both hospital discharge and follow-up in most cases. Significance. Occupational therapists can apply performance-based assessments to identify the need for rehabilitation of ADL in patients with COVID-19 during and posthospitalization.
Sensitivity study on storm modulation through a strategic use of consumer air conditioners

Storm trainings, consisting of quasi‐stationary band‐shaped precipitation systems, bring persistent local heavy rains, often causing devastating floods and landslides. Here, we show that a strategic use of consumer air conditioners can modulate a storm downstream. We numerically reproduced a storm training that damaged the Hiroshima metropolis in Japan in 2014, and conducted experiments to test the sensitivity of the storm to the initial moisture field near the surface. We propose an empirically‐derived formula for a control efficiency parameter, which can be used to estimate the impact of moisture removal on the rainfall accumulation. That formula can be different for different storms and used to assess the feasibility of modulating the target storm. It reveals that removal of half a kiloton of moisture, which could be achieved within half an hour in a city with a population of 1 million since more than one air conditioner is installed per capita in Japan, could lead to a significant reduction of the total rainfall accumulation over a 100‐km2 area of heavy rain during the Hiroshima storm event. We anticipate our results, which reveal that human activity can have a significant impact on storms, will be a starting point for considering the coupling of weather and the economy, and will contribute to the development of a sustainable society.
Mathematical Modeling of The Biodegradation of Phenol from Industrial Effluents Using Immobilized Pseudomonas putida

Synthetic chemicals are extremely harmful, particularly those man-made ones. Models are used to describe the behavior of microorganisms under different physical or chemical conditions such as temperature, pH, and water activity. Phenol is one of the potentially hazardous synthetic industrial contaminants capable of causing deteriorating effects in humans. In this paper, for the first time we present different kinetics models such as Von Bertalanffy, Baranyi-Roberts, modified Schnute, modified Richards, modified Gompertz, modified Logistics and the most recent Huang were used to get values for the above kinetic constants or parameters from simultaneous biodegradation of phenol from industrial effluents using immobilized Pseudomonas putida. All the curves present the best models with highest adjusted R2 value with the lowest RMSE and AICc value. The Accuracy and Bias Factors values were close to unity (1.0). Nearly all of the models best fit the curves indicating that Pseudomonas putida growth on phenol can be described mathematically the modelling parameters obtained can be utilized for predicting bioremediation of phenols in batch culture and perhaps in the future will be valuable in modelling growth eon industrial effluent containing phenol.
Computational Intelligence Applications in Smart Grids: Enabling Methodologies for Proactive and Self-Organizing Power Systems

This book considers the emerging technologies and methodologies of the application of computational intelligence to smart grids. From a conceptual point of view, the smart grid is the convergence of information and operational technologies applied to the electric grid, allowing sustainable options to customers and improved levels of security. Smart grid technologies include advanced sensing systems, two-way high-speed communications, monitoring and enterprise analysis software, and related services used to obtain location-specific and real-time actionable data for the provision of enhanced services for both system operators (i.e. distribution automation, asset management, advanced metering infrastructure) and end-users (i.e. demand side management, demand response). In this context, a crucial issue is how to support the evolution of existing electrical grids from static hierarchal systems to self-organizing, highly scalable and pervasive networks. Modern trends are oriented toward the employment of computational intelligence techniques for deploying advanced control, protection and monitoring architectures that move away from the older centralized paradigm to systems distributed across the field with an increasing pervasion of intelligence devices. The large-scale deployment of computational intelligence technologies in smart grids could lead to a more efficient tasks distribution amongst energy resources and, consequently, to a sensible improvement of the electrical grid flexibility. Readership: Graduate students and researchers interested in smart grids and advanced power networks.
Performance of fourteen genotypes of durum wheat under Eastern Mediterranean conditions

Durum wheat is used as raw material for many foods. Climate change might be responsible for larger or smaller changes in crop yields. For the combined assessment of climate and crop, growing degree days (GDDs) have a crucial role. Two experimental lines and twelve commercial wheat (Triticum durum) varieties from diverse backgrounds were cultivated to compare their crop properties, yield, and protein content in terms of GDDs. The experiment was established in typical Mediterranean environment, using a randomized complete block design with blocks of varieties and lines for two growing seasons. For all varieties, GDDs to head emergence was affected by factor year, whereas GDDs from head emergence to harvest were influenced by both varieties and year. Protein content (%) was not affected by genotypes. Factor of variety and interaction variety × year had an impact on vitreousness; it was ranged from 79.75 % (‘Makaras’ variety) to 44.00 % (‘Levante’ variety). Yield had no statistically significant difference among varieties/lines. In durum wheat cultivation, up to head emergence, when GDDs increased, yield would be declined in contrast to GDDs from emergence to harvest; with the increasing of GDDs to harvest, yield was climbed. Nowadays, the integrations of and interpretation of GDDs in the evaluation of crop performance seem vital.
Biomimetics Linked to Classical Product Development: An Interdisciplinary Endeavor to Develop a Technical Standard

Biomimetics is a well-known approach for technical innovation. However, most of its influence remains in the academic field. One option for increasing its application in the practice of technical design is to enhance the use of the biomimetic process with a step-by-step standard, building a bridge to common engineering procedures. This article presents the endeavor of an interdisciplinary expert panel from the fields of biology, engineering science, and industry to develop a standard that links biomimetics to the classical processes of product development and engineering design. This new standard, VDI 6220 Part 2, proposes a process description that is compatible and connectable to classical approaches in engineering design. The standard encompasses both the solution-based and the problem-driven process of biomimetics. It is intended to be used in any product development process for more biomimetic applications in the future.
Is the ‘Technological Singularity Scenario’ Possible: Can AI Parallel and Surpass All Human Mental Capabilities?

Abstract This paper explores and discusses those human mental processes and capabilities which pose a serious challenge to the actualization of the Technological Singularity scenario. It posits that the contemporary human mind-intellect 1 and its further evolution is the main issue and not AI takeover. It elaborates some relatively less explored aspects of human mental architecture and functioning that defy emulation by AI. It introduces the concept of ‘integrated autonomous intellect’ as the future evolutionary stage of the human intellect. It opts for a positive vision of the harmonious collective future(s) of human mind-intellect and AI instead of the Technological Singularity scenario.
SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI’s deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.
Multiple-Input Multiple-Output Systems for Spinning Vehicles

This paper investigates the performance of a multiple-input multiple-output (MIMO) digital communication system, when the transmitter is located on a spinning vehicle. In particular, a 2x2 MIMO system is used, with Alamouti coding at the transmitter. Both Rayleigh and Rayleigh plus line-of-sight, or Rician, models combined with a deterministic model to simulate the channel. The spinning of the transmitting vehicle, relative to the stationary receive antennas, modulates the signal, and complicates the decoding and channel parameter estimation processes. The simulated system bit error rate is the primary performance metric used. The Alamouti channel code is shown to perform better than the maximal ratio receiver combining (MRRC) and single receiver (2x1) system in some circumstances and performs similarly to the MRRC in the broadside case.