Miracle-dz/UniFAR-FTUs huggingface dataset:
Automation of Service Desk: Knowledge Management Perspective

: In the current times, the need for quality and effective service support technologies is high. We can achieve good IT service operations that support good knowledge management practices by employing automation techniques and methods to the Service Desk systems. In our position paper, we look at the literature regarding Service Desk automation in relation to knowledge management. We then propose a theoretical model of Automatized Service Desk Systems that uses several techniques to help and automatize the process of user request resolution. Our proposed system employs text mining techniques, a virtual agent, an expert system, a customer intent detector, and a ticketing system.
Comparison of grape flower counting using patch-based instance segmentation and density-based estimation with convolutional neural networks

Information on flower number per grapevine inflorescence is critical for grapevine genetic improvement, early yield estimation and vineyard management. Previous approaches to automize this process by traditional image processing techniques, are unsuccessful in the improvement of a universal system that can be applied to inflorescences with diverse morphology. In this paper, we validated the efficiency of advanced deep learning-based approaches for automatic flower number estimation. The results were analyzed on an inflorescence dataset of 204 images from four different cultivars during various growth stages. The algorithm developed on patch-based instance segmentation using Mask R-CNN, produced counting results highly correlated to manual counts (R2 = 0.96). Practically constant MAPE values among different cultivars (from 5.50% to 8.45%), implying a high robustness in this method. Achieving the fastest counting (0.33 second per image of size 512 × 512) with slightly lower counting accuracy (R2 = 0.91), the method based on object density-map using U-Net turned out to be suitable for real-time flower counting systems.
2021 Korean Thyroid Imaging Reporting and Data System and Imaging-Based Management of Thyroid Nodules: Korean Society of Thyroid Radiology Consensus Statement and Recommendations

Incidental thyroid nodules are commonly detected on ultrasonography (US). This has contributed to the rapidly rising incidence of low-risk papillary thyroid carcinoma over the last 20 years. The appropriate diagnosis and management of these patients is based on the risk factors related to the patients as well as the thyroid nodules. The Korean Society of Thyroid Radiology (KSThR) published consensus recommendations for US-based management of thyroid nodules in 2011 and revised them in 2016. These guidelines have been used as the standard guidelines in Korea. However, recent advances in the diagnosis and management of thyroid nodules have necessitated the revision of the original recommendations. The task force of the KSThR has revised the Korean Thyroid Imaging Reporting and Data System and recommendations for US lexicon, biopsy criteria, US criteria of extrathyroidal extension, optimal thyroid computed tomography protocol, and US follow-up of thyroid nodules before and after biopsy. The biopsy criteria were revised to reduce unnecessary biopsies for benign nodules while maintaining an appropriate sensitivity for the detection of malignant tumors in small (1–2 cm) thyroid nodules. The goal of these recommendations is to provide the optimal scientific evidence and expert opinion consensus regarding US-based diagnosis and management of thyroid nodules.
MPDB: a unified multi-domain protein structure database integrating structural analogue detection

With the development of protein structure prediction methods and biological experimental determination techniques, the structure of single-domain proteins can be relatively easier to be modeled or experimentally solved. However, more than 80% of eukaryotic proteins and 67% of prokaryotic proteins contain multiple domains. Constructing a unified multi-domain protein structure database will promote the research of multi-domain proteins, especially in the modeling of multi-domain protein structures. In this work, we develop a unified multi-domain protein structure database (MPDB). Based on MPDB, we also develop a server with two functional modules: (1) the culling module, which filters the whole MPDB according to input criteria; (2) the detection module, which identifies structural analogues of the full-chain according to the structural similarity between input domain models and the protein in MPDB. The module can discover the potential analogue structures, which will contribute to high-quality multi-domain protein structure modeling.
An Unmanned Aerial Carrier and Anchoring Mechanism for Transporting Companion UAVs

: This paper demonstrates an unmanned aerial carrier as well as a new anchoring mechanism for connecting and transporting companion unmanned aerial vehicles (UAVs). Establishing this platform presents unique challenges including the requirements of precise localization of the platform, real-time environment mapping system, robust ﬂight control approach, docking safety mechanism, and reliable anchor system for the companion UAV. To obtain the positioning information, a tightly-coupled visual-inertial optimization based odometry is implemented with a ﬁsheye camera and an inertial measurement unit. A 3D map is updated in real-time using an Octomap framework. A nonlinear position model predictive controller cascaded with a DJI attitude controller is implemented for the ﬂight control. Innovatively, we designed a lightweight anchoring mechanism for safe landing and reliable transportation of the companion UAV. Real-world experiments results suggest that the transportation system is a viable approach to transport the companion UAV, and that the proposed anchoring mechanism allows for reliable operation.
Relevance of Similarity Measures Usage for Paraphrase Detection

The article describes the experiments and their results using two Deep Learning (DL) models and four measures of similarity/distance, determining the similarity of documents from the three publicly available corpora of paraphrased documents. As DL models, Word2Vec was used in two variants and FastText in one. The article explains the existence of a multitude of hyperparameters and defines their values, selection of effective ways of text processing, the use of some non-standard parameters in Natural Language Processing (NLP), the characteristics of the corpora used, the results of the pairs (DL model, similarity measure) processing corpora, and tries to determine combinations of conditions under which use of exactly certain pairs yields the best results (presented in the article), measured by standard evaluation measures Accuracy, Precision, Recall and primarily F-measure.
Hybridization of Chaotic Maps and Gravitational Search Algorithm for Constrained Mechanical and Civil Engineering Design Frameworks: CGSA for Mechanical and Civil Engineering Design Optimization

The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's law of universal gravitation. It uses 10 chaotic maps for optimal global search and fast convergence rate. The advantages of CGSA has been incorporated in various Mechanical and Civil engineering design frameworks which include Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss Design (TBTD), Stepped Cantilever Beam Design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with eleven state of the art stochastic algorithms. In addition, a non-parametric statistical test namely the Signed Wilcoxon Rank-Sum test has been carried out at a 5% significance level to statistically validate the results. The simulation results indicate that CGSA shows efficient performance in terms of high convergence speed and minimization of the design parameter values as compared to other heuristic algorithms. The source codes are publicly available on Github i.e. https://github.com/SajadAHMAD1.
BENEFIT FROM AN AUDIO PROCESSOR UPGRADE IN EXPERIENCED USERS OF AN ACTIVE MIDDLE EAR IMPLANT: SPEECH UNDERSTANDING IN NOISE AND SUBJECTIVE ASSESSMENT

Background: Microphone directionality plays an important role in speech understanding in challenging acoustic environments. A new audio processor (AP) from Med-El, known as Samba, automatically detects and selects optimal settings depending on the listening situation. This clinical investigation evaluated speech understanding in noise and subjective benefit of an AP upgrade from the prior Amadé to the new Samba. Materials and Methods: Fourteen Vibrant Soundbridge users with at least 3 months experience with the Amadé AP received the new Samba AP. Speech recognition in quiet was measured using the Freiburger monosyllable test. Speech understanding in noise was assessed using the Oldenburger sentence test. Subjective benefit was determined with the Hearing Device Satisfaction Scale and the Abbreviated Profile of Hearing Aid Benefit. Results: No differences were detected in pure tone audiometry or speech recognition in quiet between the two audio processors. However, the new Samba AP performed significantly better in challenging noise situations: when speech came from the front and noise from the back, the signal-to-noise ratio (SNR) improved by 3.4 dB with the automatic mode of the Samba AP compared to the Amadé AP. When the sources of speech and noise were switched, a significant improvement of 1.8 dB SNR was observed. Based on the two questionnaires, subjective benefit in daily life and device satisfaction were comparable for the two APs. Conclusion: Due to its new features, the Samba AP gave significantly better results in challenging acoustical test situations. Even when the direction of background noise changed, the Samba AP performed better due to the new built-in adaptive directional microphones.
Histopathological Effects of Multiwall Carbon Nanotubes on Rat Liver

Methods: In this experimental study, concentrations of 10 and 20mg/kg of multiwall carbon nanotubes functionalized with carboxyl groups (10-20nm), were intraperitoneally injected into male rats in 21 stages and the control group received normal saline. Blood sampling and animal dissection were performed in two stages (24hours and 144hours after the last injection). Histological sections were prepared from the liver by hematoxylin-eosin staining and examined by optical microscopy. The concentration of thiol groups and malondialdehyde were measured and analyzed using one way ANOVA test.
Alternative Splicing of TaGS3 Differentially Regulates Grain Weight and Size in Bread Wheat

The heterotrimeric G-protein mediates growth and development by perceiving and transmitting signals in multiple organisms. Alternative splicing (AS), a vital process for regulating gene expression at the post-transcriptional level, plays a significant role in plant adaptation and evolution. Here, we identified five splicing variants of Gγ subunit gene TaGS3 (TaGS3.1 to TaGS3.5), which showed expression divergence during wheat polyploidization, and differential function in grain weight and size determination. TaGS3.1 overexpression significantly reduced grain weight by 5.89% and grain length by 5.04%, while TaGS3.2–3.4 overexpression did not significantly alter grain size compared to wild type. Overexpressing TaGS3.5 significantly increased the grain weight by 5.70% and grain length by 4.30%. Biochemical assays revealed that TaGS3 isoforms (TaGS3.1–3.4) with an intact OSR domain interact with WGB1 to form active Gβγ heterodimers that further interact with WGA1 to form inactive Gαβγ heterotrimers. Truncated isoforms TaGS3.2–3.4 , which lack the C-terminal Cys-rich region but have enhanced binding affinity to WGB1, antagonistically compete with TaGS3.1 to bind WGB1, while TaGS3.5 with an incomplete OSR domain does not interact with WGB1. Taking these observations together, we proposed that TaGS3 differentially regulates grain size via AS, providing a strategy by which the grain size is fine-tuned and regulated at the post-transcriptional level.
Vlogger’s Reputation: Connecting Trust and Perceived Usefulness of Vloggers’ Recommendation with Intention to Shop Online

The study aims to determine the impact of vlogger’s reputation on viewers’ trust on vlogger’s recommendations (TVR) and perceived usefulness of vlogger’s recommendations (PUVR). Moreover, the study examines the impact of TVR and PUVR on attitude and intention to shop online. A sample of 250 responses was collected from social media users and structural equation analysis technique was employed. The findings indicate that vlogger’s reputation has a significant role in building TVR, and PUVR and TVR and PUVR significantly influence the attitude and intention to shop online. The study also identified a positive impact of attitude towards online shopping on online shopping intention. The research provides insights to the marketers and practitioners as the findings guides on crafting promotional strategies using vloggers.
Research on federated learning method for fault diagnosis in multiple working conditions

As one of the critical components of rotating machinery, fault diagnosis of rolling bearings has great significance. Although deep learning is useful in diagnosing rolling bearing faults, it is difficult to diagnose the faults of bearings under multiple operating conditions. To overcome the above-mentioned problem, this paper designs a modular federated learning network for fault diagnosis in multiple working conditions by using dynamic routing technology as the federation strategy for federated learning of the multiple modular neural network. First, according to different working conditions, the collected multi-working condition data are divided into different groups for feeding of modular network to extract the local features under different working conditions. Then, an additional deep neural network is constructed to extract the feature involved in data without working condition division. Finally, the global adaptive feature extraction of each working condition can be obtained by designing a federated strategy based on dynamic routing technology to achieve the weights allocation scheme of the modular neural network. The bearing dataset of Case Western Reserve University is taken as a benchmark dataset to verify the effectiveness of the proposed method.
Recommendation Recovery with Adaptive Filter for Recommender Systems

: Most recommender systems are focused on suggesting the optimal recommendations rather than ﬁnding a way to recover from a failed recommendation. Thus, when a failed recommendation appears several times, users may abandon to use a recommender system by considering that the system does not take her preference into account. One of the reasons is that when a user does not like a recommendation, this preference cannot be instantly captured by the recommender learning model, since the learning model cannot be constantly updated. Although this can be to some extent alleviated by critique-based algorithms, ﬁne tuning the preference is not capable of fully expelling not-preferred items. This paper is therefore to propose a recommender recovery solution with an adaptive ﬁlter to deal with the failed recommendations while keeping the user engagement and, in turn, allow the recommender system to become a long-term application. It can also avoid the cost of constantly updating the recommender learning model.
3D-Printed Graphene-Based Bow-Tie Microstrip Antenna Design and Analysis for Ultra-Wideband Applications

In this study, the effects of graphene and design differences on bow-tie microstrip antenna performance and bandwidth improvement were investigated both with simulation and experiments. In addition, the conductivity of graphene can be dynamically tuned by changing its chemical potential. The numerical calculations of the proposed antennas at 2–10 GHz were carried out using the finite integration technique in the CST Microwave Studio program. Thus, three bow-tie microstrip antennas with different antenna parameters were designed. Unlike traditional production techniques, due to its cost-effectiveness and easy production, antennas were produced using 3D printing, and then measurements were conducted. A very good match was observed between the simulation and the measurement results. The performance of each antenna was analyzed, and then, the effects of antenna sizes and different chemical potentials on antenna performance were investigated and discussed. The results show that the bow-tie antenna with a slot, which is one of the new advantages of this study, provides a good match and that it has an ultra-bandwidth of 18 GHz in the frequency range of 2 to 20 GHz for ultra-wideband applications. The obtained return loss of −10 dB throughout the applied frequency shows that the designed antennas are useful. In addition, the proposed antennas have an average gain of 9 dBi. This study will be a guide for microstrip antennas based on the desired applications by changing the size of the slots and chemical potential in the conductive parts in the design.
Exploiting Endogenous Surface Defects for Dynamic Nuclear Polarization of Silicon Micro- and Nanoparticles

Micro- and nanoparticles of elemental, crystalline silicon represent an attractive target for a wide range of applications spanning from quantum computing to contrast agents for biomedical imaging applications. To overcome the low sensitivity of the 29Si nuclei in magnetic resonance, dynamic nuclear polarization (DNP), which exploits the endogenous surface defects as a source of polarization, can be used to temporarily boost nuclear polarization of the 29Si spin bath. In the present work, we have assessed a number of commercially available silicon micro- and nanoparticles concerning properties and characteristics under DNP conditions. It has been found that optimal physical and chemical conditions, including surface-defect concentration adjusted to the particle size, are necessary to achieve a high level of polarization enhancement.
The Influence of 3D Printing Parameters on Adhesion between Polylactic Acid (PLA) and Thermoplastic Polyurethane (TPU)

A 3D printer in FDM technology allows printing with two nozzles, which creates an opportunity to produce multi-material elements. Printing from two materials requires special consideration of the interface zone generated between their geometrical boundaries. This article aims to present the possibility of printing with PLA and TPU using commercially available filaments and software to obtain the best possible bond strength between two different polymers with respect to printing parameters, surface pattern (due to the material contact surface’s roughness), and the order of layer application. The interaction at the interface of two surfaces of two different filaments (PLA-TPU and TPU-PLA) and six combinations of patterns were tested by printing seven replicas for each. A total of 12 combinations were obtained. By analyzing pairs of samples (the same patterns, different order of materials), the results for the TPU/PLA samples were better or very close to the results for PLA/TPU. The best variants of pattern combinations were distinguished. Well-chosen printing parameters can prevent a drop in parts efficiency compared to component materials (depending on the materials combination).
Our spatial reality and God

Modern scientific models of cosmological space and the theological concept of God’s immensity seem to exclude the possibility that God himself is personally present with us humans at particular places in space. Are God and our spatial reality incompatible? Or, is it possible to conceive the connection between God and space as ‘positive’, that is, in such a way that God himself can be fully and personally present with us at particular places in space? This essay explores how this question may be addressed in a theology which accepts the results of the natural sciences and acknowledges that God is the free creator of physical space. It describes how space can be conceptualised, and presents an overview of five different views on a positive relation between God and space in recent protestant theology. It concludes by some considerations on the question whether a positive relation between God and space requires that God himself is spatial.Contribution: This article contributes to the conversation between natural science and theology by making three points. (1) The scientific understanding of cosmological space and the biblical witness of God’s personal and local presence with humans require an alternative for the traditional theological view on God and space in terms of God’s immensity and omnipresence. (2) It is argued that new theological models for the interrelation between God and space have serious weaknesses. (3) A ‘positive’ relation between God and space may be articulated in terms of the correspondence among God’s uncreated movement, multiplicity and relationality, and the movement, multiplicity and relationality in the physical space of creation.
Severe Abdominal Pain and Acne Vulgaris after the Initiation of Elexacaftor /Tezacaftor/Ivacaftor (ELX/TEZ/IVA)

DOI: 10.46998/IJCMCR.2021. 12.000294 Abstract The development of CF transmembrane conductance regulator (CFTR) modulators especially Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA), has led to significant improvement in CF care. However, as increasing number of patients are started on (ELX/ TEZ/IVA), side effects that have not been described in clinical trials are now being reported. In this case report we describe a CF patient with mild disease and remarkable complications once ELX/TEZ/IVA was started.
E-Commerce in forming Brand Image using Media Publication

The purpose of this study is to identify e-commerce in shaping brand image through publication media. The method used in this research is a descriptive method to present a complete picture of the situation related to several situations of the variables under study. The result of this study is to identify the extent to which e-commerce shapes the brand image. This research was conducted by discussing e-commerce activities in publication media to shape brand image. So it is expected that the impact of this research will result in e-commerce not only selling products but also providing information and promotion with the strength of the product.
Microneedles in Drug Delivery: Progress and Challenges

In recent years, an innovative transdermal delivery technology has attracted great interest for its ability to distribute therapeutics and cosmeceuticals for several applications, including vaccines, drugs, and biomolecules for skin-related problems. The advantages of microneedle patch technology have been extensively evaluated in the latest literature; hence, the academic publications in this area are rising exponentially. Like all new technologies, the microneedle patch application has great potential but is not without limitations. In this review, we will discuss the possible limitations by highlighting the areas where a great deal of improvements are required. Emphasising these concerns early on should help scientists and technologists to address the matters in a timely fashion and to use their resources wisely.