Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave Characterization and Probe Sensing: Parametric Study with Skin Phantom Thickness

Novel microwave and millimeter-wave devices have been recently investigated as promising diagnostic aids for skin anomaly detection. These devices rely on knowledge of skin dielectric parameters. Skin thickness varies with body location, and, hence, the goal of our work is to characterize the dielectric skin properties in the microwave range as a function of skin thickness. To do so systematically, and in a controlled laboratory environment, we developed skin phantoms models ranging from 0.5 mm to 5 mm in thickness, with 0.5 mm increments. These phantoms are placed on a fat-mimicking material and then characterized with the Keysight slim form probe over the 0.5–2.6 GHz range. Our results indicate that skin thickness, usually known from extensive anatomical resources, should be taken into account for proper interpretation of eventual dielectric characterization in vivo, as the probe's sensing volume is likely to include a complex, multi-tissue dielectric distribution.
Billing System Design of Cloud Services

Cloud computing can provide scalable computing power, storage space, and information services for a variety of applications. It has dynamic scalability, fault-tolerance and availability, which require a large number of nodes to provide support. Providing corresponding compensation mechanisms for the participating nodes can increase their enthusiasm for participating in service support. In order to provide customers with the use of cloud resources measurement and costs to better promote the development of cloud services. This paper proposes a cloud service billing system. The resource collector periodically collects resource usage information, and transmitted to the billing. The billing will calculate the corresponding resource consumption based on these data, and store these records in the database to provide user management. The resource rates vary dynamically based on the current infrastructure load and service quality. After test, this system only has partial errors in CPU billing due to the long statistical interval (1 second). The accuracy of the rest reached 95%. Keywords—cloud computing; cost; cloud billing; pricing; billing models
Reluctance Electric Machines

KEY FEATURES • Presents basic and up-to-date knowledge about the topologies, modeling, performance, design, and control of reluctance synchronous machines. • Includes information on recently introduced reluctance fluxmodulation electric machines (switchedflux, flux-reversal, Vernier, transverse flux, claw pole, magnetic-geared dual-rotor, brushless doubly fed, etc.). • Features numerous examples and case studies throughout. • Includes a comprehensive overview of all reluctance electric machines.
Sustainable supply chain management in tourism: a systematic literature review

ABSTRACT The tourism industry was seen as one of the main beneficiaries of globalisation prior to the outbreak of the Covid-19 pandemic. However, while tourism is a major driver of economic growth and social well-being, a transformation of the industry towards more sustainable practices along the tourism supply chain (TSC) is needed to ensure that sustainable development goals can be reached. As sustainable supply chain management (SSCM) practices are critical for this transformation, the present study maps previous research on SSCM in tourism within a systematic literature review. Based on an analysis of 44 peer-reviewed journal papers, we found a strong emphasis on tour operators, hotels, and transport providers being concerned with practicing SSCM while actors managing the destination (e.g., Destination Management Organizations) have rarely been studied. Sustainability practices in TSCs can be enhanced by linking traditional and non-traditional actors on the multiple tiers and levels of a tourist destination. We accordingly propose potential research avenues to stimulate future research activities.
Ultimate Boundedness of Voltage Droop Control With Distributed Secondary Control Loops

Conditions are derived for ultimate boundedness of voltage stabilization and reactive power balancing in electrical microgrids with voltage (E-Q) droop control and distributed secondary control loops. This paper shows that under a bound on the load reactive power demand, an ultimate bound set and its associated region of attraction can be computed for the deviation of the microgrid voltages with respect to their nominal values. The proposed approach employs a nonlinear model of the microgrid, departing from prevailing analyses of stability in microgrids with secondary controllers, typically based on linearization around equilibrium points. The obtained ultimate bounds serve as a performance measure guaranteeing tight voltage regulation under large variations of the load reactive power demand. We illustrate the results with examples of microgrids having different complexity, including a subnetwork of the CIGRE benchmark representing a medium-voltage rural distribution network.
Evolutionary History of DMSP Lyase-Like Genes in Animals and Their Possible Involvement in Evolution of the Scleractinian Coral Genus, Acropora

Dimethlysulfoniopropionate (DMSP) lyase is an enzyme that mediates cleavage of DMSP into dimethyl sulfide (DMS) and acrylate. DMS is an aerosol substance that may affect cloud formation, solar radiation and ocean temperatures. DMSP lyases in marine organisms, such as marine bacteria, release DMS, which might contribute to atmosphere-ocean feedback. Although DMSP lyases were first identified in marine bacteria, eukaryotic DMSP lyases or genes similar to DMSP lyase, DMSP lyase-like (DL-L) genes have been found not only in coccolithophores (Emiliania huxleyi) and symbiotic algae of the Family Symbiodiniaceae, but also in animals, including scleractinian corals (Cnidaria: Anthozoa: Hexacorallia). Comparative genomic analysis showed that gene expansion events of DL-L genes have occurred specifically in the scleractinian genus, Acropora. In the present study, we performed molecular identification of DL-L genes in Acropora digitifera. Thirteen full-length Open Reading Frames were isolated, confirming that these duplicated DL-L genes are likely expressed. A comprehensive survey of available transcriptomic databases revealed that DL-L genes have been identified not only in scleractinians (Hexacorallia), but also Octocorallia (Anthozoa) and even in a jellyfish (Cnidaria: Hydrozoa). Molecular phylogenetic analyses showed that although some sequences from cnidarian transcriptomic databases apparently originated with their symbiotic algae, cnidarian sequences from Anthozoa and Hydrozoa clustered together, indicating that these evolved from a gene in the last common ancestor of Cnidaria, dating to the Precambrian. Interestingly, cnidarian species possessing DL-L genes apparently occur only in coral reefs or shallow, warmer environments, suggesting that these genes may be essential for animals to survive in such environments. Acropora-specific duplicated DL-L genes, which originated during the past warm geological periods, may enable them to adapt to environmental changes.
Metabolic Characteristics of Hashimoto’s Thyroiditis Patients and the Role of Microelements and Diet in the Disease Management—An Overview

Hashimoto’s thyroiditis (HT) is the most common autoimmune disease and the leading cause of hypothyroidism, in which damage to the thyroid gland occurs due to the infiltration of lymphocytes. It is characterized by increased levels of antibodies against thyroid peroxidase and thyroglobulin. In this review, we present the metabolic profile, the effectiveness of micronutrient supplementation and the impact of dietary management in patients with HT. For this current literature review, the databases PubMed, Cochrane, Medline and Embase were reviewed from the last ten years until March 2022. This article provides a comprehensive overview of recent randomized controlled trials, meta-analyses, and clinical trials. Many patients with HT, even in the euthyroid state, have excess body weight, metabolic disorders, and reduced quality of life. Due to frequent concomitant nutritional deficiencies, the role of vitamin D, iodine, selenium, magnesium, iron and vitamin B12 is currently debated. Several studies have underlined the benefits of vitamin D and selenium supplementation. There is still no specific diet recommended for patients with HT, but a protective effect of an anti-inflammatory diet rich in vitamins and minerals and low in animal foods has been suggested. There is insufficient evidence to support a gluten-free diet for all HT patients. Pharmacotherapy, along with appropriate nutrition and supplementation, are important elements of medical care for patients with HT. The abovementioned factors may decrease autoantibody levels, improve thyroid function, slow down the inflammatory process, maintain proper body weight, relieve symptoms, and prevent nutritional deficiencies and the development of metabolic disorders in patients with HT.
Time-Aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in better judgment and can make sense of early diagnosis. However, the prediction performance heavily relies on effective representations from multi-dimensional time-series EHR data. Existing solutions usually focus on temporal features or inherent relations between clinical event variables or extract both information in two separate phases. This usually leads to insufficient patient feature information and results in poor prediction performance. Moreover, existing methods based on Heterogeneous Graph Neural Network usually require manual selection of proper Meta-Paths. To solve these problems, we propose the Time-aware Context-Gated Graph Attention Network (T-ContextGGAN). Specifically, we design a GNN based module with Time-aware Meta-Paths and self-attention mechanism to extract both temporal semantic information and inherent relations of EHR data simultaneously and perform automatic Meta-Path selection. To evaluate the proposed model, we extract the first 48 hour EHR data in the first Intensive Care Unit (ICU) admission of three different tasks from two open-source datasets and model various clinical variables on the proposed EHRGraph. Extensive experimental results show the proposed model can effectively extract informative features, and outperform existing state-of-art models in terms of various prediction measures. Our code is available in https://github.com/OwlCitizen/TContext-GGAN.
Woodpecker: Identifying and Fixing Android UI Display Issues

The complexity of GUI and some combinations of personalized settings make the UI display issues occur frequently. Unfortunately, little is known about the causes of UI display issues. The Android fragmentation and variety of UI components post a great challenge to repair the issue. Based on the our empirical study, this paper proposes Woodpecker to automatically detect, localize and repair UI display issues in Android apps. It detects screenshots with issues with computer vision technology, localizes buggy source code from screenshot, and repairs issues with pre-defined templates automatically. We evaluate Woodpecker with 30 real-world UI display issues, it can successfully detect 87% and repair 77% issues. We further apply Woodpecker to another 256 popular open-source Android apps, and successfully uncover 112 previously-undetected issues. It can automatically repair 106 (94%) issues, with 76 of them accepted by developers so far, while others pending (none of them is rejected).
Practical Marine Electrical Knowledge

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Clinical Implication of Tolvaptan in Patients with Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disease characterized by the growth of numerous fluid-filled cysts in the kidneys. It leads to progressive kidney enlargement and end-stage kidney disease (ESKD) [1,2]. ADPKD is a ciliopathy that involves abnormal cilia structure and function [3]. There are polycystin-1 and polycystin-2 in the kidney tubular cell protruding primary cilium. It has a role in detecting fluid flow and regulating calcium influx that activates an intracellular calcium signaling pathway [4]. In addition, increased levels of arginine vasopressin levels induced increased intracellular adenosine cyclic monophosphate (cAMP) levels in the distal tubule and collecting duct. Tolvaptan is a vasopressin receptor antagonist; it selectively blocks the binding of V2 receptors in tubular cells and reduces fluid secretion, cell proliferation, and cyst development by reducing cAMP [5]. Tolvaptan in ADPKD papISSN 2092-8335 · eISSN 2733-5380 Keimyung Med J [Epub ahead of print] https://doi.org/10.46308/kmj.2022. 00059 Original Article
Old and New Systemic Immune-Inflammation Indexes Are Associated with Overall Survival of Glioblastoma Patients Treated with Radio-Chemotherapy

Background. Systemic immunity and inflammation indexes (SI) derived from blood cells have gained increasing attention in clinical oncology as potential biomarkers that are associated with survival. Materials and methods. We tested 12 different SI using blood tests from patients with isocitrate dehydrogenase 1 and 2 wild-type glioblastomas, treated with radio-chemotherapy. The primary endpoint was their overall survival. Results. A total of 77 patients, comprising 43 males and 34 females, with a median age of 64 years (age range 26–84), who were treated between October 2010 and July 2020, were included in the present analysis (approved by a local ethics committee). In the univariate Cox regression analysis, all the indexes except two showed a statistically significant impact on OS. In the multivariate Cox regression analysis, neutrophil × platelet × leukocyte/(lymphocyte × monocyte) (NPW/LM) and neutrophil × platelet × monocyte/lymphocyte (NPM/L) maintained their statistically significant impact value. Conclusions. This univariate analysis confirms the potential of systemic inflammation indexes in patients with glioblastoma, while the multivariate analysis verifies the prognostic value of NPW/LM and NPM/L.
Vision or Threat—Awareness for Dual-Use in the Development of Autonomous Driving

In the digital age, the vision of autonomous vehicles (AVs) is vibrant. Research is being conducted worldwide to integrate AVs into our everyday lives in the future, spending considerable amounts of money in the development process. Actors from both engineering as well as social sciences are involved in this research, with technical disciplines strongly dominating. In addition to perceived progress of numerous newly developed technologies such as AVs, challenges should also be referred to. According to research analysis, the transferability of autonomous cars to the military sphere seems to be frequently forgotten or ignored (dual-use). Since not much research has been conducted in Germany on the potential deployment of autonomous driving development steps into military domains, 25 semistructured interviews with developers and researchers and actors involved in the field, were conducted in 2020. This article identifies that the majority of respondents interviewed were aware of general existing dual-use debates; however, few had reflected about dual-use issues regarding a possible transfer of their own development processes in the context of autonomous driving to military applications, intensively. One reason is the small-scale nature of research, another is the complexity of the field, which enables the engineer’s alienation from their responsibility for the artefacts’ use. Moreover, it has become clear that hardly any conversations among colleagues occur about possible misuse and that no standardized policy guidelines exist, which provide information about possible risk. To raise dual-use awareness, scientific contributions, risk education, and interdisciplinary discussions are essential.
Relationship of service quality dimensions, customer satisfaction and loyalty in e-commerce: a case study of the Shopee App

ABSTRACT The development of information technology and mobile device functions have increased the number of online shopping applications, which has increased the competition to attract customers. Service quality has been traced as an important factor that influences consumers’ buying behaviour. This present study aimed to assess the difference of four dimensions of service quality (efficiency, fulfilment, system availability, and privacy services) and to examine the relationship of service quality dimensions towards customers’ satisfaction and loyalty on Shopee App. The data collection conducted from 03rd January to 31 January 2019 were carried out by an online survey. The data were collected from 373 samples. The data were analysed using ANOVA to evaluate the difference in service quality dimensions and regression analysis to examine the relationship among service quality, satisfaction, and loyalty. This study found that the significant difference in service quality dimensions with the highest importance to customers was efficiency, followed by fulfilment, system availability, and privacy. All service quality dimensions positively influenced the customer’s satisfaction. Furthermore, the efficiency, fulfilment, and privacy services were the significant factors of customers’ loyalty to use the Shopee App. Lastly, consumers’ satisfaction positively influenced the consumers’ loyalty.
A multi-method framework for global real-time climate attribution

Abstract. Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA, highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihood of daily maximum temperatures. For instance, over 56 % of the Earth's total land area, all three framework methods agree that maximum temperatures greater than the preindustrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally, over 52 % of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of preindustrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health, and other ecosystem/human system impact studies.