Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization and Regional Differences of In-Patient Services for Peripheral Arterial Disease and Acute Limb Ischemia in Germany: Secondary Analysis of Nationwide DRG Data

Background: Peripheral arterial disease (PAD) and acute limb ischemia (ALI) pose an increasing strain on health care systems. The objective of this study was to describe the German health care landscape and to assess hospital utilization with respect to PAD and ALI. Methods: Secondary data analysis of diagnosis-related group statistics data (2009–2018) provided by the German Federal Statistical Office. Inclusion of cases encoded by the International Classification of Diseases (ICD-10) codes for PAD and arterial embolism or thrombosis. Construction of line diagrams and choropleth maps to assess temporal trends and regional distributions. Results: A total of 2,589,511 cases (median age 72 years, 63% male) were included, of which 2,110,925 underwent surgical or interventional therapy. Overall amputation rate was 17%, with the highest rates of minor (28%) and major amputations (15%) in patients with tissue loss. In-hospital mortality (overall 4.1%) increased in accordance to Fontaine stages and was the highest in patients suffering arterial embolism or thrombosis (10%). Between 2009 and 2018, the annual number of PAD cases with tissue loss (Fontaine stage IV) increased from 97,092 to 111,268, whereby associated hospital utilization decreased from 2.2 million to 2.0 million hospital days. Hospital incidence and hospital utilization showed a clustering with the highest numbers in eastern Germany, while major amputation rate and mortality were highest in northern parts of Germany. Conclusions: Increased use of endovascular techniques was observed, while hospital utilization to treat PAD with tissue loss has decreased. This is despite an increased hospital incidence. Addressing socioeconomic inequalities and a more homogeneous distribution of dedicated vascular units might be advantageous in reducing the burden of disease associated with PAD and ALI.
Analysis of Factors Related to Spiritual Psychology and Quality of Life in Patients with Inflammatory Bowel Disease Based on Artificial Intelligence IBD Nursing Technology

To investigate the characteristics of psychological symptoms in inflammatory bowel disease (IBD), to study the relationship between psychological symptoms and quality of life, and to provide some theoretical basis for the corresponding psychological treatment of IBD patients with psychiatric abnormalities. With the development of artificial intelligence (AI) and its gradual application to the medical field, it has brought new ideas to the medical development, and its research and application in IBD, including ulcerative colitis (UC) and Crohn's disease (CD), is increasing. Machine learning is used to select reasonable models and methods to help the prediction, diagnosis, treatment, and prognosis of IBD. In this study, we improved on the classical unidirectional LSTM model by adding backward and forward LSTM layers, designed a bidirectional LSTM model to improve the shortcomings of unidirectional LSTM with insufficient dependence on the posterior, introduced the contribution rate α to adjust the weight matrix of the forward LSTM layer and the backward LSTM layer, and experimentally verified the correctness and superiority of the proposed model. A total of 159 patients with IBD and 89 healthy people were collected and psychologically assessed using the general status questionnaire, the 90-item symptom checklist (SCL-90). Patients with IBD are prone to a combination of obsessive-compulsive, interpersonal, depressive, hostile, and other abnormal psychological symptoms, and their quality of life is significantly reduced; quality of life is mainly affected by disease condition, depression, interpersonal sensitivity, and paranoia, and patients in the active stage of the disease with heavy symptoms of depression, paranoia, and interpersonal sensitivity have low quality of life.
A Cognitive Deception Model for Generating Fake Documents to Curb Data Exfiltration in Networks During Cyber-Attacks

The exponential increase in the compromise of sensitive and intellectual properties alludes to the huge price the global community must pay for the digital revolution we are currently experiencing. This irrefutable reality is a major reason why cybersecurity defences continue to be a pressing and timely area of research. Traditional countermeasures of cyber defence using boundary controllers and filters such as intrusion detection, access controls, firewalls and so on, have proven ineffective. Such measures fail to account for the attacker’s inherent advantage of being increasingly techno-savvy, as well as their persistence in attempting to compromise the security of not only high-value targets, but also the vast pool of oblivious users of technology. The use of decoys and deception is one of the emerging solutions for cyber defence. Leveraging decoys and deception for security pre-date the advent of the digital revolution as centuries have witnessed the military using human decoys to deceive and successfully defeat their adversaries during wars. However, its benefits for reducing cyberattacks in these digital times have not been thoroughly investigated. One of its use requires that fake text documents are positioned in the repository of critical documents in order to mislead and catch hackers attempting to exfiltrate sensitive documents. Current methods of generating fake text documents involve using symbols, junk documents, randomly generated texts. Such approaches fail to capture the empirical and linguistic properties of language, resulting in messages that do not scale well, are not realistic, fail in the context of syntax and are semantically void. Consequently, failing to convince the attackers to believe they are the original messages. This paper presents a Cognitive Deception Model (CDM) based on a neural model which takes an input message and generates syntactically cohesive and semantically coherent independent looking but plausible and convincing decoy messages to cognitively burden and deceive the adversaries. The experimental results used to validate the models, as well as the comparison with state-of-the-art tools, show that it outperforms existing systems.
Loss calculation method for hybrid excited machines

This paper presents an experimental method for determining the main losses components in permanent magnets synchronous machines with hybrid excitation (HPMSM). To model the sources of losses is very important in order to optimize such machines and to develop robust control strategies (minimum losses optimization) which can be used in practical applications. Proposed approach was validated using time domain simulations and experiments. Experimental results clearly exhibit the benefits of including core loss model in control system of HPMSM. Streszczenie. W artykule przedstawiono eksperymentalną metodę wyznaczania głównych składników strat maszyn synchronicznych z magnesami trwałymi i wzbudzeniu hybrydowym (HPMSM). Zamodelowanie źródeł strat jest istotne dla optymalizacji tych maszyn oraz dla opracowania właściwych strategii ich sterowania (przy minimalizacja strat), które mogłyby być zastosowane w praktyce. Zaproponowany algorytm został sprawdzony teoretycznie i doświadczalnie. Wyniki badań eksperymentalnych pokazały zalety uwzględnienia strat mocy w algorytmie sterowania HPMSM. (Metoda wyznaczania strat maszyn wzbudzanych hybrydowo).
How ICT is changing the nature of the farm: a research agenda on the economics of big data.

Modern information-based technologies, such as self-driving tractors, GPS, milk robots, automated egg production, satellite data and social media, will change farm practices and agricultural structure and contribute to the prosperity and resilience of farming systems. Based on macro-trends and niche developments we argue that the food chain will become much more datadriven, based on up to date ICT. It will move away from a situation characterised by a low level of integration of data. This has a large potential impact on issues like sustainability, food safety, resource efficiency and waste reduction. The economic effects of such developments are still to be explored. At first sight it could lead to more closely integrated supply chains that makes the farmer act as a franchisee with limited freedom. But the opposite could be true as well, with more transparency and easier options for direct sales in consumer food webs, using social media and smart solutions for the ‘last mile’ delivery. Like with previous technological developments, not all farmers will invest in new skills and where technologies are saving labour, farms will get bigger. Some farms or regions will become less competitive if the basic infrastructure (e.g. in broad band internet or GPS systems) lacks. Competition between advisors could increase, if they are able to serve farmers digitally. That could also mean that a part of such value added activities moves from the most remote rural areas to regions with clusters of knowledge. As Allen and Lueck (2002) showed in their “Nature of the farm”, that family farms are characterised by a low level of specialisation of the farmer’s tasks, as the markets does not provide enough incentives to specialise. Due to a low profitability with a high level of risk and especially high transaction costs in factor markets as a result of moral hazard, family farms are competitive over more industrial holdings in most types of farming. ICT, higher food prices and demography could change that.
SmartFVM: A Fast, Flexible, and Scalable Hardware-based Virtualization for Commodity Storage Devices

A computational storage device incorporating a computation unit inside or near its storage unit is a highly promising technology to maximize a storage server’s performance. However, to apply such computational storage devices and take their full potential in virtualized environments, server architects must resolve a fundamental challenge: cost-effective virtualization. This critical challenge can be directly addressed by the following questions: (1) how to virtualize two different hardware units (i.e., computation and storage), and (2) how to integrate them to construct virtual computational storage devices, and (3) how to provide them to users. However, the existing methods for computational storage virtualization severely suffer from their low performance and high costs due to the lack of hardware-assisted virtualization support. In this work, we propose SmartFVM-Engine, an FPGA card designed to maximize the performance and cost-effectiveness of computational storage virtualization. SmartFVM-Engine introduces three key ideas to achieve the design goals. First, it achieves high virtualization performance by applying hardware-assisted virtualization to both computation and storage units. Second, it further improves the performance by applying hardware-assisted resource orchestration for the virtualized units. Third, it achieves high cost-effectiveness by dynamically constructing and scheduling virtual computational storage devices. To the best of our knowledge, this is the first work to implement a hardware-assisted virtualization mechanism for modern computational storage devices.
Evolutionary Analysis of Innovation Development in a Metropolitan Area from a Symbiosis Perspective: Empirical Research on the Shanghai Metropolitan Area

Promoting the innovative development of cities in the metropolitan area holds great signiﬁcance for China’s implementation of the “innovation-driven development” strategy. According to symbiosis theory, this paper constructed a city-based Lotka-Volterra symbiosis model and innovation-level index system. We used the Shanghai metropolitan area as a sample for empirical analysis to explore the evolution of the comprehensive development level of urban innovation under diﬀerent symbiotic relationships. The research results showed that under a reciprocal relationship, the innovative development of core city and node cities was higher than the actual level; under a mutual inhibition symbiosis or parasitic symbiotic relationship, the innovative development of core city and node cities was lower than the actual level; and under an independent symbiotic relationship, the innovative development of the core city was higher than the actual level, while that of the node cities was lower than the actual level. development level
Assessment of wavelet-SVR and wavelet-GP models in predicting the groundwater level using areal precipitation and consumption data

ABSTRACT Assessing and predicting groundwater level (GWL) fluctuations using specific models provides valuable information for water resources management and consumption planning. In this study, areal monthly data of GWL precipitation and consumptions were used to predict the GWL using genetic programming (GP), wavelet-GP, support vector regression (SVR) and wavelet-SVR models over a period of 11 years. Appropriate time lags were determined using the autocorrelation function (ACF) and cross-correlation function (CCF). It was found that consumption with a two-month lag and precipitation with a one-month lag have the greatest effect on the GWL. Discrete wavelet transform (DWT) was used to decompose time series into low- and high-frequency components for wavelet-SVR and wavelet-GP models. Two types of input structures were used for modelling (single GWL data and GWL considering consumptions and precipitation data). The results showed that the wavelet-SVR model had the best performance (according to R, RMSE and NSE) compared to the other three models, and SVR had better performance than the GP.
Inhibition of Heat Shock Protein 90 Attenuates the Damage of Blood-Brain Barrier Integrity in Traumatic Brain Injury Mouse Model

Heat shock protein 90 (HSP90) is widely found in brain tissue. HSP90 inhibition has been proven to have neuroprotective effects on ischemic strokes. In order to study the role of HSP90 in traumatic brain injury (TBI), we carried out the present study. A novel inhibitor of the HSP90 protein, 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DA), has been investigated for its function on the blood-brain barrier (BBB) damage after traumatic brain injury (TBI) in mouse models. These C57BL/6 mice were used as a TBI model and received 17-DA (0.1 mg/kg/d, intraperitoneally) until the experiment ended. To find out whether 17-DA may protect against TBI in vitro, bEnd.3 cells belonging to mouse brain microvascular endothelium were used. The HSP90 protein expressions were raised after TBI at the pericontusional area, especially at 3 d. Our study suggested that 17-DA-treated mice improved the recovery ability of neurological deficits and decreased brain edema, Evans blue extravasation, and the loss of tight junction proteins (TJPs) post-TBI. 17-DA significantly promoted cell proliferation and alleviated apoptosis by inhibiting the generation of intracellular reactive oxygen species (ROS) to downregulate cleaved caspase-3, matrix metallopeptidase- (MMP-) 2, MMP-9, and P-P65 in bEnd.3 cells after the injury. As a result, we assumed that the HSP90 protein was activated post-TBI, and inhibition of HSP90 protein reduced the disruption of BBB and improved the neurobehavioral scores in a mouse model of TBI through the action of 17-DA, which inhibited ROS generation and regulated MMP-2, MMP-9, NF-κB, and caspase-associated pathways. Thus, blocking HSP90 protein may be a potential therapeutic strategy for TBI.
Investigation of the relationship between miR-33a, miR-122, erythrocyte membrane fatty acids profile, and serum lipids with components of metabolic syndrome in type 2 diabetic patients

Background and purpose: MicroRNAs (miRNAs) are small non-coding RNA molecules acting as critical regulators of post-transcriptional gene expression. MiR-33a and miR-122 have a crucial role in cholesterol and lipid metabolism. Therefore, their dysregulation may contribute to metabolic abnormality and their inhibition may be a useful therapeutic strategy. The objective of the present study was to investigate the relationship between miR-33a, miR-122, erythrocyte membrane fatty acids profile, and serum lipids with components of metabolic syndrome in an Iranian population suffering from type 2 diabetes mellitus (T2DM). Experimental approach: Expression of miR-33a and miR-122 was measured by real-time polymerase chain reaction and erythrocyte membrane fatty acid profiles were analyzed by gas chromatography-mass spectrometry. Findings/Results: T2DM patients with and without metabolic syndrome had significantly higher miR-33a and miR-122 levels compared to controls. MiRNAs were significantly correlated with saturated fatty acid (SFAs), total SFAs/total polyunsaturated fatty acids (PUFAs) ratio, fasting plasma glucose, triacylglycerols, insulin and homeostatic model assessment of insulin resistance. In addition, there was a significant negative correlation between miR-33a and miR-122 levels and PUFAs, total PUFAs/total SFAs ratio and omega 6 fatty acids. Conclusion and implications: Considering the roles of miR-33a and miR-122 in cholesterol and lipids metabolism, it may be concluded that the measurement of their expression may be useful as a potential additional biomarker for cardiometabolic derangement in T2DM patients. In addition, these findings may suggest that the inhibition of these miRNAs by anti-miRNA therapies may be explored as a potential therapeutic strategy.
Reflexive inertia : reinventing scholarship through digital practices

Digital humanities is an emerging field whose practitioners apply digital technology to humanistic research problems. Its manifestations are diverse: from the use of online annotation tools in the collaborative study of empirical sources, the computational analysis of large corpora of textual data, to the use of provocative digital performances for exploring the twists and turns of poststructuralist theory. At the same time, such engagement with novel technologies is often full of tension. In contrast to the single-author, monograph-oriented research that characterizes established forms of scholarship, digital humanities is often practiced in collaborative, interdisciplinary projects that produces digital output rather than traditional publications. The use of digital technology thus creates exciting new possibilities to supplement and extend humanistic knowledge production, but it also entails uncommon requirements regarding the epistemic, social, and material organization of research. Drawing on a combination of ethnographic work and theories from Science & Technology Studies, this thesis investigates the conflicts that arise as scholars try to incorporate digital approaches into their established practices. Its main argument is that lasting innovations in the scholarly work process will only be possible if they are informed by a reflexive sensibility for the history and organizational specificities of the humanities.
GEO SAR Imaging of Maneuvering Ships Based on Time–frequency Features Extraction

Geosynchronous synthetic aperture radar (GEO SAR) system has the ability of observing targets for several hours in a large field of view. Complex and irregular rotating motion of the ship on sea in long aperture time makes it difficult to focus. To accomplish the imaging of maneuvering ships, a novel imaging algorithm is proposed in this article. A spatial filter constructed by convolutional neural network (CNN) is utilized to extract the time–frequency features. Combined with local generalized Radon–Fourier transform (GRFT) and phase gradient autofocus (PGA), the extraction accuracy further increases. Validation results based on simulation experiment, airborne experiment, and wave pool experiment show that the proposed algorithm achieves good imaging performance.
Probabilistic Shaping-Based Spatial Modulation for Spectral-Efficient VLC

Visible light communication (VLC) is a promising technology for 6th-generation (6G) networks because of its attractive feature such as a wide unlicensed spectrum. In this paper, a novel adaptive coded spatial modulation scheme with probabilistic shaping (PS) is proposed to approach the capacity of the spatial modulation (SM) in visible light communication (VLC) channels with intensity modulation and direct detection (IM/DD). In the proposed scheme, spatial and constellation symbols are probabilistically shaped depending on the user’s location inside the room and the optical signal-to-noise ratio (OSNR). Moreover, we optimize the channel coding rate to maximize further the achievable rate of the proposed scheme for a given OSNR. Finally, we propose an algorithm to compute the capacity-achieving distribution of the proposed scheme with unipolar <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-ary pulse amplitude modulation (PAM) signaling. The proposed scheme outperforms uniform and an orthogonal frequency-division multiplexing (OFDM) based scheme in terms of spectral efficiency (SE) and/or frame error rate (FER). For example, for 8-PAM signaling with <inline-formula> <tex-math notation="LaTeX">$N=8$ </tex-math></inline-formula> transmit antennas, the proposed scheme operates within 0.2 dB from the unipolar <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-PAM SM VLC channel signaling capacity and outperforms the uniform and OFDM based schemes in terms of FER by at least 1.1 dB and 1.3 dB at a normalized data rate of 1.33 bits per channel use per sub-carrier (b/cu/sc), respectively.
Machine Learning Based Quantitative Damage Monitoring of Composite Structure

ABSTRACT Composite materials have been widely used in many industries due to their excellent mechanical properties. It is difficult to analyze the integrity and durability of composite structures because of their own characteristics and the complexity of load and environments. Structural health monitoring (SHM) based on built-in sensor networks has been widely evaluated as a method to improve the safety and reliability of composite structures and reduce the operational cost. With the rapid development of machine learning, a large number of machine learning algorithms have been applied in many disciplines, and also are being applied in the field of SHM to avoid the limitations resulting from the need of physical models. In this paper, the damage monitoring technologies often used for composite structures are briefly outlined, and the applications of machine learning in damage monitoring of composite structures are concisely reviewed. Then, challenges and solutions for quantitative damage monitoring of composite structures based on machine learning are discussed, focusing on the complete acquisition of monitoring data, deep analysis of the correlation between sensor signal eigenvalues and composite structure states, and quantitative intelligent identification of composite delamination damage. Finally, the development trend of machine learning-based SHM for composite structures is discussed.
Ontological and conceptual challenges in the study of aesthetic experience

ABSTRACT We explain that most of the explanations that traditionally have been used to conceptually and ontologically differentiate aesthetic experience from any other are not compatible with a naturalistic framework, since they are based on transcendental idealistic metaphysics, reductions, and on the assumption that the aesthetic is an a priori special ontology in the object and the mind. However, contemporary works that propose as an alternative to apply directly evidence and theory from the science of emotions to the problem of aesthetics introduce Aesthetic Science into a new set of problematic assumptions. We argue that conceptually equating or ontologically reducing the aesthetic to the theory of rewards cannot provide a clear alternative for any Aesthetic Science to naturalize the aesthetic experience as a heterogeneous class of events that are not already explained by affective science. This practice introduces a serious danger of making the term “aesthetic” and the respective scientific field pretty weak or completely redundant and unnecessary.