Miracle-dz/UniFAR-FTUs huggingface dataset:
“Beste Grüße, Maria Meyer” — Pseudonymization of Privacy-Sensitive Information in Emails

The exploding amount of user-generated content has spurred NLP research to deal with documents from various digital communication formats (tweets, chats, emails, etc.). Using these texts as language resources implies complying with legal data privacy regulations. To protect the personal data of individuals and preclude their identification, we employ pseudonymization. More precisely, we identify those text spans that carry information revealing an individual’s identity (e.g., names of persons, locations, phone numbers, or dates) and subsequently substitute them with synthetically generated surrogates. Based on CodE Alltag, a German-language email corpus, we address two tasks. The first task is to evaluate various architectures for the automatic recognition of privacy-sensitive entities in raw data. The second task examines the applicability of pseudonymized data as training data for such systems since models learned on original data cannot be published for reasons of privacy protection. As outputs of both tasks, we, first, generate a new pseudonymized version of CodE Alltag compliant with the legal requirements of the General Data Protection Regulation (GDPR). Second, we make accessible a tagger for recognizing privacy-sensitive information in German emails and similar text genres, which is trained on already pseudonymized data.
Enriching Grammatical Error Correction Resources for Modern Greek

Grammatical Error Correction (GEC), a task of Natural Language Processing (NLP), is challenging for underepresented languages. This issue is most prominent in languages other than English. This paper addresses the issue of data and system sparsity for GEC purposes in the modern Greek Language. Following the most popular current approaches in GEC, we develop and test an MT5 multilingual text-to-text transformer for Greek. To our knowledge this the first attempt to create a fully-fledged GEC model for Greek. Our evaluation shows that our system reaches up to 52.63% F0.5 score on part of the Greek Native Corpus (GNC), which is 16% below the winning system of the BEA-19 shared task on English GEC. In addition, we provide an extended version of the Greek Learner Corpus (GLC), on which our model reaches up to 22.76% F0.5. Previous versions did not include corrections with the annotations which hindered the potential development of efficient GEC systems. For that reason we provide a new set of corrections. This new dataset facilitates an exploration of the generalisation abilities and robustness of our system, given that the assessment is conducted on learner data while the training on native data.
Leveraging Hashtag Networks for Multimodal Popularity Prediction of Instagram Posts

With the increasing commercial and social importance of Instagram in recent years, more researchers begin to take multimodal approaches to predict popular content on Instagram. However, existing popularity prediction approaches often reduce hashtags to simple features such as hashtag length or number of hashtags in a post, ignoring the structural and textual information that entangles between hashtags. In this paper, we propose a multimodal framework using post captions, image, hashtag network, and topic model to predict popular influencer posts in Taiwan. Specifically, the hashtag network is constructed as a homogenous graph using the co-occurrence relationship between hashtags, and we extract its structural information with GraphSAGE and semantic information with BERTopic. Finally, the prediction process is defined as a binary classification task (popular/unpopular) using neural networks. Our results show that the proposed framework incorporating hashtag network outperforms all baselines and unimodal models, while information captured from the hashtag network and topic model appears to be complementary.
A Constrained Bandit Approach for Online Dispatching

Online dispatching refers to the process (or an algorithm) that dispatches incoming jobs to available servers in realtime. The problem arises in many different fields. Examples include routing customer calls to representatives in a call center, assigning patients towards in a hospital, dispatching goods to different shipping companies, scheduling packets over multiple frequency channels in wireless communications, routing search queries to servers in a data center, selecting an advertisement to display to an Internet user, and allocating jobs to workers in crowdsourcing.
Videogame Hand Telerehabilitation Intervention in an Adolescent With Hemiplegic Cerebral Palsy Maintained Hand Function and Forearm Bone Health 14 Months After an In-Home Virtual-Reality

Virtual reality videogames can be used to motivate rehabilitation, and telerehabilitation can be used to improve access to rehabilitation. These uses of technology to improve health outcomes are a burgeoning area of rehabilitation research. So far, there is a lack of reports of long-term outcomes of these types of interventions. The authors report a 15-year-old boy with hemiplegic cerebral palsy and epilepsy because of presumed perinatal stroke who improved his plegic hand function and increased his plegic forearm bone health during a 14-month virtual reality videogame hand telerehabilitation intervention. A total of 14 months after the intervention ended, repeat evaluation demonstrated maintenance of both increased hand function and forearm bone health. The implications of this work for the future of rehabilitation in children with neurological disabilities are discussed in this article.
Effect of oxidative ageing on the thermophysical and electrical properties of the nanofluid with statistical analysis of AC breakdown voltage

In this study, the prediction of the breakdown probability of a new batch of nanofluid (NF), namely exfoliated hexagonal boron nitride based NF is performed. Weibull two-parameter model distribution function has been used to predict the AC breakdown voltage (ACBDV) probability percentage at 5, 10, 50 and 63.2% of failure. To verify the effect of ageing on the other allied properties like dielectric dissipation factor, resistivity, interfacial tension, acid number and thermal conductivity of the insulating NF, oxidative ageing test has been performed at three different ageing durations and compared with the existing mineral oil (MO). It is observed that the new batch of NF has the superior properties to be new age insulating oil compared to the MO. A significant enhancement in the ACBDV and thermal conductivity of the NF compared to MO is observed. Hydrophobic nature of the nanoparticle (NP) which has the minimum affinity towards the polar contamination hinders early ageing degradation in the NF. The enhancement in ACBDV of NF is due to the stimulated charge accumulation on the surface of the NP of the NF. The high aspect ratio of the exfoliated NP surface enhances the heat transfer capabilities in the NF.
Unbiased Boosting Estimation for Censored Survival Data

: Boosting methods have been broadly discussed for various settings, and most methods handle data with complete observations. Although some methods are available for survival data with censored responses, they tend to assume a speciﬁc model for the survival process, and most provide numerical implementation procedures without rigorous theoretical justiﬁcations. In this paper, we develop an unbiased boosting estimation method for censored survival data, without assuming an explicit model, and explore three strategies for adjusting the loss functions, while accommodating censoring eﬀects. We implement the proposed method using a functional gradient descent algorithm, and rigorously establish our theoretical results, including the consistency and optimization convergence. Our numerical studies show that the proposed method exhibits satisfactory performance in ﬁnite-sample settings.
Identification of drought-responsive phenolic compounds and their biosynthetic regulation under drought stress in Ligularia fischeri

Ligularia fischeri, a leafy edible plant found in damp shady regions, has been used as an herbal medicine and is also consumed as a horticultural crop. In this study, we investigated the physiological and transcriptomic changes, especially those involved in phenylpropanoid biosynthesis, induced by severe drought stress in L. fischeri plants. A distinguishing characteristic of L. fischeri is a color change from green to purple due to anthocyanin biosynthesis. We chromatographically isolated and identified two anthocyanins and two flavones upregulated by drought stress using liquid chromatography-mass spectrometry and nuclear magnetic resonance analyses in this plant for the first time. In contrast, all types of caffeoylquinic acids (CQAs) and flavonol contents were decreased under drought stress. Further, we performed RNA sequencing to examine the molecular changes in these phenolic compounds at the transcriptome level. In an overview of drought-inducible responses, we identified 2,105 hits for 516 distinct transcripts as drought-responsive genes. Moreover, differentially expressed genes (DEGs) associated with phenylpropanoid biosynthesis accounted for the greatest number of both up- and downregulated DEGs by Kyoto Encyclopedia of Genes and Genomes enrichment analysis. We identified 24 meaningful DEGs based on the regulation of phenylpropanoid biosynthetic genes. Potential drought-responsive genes included upregulated flavone synthase (LfFNS, TRINITY DN31661 c0 g1 i1) and anthocyanin 5-O-glucosyltransferase (LfA5GT1, TRINITY DN782 c0 g1 i1), which could contribute to the high levels of flavones and anthocyanins under drought stress in L. fischeri. In addition, the downregulated shikimate O-hydroxycinnamolytransferase (LfHCT, TRINITY DN31661 c0 g1 i1) and hydroxycinnamoyl-CoA quinate/shikimate transferase (LfHQT4, TRINITY DN15180 c0 g1 i1) genes led to a reduction in CQAs. Only one or two BLASTP hits for LfHCT were obtained for six different Asteraceae species. It is possible that the HCT gene plays a crucial role in CQAs biosynthesis in these species. These findings expand our knowledge of the response mechanisms to drought stress, particularly regarding the regulation of key phenylpropanoid biosynthetic genes in L. fischeri.
A Comparative Study between Erector Spinae Plane Block with General Anesthesia versus Conventional Intravenous Analgesics with General Anesthesia in Patients undergoing Conserving Surgery for Breast Cancer

ABSTRACT Background Acute pain is a common concern after breast surgery, and despite the conventional management of pain with different analgesics, many patients have poor responses. Paravertebral, epidural, pectoral and serratus plane blocks are the regional blocks, which are used commonly to control pain after breast surgeries. Each of these techniques has its advantages and disadvantages. Erector spinae plane block (ESPB) is a new regional approach which can provide analgesia to the chest wall, by which local anesthetic drugs can be deposited in the plane between erector spinae muscle and the transverse process producing hemithoracic analgesia. This study was intended to assess the efficacy of ESPB for postoperative analgesia after breast surgeries. Results Fifty-four patients were enrolled in this study and divided into two groups, 27 patients in each group. Group B patients received general anesthesia with ESPB and group C patients received general anesthesia with conventional intravenous analgesia. Group B showed statistically significant reduction in total opioid consumption at 24 hours postoperatively, lower VAS score and improved hemodynamic parameters in the first 8 hours postoperatively. Conclusions Ultrasound-guided ESPB performed at the level of T5 was found to be effective and safe in controlling postoperative pain after breast surgery.
Online music business: The relationship between perceived benefit, perceived sacrifice, perceived value, and purchase intention

The development of information technology has changed the business model of firms in the industry that are in contact with technology. One of them is in the music industry, which currently has penetrated the online platform with a series of accompanying issues. This study uses a conceptual approach to Technology Acceptance Model to understand this relationship. Although in fact similar research has been carried out by a number of researchers, this topic remains an interesting object to study considering that technology acceptance cannot be separated from the environment in which consumers are located. This research was conducted with a quantitative approach to test the research hypotheses using data obtained from questionnaires distributed to 102 respondents spread across the Yogyakarta area. This study found that perceived usefulness and perceived price do not have a significant influence on perceived value. On the other hand, perceived playfulness and perceived ease of use have a significant influence on perceived value. This finding emphasizes that the online music business is more in touch with entertainment products. With respondents who are dominated by young people in a developing country, it is shown that pleasure is a highly prioritized part of overcoming other considerations, especially price and usability. Another finding is that the greater the perceived value of online music, the higher the intention in purchasing the product would be. This study adds to the literature and information about how consumers respond in different regions (Yogyakarta, Indonesia) as one the developing countries on the factors that affect the perceived value and purchase intention, thus it can be used to test the generality of the results of previous studies.
Current sharing and stability in an extremely low AC-loss MgB2 conductor

In the push to develop high power electric aircraft, superconducting technology promises to significantly reduce mass and volume of motors and generators. However, challenges related to AC-loss and thermal management are a significant factor in preventing the proliferation of aerospace superconducting technologies. Increasing the resistance of the metal matrix stabilization has only gone so far in reducing coupling currents for higher frequency applications. In this research, Multiphysics simulations of a single composite filament were used to investigate stability decreases when using very high thermal conductivity electrical insulator (CsI) or metal-to-insulator transitioning material (V2O3) to replace the slightly resistive metal matrix typically used for a low AC loss MgB2 composite wire. The insulators separate the MgB2 filaments entirely, only allowing transient current sharing to occur with the high purity Nb diffusion barrier or with the metallic state V2O3. These simulations show that for these very low AC-loss composites at 20 K, instability will become a major issue due to reductions in current sharing. With higher electrical conductivity metal-to-insulator materials, higher thermal conductivity impregnation materials, and thicker metallic diffusion barriers it may be possible to find a reasonable balance between AC-loss and stability.
Fractional Flow Reserve Cardio-Oncology Effects on Inpatient Mortality, Length of Stay, and Cost Based on Malignancy Type: Machine Learning Supported Nationally Representative Case-Control Study of 30 Million Hospitalizations

Background and Objectives: There are no nationally representative studies of mortality and cost effectiveness for fractional flow reserve (FFR) guided percutaneous coronary interventions (PCI) in patients with cancer. Our study aims to show how this patient population may benefit from FFR-guided PCI. Materials and Methods: Propensity score matched analysis and backward propagation neural network machine learning supported multivariable regression was performed for inpatient mortality in this case-control study of the 2016 National Inpatient Sample (NIS). Regression results were adjusted for age, race, income, geographic region, metastases, mortality risk, and the likelihood of undergoing FFR versus non-FFR PCI. All analyses were adjusted for the complex survey design to produce nationally representative estimates. Results: Of the 30,195,722 hospitalized patients meeting criteria, 3.37% of the PCIs performed included FFR. In propensity score adjusted multivariable regression, FFR versus non-FFR PCI significantly reduced inpatient mortality (OR 0.47, 95%CI 0.35–0.63; p < 0.001) and length of stay (LOS) (in days; beta −0.23, 95%CI −0.37–−0.09; p = 0.001) while increasing cost (in USD; beta $5708.63, 95%CI, 3042.70–8374.57; p < 0.001), without significantly increasing complications overall. FFR versus non-FFR PCI did not specifically change cancer patients’ inpatient mortality, LOS, or cost. However, FFR versus non-FFR PCI significantly increased inpatient mortality for Hodgkin’s lymphoma (OR 52.48, 95%CI 7.16–384.53; p < 0.001) and rectal cancer (OR 24.38, 95%CI 2.24–265.73; p = 0.009). Conclusions: FFR-guided PCI may be safely utilized in patients with cancer as it does not significantly increase inpatient mortality, complications, and LOS. These findings support the need for an increased utilization of FFR-guided PCI and further studies to evaluate its long-term impact.
Near-Field Microwave Scattering Formulation by a Deep Learning Method

A deep learning (DL) method is applied to modeling electromagnetic (EM) scattering for microwave breast imaging (MBI). The neural network (NN) accepts 2-D dielectric breast maps at 3 GHz and produces scattered-field data on an antenna array composed of 24 transmitters and 24 receivers. The NN was trained by 18000 synthetic digital breast phantoms generated by generative adversarial network (GAN), and the scattered-field data pre-calculated by method of moments (MOM). Validation was performed by comparing the 2000 NN-produced datasets isolated from the training data with the data computed by MOM. Finally, data generated by NN and MOM were used for image reconstruction. The reconstruction demonstrated that errors caused by NN would not significantly affect the image result. But, the computational speed of NN was nearly 104 times faster than the MOM, indicating that DL has the potential to be considered as a fast tool for EM scattering computation.
Challenges faced by female entrepreneurs: The case of the South African learner transport industry

This study aimed to explore the challenges faced by female entrepreneurs operating in the learner transport industry in South Africa, specifically in the Gauteng province. The study adopted a qualitative research approach by making use of semi-structured interviews with 15 female entrepreneurs operating in the learner transport industry in the Gauteng province. A non-probability sampling approach was adopted by means of convenience sampling. Data was analysed through thematic content analysis, using Atlas.ti. Findings from the study indicated that female entrepreneurs in the learner transport industry face several challenges such as high operational costs, a male-dominated industry, being disrespected or undermined by customers, a lack of trust and commitment, being belittled, as well as a lack of opportunity awareness. Participants also indicated a need for government intervention in the industry due to prevailing working conditions. The findings of the study contribute both to the industry, as well as the existing body of knowledge. The findings from the study will assist the national government to collaborate with regional governmental transport agencies, as well as with regional industry associations in an effort to improve the working environment for female entrepreneurs, as well as grow the learner transport industry in a more structured and regulated manner. The industry, despite being regulated by regional transport associations, is currently unstructured.
Twenty-Five Years of the Midurethral Sling: Lessons Learned

Midurethral slings (MUS) are widely accepted for the surgical treatment of stress urinary incontinence (SUI) in cases where conservative treatment has failed. They have been shown to be a safe and effective surgical treatment for the management of SUI. However, there have recently been growing concerns regarding the safety profile of mesh procedures, generating international debate and leading to national inquiries into the effectiveness and safety of mesh implants. A multitude of clinical, technical, manufacturer-related, and other health jurisdictional factors are involved in the outcomes of MUS. Appropriate patient selection and informed consent to all treatment options, with proper guidance from healthcare providers, are critical for empowering women to choose an appropriate treatment option based on a personalized decision.
Chokeberry (A. melanocarpa (Michx.) Elliott)—A Natural Product for Metabolic Disorders?

Abnormal metabolism of substances in the body can result in metabolic disorders which include obesity, cardiovascular diseases, diabetes, hypertension, chronic kidney disease, liver disease, or cancer. Foods rich in antioxidants can help to prevent and treat various types of disorders. Chokeberry fruits are rich in polyphenols, especially cyanidins, and therefore, can show a beneficial health effect. The aim of this study was to summarize and systematize reports about the effects of chokeberry on various metabolic parameters. Studies from 2000 to 2021, published in the PubMed and Google Scholar databases, were reviewed. The review of studies shows that chokeberry may have a positive effect in dyslipidemia and hypertension and may increase the body’s antioxidant defense mechanisms. The anti-inflammatory effect, in turn, may translate into a reduction in the risk of metabolic disorders over a longer period of use. Changes in glucose levels were reported by studies in which the intervention lasted more than 10 weeks in patients with carbohydrate metabolism disorders. The effects of protecting the liver, inhibiting platelet aggregation, lowering uric acid levels, and having a protective effect on the kidneys require additional confirmation in human clinical trials. Consumption of chokeberry fruit did not impact on anthropometric measurements; however, it seems that chokeberry fruit can be recommended in many metabolic disorders due to the richness of bioactive ingredients.
BIOREMEDIATION OF LEAD IN WATER BY TWO PURE MICROORGANISMS AND BACTERIA-YEAST CONSORTIUM

Water pollution by heavy metals continues to be a problem of environmental interest, so evaluating more efficient alternatives for their removal is of high importance. This paper presents the results obtained when applying microbial bioremediation processes through biosorption and bioaccumulation for the removal of lead in water through the use of the bacteria Pseudomonas putida and the yeast Saccharomyces cerevisiae , in pure culture, in which case the most efficient processes were applied in the bacteria-yeast consortium. The highest removals were achieved with the biosorption process, using autoclaved inactivated Pseudomonas putida , where a 100% removal was obtained within 24 hours, whereas with Saccharomyces cerevisiae inactivated in the water bath a removal of 90.23% was obtained. Subsequently, the evaluation of the microorganisms in the consortium was carried out, obtaining a lead removal of 52.11%, demonstrating that the use of pure cultures of such inactivated microorganisms is an efficient, fast, reliable and low-cost alternative, since the use of inactivated microorganisms reduces the use of culture media for their preservation. Resumen