Miracle-dz/UniFAR-FTUs huggingface dataset:
A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

In the big data era, traditional auditing methods are facing challenge such as limited audit scope, uneven distribution of audit power, and insufficient audit analysis. To pursue high efficiency, the utilization of big data analysis technique in financial auditing has been a novel tendency in this area. The deep learning has been popular in many areas due to its high freedom degree. Thus, this paper employs a typical deep learning model convolution neural network (CNN), and proposes a big data-driven financial auditing method using CNN. Specifically, the strong ability of feature abstraction of CNN is leveraged to extract multi-level features in materials, such as visual features, textual features, etc. Then, the multi-source features from auditing materials can be well fused for final discrimination. Some simulation experiments are conducted on real-world financial auditing scenes for assessment. And the results show that the designed the proposed financial auditing method possesses relatively high auditing accuracy.
A Pharmacoepidemiological Study of Myocarditis and Pericarditis Following the First Dose of mRNA COVID-19 Vaccine in Europe

This study assessed the myocarditis and pericarditis reporting rate of the first dose of mRNA COVID-19 vaccines in Europe. Myocarditis and pericarditis data pertinent to mRNA COVID-19 vaccines (1 January 2021–11 February 2022) from EudraVigilance database were combined with European Centre for Disease Prevention and Control (ECDC)’s vaccination tracker data. The reporting rate was expressed as events (occurring within 28 days of the first dose) per 1 million individuals vaccinated. An observed-to-expected (OE) analysis quantified excess risk for myocarditis or pericarditis following the first mRNA COVID-19 vaccination. The reporting rate of myocarditis per 1 million individuals vaccinated was 17.27 (95% CI, 16.34–18.26) for CX-024414 and 8.44 (95% CI, 8.18–8.70) for TOZINAMERAN; and of pericarditis, 9.76 (95% CI, 9.06–10.51) for CX-024414 and 5.79 (95% CI, 5.56–6.01) for TOZINAMERAN. Both vaccines produced a myocarditis standardized morbidity ratio (SMR) > 1, with the CX-024414 vaccine having a greater SMR than TOZINAMERAN. Regarding TOZINAMERAN, SMR for pericarditis was >1 when considering the lowest background incidence, but <1 when considering the highest background incidence. Our results suggest an excess risk of myocarditis following the first dose of the mRNA COVID-19 vaccine, but the relationship between pericarditis and the mRNA COVID-19 vaccine remains unclear.
Delimitation of central cities influenced regions based on weighted-Voronoi diagram

Based on analyzing the existing methods of delimitating influenced regions of cities, the complex components for calculating the central-strength value of each central city are selected; and the strength is calculated by means of principal component analysis. A new delimitation method of the central cities influenced region is given with help of weighted Voronoi diagram and breakpoint theory.
Game-theory strategies for open-source Infrastructure-as-Code

Infrastructure-as-Code can be designed, developed, and managed with an Open-Source Software approach. While openly embraced by academia, the OSS philosophy typically elicits some skepticism among industrial partners who want to ensure that adopting it will not enable their market competitors or harm them in any way, like by releasing valuable intellectual property into the wild.In this paper, we study this phenomenon from the perspective of game-theory, to then draw conclusions on what information to share, when, why, how, and with whom, so that risks are minimized, and benefits are maximized for each, and every partner engaged in IaC-focused collaborative projects that make use of an OSS approach.
COVID-19 Vaccine Acceptance, Attitude and Perception among Slum and Underserved Communities: A Systematic Review and Meta-Analysis

This systematic review summarises the literature on Coronavirus Disease 2019 (COVID-19) vaccination, including acceptance, uptake, hesitancy, attitude and perceptions among slum and underserved communities. Relevant studies were searched from PubMed, Scopus, Web of Science and Google Scholar, following a pre-registered protocol in PROSPERO (CRD42022355101) and PRISMA guidelines. We extracted data, used random-effects models to combine the vaccine acceptance, hesitancy and uptake rates categorically, and performed meta-regression by R software (version 4.2.1). Twenty-four studies with 30,323 participants met the inclusion criteria. The overall prevalence was 58% (95% CI: 49–67%) for vaccine acceptance, 23% (95% CI: 13–39%) for uptake and 29% (95% CI: 18–43%) for hesitancy. Acceptance and uptake were positively associated with various sociodemographic factors, including older age, higher education level, male gender, ethnicity/race (e.g., Whites vs African Americans), more knowledge and a higher level of awareness of vaccines, but some studies reported inconsistent results. Safety and efficacy concerns, low-risk perception, long distance to vaccination centres and unfavourable vaccination schedules were prominent reasons for hesitancy. Moreover, varying levels of attitudes and perceptions regarding COVID-19 vaccination were reported with existing misconceptions and negative beliefs, and these were strong predictors of vaccination. Infodemic management and continuous vaccine education are needed to address existing misconceptions and negative beliefs, and this should target young, less-educated women and ethnic minorities. Considering mobile vaccination units to vaccinate people at home or workplaces would be a useful strategy in addressing access barriers and increasing vaccine uptake.
Mid-infrared supercontinuum generation up to 4.6 µm using step-index indium fluoride fiber pumped by a femtosecond fiber laser near 2 µm

We report mid-infrared supercontinuum (SC) generation in a dispersion-engineered step-index indium fluoride fiber pumped by a femtosecond fiber laser near 2 μm. The SC spans 1.8 octaves from 1.25 μm to 4.6 μm with an average output power of 270 mW. The pump source is an all-fiber femtosecond laser that generates sub-100 fs pulses at 50 MHz repetition rate with 570 mW average power. The indium fluoride fiber used for SC generation is designed to have a zerodispersion wavelength close to 1.9 μm. Two fiber lengths of 30 cm and 55 cm are selected for the SC generation experiments based on the numerical modelling results. The measured spectra and the numerical modelling results are presented showing good agreement for both lengths. The femtosecond pumping regime is a key requirement for generating a coherent SC. We show by modelling that the SC is coherent for a pump with the same pulse width and energy as our fiber laser and added quantum-limited noise. The results are promising for the realization of coherent and high-repetition-rate SC sources, two conditions that are critical for spectroscopy applications using FTIR spectrometers. Additionally, the entire SC system is built using optical fibers with similar core diameters, which enables integration into a compact platform.
Fast-transient Capacitor-Less LDO in 0.18µm SOI Technology

Based on 0. 18µm SOI process, a fast transient capacitor-less is designed. An LDO with 1.6-2.3 voltage input range, 0-100mA load and 1.5V output voltage is designed. The circuit includes a bandgap reference circuit, a folded structure error amplifier, a super-gm source follower, an active capacitor circuit and an output power stage. Simulation results show that the proposed LDO is stable over the full load range, with a worst-case loop gain of 85dB, PM of 87deg, and GBW>100kHz. This design achieves a load regulation of $\boldsymbol{3\mu}\mathbf{V}/\mathbf{mA}$, a linear regulation of 9mV/V and a PSR of -33dB. The transient simulation results show that the load jumps from 0 to 100mA in $\boldsymbol{1\mu}\mathbf{s}$, the overshoot voltage is 57mV, the undershoot voltage is 112mV, and the settling time is $< \boldsymbol{1.5\mu} \mathrm{s}$. The quiescent current is $\boldsymbol{32\mu}\mathbf{A}$ when the LDO is on, and 630nA when the LDO is off. The temperature characteristic of this circuit is 8ppm/°C, and the circuit layout area is 0.063mm2.
Understanding how social discussion platforms like Reddit are influencing financial behavior

This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures to assess user participation, influence and topic affinities specifically. Our study also maps user-generated content to components of behavioral finance. While these types of information are usually gathered through surveys, it is obvious that large scale data analysis from social media can reveal many potentially unknown or rare insights. Characterising users based on their platform behavior to provide critical insights about how communities are formed and trust is established in these platforms using graphical analysis is also studied.
Effect of Compression Garment and Low Intensity Exercise on Upper Limb Function and Lymphedema among Post-Mastectomy Women: A quasiexperimental Two Arm Study

1.ABSTRACT Background : Lymphedema is one of the riskiest and most prevalent complications post-mastectomy that interfere upper limb function which negatively affect their quality of life. Aim: Evaluate the effect of compression garment (CG) along with shoulder exercise on reducing lymphedema and enhancing upper limb function among post mastectomy women. Method: Research design : A quasi experimental research design with a control group was used. Subjects : A total of 120 women with breast cancer were evenly and randomly allocated to the intervention and control group. Women in the interventional arm were directed to apply compression garmet during exercise for six months post operatively. The control group received routine hospital postoperative care. Tools: 1. Demographic and health relevant form, 2. Shoulder Pain and Disability index questionnaire, 3. Shoulder flexibility and grip strength, 4. lymphedema and arm circumference scale. Results : At a 6-month interval, the occurrence of lymphedema was markedly lower in the intervention group (p = 0.000) compared to their control and there were statistically significant differences between study groups as regard shoulder pain and upper limb function (p=0.000). Conclusion : Post mastectomy exercises combined with compression garment is very effective in preventing lymphedema and improving upper limb function in post-mastectomized women. Recommendations and Relevance to clinical practice : Low-intensity exercises and compression garment is a safe, effective, non-invasive method that should be incorporated into the management of post-mastectomy patients to prevent lymphedema and enhance upper limb function.
Pleniglacial dynamics in an oceanic central European loess landscape

Abstract. Loess–palaeosol sequences (LPSs) of the oceanic-influenced European loess belt underwent frequent post-depositional processes induced by surface runoff or periglacial processes. The interpretation of such atypical LPSs is not straightforward, and they cannot be easily used for regional to continental correlations. Within the last few years, however, such sequences gained increased attention, as they are valuable archives for regional landscape dynamics. In this study, the Siersdorf LPS was analysed using a multi-proxy approach using sedimentological, geochemical, and spectrophotometric methods combined with luminescence dating and tentative malacological tests to unravel Pleniglacial dynamics of the Lower Rhine Embayment. A marshy wetland environment for the late Middle Pleniglacial to the early Upper Pleniglacial was shown by colour reflectance and grain size distribution. Age inversions from luminescence dating paired with geochemical and sedimentological data reveal long-lasting erosional processes during the early Upper Pleniglacial, which were constrained to a relatively small catchment with short transport ranges. The upper sequence shows typical marker horizons for the study area and indicate harsh, cold-arid conditions for the late Upper Pleniglacial. In comparison with other terrestrial archives, the Siersdorf LPS shows that the Lower Rhine Embayment was more diverse than previously assumed, regarding not only its geomorphological settings and related processes but also its ecosystems and environments.
Semantic Segmentation of seafloor images in Philippines based on semi-supervised learning

Semantic segmentation of marine images can be used to describe seafloor scenes and monitor marine creatures. However, preparing human-annotated datasets for image segmentation is time-consuming task. Therefore, this paper proposes a semi-supervised semantic segmentation algorithm based on the combination of Mean-Teacher and U-Net models to classify seafloor images collected in Philippines. The method will train and validate on two parts of the image. On the one hand, for images containing categories of coral, sea urchin, sea stars, and others (including sediment and seagrass), ordinary labeling is used for training and validation. On the other hand, for images only including seagrass and sediment categories, manual labeling of seagrass categories is particularly difficult. In order to overcome this barrier, based on the characteristics of this type of images, K-means clustering algorithm is used to obtain labeled dataset for training and validation. Compared with the U-Net based supervised method, the semi-supervised method proposed in this paper achieves good results and accuracy values even with fewer labeled images.
Performance of Pristine versus Magnetized Orange Peels Biochar Adapted to Adsorptive Removal of Daunorubicin: Eco-Structuring, Kinetics and Equilibrium Studies

Drugs and pharmaceuticals are an emergent class of aquatic contaminants. The existence of these pollutants in aquatic bodies is currently raising escalating concerns because of their negative impact on the ecosystem. This study investigated the efficacy of two sorbents derived from orange peels (OP) biochar (OPBC) for the removal of the antineoplastic drug daunorubicin (DNB) from pharmaceutical wastewater. The adsorbents included pristine (OPBC) and magnetite (Fe3O4)-impregnated (MAG-OPBC) biochars. Waste-derived materials offer a sustainable and cost-effective solution to wastewater bioremediation. The results showed that impregnation with Fe3O4 altered the crystallization degree and increased the surface area from 6.99 m2/g in OPBC to 60.76 m2/g in the case of MAG-OPBC. Placket–Burman Design (PBD) was employed to conduct batch adsorption experiments. The removal efficiency of MAG-OPBC (98.51%) was higher compared to OPBC (86.46%). DNB adsorption onto OPBC followed the D–R isotherm, compared to the Langmuir isotherm in the case of MAG-OPBC. The maximum adsorption capacity (qmax) was 172.43 mg/g for MAG-OPBC and 83.75 mg/g for OPBC. The adsorption kinetics for both sorbents fitted well with the pseudo-second-order (PSO) model. The results indicate that MAG-OPBC is a promising adsorbent for treating pharmaceutical wastewater.
STUDY OF THE RELATION BETWEEN INTERFACIAL REACTION AND FORMATION OF SCHOTTKY BARRIERS

Electrical measurements (DLTS and C-V) were combined with surface analysis techniques (AES and SIMS) to study the reactions, atomic structure, the distribution of defect/impurity and the Schottky barrier heights at Pt/Si and Pt-silicides/Si interfaces. The relation between the interracial reaction and the formation of Schottky barriers is discussed in this paper in detail.
Clustering approach to protein folding problem and dynamics of the tropical atmosphere

Many different multidimensional and multivariate data-sets are studied currently in almost all research areas. Usually, the aim of such studies is to reveal meaningful patterns and to gain an insights about special characteristics of the particular data-set. It is usually done by scanning over the elements of the data-set at large and bringing up groups or clusters of these elements that exhibit correlations across the variables or dimensions. In this thesis I use different techniques to address two problems: protein folding and the dynamics of the tropical atmosphere. Biological research has generated vast quantities of protein sequences. One of the most important characteristics of the certain protein is its function. It is crucial to understand it in order to use protein for pharmaceutical, engineering or research purposes. Protein folding problems, which predict the spatial structure and the corresponding function of a protein from its amino acid sequence alone, are then critical in biological studies. In this work I study several proteins: start with a penta-alanine molecule, which consists of an acetyl cap group, a methylamide cap group and five residual alanine groups, I then study three polyalanine peptides. Using pairwise correlation, clustering techniques, minimal spanning tree and other techniques, I was able to identify the moments of folding and precursors of the folding event for some of the mentioned molecules. In the Tropics seasonal variations are mainly governed by precipitation, since temperature and day length remain relatively constant throughout the year. The Madden-Julian oscillation (MJO) is the major tropical intraseasonal variability with extensive meteorological impacts, that keeps on puzzling the climate research community on both theoretical and modeling fronts. Together with monsoons, known as dominant water distributors, MJO is an important contributor to the rainfall cycle. In this work, I study dynamics of the MaddenJulian oscillation and its interaction with monsoons. I use satellite data collected by the Tropical Rainfall Measurement Mission (TRMM). Using clustering techniques I managed to identify MJO events, measure its parameters, show relation between magnitudes of MJO and monsoon.
Multiwavelength Raman fiber laser based on polarization maintaining fiber loop mirror and random distributed feedback

We experimentally demonstrate a configuration to realize multiwavelength generation in a Raman fiber laser structure. The system includes a fiber loop mirror (FLM) containing a piece of polarization maintaining (PM) single mode fiber, and a 1 km-long passive fiber which is core pumped by a 1120 nm pump laser providing Raman amplification and random distributed feedback via Rayleigh scattering. Up to 1 W multiwavelength laser output is realized due to interference between modes supported by the FLM. As a consequence, a 35 nm bandwidth multiwavelength spectrum from 1155 to 1190 nm with ~0.75 nm spacing (OSNR ~10 dB) is obtained.
Generalized Trapezoidal Fuzzy Number with its Arithmetic Operations and its Application in Fuzzy System Reliability Analysis

In this paper generalized fuzzy numbers with arithm etic operations are described. An electric network model of a dark room is considered in fuzzy environment. The model consists of one switch, four light bulbs in a series connection. Th e reliability of components of the above model is considered as GTrFNs. Numerical example is provided to illustrate the model .
Identification of Interface Residues Involved in Protein-protein Interactions Using Nave Bayes Classifier

The identification of interface residues involved in protein-protein interactions(PPIs)has broad applica-tion in rational drug design and metabolic etc.A nave Bayes classifier for PPIs prediction with features including protein sequence profile and residue accessible surface area is proposed.This method adequately uses the character of nave Bayes classifier which assumes independence of the attributes given the class.The test results on a diversity dataset made up of only hetero-complex proteins achieve 68.1% overall accuracy with a correlation coefficient of 0.201,40.2% specificity and 49.9% sensitivity in identify interface residues as estimated by leave-one-out cross-validation.This result indicates that the method performs substantially better than chance(zero correlation). Exam-ination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying protein-protein sites.
Neither work nor leisure: temporalities and life world realities of split shift work in the Austrian care sector

ABSTRACT Employment conditions have been subject to far-reaching flexibilization and fragmentation in recent decades. One of the many ways to make labor flexibly available and cost-effective is to divide the working day using split shifts. In this qualitative study on the home care sector in Austria, we investigate the workers’ experiences of split shifts as example of fragmented work and unsocial working times. On an empirical level, the findings show that split shifts imply severe challenges for the workers. On a conceptual level, the research emphasizes the need to consider complex and subjective dimensions of time in researching working times. Our findings suggest that even seemingly clear delineations between work and non-work time are in fact fragile and ambivalent. In that sense, the interruption between the two shifts in split shift work is neither work nor real leisure.
Face masks for respiratory viral illness prevention in healthcare settings: a concise systemic review and meta-analysis

Background and Objectives: There are conflicting views regarding face mask guidelines amongst healthcare staff to prevent transmission of coronavirus disease 2019 (COVID-19), influenza and other respiratory viral infections (RVIs). We conducted a thorough meta-analysis to statistically compare mask use versus no mask use efficacy for RVIs in healthcare settings. Materials and Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were used for selecting researches published between 2003 and June 2022 from different databases, including Publisher Medline (PubMed), Web of Science, etc.; 6 studies qualified for inclusion. Data was pooled from in vivo randomized control, case-control and observational studies dealing with the relationship between face mask use and no use by patients or health personnel and RVI prevention in healthcare setups. Results: The fixed and random-effects model was carried out to determine pooled odds ratios (ORs) and their respective 95 percent confidence intervals (CIs). The results revealed that wearing a face mask significantly reduced the risk of contracting a respiratory viral illness in hospital settings, with pooled OR (95% CI) of 0.11 (0.04 to 0.33) (probability value (P) <0.08). Conclusion: Masks largely succeeded in stopping respiratory virus transmission, as evidenced by the meta-analysis of 6 studies (a total of 927 individuals).
Measurement Matrix of Compressive Sensing Based on Bacterial Foraging Optimization

Compressive Sensing (CS) is a new sampling theory which captures signals at sub-Nyquist rate without jeopardizing signal recovery. To guarantee exact recovery from compressed measurements, specific matrix should be chosen according to the Restricted Isometry Property (RIP) to implement the sensing procedure. In this work, we optimize measurement matrix based on Bacterial Foraging Optimization (BFO) and compare some optimized measurement matrices for different signal to find the intrinsic structural measurements of universal laws. Experiment results show the proposed BFO-based detectors can significantly improve the detection performance with less measurement than the traditional compressive detectors without using BFO.