Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Point Reliability Analysis in LV Distribution Networks with P2P Energy Trading

Although there are several reliability studies for LV distribution networks, almost all of them have focused on the feeder or system reliability indices. However, there are several different types of customers in an LV distribution grid, either from a consumption point of view or from a priority (importance) point of view. Consequently, P2P trading impacts on the endpoint reliability improvements may have different significance for the customers. This study addresses the load point reliability parameters in a low voltage distribution network with P2P energy trading. They are calculated for the European LV test feeder, where the Turkey electricity market prices and conditions with a realistic future estimation of the national grid are used in prosumers’ strategies and pricing. The results are discussed, and the potential benefits of the P2P energy transaction are quantified.
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies on expert knowledge and thresholds. However, data is viewed isolated and complex, multivariate relationships are neglected. In this work, we present an autoencoder based end-to-end workflow for anomaly detection suitable for multivariate time series data in large industrial cooling systems, including explained fault localization and root cause analysis based on expert knowledge. We identify system failures using a threshold on the total reconstruction error (autoencoder reconstruction error including all sensor signals). For fault localization, we compute the individual reconstruction error (autoencoder reconstruction error for each sensor signal) allowing us to identify the signals that contribute most to the total reconstruction error. Expert knowledge is provided via look-up table enabling root-cause analysis and assignment to the affected subsystem. We demonstrated our findings in a cooling system unit including 34 sensors over a 8-months’ time period using 4-fold cross validation approaches and automatically created labels based on thresholds provided by domain experts. Using 4-fold cross validation, we reached a F1-score of 0.56, whereas the autoencoder results showed a higher consistency score (CS of 0.92) compared to the automatically created labels (CS of 0.62) – indicating that theanomaly is recognized in a very stable manner. The automatically created labels, however, detected anomaly earlier. The main anomaly was found by the autoencoder and automatically created labels, and was also recorded in the log files. Further, the explained fault localization highlighted the most affected component for the main anomaly in a very consistent manner.
Is digitalisation of public health and social welfare services reinforcing social exclusion? The case of Russian-speaking older migrants in Finland

Governments are rapidly digitalising public services to increase cost-effectiveness of the public sector. This study examines older migrants’ use of digital public health and social welfare services from the perspective of social exclusion. The study uses a mixed methods approach, drawing on representative survey data of Russian-speaking migrants in Finland and qualitative interviews with third-sector representatives who assist Russian-speaking migrants with digital service use. Our quantitative results show that a sizeable proportion of Russian-speaking older adults are excluded from digital services. In particular, those with lower socio-economic status, poor local language skills and without Finnish education are at higher risk of exclusion. Our qualitative results describe the multiple ways the exclusion from digital services intersects with other disadvantages in the everyday lives of Russian-speaking older adults. We argue that digitalisation of these services may foster social exclusion and endanger the realisation of these people's social rights.
Estimation of Washing Performance in Washing Machines with Neural Networks

There are many standard procedures that washing machines must provide. One of them is the washing performance, which shows the cleanliness of the laundry. This measurement, called the washing performance index, must meet the criteria determined according to the relevant standard in repeated tests. However, the washing and cleaning process is a complex process affected by many parameters such as mechanical effects, chemical effects, temperature, and amount of water. We propose an approach to determine the effect of these multi-parameter effects on washing performance and estimate the washing performance of the washing machine without the need for trial tests before the algorithm design. The approach uses artificial neural network algorithms to estimate washing performance by varying the relevant input parameters accurately. In order to establish the structure of the neural network, we have performed experimental tests on washing machines with different features and different input parameters and used them for training, validation, and test sets. According to the results obtained, it has been shown that it is possible to predict the washing performance index with neural networks accurately.
Vision based Human Activity Recognition using Deep Neural Network Framework

—Human Activity Recognition (HAR) has become a well-liked subject in study as of its broad application. With the growth of deep learning, novel thoughts have emerged to tackle HAR issues. One example is recognizing human behaviors without exposing a person's identify. Advanced computer vision approaches, on the other hand, are still thought to be potential development directions for constructing a human activity classification approach from a series of video frames. To solve this issue, a deep learning neural network technique using Depthwise Separable Convolution (DSC) with Bidirectional Long Short-Term Memory (DSC-BLSTM) is proposed here. The redeeming features of the proposed network system comprises a DSC convolution that helps to reduce not only the number of learnable parameters but also computational cost in together training and testing method The bidirectional LSTM process can combine the positive and the negative time direction. The proposed method comprises of three phases, which includes Video data preparation, Feature Extraction using Depthwise Separable Convolution Neural Network algorithm and DSC-BLSTM algorithm. The proposed DSC-BLSTM method obtains high accuracy, F1-score when compared to other HAR algorithms like MC-HF-SVM, Baseline LSTM Bidir-LSTM algorithms.
State of Health Forecasting of Heterogeneous Lithium-ion Battery Types and Operation Enabled by Transfer Learning

Due to the global transition to electromobility and the associated increased use of high-performance batteries, research is increasingly focused on estimating and forecasting the state of health (SOH) of lithium-ion batteries. Several data-intensive and well-performing methods for SOH forecasting have been introduced. However, these approaches are only reliable for new battery types, e.g., with a new cell chemistry, if a sufficient amount of training data is given, which is rarely the case. A promising approach is to transfer an established model of another battery type to the new battery type, using only a small amount of data of the new battery type. Such methods in machine learning are known as transfer learning. The usefulness and applicability of transfer learning and its underlying methods have been very successfully demonstrated in various fields, such as computer vision and natural language processing. Heterogeneity in battery systems, such as differences in rated capacity, cell cathode materials, as well as applied stress from use, necessitate transfer learning concepts for data-based battery SOH forecasting models. Hereby, the general electrochemical behavior of lithium-ion batteries, as a major common characteristic, supposedly provides an excellent starting point for a transfer learning approach for SOH forecasting models. In this paper, we present a transfer learning approach for SOH forecasting models using a multilayer perceptron (MLP). We apply and evaluate it on the method presented by von Bülow, Mentz, and Meisen (2021) using five battery datasets. In this regard, we investigate the optimal conditions and settings for the development of transfer learning with respect to suitable data from the target domain, as well as hyperparameters such as learning rate and frozen layers. We show that for the transfer of a SOH forecasting model to a new battery type it is more beneficial to have data of few old batteries, compared to data of many new batteries, especially in the case of superlinear degradation with knee points. Contrarily to computer vision freezing no layers is preferable in 95% of the experimental scenarios.
Liquid Biopsy in Prostate Cancer Management—Current Challenges and Future Perspectives

Simple Summary Prostate cancer (PCa) is a widespread malignancy, representing the second leading cause of cancer-related death in men. In the last years, liquid biopsy has emerged as an attractive and promising strategy complementary to invasive tissue biopsy to guide PCa diagnosis, follow-up and treatment response. Liquid biopsy is employed to assess several body fluids biomarkers, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating tumor DNA (ctDNA) and RNA (ctRNA). This review dissects recent advancements and future perspectives of liquid biopsy, highlighting its strength and weaknesses in PCa management. Abstract Although appreciable attempts in screening and diagnostic approaches have been achieved, prostate cancer (PCa) remains a widespread malignancy, representing the second leading cause of cancer-related death in men. Drugs currently used in PCa therapy initially show a potent anti-tumor effect, but frequently induce resistance and PCa progresses toward metastatic castration-resistant forms (mCRPC), virtually incurable. Liquid biopsy has emerged as an attractive and promising strategy complementary to invasive tissue biopsy to guide PCa diagnosis and treatment. Liquid biopsy shows the ability to represent the tumor microenvironment, allow comprehensive information and follow-up the progression of the tumor, enabling the development of different treatment strategies as well as permitting the monitoring of therapy response. Liquid biopsy, indeed, is endowed with a significant potential to modify PCa management. Several blood biomarkers could be analyzed for diagnostic, prognostic and predictive purposes, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating tumor DNA (ctDNA) and RNA (ctRNA). In addition, several other body fluids may be adopted (i.e., urine, sperm, etc.) beyond blood. This review dissects recent advancements and future perspectives of liquid biopsies, highlighting their strength and weaknesses in PCa management.
Microcarrier-Based Culture of Human Pluripotent Stem-Cell-Derived Retinal Pigmented Epithelium

Dry age-related macular degeneration (AMD) is estimated to impact nearly 300 million individuals globally by 2040. While no treatment options are currently available, multiple clinical trials investigating retinal pigmented epithelial cells derived from human pluripotent stem cells (hPSC-RPE) as a cellular replacement therapeutic are currently underway. It has been estimated that a production capacity of >109 RPE cells annually would be required to treat the afflicted population, but current manufacturing protocols are limited, being labor-intensive and time-consuming. Microcarrier technology has enabled high-density propagation of many adherent mammalian cell types via monolayer culture on surfaces of uM-diameter matrix spheres; however, few studies have explored microcarrier-based culture of RPE cells. Here, we provide an approach to the growth, maturation, and differentiation of hPSC-RPE cells on Cytodex 1 (C1) and Cytodex 3 (C3) microcarriers. We demonstrate that hPSC-RPE cells adhere to microcarriers coated with Matrigel, vitronectin or collagen, and mature in vitro to exhibit characteristic epithelial cell morphology and pigmentation. Microcarrier-grown hPSC-RPE cells (mcRPE) are viable; metabolically active; express RPE signature genes including BEST1, RPE65, TYRP1, and PMEL17; secrete the trophic factors PEDF and VEGF; and demonstrate phagocytosis of photoreceptor outer segments. Furthermore, we show that undifferentiated hESCs also adhere to Matrigel-coated microcarriers and are amenable to directed RPE differentiation. The capacity to support hPSC-RPE cell cultures using microcarriers enables efficient large-scale production of therapeutic RPE cells sufficient to meet the treatment demands of a large AMD patient population.
EVALUATION OF ROAD MARKINGS RETOREFLECTION MEASURING METHODS

Modern traffic demands the safe movement of vehicles under normal conditions and especially at night and in reduced visibility (fog, rain, sleet, etc.). Quality and quantity of participants visual guidance in traffic directly depends on the visibility and the reflective properties of road markings are of crucial importance. Using the latest methods and procedures of testing road markings a high and constant quality level can be achieved, and thus the security level of individual roads can be raised. One of the most important elements for testing the quality of road markings is testing day and night visibility of road markings. These tests can be done in two ways: method for static test of road markings reflection (daytime and night-time visibility) and dynamic method for testing retroreflection of road markings (night-time visibility). Aim of this article is to describe and evaluate mentioned methods for measuring retroreflection of road markings.
Design and Implementation of Authentication System Using Deep Convoluted Siamese Network

The majority essential prerequisite in this day is to conquer the various sorts of threats. Human behavioral and physiological components have the biggest alternative to overcome these security issues. In any case, the current biometric authentication methodology like fingerprints, faces and iris are profoundly complex methods. So in this paper, a new authentication system i.e. Finger vein authentication with the help of Deep Convoluted Siamese network has been proposed. The region of interest was done by threshold value over captured images by NIR and CCD cameras. After that, the Deep convoluted Siamese network is used to compare and contrast between two images to predict whether the two images are similar or not. The modern Siamese network uses a "Triplet Loss Function". In this triplet loss function, three fundamental images (Anchor Image, Positive Image and Negative Image) are considered where each image is a pixel matrix. The detection accuracy for testing and training is 97.2% and 96.4 % which is compared by utilizing several machine learning techniques (ANFIS, SVM, MLP and Global mapping/SVM) accuracy. It is clear from the comparative analysis that the proposed method gives better results than other algorithms. The proposed methodology TPR, FPR, FNR and TNR are 0.916, 0.027, 0.041 and 0.027 respectively. From these values, it is obvious that the proposed model gives better results, as the TP value is higher while the FP, FN and TN value are lower.
Proxy Learning of Visual Concepts of Fine Art Paintings from Styles through Language Models

We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. The formal analysis is fundamental for understanding art, but developing such a system is challenging. Paintings have high visual complexities, but it is also difficult to collect enough training data with direct labels. To resolve these practical limitations, we introduce a novel mechanism, called proxy learning, which learns visual concepts in paintings through their general relation to styles. This framework does not require any visual annotation, but only uses style labels and a general relationship between visual concepts and style. In this paper, we propose a novel proxy model and reformulate four pre-existing methods in the context of proxy learning. Through quantitative and qualitative comparison, we evaluate these methods and compare their effectiveness in quantifying the artistic visual concepts, where the general relationship is estimated by language models; GloVe or BERT. The language modeling is a practical and scalable solution requiring no labeling, but it is inevitably imperfect. We demonstrate how the new proxy model is robust to the imperfection, while the other methods are sensitively affected by it.
The Minimum Space Standard: Proposing New House Floorplan on Dwelling Activities in Greater Jakarta Region, Indonesia

ABSTRACT This study produces a modest space requirement of the housing unit’s floor plan to assess housing quality and ensure the occupants’ health and well-being. In the urgency of acknowledging the need for quality housing due to the issue of human health and well-being, Indonesia still endures several setbacks. Previous studies suggest that ergonomics emergence becomes a practical perspective as it recognises that the built environment – in this case, housing – is tightly intertwined with human health and well-being. We analysed and regrouped 12 main activities in urban dwelling through survey and questionnaires to form a minimum yet efficient layout regarding furniture usage, occupants’ anthropometry and activity’s space requirement, and circulation space. Results reveal sufficient internal space minimums requiring 30.57 m2 area within interior boundaries, while 37 m2 include external walls. We propose a new floor plan based on 12 main activities while analysing the building performance regarding healthy space requirement in further research.
Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions

ABSTRACT Supply chains (SCs) operate in a highly disruptive environment, where they face a variety of disruptions in product and cash flows. In such an environment, determining suitable inventory and cash replenishment policies ensures that cash and inventory are at the right place at the right time and provides a productive SC with high customer service levels. In this study, we first examine the impact of the disruptions in physical and financial flows on SC performance. We then, investigate the potential of a SC digital twin framework to help decision-makers in managing inventory and cash throughout the SC during disruption, currently absent from the literature. The proposed SC digital twin framework integrates machine learning (ML) and simulation to identify the inventory and cash replenishment policies that minimise the impact of the disruptions on SC performance. This approach proves effective in a SC disrupted by demand increase, capacity reduction, and credit purchase increase. Results show that employing the SC digital twin leads to a noticeable reduction in the cash conversion cycle for upstream members of the SCs. We observe that the cash conversion cycle for the upstream SC members is greatly impacted by the inventory policy employed by their immediate downstream members.
Silver Ink Printed Logarithmic Spiral Antenna

Innovative manufacturing processes based on printing technology are gaining in importance and already evolve to a key technology for electrical components. In this paper we compare a broadband antenna design in the high frequency regime from 1 GHz to 8 GHz. One antenna was designed and fabricated on a copper coated printed circuit board based on a conventional wet etching process and two antennas of equal design were fabricated with a silver ink-based printing process. Typical antenna parameters such as reflection coefficient, realized antenna gain, and radiation pattern were investigated for the performance comparison of the two different manufacturing techniques. Frequency dependent deviations in impedance matching were found as well as a reduced antenna gain behavior over the entire frequency band of the silver ink printed antennas. The greatest and frequency dependent deviations are at low frequencies from 1 GHz to 2 GHz and at higher frequencies starting at 6 GHz. Between these frequency regimes the observed deviations are comparably constant over frequency.
Security band-aids: more cost-effective than "secure" coding

Patching systems against the latest virus is a full-time job, and most corporations have heavier near-term problems facing them. The war between hackers and software is being fought on the front lines-in the users' trenches. But hunting down the "engineers" who write bad software won't win this war, at least not in the short run. With the best of intentions, development shops are trying to address bad software by learning secure coding practices. Application security tools are the most effective way your organization can protect itself today. Building more secure software is a goal, but it won't stop the virus that gets released tomorrow. It comes down to this: secure coding practices are not going to produce 100 percent bug-free software. Thus, application security tools should always play a part In your risk mitigation plan.
Migraines Epilepsy Dementia Rheumatologic Rheumatoid arthritis Systemic lupus erythematosus Pulmonary Chronic obstructive pulmonary disease Asthma Pulmonary circulation disorders Endocrine Diabetes 23 Hypothyroidism Renal Renal failure Fluid and electrolyte disorders Gastrointestinal Liver diseases 

Objective: To determine the prevalence of selected comorbidities in patients with open-angle glaucoma (OAG) and whether these comorbidities are more prevalent among individuals with OAG than those without OAG. Design: A retrospective, nationwide, case-control study using an administrative database. Participants: The study group comprised 76 673 OAG patients. The comparison group comprised 230 019 subjects matched to the study cohort. Methods: Data were collected retrospectively from the Taiwan National Health Insurance Research Database. The study cohort comprised all patients with a diagnosis of OAG (International Classification of Diseases, 9th Revision, Clinical Modification codes 365.1–365.11) in 2005 (n 76 673). The comparison cohort comprised randomly selected patients (3 for every 1 OAG patient; n 230 019) matched with the study group in terms of age, gender, urbanization level, and monthly income. In total, 31 medical comorbidities were selected based mainly on the Elixhauser Comorbidity Index. Separate conditional logistic regression analyses were used to estimate the adjusted odds ratio for each of the medical comorbidities between patients with and without OAG. Main Outcome Measures: The prevalences of selected comorbidities. Results: More than half (50.5%) of the OAG patients had hypertension, and more than 30% had hyperlipidemia or diabetes (30.5% and 30.2%, respectively). The prevalences of 28 of 31 comorbidities were significantly higher for OAG patients than subjects without glaucoma after adjusting for age, gender, urbanization level, and monthly income. The adjusted odds ratio was more than 1.50 for hypertension, hyperlipidemia, systemic lupus erythematosus, diabetes, hypothyroidism, fluid and electrolyte disorders, depression, and psychosis. Among the studied comorbidities, the prevalence difference of the OAG group minus the control group was 3% or higher for hypertension, hyperlipidemia, stroke, diabetes, liver disease, and peptic ulcer. Conclusions: Open-angle glaucoma patients are significantly more likely to have comorbidities, many of which can be life threatening or can affect the quality of life appreciably. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article. Ophthalmology 2010;117:2088–2095 © 2010 by the American Academy of Ophthalmology.