Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaches for Monitoring the Energy Consumption with Machine Learning Methods

In times of rising energy costs and increasing customer awareness of sustainable production methods, many manufacturers take measures to reduce their energy consumption. However, after the realization of such activities the energy demand often tends to increase again due to e.g. leaks, clogged filters, defect valves or suboptimal parameter settings. In order to prevent this, it is necessary to quickly identify such increases by continuously monitoring the energy consumption and counteracting accordingly. Currently, the monitoring is either performed manually or by setting static threshold values. The manual control can be time consuming for large amounts of sensor data. By setting static threshold values only a fraction of the inefficiencies are disclosed. Another option is to use anomaly detection methods from the area of machine learning, which compare the actual sensor values with the expected ones. In this paper an overview about existing anomaly detection methods, which can be applied for this purpose, is presented.
Flexible, Fully Printable, and Inexpensive Paper-Based Chipless Arabic Alphabet-Based RFID Tags

This work presents the design and analysis of newly developed reconfigurable, flexible, inexpensive, optically-controlled, and fully printable chipless Arabic alphabet-based radio frequency identification (RFID) tags. The etching of the metallic copper tag strip is performed on a flexible simple thin paper substrate (ϵr = 2.31) backed by a metallic ground plane. The analysis of investigated tags is performed in CST MWS in the frequency range of 1–12 GHz for the determination of the unique signature resonance characteristics of each tag in terms of its back-scattered horizontal and vertical mono-static radar cross section (RCS). The analysis reflects that each tag has its own unique electromagnetic signature (EMS) due to the changing current distribution of metallic resonator. This EMS of each tag could be used for the robust detection and recognition of all realized 28 Arabic alphabet tags. The study also discusses, for the first time, the effect of the change in font type and size of realized tags on their EMS. The robustness and reliability of the obtained EMS of letter tags is confirmed by comparing the RCS results for selective letter tags using FDTD and MoM numerical methods, which shows very good agreement. The proposed tags could be used for smart internet of things (IoT) and product marketing applications.
Field Test of In Situ Groundwater Treatment Applying Oxygen Diffusion and Bioaugmentation Methods in an Area with Sustained Total Petroleum Hydrocarbon (TPH) Contaminant Flow

Contamination of groundwater by petroleum hydrocarbons is a widespread environmental problem in many regions. Contamination of unsaturated and saturated zones could also pose a significant risk to human health. The main purpose of the study was to assess the efficiency of biodegradation of total petroleum hydrocarbon (TPH) in situ, in an area with loam and sandy loam soils, and to identify features and characteristics related to groundwater treatment in an area with a persistent flow of pollutants. We used methods of biostimulation (oxygen as stimulatory supplement) and bioaugmentation to improve water quality. Oxygen was added to the groundwater by diffusion through silicone tubing. The efficiency of groundwater treatment was determined by detailed monitoring. Implementation of the applied measure resulted in an average reduction in TPH concentration of 73.1% compared with the initial average concentration (4.33 mg/L), and in the local area, TPH content was reduced by 95.5%. The authors hope that this paper will contribute to a better understanding of the topic of groundwater treatment by in situ biodegradation of TPH. Further studies on this topic are particularly needed to provide more data and details on the efficiency of groundwater treatment under adverse geological conditions.
The Link between Tax Revenue Components and Economic Growth: Evidence from South Africa. An ARDL Approach

This study examined the link between tax revenue components and economic growth in South Africa, utilizing time series data for the period of 22 years. The stationarity of the variables was established using the Phillips-Perron (PP) unit root test, and the existence of long-run and short-run equilibrium conditions was tested using the Autoregressive Distributed Lag (ARDL) model. As a proxy for economic growth, the study used the real GDP growth rate as the dependent variable, with company income tax, personal income tax, taxes on international trade and transactions, taxes on income, profits, and capital gains tax, foreign direct investment, inflation, and gross savings as the independent variables. According to the PP findings, none of the variables are integrated at a higher order than one, i.e. (1). All variables are found to be cointegrated, and all explanatory variables have a long-run link with economic growth. According to the ARDL findings, company income tax, personal income tax, and taxes on international trade and transactions all have a positive long-run and short-run link with economic growth, whereas capital gain tax, foreign direct investment, and gross savings all have a negative long-run and short-run link with economic growth. The long-run coefficient is negatively related to RGDP, while the short-run coefficient revealed a positive link between inflation and economic growth, among other findings. Heteroskedasticity and autocorrelation are not present in our model, according to diagnostic tests. The CUSUM and CUSUMSQ values indicate that the model is structurally sound.
Potential risk zone for anthropogenic mortality of carnivores in Gandaki Province, Nepal

Abstract Anthropogenic pressures in human‐dominated landscapes often contribute to wildlife mortality. Carnivores are especially vulnerable to human‐induced mortality due to the perceived threat to livestock and humans. Despite having widespread conservation implications, carnivore mortality data have been largely underutilized within Nepal. This study utilized Maxent to identify high‐risk areas and explore the contribution of habitat attributes associated with carnivore mortality using the casualty database within the Gandaki province of central Nepal. We categorized the risk to carnivore species in three taxonomic groups, Felid, Viverridae, and Herpestidae, and identified a 3704‐km2 area within the province at high risk for carnivore casualty. The middle mountains were the riskiest physiographic zone, and the Annapurna Conservation Area represented the largest risk zone among the four protected areas. Agricultural land was the most problematic area in terms of carnivore casualty. The human population was positively associated with high‐risk areas and the number of casualties, whereas protected area cover had a negative association. This study identified that the common leopard was at the highest risk of mortality and therefore would benefit from the implementation of an action plan and species‐specific conservation strategies, especially within identified high‐risk zones. An expansion of protected areas in the middle mountain region would serve to greatly reduce carnivore casualty. Species distribution modeling can be further used with national‐level spatial and temporal mortality data to identify the most prominent casualty times and pinpoint potential casualty locations throughout the country.
Singing Voice Detection: A Survey

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection.
A numerical flat plate friction line and its application

This paper studies the regression of a numerical two-dimensional flat plate friction line using the RANS method with the SST k-turbulence model.Numerical simulations with different inlet turbulence kinetic energies are first conducted.Comparing with the experimental data,the finest grid and the appropriate inlet turbulence kinetic energy are selected to compute the flat plate friction resistance at 14 Reynolds numbers.Two numerical friction lines are obtained by the least squares root fitting method and one similar to that of the ITTC-1957 line and the cubic polynomials in logarithmic scales,and the results are compared to the friction line proposals available in the open literature.Finally,the full scale viscous resistance predictions of DTMB5415,KVLCC2,SUBOFF are compared between the numerical friction line and the friction line proposals available in the open literature based on the form factor approach.It is shown that the form factor keeps relatively constant via the numerical friction line for the bare hull,but the form factor concept in extrapolating the model test results is not appropriate for appended hulls.It is suggested that for computing a form factor numerically,it is best to use a numerical friction line.
Innovativeness of Free/Open Source Solutions: Evidence from an Alternative Methodology

The issue of innovation processes taking place in the software sector is currently widely debated. Challenging questions arise about what products/services have to be considered innovative, and whether a specific artefact is innovative or not. In this framework, the widespread success of the Free/Open Source Software (FOSS) put forward new research issues, dealing with whether and how programs developed according to the new production paradigm turn out to be more innovative than traditional ones. In this framework, this paper aims at contributing to the literature by addressing three main research questions: (i) are software solutions produced by Small and Medium Enterprises (SMEs) innovative? (ii) What kinds of innovations are implemented? And, finally, (iii) are programs based on FOSS more innovative than proprietary ones? Basing on a sample of 134 software solutions produced by Italian SMEs and using an original methodology to asses the problem of evaluating innovation in the software field, we provides some first insights of what emerges if we set aside the traditional innovation indicators and endower to build alternative metrics, specifically developed to target the complexity of the innovation processes in the software markets.
An Integrated Monitor-Diagnosis-Reconfiguration Scheme for (Semi-) Autonomous Systems

A nested monitoring, diagnosis and reconfiguration (MDR) scheme is proposed for a Recursive Nested Behavior based Control structure (RNBC)constituting a generic system architecture for (semi-) autonomous mobile systems. Each behavior layer within the RNBC structure is associated with a MDR schema, which is responsible to ensure the dependability of every single layer. An online dependability measurement and diagnosis procedure is integrated into monitor and diagnosis blocks under consideration of performance and safety acceptability factors. The reconfiguration blocks within the MDR-scheme switch from components with unacceptable behavior to redundant components, which may have degraded performance but more robust and safe behavior. The MDR blocks at each layer are nested through unified interfaces in order to utilize the distributed modeling of system behavior and to facilitate the system design and implementation process. In a small case study the MDR scheme is demonstrated for an assistant wheelchair on the body velocity control and axis velocity control levels. Simulation results show the feasibility and effectiveness of the approach.
A framework and road map for rapid start-up and completion of a COVID-19 vaccine trial: A single clinical trial site experience

Abstract The COVID-19 global pandemic required the rapid development of vaccines with a quick start up of phase 1–3 studies with large enrollment targets. The University of California San Diego was identified as a site for the phase 3 trial of the mRNA-1273-SARS-CoV-2 vaccine. There were many challenges with scaling up a large-scale clinical trial in such a short time. This report describes the processes and procedures that were implemented to successfully complete the enrollment target in under 10 weeks. This required the team to identify existing tools that could rapidly be accessed to develop a database, scheduling system, effective communication, document management, staff time tracking/efficiency, subject scheduling/tracking, project management, and accrual/study performance. The outcome of these efforts resulted in rapid enrollment and study completion in a short time. The lessons learned from this experience can be used by other clinical trial sites faced with similar challenges.
The global epidemiology of chikungunya from 1999 to 2020: A systematic literature review to inform the development and introduction of vaccines

Chikungunya fever is an acute febrile illness that is often associated with severe polyarthralgia in humans. The disease is caused by chikungunya virus (CHIKV), a mosquito-borne alphavirus. Since its reemergence in 2004, the virus has spread throughout the tropical world and several subtropical areas affecting millions of people to become a global public health issue. Given the significant disease burden, there is a need for medical countermeasures and several vaccine candidates are in clinical development. To characterize the global epidemiology of chikungunya and inform vaccine development, we undertook a systematic literature review in MEDLINE and additional public domain sources published up to June 13, 2020 and assessed epidemiological trends from 1999 to 2020. Observational studies addressing CHIKV epidemiology were included and studies not reporting primary data were excluded. Only descriptive analyses were conducted. Of 3,883 relevant sources identified, 371 were eligible for inclusion. 46% of the included studies were published after 2016. Ninety-seven outbreak reports from 45 countries and 50 seroprevalence studies from 31 countries were retrieved, including from Africa, Asia, Oceania, the Americas, and Europe. Several countries reported multiple outbreaks, but these were sporadic and unpredictable. Substantial gaps in epidemiological knowledge were identified, specifically granular data on disease incidence and age-specific infection rates. The retrieved studies revealed a diversity of methodologies and study designs, reflecting a lack of standardized procedures used to characterize this disease. Nevertheless, available epidemiological data emphasized the challenges to conduct vaccine efficacy trials due to disease unpredictability. A better understanding of chikungunya disease dynamics with appropriate granularity and better insights into the duration of long-term population immunity is critical to assist in the planning and success of vaccine development efforts pre and post licensure.
Ultrasound-guided Hydrodissection of Sural Nerve for Foot Pain

Background: Sural nerve entrapment is an impor­tant but infrequent cause of pain. The sural nerve provides sensation to the posterolateral aspect of the leg, lateral foot and fifth toe. Sural nerve entrapment can be challenging to treat and can cause significant limitation. We present a case of sural nerve entrapment resistant to conservative management that was effectively treated by percutaneous ultrasound guided hydrodissection of the sural nerve. Case Report: A 57 year old male came with complaints of pain and tingling sensation on both lower limbs with 50% decrease in sensation to touch (right > left) in lateral aspect of both foot. The patient had tried several conservative modalities with no success. We performed percutaneous ultrasound guided hydrodissection of the sural nerve and the patient reported complete improvement in his pain. Conclusion: Percutaneous ultrasound guided hydrodissection of the sural nerve, is a safe and effective treatment for patients with sural nerve entrapment that does not respond to conservative therapy. However, studies are needed to elucidate its effectiveness and safety profile.
Approximate Diagonal Integral Representations and Eigenmeasures for Lipschitz Operators on Banach Spaces

A new stochastic approach for the approximation of (nonlinear) Lipschitz operators in normed spaces by their eigenvectors is shown. Different ways of providing integral representations for these approximations are proposed, depending on the properties of the operators themselves whether they are locally constant, (almost) linear, or convex. We use the recently introduced notion of eigenmeasure and focus attention on procedures for extending a function for which the eigenvectors are known, to the whole space. We provide information on natural error bounds, thus giving some tools to measure to what extent the map can be considered diagonal with few errors. In particular, we show an approximate spectral theorem for Lipschitz operators that verify certain convexity properties.
On-LineAuto Monitoring System Of Environmental Noise

on-line auto monitoring of noise is trend and final goal of noise monitoring in our country. This problem is researched deeply by information centre of Harbin environmental protecting bureau. The article recounts the structure of on-line auto monitoring system of noise, and discusses the difficulties that meet during exploitation and the problems which need to be solved eagerly.
Analysis of Businesses Satisfaction and Loyalty To The Service Quality of Agriculture Tool and Machinery Testing

System standardization in testing and monitoring agricultural machinery an attempt to anticipate the increasing use of agricultural machinery who do not meet the technical and economical feasibility standards. At this time there are many agricultural machinery from foreign and domestic untested and not in accordance with the Indonesian National Standard (SNI). Agricultural Machinery Quality Testing Center (BPMA) is one organization that conducts testing agriculture machinery But since the beginning of the current set up has never conducted a survey on customer satisfaction. Yet over time, occurs complaints submitted by businesses agricultural machinery directed to the BPMA. This research examines the relationship between service quality, satisfaction and loyalty businesses that apply for testing equipment and agricultural machinery in the Ministry of Agriculture . Respondents were used in this study are the businesses that produce agricultural machinery as 88 respondents, that all respondents should be the following testing procedures from the beginning until the publication of the report of test results. Based on the results if the data using SEM-based variantis found that the quality of service (tangible, responsiveness, assurance, and empathy) a significant effect on customer satisfaction, and satisfaction significantly influence loyalty.
Training Strategies for Neural Multilingual Morphological Inflection

This paper presents the submission of team GUCLASP to SIGMORPHON 2021 Shared Task on Generalization in Morphological Inflection Generation. We develop a multilingual model for Morphological Inflection and primarily focus on improving the model by using various training strategies to improve accuracy and generalization across languages.
Multi-GBS: A massively multiplexed GBS-based protocol optimized for large, repetitive conifer genomes

Reduced representation library approaches are still a valuable tool for breeding and population and ecological genomics, even with impressive increases in sequencing capacity in recent years. Unfortunately, current approaches only allow for multiplexing up to 384 samples. To take advantage of increased sequencing capacity, we present Multi-GBS, a massively multiplexable extension to Genotyping-by-Sequencing that is also optimized for large conifer genomes. In Norway Spruce, a highly repetitive 20Gbp diploid genome with high population genetic variation, we call over a million variants in 32 genotypes from three populations, two natural forest in the Alps and Bohemian Alps, and a managed population from southeastern Austria using the existing TASSEL GBSv2 pipeline. Metric MDS analysis of replicated genotypes shows that technical bias in resulting genotype calling is minimal and that populations cluster in biologically meaningful ways.
Students make a plan: ICT access and social and academic uses in higher education

This paper reports on the findings of ICT access issues and social and academic uses in higher education, undertaken as part of a study in 2007 in three dissimilar South African higher education contexts. This diversity provided insight into a highly differentiated student body, varied contexts, different infrastructures and historically distinct backgrounds, thus providing a rich data set. The study focused on forms of “thick” access including both computers and cell phones. Access to the Internet via cell phones proved unexpectedly high, and was undifferentiated across socio-economic background. Findings challenge a staged model to ICT access and use suggesting that take up and use are complex, with students proving enterprising in meeting their educational needs, satisfying their curiosity and finding ways to participate in the “information age” even in difficult environments.
BSIRNet: A Road Extraction Network with Bidirectional Spatial Information Reasoning

Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.