Miracle-dz/UniFAR-FTUs huggingface dataset:
Lamb wave based damage detection in metallic plates using multi-headed 1-dimensional convolutional neural network

Lamb wave based damage diagnosis holds potential for real-time structural health monitoring; however, analysing the Lamb wave response possess challenge due to its complex physics. Data-driven machine learning (ML) algorithms are often more effective in identifying the damage-related features from these complex responses. However, in analysing such complex responses the ML algorithms requires extensive data pre-processing and are often not suitable for real-time damage detection. This paper presents a deep learning multi-headed 1-dimensional convolutional neural network (1D-CNN) architecture capable to operate directly on raw discrete time-domain Lamb wave signals recorded from a thin metallic plate. The multi-headed configuration consisting of two parallel 1D-CNN layers is capable to learn higher order damage-related features and enhances robustness of overall classification performance. To train the adopted 1D-CNN algorithm a diverse database is also constructed consisting 216 numerically and 24 experimentally generated responses of a thin 1.6 mm Al-5052 plate structure. The diversification of training database is achieved by varying parameters like scanning length, scanning frequency and adding different levels of white noises to the captured responses. Later, the trained 1D-CNN architecture is tested against two separated unseen test-databases. The first test database consist of experimentally generated 12 samples with notch-like damage and 12 samples of pristine condition. The proposed 1D-CNN classifier generalizes well on the unseen samples and decisively predicts the outcome for 23 out of 24 samples of first test database. The second test database consists of 108 unseen FE simulated samples capturing additional damage scenarios. In the second test phase, the model has correctly predicted the condition of all the 108 samples.
Contribution of Topography and Incident Solar Radiation to Variation of Soil and Plant Litter at an Area with Heterogeneous Terrain

Os processos naturais que determinam os atributos do solo e da serapilheira sao controlados por diversos fatores. Entretanto, pouca atencao tem sido dada para diferenciar o efeito de fatores ambientais de efeitos da estrutura espacial da area sobre a distribuicao de atributos do solo e da serapilheira em ecossistemas tropicais em topografias heterogeneas. O objetivo deste estudo foi avaliar padroes de variacao dos atributos quimicos do solo e da serapilheira em uma area tropical que intercepta niveis distintos de incidencia solar ao longo do ano, dado que sua topografia possui encostas predominantemente voltadas para direcoes geograficas opostas. Dados de solo (pH, C, N, P, H+Al, Ca, Mg, K, Al, Na, areia e silte) e serapilheira (N, K, Ca, P e Mg) foram coletados junto as coordenadas geograficas (para modelar a estrutura espacial) de 40 unidades amostrais estabelecidas em dois sitios compostos, respectivamente, de encostas voltadas para noroeste e sudeste (20 unidades em cada). Os atributos do solo e da serapilheira variaram mais entre encostas voltadas para orientacoes geograficas semelhantes do que entre encostas voltadas para orientacoes opostas. Tanto a incidencia solar quanto a estrutura espacial da area foram relevantes para explicar os padroes detectados de variacao do solo e da serapilheira. As contribuicoes individuais da incidencia solar para explicar a variacao dos atributos avaliados sugerem que esse e outros fatores ambientais podem ter papel particularmente relevante na determinacao da distribuicao do solo e da serapilheira em areas tropicais com topografia heterogenea. Este estudo corrobora ainda que a estrutura espacial da area tambem tem papel importante na distribuicao do solo e da serapilheira neste tipo de paisagem, o que parece consistente com a acao de mecanismos relacionados a movimentacao da agua nesses locais.
Accounting and Smart System: Functional Evaluation of ISO/IEC 25010:2011 Quality Model (a Case Study)

Agile, flexible, and adaptable are important elements in the era of industrial 4.0, and vastly become new objectives by all entrepreneurs in developing their business information systems. As the most productive market in the global economy, Small and Medium Enterprises (SME) plays a vital role in developing countries. An ideal SME must have a standardized in the planning system and have an optimal quality of data model to run the business process and its operation. In the accounting system, all transaction data must be integrated to become a source of data for further processes such as recording sales or purchases, financial reporting, and generating invoices. The absence of technology and automated system in the accounting processes of SMEs, frequently waste both time, assets, and resources to be finished. Open-source ERP system implementation is often to be the initial step for SMEs to create a smart system that automating the business processes and operation’s needs. However, accurate parameters are needed to achieve an optimal system and data quality. The objective of this study is to evaluate the functional suitability level of smart ERP in the SME sector based on ISO 25010: 2011 standardization, and to produce good quality systems, as a result of the success of smart ERP systems implementation in automating business processes and integrating data across departments.
The impact of problem based learning model through e-learning on students’ critical thinking ability

This research is motivated by long distance learning and the importance of critical thinking ability. Several previous studies relating to critical thinking ability were about understanding students’ critical thinking ability based on gender and students’ characteristics of lower and upper group critical thinking ability. However, almost no research could be found on students’ critical thinking ability who have learned with problem-based learning model through e-learning and students’ characteristics of middle critical thinking ability. Therefore, this study aims to identify students’ critical thinking ability who have learned with problem-based learning model through e-learning. The method in this study was qualitative with case-study approach. We gave a learning with problem based learning model through e-learning and a written test to 36 students on 8th grade students of one junior high school in Banyumas, Central Java. Then, we conducted interviews to 2 upper group students, 2 middle group students, and 2 lower group students. The results show that the students with upper, middle, and lower group critical thinking ability have different characteristics in each indicator of critical thinking ability. Meanwhile, the student who have learned with problem-based learning models through e-learning dominant in middle group critical thinking ability.
Proactive repair crew deployment to improve transmission system resilience against hurricanes

Extreme weather events, such as hurricanes, impose severe impacts on power systems. A rapid repair for power systems is desired after hurricanes pass to mitigate load-shedding losses. This study proposes a proactive repair crew deployment model to prepare for post-disaster repair and restoration. A two-stage stochastic optimisation framework is designed. In the ﬁrst stage, the repair crews are allocated to candidate locations ahead of a hurricane. In the second stage, the repair dispatch and load restoration are implemented under several damage scenarios to support the ﬁrst-stage decision-making. A novel repair crew routing method is proposed which considers repair crew cooperation on important damaged components to enable earlier restoration of critical load. A linear-programming approximation of AC power ﬂow is applied to model the restoration of the transmission system in which the generator start-up characters and security constraints are incorporated. A scenario-based decomposition algorithm is applied to solve the stochastic problem and a preprocessing method to identify necessary components is proposed to further reduce computation time. Case studies validate the effectiveness of the proposed model in improving transmission system resilience.
Instagram Users’ Information Acceptance Process for Food-Content

Taking pictures of food and sharing them on social networking services has now become a general consumer trend. In particular, many companies are interested in Instagram marketing due to the increase in users and word-of-mouth effect generated by using hashtags. Therefore, this paper aims to investigate Instagram users’ food-content acceptance processes by applying the information adoption model. Sample data for this study was collected by an online survey company and a total of 333 valid responses were analyzed. The study found significant relationships between food-content quality—accuracy, relevance, and conciseness—and information usefulness. Among the factors constituting source credibility, source trustworthiness and hashtag scalability were found to have a significant relationship with information usefulness. Information adoption mediated the relationship between information usefulness and continued use of food-content Instagram/information sharing intentions. The theoretical and managerial implications based on these findings can contribute to designing marketing strategies.
Definition of Healthy Ranges for Alanine Aminotransferase Levels: A 2021 Update

The changing epidemiology of liver disease, and modifications in the recommended analytical methodology call for a re‐evaluation of the upper reference limits (URLs) of alanine aminotransferase (ALT) levels. Using the same approach consolidated 20 years ago to define the healthy population, we defined the URL for the newly recommended International Federation of Clinical Chemistry (IFCC) standardized test. In a cross‐sectional study, we examined 21,296 apparently healthy blood donors (age 18‐65 years) and calculated the sex‐specific URL by the 95th percentile in individuals without risk factors for liver disease. These were tested for the ability to predict liver damage in a subset of 745 participants with dysmetabolism, in an independent cohort of 977 unselected donors, and in 899 patients with chronic liver disease. ALT levels were measured by the IFCC test. Male sex, body mass index, glucose, lipids, ferritin, hypertension, and younger age were independent ALT predictors (P < 0.001). Updated URLs were identified at 42/30 U/L in males/females, approximately 30% lower than those currently recommended by the IFCC. Due to improved sensitivity, they conferred the ability to detect steatosis and significant fibrosis in individuals with dysmetabolism (odds ratio [OR] = 2.31, range 1.40‐3.80, P = 0.001; and OR = 3.35, range 1.19‐9.42, P = 0.021; respectively), although with a limited accuracy, and significant fibrosis in unselected donors (OR = 2.32, 1.02‐5.31, P = 0.045). Updated URLs had a moderate to high accuracy to discriminate liver conditions (area under the receiver operating characteristic curve = 0.81, range 0.78‐0.91). Conclusion: Updated URLs by the IFCC method were lower than those calculated in initial studies, but higher than those in use with the recommended old, nonstandardized method, and were able to better predict liver disease. The limited awareness that different techniques are still in use should be regarded as a possible source of medical errors.
American Based Research Journal Vol-7-Issue-9 Sep-2018 ISSN (2304-7151)

Abstract In modern business environment due to rapid technological changes, innovation, policies of government companies facing tremendous challenges, specially small and medium enterprise (SMEs). The Indian SMEs try to integrate their business process strategically within their supply chain network. Despite the fact that intensive research has been done in supply chain management (SCM) a little research has been done so far in which this issue is discuss with respect to a SMEs in India. The study examines the impact of supply chain strategy and flexibility on the SMEs business performance in the Indian automobile SMSs. A quantitative research survey was conducted among 202 SMEs owners/managers. SPSS 23.0 was used to analyze the data. Structural path modeling (SEM) was conducted to assess the proposed model analysis and to test the statistically significant relationship of the hypothesis. This research is an attempt to bridges the gap and deficiency on the research done previously in this issue and particularly about the relationship between supply chain flexibility dimensions on supply chain performance. Hence, the first and foremost objective of this study is to find how supply chain strategy (SCS) and supply chain flexibility (SCF) dimensions are related with supply chain performance (SCP) dimensions. Furthermore, innovative strategy and efficient and effective supply chain flexibility is directly related to improve supply chain performance. This research is based on research done by Fantazy et al. (2009) in his research he suggested that further investigation can be made on his research in a different geographical region. To widen understanding on this topic and to go after the recommendation of the preceding research, this research closely related to Fantazy et al. (2009) and this research is done on the SMEs of Indian automotive industries which in current situation having much importance. The research study results revealed that chain strategy and flexibility positively impacted performance of supply chain and their by influence SMEs business performance
Soil attributes and microbial diversity on 28 years of continuous and interrupted for 12 months of pig slurry application

The pig slurry (PS) application as an organic fertilizer in Brazilian soils occurs for many years and the effects of this application are known, but in this study our objective was to evaluate the microbiological attributes and microbial diversity of the soil after the interruption of the PS application. The PS rates were 0, 30 and 60 m3 ha-1 yr-1 and in 2016 the plots were divided and the PS application was stopped in half of the plot, generating the factorial scheme 2 × 2, being two PS rates (30 and 60 m3 ha-1 yr-1) and two application conditions (continuous or interrupted). Two soil samples were taken at 5 and 12 mo after interruption. The PS interruption reduced the microbial biomass C (MBC) by 5% and 14% after 5 and 12 mo, respectively. Microbial biomass N (MBN) was reduced too by 32% and 29% due to the PS interruption. The soil basal respiration (SBR) decreased when PS application was interrupted. The metabolic quotient (qCO2) did not change with the PS interruption but was high in the 60 m3 ha-1 yr-1. The bacteria and archaea genetic profiles showed similarity above 69% between continuous and interrupted applications. The bacterial diversity and richness index were higher than those found for fungal and archaeal communities. In contrast, the interruption had a strong effect on the soil fungi, which had diversity reduced due to the increase of the species dominance. The PS interruption up to 12 mo has an effect on the biomass, diversity, and activity of the microorganisms.
Nurse’s Attitude Towards Palliative Care and Associated Factors in Ethiopia: a Systematic Review and Meta-analysis

Background: Palliative care significantly improves the distressing symptoms of patients, especially those with cancer, heart disease, renal disease, and liver disease, and its need is increasing worldwide due to the growing burden of chronic disease. Nurses with an unfavorable attitude towards palliative care cannot skilfully assess the patient's needs, effective communication, and adequately address the patient's problems. Thus, this study was amid to assess the nurse’s level of attitude towards palliative care in Ethiopia.Method: PubMed/MEDLINE, HINARI, EMBASE, Scopus, Google Scholar, and African Journals Online (AJOL) were the databases used to search for articles. Cochrane I2 statistics and Eggers test was done to check heterogeneity and publication bias, respectively. Subgroup analysis and sensitivity analysis were also done to detect the source of heterogeneity and an influential study, respectively. Result: The pooled prevalence of nurse’s attitudes towards palliative care was 67.15% (95% CI: 54.75-79.54. Palliative care training was a significantly associated factor with the level of nurse’s attitude towards palliative care. Accordingly, nurses who have taken palliative care training (AOR=2.53; 95% CI; 1.88-3.40) was found a significantly associated factor with the level of nurse’s attitude towards palliative care.Conclusion: More than half of the nurses had a favorable attitude towards palliative care. Palliative care training was a significantly associated factor with the nurse’s level of attitude towards palliative care. Thus, palliative care training and improving nurse’s careers through continuous professional development should be given regularly for nurses to improve their knowledge about palliative care.
Use of augmented reality-enabled prototyping of cyber-physical systems for improving cyber-security education

The use of augmented reality-enabled scenarios in cybersecurity teaching is proposed in the article to respond to new requirements for the rapid adoption of new technologies and profound knowledge of cybersecurity issues by professionals. Implementation of project-type activities based on real cybersecurity issues in application fields of cyber-physical systems is suggested to improve the competence forming. A use-case of agricultural cyber-physical system of systems is discussed as a viable example of augmented reality-enabled prototyping of cybersecurity risk-aware architecture. The necessary steps are analysis of general and business-specific tasks on cybersecurity, creation of a list of competencies, formalized in educational standards and curricula, development of gaming scenarios for the formation of hard and soft skills, development of the scenario management system for AR interfaces. The system using AR tools can be easily adapted to different cybersecurity training activities. Industrial cyber-physical systems may be vulnerable due to insecure wireless connectivity, lack of encryption, inadequate access policy. The project-based learning complex is focused on the implementation of a data acquisition, storage and processing platform for new sensor networks and instruments. Representing all the diverse information on different layers will be greatly improved by use of the developed holographic projection AR tools.
Image restoration model using Jaya-Bat optimization-enabled noise prediction map

Image restoration approaches are introduced to restore the latent clear images from the degraded images. However, the performance of the existing approaches remains an open problem, which may leads to the further development of advanced image restoration techniques. Therefore, an effective image restoration method is developed for restoring the input image from various noises, like impulse noise and random noise. The generation of pixel map, identiﬁcation of noisy pixel, and the enhancement of pixel are the three major phases involved in the proposed method. Initially, the noisy pixel map generation is performed from the input image, and then the noisy pixels are identiﬁed based on deep convolutional neural network, which is trained by the proposed Jaya-Bat algorithm. The Jaya-Bat algorithm is developed by combing the Jaya optimization algorithm and Bat algo-rithm. Once the noisy pixels are identiﬁed, the pixel enhancement is done using the neuro fuzzy system. The experiment is carried out using Statlog (landsat satellite) dataset, and the developed method achieves the maximal peak signal to noise ratio of 51.03 dB, maximal structural similarity index of 0.848 for the image with random noise, and the maximal second derivative like measure of enhancement 62.96 dB with impulse noise, respectively.
COMPARATIVE ANALYSIS OF VM PLACEMENT AND MIGRATION ALGORITHMS IN VM CONSOLIDATION

With the increasing number of Internet of Things (IoT) devices, data centers are experiencing immense augmentation in the hardware devices with an increase in the traffic to the cloud infrastructures. To handle this growth and to satisfy users demand, data centers require more energy. The IoT devices produce vast data which needs to be handled properly by the data centers which in turn is responsible for increase in the power consumption at the data centers Management and reduction of this energy is quite a challenging task for the managers and the designers of the data centers as increasing cost of data centers is posing a major hindrance. One major aspect that needs to be taken into consideration is the sharing of the data center resources which is fundamentally achieved by the consolidation of the resources. The analysis done will conclude that consolidation plays an important role in the reduction of energy consumption of a data center.
Cyanobacteria net community production in the Baltic Sea as
inferred from proﬁling pCO2 measurements

Abstract. Organic matter production by cyanobacteria blooms is a major environmental concern for the Baltic Sea as it promotes thespread of anoxic zones. Partial pressure of carbon dioxide (pCO2) measurements carried out on Ships of Opportunity (SOOP) since 2003 have proven to be a powerful tool to resolve the carbon dynamics of the blooms in space and time. However, SOOP measurements lack the possibility to directly constrain the depth–integrated net community production (NCP) due to their restriction to the sea surface. This study tackles the resulting knowledge gap through (1) providing a best–guess NCP estimatefor an individual cyanobacteria bloom based on repeated profiling measurements of pCO2 and (2) establishing an algorithm to accurately reconstruct depth–integrated NCP from surface pCO2 observations in combination with modelled temperature profiles. Goal (1) was achieved by deploying state–of–the–art sensor technology from a small–scale sailing vessel. The low–cost and flexible platform enabled observations covering an entire bloom event that occurred in July and August 2018 in the Eastern Gotland Sea. For the biogeochemical interpretation, recorded pCO2 profiles were converted to CT*, which is the dissolved inorganic carbon concentration normalised to alkalinity. We found that the investigated Nodularia–dominated bloom event had many biogeochemical characteristics in common with blooms in previous years. In particular, it lasted for about three weeks, caused a CT* drawdown of 80 μmol kg−1, and was accompanied by a sea surface temperature increase of 10 °C. The novel finding of this study is the vertical extension of the CT* drawdown up to 12 m water depth. Integration of the CT* drawdown across this depth and correction for vertical fluxes permit a best–guess NCP estimate of ~1.2 mol–C m−2. Addressing goal (2), we combined modelled hydrographical profiles with surface pCO2 observations recorded by SOOP Finnmaid within the study area. Introducing the temperature penetration depth (TPD) as a new parameter to integrate SOOP observations across depth, we achieve a reconstructed NCP estimate that agrees to the best–guess within 10 %. Applying the TPD approach to almost two decades of surface pCO2 observations available for the Baltic Sea bears the potential to provide new insights into the control and long–term trends of cyanobacteria NCP. This understanding is key for an effective design and monitoring of conservation measures aiming at a Good Environmental Status of the Baltic Sea.