Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Carbapenemase Producing of Klebsiella pneumoniae Isolated in Blood Cultures By Matrix Assisted Laser Desorption/Ionization-flight Time Mass Spectrometry

Introduction: Klebsiella pneumoniae can cause Healthcare Related Infections (HRIs), especially by carbapenemase producing microorganisms. Rapid methods are needed to determine carbapenem resistance. MALDI-TOF MS is a faster diagnostic method than conventional methods. It is expected that routine use and standardization of this diagnostic test in laboratories will contribute to the rapid determination of carbapenemase resistance. In this study, it was aimed to determine carbapenemase enzyme production by MALDI-TOF MS technique in K. pneumoniae strains. Materials and Methods: Carbapenem susceptible (n= 40) and non-susceptible (n= 96) K. pneumoniae strains isolated in the Medical Microbiology Laboratory between January 2016 to December 2017 were included into the study. Detecting of production of carbapenemase enzyme was investigated by MALDI-TOF MS technique by comparing with the polymerase chain reaction (PCR). Results: PCR analysis showed that 93 of the carbapenem resistant isolates had OXA-48 gene and three of them had NDM gene. In the MALDI-TOF MS analyzes of the 96 isolates with genotypic resistance, 88 were resistant and three were susceptible. Five isolates of carbapenem resistant with PCR could not be identified as resistant or sensitive by this method. Forty isolates susceptible to carbapenem were determined to be susceptible by MALDI-TOF MS. Sensitivity, specificity, positive predictive, negative predictive values were 96.7%, 100%, 100%, 93% respectively. Conclusion: It appears that MALDI-TOF MS determines carbapenemase production with high specificity and sensitivity. We think it will be beneficial to complete standardization studies and use them in routine practice.
Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.
Applied Strategy to Characterize the Energy Improvement Using PATs in a Water Supply System

Sustainable development has been an idea raised in recent years. The results are related to the improvement and the use of new technologies to maximize efficiency in water management. However, energy consumption has been increasing as a consequence of new management and uses of water. Especially in pressurized water distribution systems, the use of pressure reduction valves (PRVs) increases the water usage efficiency but it decreases the energy consumption efficiency, since the valves dissipate energy that could be recovered. This research presents a proposal of a recovery system based on the installation of pumps used as turbines (PATs). These machines are located in different points of the high-pressure water distribution system in the Valencia Metropolitan System (Spain). An annual estimate of the theoretical recoverable energy as well as the “ideal” pump for each point were proposed. The theoretical recovered energy value was 847,301 kWh/year for a specific analyzed point. Besides, the characteristic curves of the PATs from a selected point were determined, estimating an improvement in the sustainable indexes. The calculus of these green parameters showed that the implementation of this solution caused a reduction in consumed energy of 1.50 kWh/m3.
Harmonic generation at the nanoscale

Nonlinear photon conversion is a fundamental physical process that lies on the basis of many modern disciplines, from bioimaging and theranostics in nanomedicine to material characterization in materials science and nanotechnology. It also holds great promise in laser physics with applications in information technology for optical signal processing and in the development of novel coherent light sources. The capability to efficiently generate harmonics at the nanoscale will have an enormous impact on all these fields, since it would allow one to realize much more compact devices and to interrogate matter in extremely confined volumes. Here, we present a perspective on the most recent advances in the generation of nonlinear optical processes at the nanoscale and their applications, proposing a palette of future perspectives that range from material characterization and the development of novel compact platforms for efficient photon conversion to bioimaging and sensing.
LSPARCOM: deep unfolded super-resolution microscopy

The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization. However, this is achieved at the cost of lengthy imaging times, limiting temporal resolution. In recent years, a variety of approaches have been suggested to reduce imaging times, ranging from classical optimization and statistical algorithms to deep learning methods. Classical methods often rely on prior knowledge of the optical system and require heuristic adjustment of parameters or do not lead to good enough performance. Deep learning methods proposed to date tend to suffer from poor generalization ability outside the specific distribution they were trained on, and tend to lead to black-box solutions that are hard to interpret. In this paper, we suggest combining a recent high-performing classical method, SPARCOM, with model-based deep learning, using the algorithm unfolding approach which relies on an iterative algorithm to design a compact neural network considering domain knowledge. We show that the resulting network, Learned SPARCOM (LSPARCOM), requires far fewer layers and parameters, and can be trained on a single field of view. Nonetheless it yields comparable or superior results to those obtained by SPARCOM with no heuristic parameter determination or explicit knowledge of the point spread function, and is able to generalize better than standard deep learning techniques. Thus, we believe LSPARCOM will find broad use in single molecule localization microscopy of biological structures, and pave the way to interpretable, efficient live-cell imaging in a broad range of settings.
Optimizing the Sustainability of Renewable Energy: A Review on the Impart of Internet of Things

The problems of energy usage wastage, conservation and optimization has always been there. Most significantly in third world countries with a high level of improper energy resource management. The world has become a global village and tending toward the transformational use of technology assets and materials to optimize the usage of renewable energy; in this case the Internet of things. Renewable Energy are actually energy source that are continually replenished naturally by nature. The internet of things are interconnected system through which hardware system device are given instruction for instance to reduce energy wastage; the potential for further reduction of fossil fuel usage and wasted energy is become cosmic. Optimization here is the appropriate management of the energy generated by energy sources through use of internet of things. The modern technology such as the internet of things is to help harness and improve in management by switching between renewable energy sources alongside the enhancement in making effective energy source at given time and energy consuming device. Review Article Chukwuemeka et al.; AJRCOS, 5(4): 44-52, 2020; Article no.AJRCOS. 57557 45
DOES ENVIRONMENTAL, SOCIAL AND GOVERNANCE PERFORMANCE INFLUENCE ECONOMIC PERFORMANCE?

The purpose of this paper is to evaluate the influence of environmental, social and governance performance on the economic performance of the Standard & Poor’s 500 companies. Structural equation modeling and linear regression have been utilized to measure the overall and individual influence of environmental, social and governance (ESG) performance on economic performance using longitudinal data comprising the years from 2010 to 2015. The overall ESG model had a significant relationship on economic performance. Furthermore, the findings of this study show that social and governance performance significantly affects economic performance in all regression models. However, environmental performance failed to show a significant relationship. The research contributes to the literature by providing insights for investors, managers and employees about the influence of ESG performance on company performance.
A review of solution combustion synthesis: an analysis of parameters controlling powder characteristics

The solution combustion synthesis technique is a versatile method for the production of powders used in a variety of applications. It has been used to produce hundreds of compounds, thus demonstrating its versatility, especially for the preparation of oxides, and is now a workhorse technique in materials science. Its success resides in the ease of implementation, high-throughput, the versatility of chemistries, and capacity for the production of high-surface area powders. The main limitations of the technique include problems with powder agglomeration, possible lack of control of powder morphologies, and the presence of leftover organic impurities from incomplete combustion. In this contribution, we review the influence of a variety of factors of relevance for the technique, including the type of fuel, fuel-to-oxidizer ratio, and combustion temperature, as well as the presence of additives and other special considerations, with a particular focus on the crystallite size and particle size of the resulting powders.
The Effect of Undergraduate Major Choices on the Earnings of Sub-Saharan African Immigrant and Native-Born College Graduates

A very high percentage of sub-Saharan African college-graduate immigrants in the United States have college degrees in science, technology, engineering, and mathematics (STEM) disciplines compared with native-born college graduates. This study uses a pooled cross-section (2013–2018) from the American Community Survey to compare the distribution of undergraduate majors of sub-Saharan African immigrants and native-born college graduates. We estimate ordinary least square (OLS) earnings functions that include detailed college major variables. We find that undergraduate major area of study is a significant predictor of earnings and that there is an overrepresentation of sub-Saharan African immigrants with high-paying undergraduate majors. However, after controlling for human capital differences, college-educated African immigrants have not achieved pay equity with their native-born counterparts. JEL Classifications: J61, J15, J26, I21
Business model designs, big data analytics capabilities and new product development performance: evidence from China

PurposeAlthough the business model (BM) has become a top priority in management research, existing literature still offers a confusing and partial picture about how to leverage BM designs for new product development (NPD) because of two limitations. First, research has paid little attention to different BM designs' effects on NPD performance. Second, few empirical studies have examined the moderating roles of firms' learning capabilities, such as big data analytics capabilities (BDA capabilities). This study aims to investigate the effects of BM novelty design and BM efficiency design on NPD performance and the ways in which BDA capabilities moderate these effects.Design/methodology/approachA literature review provides the model and hypotheses. Using a sample of 208 Chinese firms, the authors conducted an empirical test following multiple regression analysis.FindingsThe results demonstrate that BM novelty design has a positive effect on NPD performance while BM efficiency design takes the form of an inverted U-shape. Moreover, BDA capabilities (i.e. BDA technology capability and BDA management capability) have complicated moderating effects on BM novelty design- and BM efficiency design-NPD performance relationships.Research limitations/implicationsThe results may be affected by both the context (solely in China) and type (cross-sectional) of the data set. This study has explored the moderating effects of BDA capabilities, further studies considering other significant practices such as social media usage, could yield richer insights that would help validate the results of this study.Practical implicationsFirst, we suggest that managers should be explicitly aware of the different impacts of BM novelty design and BM efficiency design on NPD performance. Second, this study encourages managers to build relevant BDA capabilities to work with BM designs to improve NPD performance.Originality/valueThis is one of the first studies to investigate BM designs' complicated influences on NPD success and explore BDA capabilities' moderating effects on the BM design-NPD performance linkage.
Assesment of Spontaneous Heating Susceptibility of Coals Differential Thermal Analysis

The auto-oxidation of coal is a complex physico-chemical process which is accompanied by the absorption of oxygen, formation of coal oxygen complexes and their decomposition leading to the liberation of heat. This complexity of the process is enormous because of the great diversity in the coal substance with the associated mineral matter and the condition of the heterogeneous mass, concurrent and over lapping reactions takes place which are very difficult to separate out. The rate of oxidation of coal at ambient temperature gives a measure of the proneness of coal to auto oxidation. From the economics stand point, even a small incidence in auto oxidation causes loss of production when it becomes necessary to seal off a section or a district resulting in the possible loss of machinery and the deterioration of reserves is potentially great. Fires in coal mines are one of the serious problems of Indian coal mining industry. In India every year many incidence of the fire occur in the underground and surface due to spontaneous combustion. Mostly the fire occurring due to spontaneous combustion per year is a function of the quality of the coal and the circumstances which it is subjected. They endanger not only the valuable lives of men in mine but also cause considerable economic losses to the organization affected by them. History of coal mines fires can be traced back to the year 1865, when the first fire was reported in Raniganj coal fields. Over 140 years fires have been reported till the year1967 from both Jharia and Raniganj coal fields and superior quality non-cocking coal in Raniganj coal fields. Various methods have been used in laboratory for the study of relative tendency of coals to heat spontaneously. Most of the methods are based on the measurement of the oxidation rate and ignition temperature. The propensity of different coal to self heating has been studied in the laboratory using crossing point temperature, different thermal analysis, oxygen absorption method etc. the result of the test analyzed according to readily recognizable characteristics temperature and rates of heating under non adiabatic condition. These simple indices have historically been considered to be prediction of self heating liability.
Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes

Netflix has produced many TV series, one of which is Money Heist. This series has four seasons with a total of 38 episodes. The fourth season was released on April 3, 2020, which has eight episodes. The fourth season of Money Heist is 31.73 times more demand than the average series around the world. However, despite the many requests for the fourth season of the Money Heist series, there are still some negative comments made by the connoisseurs of the Money Heist series. In the YouTube comments column on the Netflix channel, there are still many who comment neutral and provide positive comments on this series. Therefore, there needs to be a method in which viewers' comments or opinions can be analyzed in order to be able to classify the opinions they make about this series by conducting sentiment analysis using the Naive Bayes algorithm. Based on the results of research conducted, Naive Bayes can be said to be successful in conducting sentiment analysis because it achieves results of 81%, 74.83%, and 75.22% for accuracy, precision, and recall, respectively.
A Study on the Awareness of Reflective History in Education: The Case of Nicosia

This study was carried out in Nicosia to evaluate the society’s awareness of history reflected in education. The research was conducted to determine the differentiated historical effects through education to create the city brand identity of, Nicosia, symbolizing both the northern and southern parts. Semiotics and descriptive analysis techniques, which are among qualitative research methods, were used. The fact that there has been no research in terms of city brand identity reflected through education covering both the northern and southern parts of Nicosia makes the study unique. This research aims to promote interdisciplinary social representation that addresses the ways in which social and cultural respond to the needs of the people on the island as a whole after obtaining history lessons through education. Both Greek and Turkish society chosen randomly has revealed information about the city identity and brand identity regarding their history education. It was found out that all of the participants learnt the traces of many different civilizations dominated the island of Cyprus are the common intersection points of the city. Therefore, an integrated logo was proposed as a graphic design study for the city brand identity as a reflection of history teaching and learning, which may cover all of the northern and southern parts’ cultural symbols of Nicosia.
Yield of green super rice (GSR) lines under legowo and squared planting system

Green Super Rice (GSR) is rice genotypes designed to have high yield which is stable under low input Conditions. Legowo was reported to give higher yield compared to The square planting system. This research aimed to study The yield of 10 GSR lines along with four checks. i.e. Situ Bagendit, Ciherang, Inpari 8, and Inpari 13 under legowo 2: 1 (20 cm x 10 cm x 40 cm) and square (20 cm x 20 cm) planting system. The experiment was conducted in ICRR experimental station in Sukamandi during DS 2012 following split-plot design of two replications with The spacing system as the main plot and genotype as a subplot. Transplanting was conducted at 21 days after sowing into 3 m x 7 m plots of 1 - 3 seedlings/hill. The results showed that the interaction between genotypes and spacing systems was identified only for plant height. There were differences among genotypes on plant height, tiller number, unfilled grain/panicle, 1000 grain weight and yield. The spacing systems affected The tiller number and yield. Huanghuazhan (8.81 t/ha) had highest yield, higher than The best check Ciherang (7.81 t/ha). FFZ1, IR 83140-B-11-B, IR 83142-B-19-B, WTR 1 dan Zhongzu 14 considered to be adapted for legowo planting system.
Acetaldehyde in Polyethylene Terephthalate (PET) Bottled Water: Assessment and Mitigation of Health Risk for Consumers

This study was designed to investigate the levels of acetaldehyde residues in polyethylene terephthalate bottled water and its significance in terms of consumer health. We analyzed 104 samples collected throughout Italy, so as to be representative of the national market. Parameters such as CO2 level, shelf life, weight of the empty bottle and distance from the production sites to the point of sale were also collected. Although the levels of acetaldehyde complied with the limits established by Italian legislation, they varied widely, with concentrations ranging from 0.41 to 76.2 µg/L. An assessment of safety for human health, using the margin of exposure approach, showed that the amount of acetaldehyde in bottled water is unlikely to be of any safety concern for human health. The acetaldehyde residues were mainly due to CO2 levels which influence solubility of acetaldehyde in water. They are also related to the size of the bottle and the distance from the store, but not to the shelf life, at least for 40 days. The findings suggest some good practices for a better product from the point of view of public health, like polymer quality and limitation of transport distances.
Prevalence of Post Traumatic Stress Disorder among School-age Adolescent

Aim: After a traumatic event, the person can develop post-traumatic stress disorder (PTSD), the purpose of the study is to assess the prevalence of PTSD in adolescents in public middle schools of the prefecture of Salé in Morocco and study anxiety and depression which are the comorbid disorders of the PTSD. The survey was carried out from March to June 2017. Methods: 523 students were selected by the cross-sectional method from fifteen schools that were randomly selected, the age of the students vary between 12 and 17 years. For the survey, standardized questionnaires (the socio-demographic data, the Life Events Checklist, the CPTS-RI (Children's Post Traumatic Stress Reaction Index), the STAIY (State Trait Inventory Anxiety Form Y) and the CDI (Children Depression Inventory) were used which were filled in by the students. Results: The prevalence of PTSD was 70.4% in the students who have PTSD. We found that the prevalence in boys was 46.74%, while in girls it was 53.26%. In addition to that, 81% of students found to be anxious and 51.8% of students have depression. Conclusion: There is a high prevalence of post traumatic stress disorder among adolescents, there are practical implications for the support and care of these adolescents.