Miracle-dz/UniFAR-FTUs huggingface dataset:
Cavitation Prevention Potential of Hydromechanical Pressure Compensation Independent Metering System with External Active Load

This article studies the cavitation performance and preventing method of the hydromechanical pressure compensation independent metering system (HPCIMS). Compared with the conventional load sensing system (CLSS), the meter-in and meter-out orifices of HPCIMS can be regulated independently. A quasi-static behavior analysis of cavitation performance was applied to the HPCIMS and CLSS. The meter-in pressure equation of HPCIMS showed that keeping the ratio of the meter-in and meter-out orifices greater than the minimum value can avoid the cavitation phenomenon. Systems parameters were then kept as constant, and the key parameters related to cavitation performance of the two systems were compared by varying external force. Comparison results show that the cavitation phenomenon in the meter-in chamber of CLSS with the external active load is inevitable, but in HPCIMS, it can prevent the cavitation phenomenon by changing the ratio of the meter-in and meter-out orifices, so the HPCIMS has the cavitation prevention potential.
The analysis of implementation in public relations to establishment the effort of the brand image coworking (a study at coworking space ETOS of Yogyakarta)

ETOS co working space, one of the workspaces located in Yogyakarta that carries a simple concept, but is still in demand by the public. Manyhave emerged co-working spaces with attractive concepts and are in demand by many people. ETOS still carries the concept of "real co-working space" and competes with its competitors, and in less than a year ETOS has already received regular clients and customers every day. With the title "The Analysis of the Implementation Public Relations to Develop The Effort of The Brand Image Co working space" with the formulation of the problem of how to use the role of public relations in an effort to increase the brand image of the ETOS co working space in Yogyakarta with the aim of research, namely to determine the use of the role of public relations in an effort to improve brand image co working space ETOS. This research uses descriptive qualitative research methods. Data collection techniques are carried out using observation, interviews, and documentation. This research process focuses on utilizing the role of public relations in an effort to increase the brand image of the co-working space at ETOS. By using several data analysis techniques such as data reduction, display data, and conclusion drawing / verification. For data validation, this study used triangulation techniques. Based on the research results obtained in the field, it is concluded that in an effort to improve the brand image of the ETOS co working space , ETOS Public Relations takes advantage of the role of public relations , namely, expert prescribe, communication facilitator, problem solving facilitator, and communication technician. ETOS PR also implements management public relations in this case planning, implementation, and evaluation.
Smart monitoring of strengthened beams made of ultrahigh performance concrete using integrated and nonintegrated acoustic emission approach

Ultrahigh performance concrete (UHPC) is one of the recent advancements in construction building materials and popularly used as a precast applications or strengthening material. The current paper attempts to improve understanding about fracture and failure mechanisms of UHPC application as a strengthening material using acoustic emission (AE) technique. The paper proposes an integrated and nonintegrated approach to relate the mechanical energy with AE energy for (i) undamaged reinforced concrete (RC) beams and (ii) damaged RC beams strengthened with UHPC. The integrated approach evaluates the damage mechanisms by evaluating sentry function (logarithmic ratio of mechanical and acoustic energy), while nonintegrated approach considers superimposition of flexure and AE response, load versus AE energy behavior and damage index. To validate the proposed approach, experiments are conducted on (i) control RC beams and (ii) RC beams strengthened with UHPC layers of t, 1.5t, and 2t thickness (where t = 10 mm). From the study, it is found that analysis of structures using both the approaches can overcome corresponding limitations. Hence, to completely characterize the damage, both methods are recommended.
Genetics components of rice root architecture and carbon isotopic fractionation parameters: a tracer for breeding in a water-saving irrigation management

Phenotyping is the major bottleneck in the effort to develop varieties of rice ( Oryza sativa L.) suitable for growing under a water-saving irrigation management, such as alternate wetting and drying irrigation techniques (AWD). To analyze if the genotypic variability for carbon isotope discrimination (CID) in rice leaves could be used as a relatively high-throughput tracer to early select superior genotypes highlighting improved root architecture traits when submitted to AWD, a set of twenty varieties grown under semi-natural conditions were submitted to two water irrigation regimes, continuous flooding (CF) and AWD cycles. Coefficients of genetic variance ( π2 g) obtained for root architecture, micro-morphological and physiological traits were significant for all of them regardless of the adopted irrigation system, except to mean root diameter. The three significant principal components (PCs) with eigenvalue > 1, explain most of the total variation across cycles and water regimes. For most of analyzed traits, the values of heritability coefficients were higher regardless of adopted irrigation management and trait category; for CID, the magnitudes of broad heritability at an individual level (greater than 0.80) were similar in the two irrigation techniques, evidencing that the success of the selection is independent of irrigation management. The higher CID values after three AWD cycles are associated with varieties with higher total root length and volume. To our knowledge, this is the first study demonstrating the potential application of CID as a tracer to select root architecture traits in rice when water-saving irrigation management is of concern.
Theoretical atomic radii of elements (H-Cm): A non-relativistic study with Gaussian basis set using HF, post-HF and DFT methods

We calculated the most probable radius of an atom for elements from H to Cm. The calculations were carried out by using non-relativistic, spin polarized, HF, MP2 and DFT methods with all electron Gaussian basis set. Periodicity of atomic radii was correlated with the experimental first ionization energies. This non-relativistic atomic radii were also compared with other theoretical atomic radii. With respect to the Dirac-Slater data, our values were in good agreement with the elements up to Sn. Relationship with van der Waals radii of noble gases was discussed. Anomalous properties of Gd and Pd were examined. Linearity of lanthanide contraction of elements with 4f electrons is illustrated. This linearity is found independent of the extent of electron correlation.
Texture descriptors for better face recognition performance

Due to, the great electronic development, which reinforced the need to define people’s identities, different methods, and databases to identification people’s identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40,401 and 10 subjects taken from ORL, UFI and Self-Created databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database as standard database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance. Hereby, the LBP based biometric system performs excellence recognition accuracy even with difficult face images that contain different expressions, different poses, and varied illumination like the UFI dataset.
On the surface vis impressa caused by a fluid-solid contact

It is well-known that, nano-mechanics should take into account not only physical phenomena occuring within the bulk but, first of all, the physical phenomena appropriate for a surface of two materials contact. The huge volume density of internal surfaces as well countours lines located within the nanomaterial results in our interest in, apart from classical form of mass, momentum and entropy transport, those modes of transportation where a carrier of physical property follows a free path having of a dimension greater than nanostructure characteristic dimension. The mode of transport dominated by mechanical, thermal and electrical slip of carried bounding off walls (a surface of separation) is called usually in physics ”a ballistic mode”. In the paper the appropriate Newtonian surface vis impressa responsible for the ballistic mode of transport is defined, classified and explained. We postulate that generally surface vis impressa can be additivelly splited onto friction and mobility forces.
Preparation and Tribological Behavior of Hydrophobic Lanthanum Borate Nanosheets in Rapeseed Oil

Oleic acid-capped lanthanum borate (abbreviated as OA/LaBO3·H2O) nanosheets were prepared by the hydrothermal method. The microstructures of as-prepared OA/LaBO3·H2O were characterized by means of SEM, TEM, EDS, FTIR and XRD, respectively. Moreover, the friction and wear properties of OA/LaBO3·H2O as a lubricant additive in rapeseed oil were evaluated on a four-ball tribotester. The tribochemical characteristics of worn surfaces were investigated by SEM and XPS. The results showed that the hydrophobic OA/LaBO3·H2O nanosheets exhibited their morphology with a diameter in the range of 100 nm to 300 nm and a thickness of about 25 nm, and displayed excellent dispersing stability in rapeseed oil. In the meantime, the rapeseed oil doped with OA/LaBO3·H2O nanosheets markedly decreased the friction and wear of steel balls, and the optimal friction-reducing and antiwear ability of rapeseed oil was obtained at an OA/LaBO3·H2O content of 1.0%. The outstanding tribological performance of OA/LaBO3·H2O in rapeseed oil was attributed to the formation of a composite boundary lubrication film mainly composed of lubricious tribochemical species of B 2O3, La2O3 and Fe2O3, and deposits of OA/LaBO3·H2O nanosheets as well as the adsorbates of rapeseed oil on rubbed surfaces.
Geometric morphometrics reveal sister species in sympatry and a cline in genital morphology in a ghost spider genus

Morphological boundaries between species of the maculatipes‐group in the ghost spider genus Sanogasta Mello‐Leitão are ambiguous, and the most widespread species, S. maculatipes, is unusually variable and may represent multiple cryptic species. To resolve these issues, we perform a geometric morphometric analysis on the female genitalia of the group, visualizing and testing for differences in shape between described species, and between putative cryptic species within S. maculatipes. We complement this with a multi‐locus phylogenetic analysis of the group, to place the morphological results in a phylogenetic context. Our study reveals that two species in the group, S. alticola and S. mandibularis, are morphologically and molecularly distinct lineages that can be objectively distinguished. However, we reveal that S. maculatipes actually consists of two widespread sister species which occur in sympatry throughout the grasslands of northern Argentina. We further discover a geographical cline in the shape of the female genitalia of one of these sister species, such that specimens from the east and west of the range display morphological differences comparable to those between species, despite being virtually identical at the COI locus. Our study demonstrates the strength of a geometric morphometric approach for delimiting species in species‐complexes where morphological differences are subtle and confounding factors such as overlapping ranges, allometry and high levels of intraspecific variation are present.
The Impact of Interest Rate Liberalization on the Systemic Risk—Based on the Panel Data of Commercial Banks

This paper uses coefficient of variation method and dynamic CoVar model to measure the level of interest rate liberalization and the systemic risk of commercial banks, and then based on the quarterly panel data of 14 listed commercial banks from the first quarter of 2009 to the fourth quarter of 2018, it empirically analyzes the impact of interest rate marketization process on the systemic risk of commercial banks. The results show that: the process of interest rate liberalization in China has significantly increased the overall systemic risk of commercial banks, and the robustness test further supports this conclusion.
Research on Self-compensating Oil Sleeve for Hydraulic Chuck

Based on the oil supply principle of hydraulic chuck, analysis of the structural characteristics and failure reasons of the oil distribution sleeve showed that the main reason for the increase of the leakage of the oil sleeve is the increase of the value of the seal gap caused by the increase of the temperature and pressure of the hydraulic oil. The self-compensating oil sleeve for the rotary oil distribution of hydraulic chuck is designed by theoretical calculation, and the deformation of the self-compensating oil distribution sleeve and the common oil distribution sleeve are compared and analyzed by finite element software, and the sealing performance of the two kinds of oil sleeve is compared and analyzed by test. The results show that the sealing gap of the self-compensating oil sleeve has dynamic stability compared with that of the ordinary oil sleeve, and the leakage of the self-compensating oil sleeve is stable under the working condition, which can provide reference for the design innovation of the rotary oil distribution sleeve for the chuck of the full hydraulic drilling machine.
CT scan image segmentation based on hounsfield unit values using Otsu thresholding method

The use of medical images in diagnosing and analyzing various cases in the medical field is commonly used. In certain cases, the image used is not limited to two-dimensional images, but sometimes requires the use of three-dimensional images. CT scan image is an image that has several image slices that can be reconstructed into a three-dimensional image. In the reconstruction process, segmentation plays an important role to get a good reconstruction result and reduce the resulting noise. This study aims to develop a method used in CT scan image segmentation, with the hope that it can simplify the diagnosis process performed by doctors using two-dimensional images or those that have been constructed into three-dimensional images. The main method developed is the Otsu Thresholding method based on the threshold value, which is combined with the Hounsfield unit (HU) value which will be the input for the segmentation process. The image used is a thorax CT scan image with the final goal to get the results of heart segmentation. The results obtained based on the calculation of balanced accuracy for the 30 data tested had an average of 72.54%. The highest result of balanced accuracy for heart segmentation was obtained by data 4 of 77.43%, while the lowest result was obtained by data 29 of 69.1%.
Big Data, Marketing Analytics, and Firm Marketing Capabilities

ABSTRACT While big data, marketing analytics, and firm marketing capabilities are all potential drivers of competitive advantage, there is limited research that investigates the interrelationship between them. This study aims to address this gap by examining the mechanisms through which big data and marketing analytics can be used to enhance firm marketing capabilities. Drawing on the dynamic capability view, a research model is developed and tested based on an analysis of 316 survey responses. The findings demonstrate positive effects of the use of big data on the use of marketing analytics, and the latter’s effect on firm marketing planning, marketing implementation, brand management, customer relationship management, and product development management. This study helps advance our understanding of firm marketing capability-enhancing processes through the use of big data and marketing analytics. It also provides practical implications to guide firms in using big data and marketing analytics to improve their marketing capabilities.
Current status data with two competing risks and missing failure types: a parametric approach

ABSTRACT Missing cause of failure is a common problem in competing risks data. Here we consider a general missing pattern in which one observes a set of possible causes containing the true cause. In this work, we focus on the parametric analysis of current status data with two competing risks and the above-mentioned missing pattern. We make some simpler assumptions on the conditional probability of observing a set of possible causes of failure given the true cause and carry out maximum-likelihood estimation of the model parameters. Asymptotic properties of the maximum-likelihood estimators are also discussed. Simulation studies are performed to study the finite sample properties of the estimators and also to investigate the role of the monitoring time distribution. Finally, the method is illustrated through the analysis of a real data set.
Exploring the Interactions between Online Shopping, In-Store Shopping, and Weekly Travel Behavior using a 7-Day Shopping Survey in Lisbon, Portugal

The steady growth of online shopping in the last decades has led to an impact on personal travel and on freight transport that is yet to be fully grasped. Previous research on the subject offers mixed findings, with several studies pointing to complementarity between online and in-store shopping, while others suggest substitution, modification, or neutrality. Using data from a 7-day shopping survey in Lisbon, Portugal, which involved 400 respondents, this paper applies structural equation modeling to explore the relationships among online shopping and in-store shopping preferences, while also considering the period of the week in which the purchases took place, since it is expected that the interaction between shopping and other personal travel behavior varies between weekdays and weekends. The result shows that online shopping preference leads to more online purchases, while in-store shopping preference leads to more in-store purchases. Furthermore, online shopping on weekdays has a positive association with both online and in-store shopping on weekends, which supports a complementarity effect. This effect is linked to a younger population, which commutes by car, and lives in less central areas. Since deliveries are becoming increasingly faster, while living centrally is becoming progressively more difficult, complementarity might give way to substitution, with the foreseeable challenges to maintaining street vitality, if this issue is not addressed timely by policymakers.
Study of effects of radial load on vibration of bearing using time-Domain statistical parameters

Rolling element bearings are important parts of rotating machineries. Any defect in their elements causes vibration and damage. Many vibration signal analysis techniques are available for detection and diagnosis of defects in machineries. In time domain vibration analysis techniques, various statistical parameters such as RMS, crest factor, skewness, kurtosis etc. are used for defect detection in bearings. Bearing related parameters like defect type, defect size, shaft speed, radial load etc. affect the bearing vibrations. In this paper, the effects of change in bearing radial load on various time-domain statistical parameters are analyzed. New combination of indicators like Kurtosis × RMS, Kurtosis × Peak, RMS × Peak and new indicators developed by researchers TALAF and THIKAT are analyzed for change in radial load on bearing. Also the effect of outer race defect in bearing on statistical parameters is analyzed. In this paper, the bearing defect data sets with outer and inner race defects provided by Society for Machinery Failure Prevention Technology are used. The results show that these parameters can be used as condition indicators for early fault detection in bearings.
CAE-based classification method of electric power business

Aiming at the problem that abnormal data in the business classification of power data communication networks will reduce the business classification accuracy, a classification method based on convolutional autoencoder combined with lightGBM was proposed. First, the flow features of the electric power business were extracted through the convolutional autoencoder. Then, anomaly detection was realized according to the relationship between the reconstruction loss of the convolutional autoencoder and the set threshold. Finally, the electric power business was classified through the LightGBM classifier. The Moore data set used for simulation verification. The results show that the proposed method can effectively detect abnormalities, thereby improving the accuracy of the electric power business classification.
Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.