Miracle-dz/UniFAR-FTUs huggingface dataset:
An Experimental Study of Negative Drift Force Acting on a Floating OWC “Backward Bent Duct Buoy”

The utilization of renewable energy is required immediately since emissions of carbon dioxide are being restricted. To this end, we are investigating the ocean wave energy converter, especially the floating OWC “Backward Bent Duct Buoy” (BBDB). The BBDB, proposed by Masuda in 1986, is a wave energy converter of the ‘moored floating oscillating water column’ type that is composed of an air chamber, an L-shaped bent duct, a buoyancy chamber, and a turbine. The BBDB has certain positive characteristics. Firstly, the primary conversion performance of the BBDB is better than other floating OWCs. Secondly, the length of the BBDB is shorter than other floating OWCs. Thirdly, as the BBDB advances in the incident wave direction with slow speed waves because of the negative wave drift force, the mooring cost can be reduced. In this research, experiments under a various wave periods were carried out to clarify the characteristics and cause of the generation of negative drift force acting on a BBDB in regular waves with a two-dimensional wave tank at Saga University. The length of the BBDB model is 85cm. To measure the wave drift force, the model is moored with horizontal wire-springs. The motions of the BBDB, such as surge, heave, and pitch, are measured by remotely using image processing. The fluid velocity around the BBDB is measured by using particle image velocimetry (PIV). Motion tests of the BBDB without mooring are also carried out to measure the horizontal velocity of the BBDB in waves. From the experimental results, the characteristics and causes of the generation of negative drift force acting on the BBDB in regular waves are discussed.Copyright © 2008 by ASME
Preprocessing and Solving Finite Element Analysis for NVH Prediction

Current vehicle development period has become shorter; therefore, virtual testing has been considered as vital to assist design decision at the early stage of the development. Over the last three decades, Finite Element Method (FEM) is widely used to predict the Noise and Vibration level of a vehicle. With the latest technology of Computer Aided Engineering (CAE) simulation, the calculation time taken for NVH analysis can be reduced from few days to only few hours. This paper presents current simulation technique for automotive development using Altair Hyperworks as preprocessing tool for vehicle modeling as well as application of NASTRAN as calculation solver. Normal Mode analysis is conducted on the Trimmed Body to investigate the natural frequency of the steering and the vehicle resonance. The result obtained is comparable with the actual prototype testing result to present the level of correlation. Coupling structural-acoustic analysis also conducted to predict the Noise Transfer Function (NTF) at the driver's ear.
Leaf lettuce (Lactuca sativa L. ‘L-121’) growth in hydroponics with different nutrient solutions used to generate ultrafine bubbles

Abstract Fine bubble (FB) and ultrafine bubble (UFB) generators have been installed in hydroponic systems to promote plant growth. However, their application is limited because the mechanism of the effect of UFBs is not well understood. We aimed to evaluate the effect of UFBs on promoting leaf lettuce growth in hydroponics using nutrient solutions prepared with tap water (Control) and two UFB water—tap water (standard, STD) and calcium nitrate solution (original, ORL). We then compared the differences among the three treatments under the same nutrient concentration and environmental conditions. The shoot fresh weights, dry weights, nutrient element (Zn, K, Mn, Fe, Cu, Ca, and Mg) concentrations, and total nutrient element quantity were measured after harvesting. The shoot fresh and dry weights were higher in the STD and ORL treatments than in the control, but no difference was noted between the STD and ORL treatments. Plant nutrient element uptake was higher in the STD and ORL treatments than in the Control treatment. Additionally, Mn uptake differed between the STD and ORL treatments. The results indicated that plant growth promotion in the STD and ORL treatments was affected by reactive oxygen species generated by UFBs. UFB properties also allowed for the supply of sufficient nutrients enabling rapid growth. Furthermore, we suggest that the type of solution used altered the properties of the FBs and UFBs, which in turn influenced the uptake of nutrients by the plants.
Numerical Analysis of Vertical Water Impact of a Spherical Projectile

Information related to the water impact forces is crucial in the design of missiles, seaplanes, aircraft, and spacecraft and also has relevance to the design of marine structures that must withstand slamming loads. In this paper numerical modeling of vertical water impact of a spherical projectile is presented using LS-DYNA explicit dynamics software package. The LS-DYNA package allows the fluid to be modeled using an Eulerian formulation. The LS-DYNA predictions for impact coefficient are compared with the analytical and experimental data.
The mutation of the Toyota Production System: adapting the TPS at Hyundai Motor Company

This paper examines the spread of Toyota Production System (TPS) through Korea, by focusing on the experience of Hyundai Motor Company. Drawing upon data obtained from field research, this case study interprets the adoption of TPS from an evolutionary perspective, arguing that the emulation of TPS should not entail adopting TPS as Toyota developed it originally, but to develop one's own production model. Over the past 40 years, Hyundai has developed its own production model, Hyundai Production System (HPS), initially emulating TPS, followed by re-interpreting and modifying TPS to adapt to the company's unique circumstances. HPS is a mutated form of TPS. The Hyundai case reveals that the adoption of TPS involves a complex evolutionary process of organizational learning and interpretation. This case sheds light on the possibility of various paths toward lean production, and demonstrates that both external and internal factors combine to form a complicated causal chain, influencing the ‘mutated’ emulation of TPS and generating a certain pattern of path-dependence in the evolutionary trajectory of a particular production model.
Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments

The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge–response scheme. We propose a novel approach to convolutional phoneme classifier training, which ensures high phoneme recognition accuracy even for significantly simplified network architectures, thus enabling efficient utterance verification on resource-limited hardware, such as mobile phones or embedded devices. We consider Deep Convolutional Neural Networks operating on windows of speech Mel-Spectrograms as a means for phoneme recognition, and we show that one can boost the performance of highly simplified neural architectures by modifying the principle underlying training set construction. Instead of generating training examples by slicing spectrograms using a sliding window, as it is commonly done, we propose to maximize the consistency of phoneme-related spectrogram structures that are to be learned, by choosing only spectrogram chunks from the central regions of phoneme articulation intervals. This approach enables better utilization of the limited capacity of the considered simplified networks, as it significantly reduces a within-class data scatter. We show that neural architectures comprising as few as dozens of thousands parameters can successfully—with accuracy of up to 76%, solve the 39-phoneme recognition task (we use the English language TIMIT database for experimental verification of the method). We also show that ensembling of simple classifiers, using a basic bagging method, boosts the recognition accuracy by another 2–3%, offering Phoneme Error Rates at the level of 23%, which approaches the accuracy of the state-of-the-art deep neural architectures that are one to two orders of magnitude more complex than the proposed solution. This, in turn, enables executing reliable presentation attack detection, based on just few-syllable long challenges on highly resource-limited computing hardware.
Can Cranioplasty Be Considered a Tool to Improve Cognitive Recovery Following Traumatic Brain Injury? A 5-Years Retrospective Study

Cranioplasty (CP) is a neurosurgical intervention of skull repairing following a decompressive craniectomy. Unfortunately, the impact of cranioplasty on cognitive and motor function is still controversial. Fifteen TBI subjects aged 26–54 years with CP after decompressive craniectomy were selected in this observational retrospective study. As per routine clinical practice, a neuropsychological evaluation carried out immediately before the cranioplasty (Pre CP) and one month after the cranioplasty (T0) was used to measure changes due to CP surgery. This assessment was performed each year for 5 years after discharge in order to investigate long-term cognitive changes (T1-T5). Before cranioplasty, about 53.3% of subjects presented a mild to severe cognitive impairment and about 40.0% a normal cognition. After CP, we found a significant improvement in all neuropsychological test scores. The more significant differences in cognitive recovery were detected after four years from CP. Notably, we found significant differences between T4 and T0-T1, as well as between T5 and T0-T1-T2 in all battery tests. This retrospective study further suggests the importance of CP in the complex management of patients with TBI showing how these patients might improve their cognitive function over a long period after the surgical procedure.
Alteration of Resting Electroencephalography by Acute Caffeine Consumption in Early Phase Psychosis

Individuals with schizophrenia use twice as much caffeine on average when compared to healthy controls. Knowing the high rates of consumption, and the potential negative effects of such, it is important we understand the cortical mechanisms that underlie caffeine use, and the consequences of caffeine use on neural circuits in this population. Using a randomized, placebo controlled, double-blind, repeated measures design, the current study examines caffeine's effects on resting electroencephalography (EEG) power in those who have been recently diagnosed with schizophrenia (SZ) compared to regular-using healthy controls (HC). Correlations between average caffeine consumption, withdrawal symptoms, drug related symptoms and clinical psychosis symptoms were measured and significant correlations with neurophysiological data were examined. Results showed caffeine had no effect on alpha asymmetry in the SZ group, although caffeine produced a more global effect on the reduction of alpha2 power in the SZ group. Further, those with more positive symptoms were found to have a greater reduction in alpha2 power following caffeine administration. Caffeine also reduced beta power during eyes closed and eyes open resting in HC, but only during eyes closed resting conditions in the SZ group. These findings provide a descriptive profile of the resting EEG state following caffeine administration in individuals with schizophrenia. The findings ultimately suggest caffeine does not affect alpha or beta power as readily in this population and a higher dose may be needed to achieve the desired effects, which may elucidate motivational factors for high caffeine use.
Evolving Legacy System Features into Fine-Grained

is a constant need for practical, efficient and cost-effective software evolution techniques. We propose a novel evolution methodology that integrates the concepts of features, regression tests, and component-based software engineering (CBSE). Regression test cases are untapped resources, full of information about system features. By exercising each feature with their associated test cases using code profilers and similar tools, code can be located and refactored to create components. These components are then inserted back into the legacy system, ensuring a working system structure. This methodology is divided into three parts. Part one identifies the source code associated with features that need evolution. Part two deals with creating components and part three measures results. By applying this methodology, AFS has successfully restructured its enterprise legacy system and reduced the costs of future maintenance. Additionally, the components that were refactored from the legacy system are currently being used within a web-enabled application.
Evaluation of OCR Prediction Methods utilizing PCPT Data

The purpose of this study is to evaluate the capability of representative existing methods through which OCR of clayey soils can be predicted utilizing PCPT data. The existing methods include Schmertmann method, Chen and Mayne method using pore water pressures measured immediately behind the cone base , Lunne et al. method, and the latest Abu-Farsakh method. The lab. and in-situ test results, conducted in Incheon port area, were used for the study. The predicted OCR values from the methods were compared with the reference values estimated from laboratory oedometer test. Lunne et al.(k
Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.
Morphology and Mechanical Property of Electrospun PA 6/66 Copolymer Filament Constructed of Nanofibers

A technique for spinning continuous filaments constructed of nano-scale fibers instead of non-woven obtained by general electrospinning was described in the paper. Polyamide 6/66 copolymer solution in 88% formic acid was used as the spinning material. The relationships between the fiber microstructures, filament mechanical properties and spinning parameters: spinneret tip to collector vertical distance (TD), rotation speed (RS) were investigated. Post-drawing was performed to the as-electrospun continuous filament. The research results revealed that increase of TD and RS could improve the fiber’s uniaxial alignment and crystal structure, consequently the strength of the filament was enhanced. After being post-drawn, the stress and strain at break of the electrospun polyamide 6/66 copolymer filament was well improved.
Culture versus Technology: Bridging the Gap to Improve COVID-19 Prevention and Care based on the COVID-19 Task Force Perspectives

BACKGROUND: The diversity of Indonesians’ cultural backgrounds is a challenge for Indonesia’s coronavirus disease (COVID)-19 Task Force efforts to prevent COVID-19. The “infodemic” or misleading information in the community generates both positive and negative responses toward COVID-19. AIM: The study aims to explore the extent of Indonesia’s COVID-19 prevention and care efforts based on the perspective of the COVID-19 Task Force officers, as the frontline in society. METHODS: This descriptive qualitative study explored the extent of COVID-19 prevention and care based on the perspective of 16 COVID-19 Task Force participants from each region in Sumedang, Indonesia. Data collection was undertaken during the pandemic through semi-structured interviews using telephone (n = 13) and face-to-face (n = 3) approaches. RESULTS: Four themes emerged: community sentiment toward COVID-19, origin of COVID-19 in the region, collective cultural activities in local communities, and the use of information and communication technology related to COVID-19. CONCLUSION: Collaboration with religious leaders and community leaders to integrate the concept of cultural care with the use of technology can be developed to improve COVID-19 prevention and care.
Automated identification of complex undercut features for side-core design for die-casting parts

This article describes automated identification, classification, division and determination of release direction of complex undercut features of die-cast parts. The proposed system uses the concepts of visibility and accessibility to identify undercut features from a B-rep model of a die-casting part. The undercut features are then classified using a rule-based algorithm. Thereafter, the identified complex undercut features are separated into simple ones. Finally, the release direction for each simple undercut feature is determined and those having common release direction are grouped. The proposed system is implemented on case study die-cast parts, and the results are verified. This article would help bridge the design-manufacturing integration gaps in the die-casting process.
Exploiting spatial diversity in MIMO radars with collocated antennas

MIMO systems have revolutionized wireless communications resulting in unprecedented channel capacity. This breakthrough led researchers in radar as well as wireless communications communities to investigate the applicability of MIMO systems to radar. Preliminary research is showing that the full benefits of MIMO technology is realized when antenna spacing results in a decorrelated target scattering matrix. This requires antenna placement such that each receiver is observing an independent view of the target. Research is also showing that suboptimal improvements can be attained when the scattering matrix is partially correlated. This situation arises when antennas are collocated. In this work, we investigate the feasibility of MIMO Radar technology when antenna placement is quite restricted, such as in phased-array antennas. We extend the theoretical results for the correlation coefficients derived for statistical MIMO radar. We apply these results to assess the degree of decorrelation that can be achieved with Phase-Array antennas. We quantify our results as a function of antenna element spacing, frequency band and target RCS. In addition, we quantify the degree of decorrelation that is achievable by antennas that are typical in a tactical missile environment. Our results show that even when the antennas are quite small, it is possible to achieve a significant degree of decorrelation for a certain class of targets and certain frequency bands.
Accurate Absolute Measurements of Liquid Water Content (LWC) and Ice Water Content (IWC) of Clouds and Precipitation with Spectrometric Water Raman Lidar

A detailed description is given of how the liquid water content (LWC) and the ice water content (IWC) can be determined accurately and absolutely from the measured water Raman spectra of clouds. All instrumental and spectroscopic parameters that affect the accuracy of the water-content measurement are discussed and quantified, specifically, these are the effective absolute differential Raman backscattering cross section of water vapor (π)/dΩ, and the molecular Raman backscattering efficiencies ηliq and ηice of liquid and frozen microparticles, respectively. The latter two are determined following rigorous theoretical approaches combined with RAMSES measurements. For ηice, this includes a new experimental method which assumes continuity of the number of water molecules across the vertical extent of the melting layer. Examples of water-content measurements are presented, including supercooled liquid-water clouds and melting layers. Error sources are discussed, one effect that stands out is interfering fluorescence by aerosols. Aerosol effects and calibration issues are the main reasons why spectral Raman measurements are required for quantitative measurements of LWC and IWC. The presented study lays the foundation for cloud microphysical investigations, and for the evaluation of cloud models or the cloud data products of other instruments. As a first application, IWC retrieval methods are evaluated that are based on either lidar extinction or radar reflectivity measurements. While the lidar-based retrievals show unsatisfactory agreement with the RAMSES IWC measurements, the radar-based IWC retrieval which is used in the Cloudnet project performs reasonably well. On average, retrieved IWC agrees within 20% to 30% (dry bias) with measured IWC.
Accumulation of water on the vehicle deck

A mathematical model is presented for the accumulation of water on the vehicle deck of a damaged ro-ro (roll on-roll off) vessel. The model leads to a simple theoretical relationship between the mean elevation of water accumulated on the deck above the sea level, freeboard at the damage opening (including negative freeboard) and the significant height of relative motion due to ship motions. The two characteristic values are indispensable for the theoretical justification of the static equivalency method, used for the assessment of damage stability of ro-ro vessels. Standard deviation of the elevation of water is also provided, derived from the theoretical analysis.
Anxiety, Perceived Stress, and Resilience during the COVID-19 Pandemic: Population Estimates of Persons Presenting to a General Practitioner in Romania

Facing the COVID-19 pandemic, individuals are experiencing severe mental distress. Thus, during the last year, drastic changes occurred in everyday life of every human being. Following social distancing and economic insecurity, significant increases in mental health concerns (loneliness, anxiety, depression, or insomnia) have developed. The objective of this study was to explore the anxiety, perceived stress, and resilience in a population presenting at the general practitioner, during the COVID-19 pandemic. Data were collected between February and April 2021 and 440 individuals who presented to the general practitioner were evaluated. Concerning anxiety level, almost half of the respondents (49,3%, N = 217) scored above the threshold value on the anxiety scale (mild intensity 38.6%, moderate intensity 9.9%, severe intensity 0.8%). Having a low level of resilience, as well as experiencing a high level of stress, are both predictive of the occurrence of high anxiety (p < 0.001, r = −0.551 and p < 0.001, r = 0.622, respectively). Furthermore, resilience is negatively related to perceived stress (p < 0.001, r = −0.676). It is critical in the current crisis to recognize those at risk of developing mental illnesses, taking into consideration the various socioeconomic classes, as well as to maintain and improve the general public’s mental health using appropriate psychological interventions.