Miracle-dz/UniFAR-FTUs huggingface dataset:
Studi Literatur Potensi Pemanfaatan Terak Nikel (Slag Nikel) sebagai Agregat pada Mortar dan Beton

The increasing of infrastructure development has an impact on increasing demand for construction materials. Generally, construction materials come from nature and still being exploited, which could lead to environmental degradation. Visually, nickel slag resembles gravel and sand which can be used as an alternative material. Therefore, this literature study aims to determine the characteristics and potential utilization of nickel slag gathered from Indonesian lateritic nickel ore and its application as an aggregate in mortar and concrete. The method used in this study is a review of several literatures from a reputable digital publication database with the keywords "terak nikel" and "nickel slag". In addition, several criteria are also used as filters in order to get the more focused literatures. Although the fluctuations in its mechanical properties was reported, it can still be overcome by using slag nickel slag as coarse and fine aggregate simultaneously. The results also show that nickel slag can be used as fine or coarse aggregate in concrete up to 100% content as well and as fine aggregate in asphalt application mortars up to 50% content.
Numerical Simulations of Chloride Transport in Concrete Considering Connectivity of Chloride Migration Channels in Unsaturated Pores

This study aimed to develop a method for the reasonable prediction of chloride ingress into concrete under unsaturated conditions. To achieve this, the connectivity of both the pore networks and the water paths in hardened cement paste were investigated and modeled in the existing numerical analytical system. Previous measurement results for the continuous porosity in hardened cement paste were reorganized, and the relationship between the total porosity and continuous porosity was clarified and formulated as the pore connectivity. In addition, the connectivity of the liquid water in unsaturated pore structures was formulated referring to previous numerical studies, suggesting that the connectivity of the liquid water decreases at a lower degree of saturation. Furthermore, by calculating the chloride transport considering the pore connectivity and the liquid water connectivity under unsaturated conditions, the chloride penetration into unsaturated concrete, including an airborne chloride environment, could be reproduced more realistically than was previously possible.
THE PERSPECTIVES OF CLINICIANS ON ENRICHING PATIENT EXPERIENCES IN A CLINICAL CONTEXT: A QUALITATIVE STUDY

Abstract Understanding the viewpoint of clinicians in a healthcare setting is a vital task to comprehend the success of patients undergoing physical rehabilitation. Addressing user experience is an essential activity which designers undertake when formulating product specifications at the early stages of product design. The involvement of clinicians during the product use-phase influences the progress and achievements attained by the patients throughout their rehabilitation journey. Several clinicians (n=16) were asked to participate in a qualitative study to evaluate the performance of different personae in activities of daily living. The experiences of patients who use rehabilitation products as part of their therapy session at the clinic were evaluated. Based on this evaluation, a set of clinician requirements were formulated to reflect the overall experience when using rehabilitation products. Understanding these requirements brought about several implications to be considered during the design cycle. Through these considerations and by adopting a user-centred design approach, designers are in a better position to design innovative products targeted at providing a high-quality user experience which increases customer satisfaction.
An Integrated Framework for Risk‐Based Analysis of Economic Impacts of Drought and Water Scarcity in England and Wales

Drought and water scarcity pose a risk to the economy, particularly sectors where water is a crucial input to the production process. This paper describes integrated simulations of water shortages from a dynamic national water resource systems model with a static economic input‐output (I‐O) model to assess total drought risk to the economy. To quantify the economic risks of drought and water scarcity, the analysis: (a) integrates a large ensemble of simulated meteorological droughts, propagated through a national hydrological model and water resource systems model to simulate water availability from reservoirs and groundwater; (b) assesses implications of restrictions on water availability for multiple water user categories, representing natural and human contributions to water scarcity; (c) investigates how water shortages propagate into economy‐wide direct and indirect impacts. The study focuses on England and Wales where the water supply system is considered under strain, with growing recognition of the economic risk of prolonged and widespread shortages. The direct Expected Annual Loss (EAL) to water users, averaged over an ensemble equivalent to 2800 years of synthetic daily weather, is estimated to be £11.7 million in the 2011 base‐year. Accounting for indirect economic losses results in a total EAL of £30.2 million in 2011. The most severe event simulated results in a total loss of £1.4 billion in 2011, equivalent to 0.11% of GVA. The analysis provides a framework to assess what the economically efficient level of system reliability should be, and for deciding upon appropriate water management regulations and strategic investments.
Diagnostic Performance of Magnetic Resonance Imaging for Preoperative Local Staging of Penile Cancer: A Systematic Review and Meta-Analysis

Invasion of the tunica albuginea (TA) and/or urethra are key factors in determining the feasibility of organ-preserving surgery in penile cancer (PC). Magnetic resonance imaging (MRI) appeared to be a promising technique for preoperative local staging. We performed a systematic review (SR) and pooled meta-analysis to investigate the diagnostic performance of MRI in preoperative local staging of primary PC. An SR up to May 2021 was performed according to the PRISMA statement. The diagnostic performance of MRI was evaluated according to TA invasion, urethra invasion, and pT-stage ≥ 2. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) from eligible studies were pooled and summary receiver operating characteristic (SROC) curves were constructed. Overall, seven qualified studies were deemed suitable. Diagnostic performance of MRI showed an accuracy of 0.89 for TA invasion (sensitivity 0.78, PPV 0.79, specificity 0.91, and NPV 0.90); an accuracy of 0.88 for urethra invasion (sensitivity 0.65, PPV 0.46, specificity 0.86, and NPV 0.93); an accuracy of 0.90 for pT ≥ 2 (sensitivity 0.86, PPV 0.84, specificity 0.70, and NPV 0.73). Currently available evidence indicates that MRI might be a one-stop shop for local staging of primary PC and play a central role with regard to conservative surgical management.
Appropriate Drone Flight Altitude for Horse Behavioral Observation

Recently, drone technology advanced, and its safety and operability markedly improved, leading to its increased application in animal research. This study demonstrated drone application in livestock management, using its technology to observe horse behavior and verify the appropriate horse–drone distance for aerial behavioral observations. Recordings were conducted from September to October 2017 on 11 horses using the Phantom 4 Pro drone. Four flight altitudes were tested (60, 50, 40, and 30 m) to investigate the reactions of the horses to the drones and observe their behavior; the recording time at each altitude was 5 min. None of the horses displayed avoidance behavior at any flight altitude, and the observer was able to distinguish between any two horses. Recorded behaviors were foraging, moving, standing, recumbency, avoidance, and others. Foraging was the most common behavior observed both directly and in the drone videos. The correlation coefficients of all behavioral data from direct and drone video observations at all altitudes were significant (p < 0.01). These results indicate that horse behavior can be discerned with equal accuracy by both direct and recorded drone video observations. In conclusion, drones can be useful for recording and analyzing horse behavior.
Increased Activity of 5-Enolpyruvylshikimate-3-phosphate Synthase (EPSPS) Enzyme Describe the Natural Tolerance of Vulpia myuros to Glyphosate in Comparison with Apera spica-venti

Rattail fescue (Vulpia myuros (L.) C.C. Gmel. ) is a self-pollinating winter annual grassy weed of winter annual crops. The problems with V. myuros are mostly associated with no-till cropping systems where glyphosate application before sowing or emergence of the crop is the most important control measure. Ineffective V. myuros control has been reported following glyphosate applications. Experiments were performed to study the effectiveness of glyphosate on V. myuros, and determine the causes of the lower performance of glyphosate on V. myuros compared to other grass weeds. Estimated GR50 values demonstrated that V. myuros was less susceptible to glyphosate than Apera spica-venti regardless of the growth stage. Within each species, glyphosate efficacy at different growth stages was closely related to spray retention. However, the low susceptibility to glyphosate in V. myuros was not caused by lower retention as previously suggested. A significantly lower shikimic acid accumulation in V. myuros compared to A. spica-venti was associated with a higher activity of the EPSPS enzyme in V. myuros. Nevertheless, the relative responses in EPSPS activity to different glyphosate concentrations were similar in the two grass species, which indicate that EPSPS from V. myuros is as susceptible to glyphosate as EPSPS from A. spica-venti suggesting no alternation in the binding site of EPSPS. The results from the current study indicate that V. myuros is less susceptible to glyphosate compared to A. spica-venti, and the low susceptibility of V. myuros is caused by an increased EPSPS enzyme activity.
Purification of oncolytic measles virus by cation-exchange chromatography using resin-based stationary phases

ABSTRACT Cancer therapy with oncolytic measles virus (MV) requires high titers of pure, infectious virus particles (~108 times more particles per dose than the measles vaccine). A gentle but efficient downstream purification process is therefore essential. Here we investigated the suitability of ion-exchange chromatography for the purification of MV using resin-based stationary phases. The success of purification was evaluated based on the infectious virus yield, the ratio of infectious to total MV particles, and the quantity of major contaminants (host cell DNA and total protein). After testing different anion-exchange, cation-exchange and hybrid materials, we found that the cation-exchanger Eshmuno ® CPX (Merck) was the most promising resin. By optimizing the pH of the feed and buffers, and reducing the flow rate, we achieved a yield of 80.7% infectious MV particles while reducing the ratio of infectious to total MV particles by only 16.8%. We also removed 98.3% of the total protein and 80.5% of the host cell DNA. Although resin-based chromatography is not yet typically used for virus purification, we have developed a promising new approach to improve the efficiency of MV purification during downstream processing.
Characterization of Normal and Malignant Breast Tissues utilizing Hyperspectral Images and Associated Differential Spectrum Algorithm

Breast malignancy is the most pervasive disease and a significant reason for death in women around the world. Recently, Photonic technologies play a vital role in medical applications. This study presents an outline of recent outcomes on the magnitude of breast tissue optical properties. We established an optical system setup utilizing a hyperspectral (HS) camera with poly-chromatic source lights with wavelength (380~1050 nm) for this investigation. Measuring the diffuse reflect ion (Ŗd) of th e investigated ex vivo breast sample to select the optimum spectral image to differentiate between the normal and tumor in the near infra-red and visible (NIR – VIS) spectrum. Finally, applying the custom algorithm to increase the image contrast and applying contour delineation of the malignant regions. The experimental analysis indicates key spectroscopic variations between normal tissue and malignant region in range (550~650 nm). Although, after data normalization, there was noticeable variation at three ranges (630 – 680 nm), (720 – 770 nm), and (830 – 880 nm). The calculated standard deviation (Şd) between the normal and cancer tissue to validate the selective ranges shows that the highest contrast at wavelength 680 nm. However, the histogram analysis illustrates that the spectral image at 600 nm was higher contrast and wavelength 400 nm was the lowest contrast from the select seven-spectral images (400, 500, 600, 700, 800, 900, 1000 nm) to avoid the processing time of the captured HS 128-frames. The proposed potential method could provide promising results on the investigated breast sample optical properties in the diagnostic applications to assist the pathologist and the surgeon. Where the optimum wavelength at 680 nm for diagnostic applications and the ideal spectral image at 600 nm discriminate between the normal and malignant tissue. © 2021 Journal of Biomedical Photonics & Engineering.
Effect of oxygen enrichment and CO2 dilution on laminar methane flame velocities

This study presents the effect of oxygen enrichment and CO2 dilution on laminar methane flame velocity. Experiments were carried out in a stainless steel combustion chamber at atmospheric pressure and 300 K. The methane was mixed with a mixture of O2, CO2 and N2. The ratio O2/(O2+N2) was varied from 21% to 100% and the volume fraction of CO2 in the mixture was varied from 0% to 40%, for an equivalence ratio of 0.7. The classical shadowgraphy technique was used to detect the reaction zone to deduce the un-stretched burning velocity, from the nonlinear methodology. Laminar flame velocities were deduced from this analysis. Laminar flame velocities were also calculated and compared to experiments.
Deep Learning-based Electric Appliances Identification from their Switching-On Current Waveforms

- The field of non-intrusive load monitoring offers a multitude of methods for investigating and diagnosing energy demand per appliance. Thus, energy-aware strategies can be derived and implemented. With the widespread of smart meters, the rich information of the main current variation is within reach for many households. Through continuous analysis of the main current waveform, switching-on loads can be identified, and energy-saving practices can be devised. This paper proposes a deep learning model, a Convolutional Siamese neural network for appliance classification based on the WHITED raw high-frequency current dataset. The model is trained on pairs of appliance, measuring their similarity. Based on that, the appliance is identified. With minimal data preprocessing, an F1 macro measure of 0.95 was achieved on the training appliances, and a 0.79 score on previously unseen devices.
Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform

Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as ReMiDvne. Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. ReMiDvne then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that ReMiDvne outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, ReMiDvne improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45, 40.45, and 299.03 percent, respectively, and reduces the average rate of cost by up to 64.16 percent. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of ReMiDvne to efficiently reduce communication costs by up to 45.93 and 63.43 percent for download and upload, respectively.
An active-set proximal quasi-Newton algorithm for ℓ1-regularized minimization over a sphere constraint

The -regularized minimization has been widely used in many data science applications, and certain special constrained -regularized minimizations have also been proposed in some recent applications. In this paper, we consider a sphere constrained -regularized minimization, which can arise in image processing, signal recognition and sparse principal component analysis. Viewing the sphere as a simple Riemannian manifold, manifold-based methods for non-smooth minimization can be applied to solve such a problem, but may still encounter slow convergence in some situations. Our objective of this paper is to propose a new and efficient active-set proximal quasi-Newton method for this problem. The idea behind is to speed up the convergence by separately handling the convergence of both the active and inactive variables. In particular, our method invokes a procedure to effectively estimate active and inactive variables, and then designs the search directions based on proximal gradients and quasi-Newton directions to efficiently treat the convergence of the active and inactive variables, respectively. We show that under some mild conditions, the global convergence is guaranteed, and the complexity is also performed to reveal the computational efficiency. Numerical experience on the -regularized quadratic programming and sparse principal component analysis on both synthetic and real data demonstrates its robustness and efficiency.
Patterns of depredation in the Hawai‘i deep‐set longline fishery informed by fishery and false killer whale behavior

False killer whales (Pseudorca crassidens) depredate bait and catch in the Hawai‘i-based deep-set longline fishery, and as a result, this species is hooked or entangled more than any other cetacean in this fishery. We analyzed data collected by fisheries observers and from satellite-linked transmitters deployed on false killer whales to identify patterns of odontocete depredation that could help fishermen avoid overlap with whales. Odontocete depredation was observed on ˜6% of deep-set hauls across the fleet from 2004 to 2018. Model outcomes from binomial GAMMs suggested coarse patterns, for example, higher rates of depredation in winter, at lower latitudes, and with higher fishing effort. However, explanatory power was low, and no covariates were identified that could be used in a predictive context. The best indicator of depredation was the occurrence of depredation on a previous set of the same vessel. We identified spatiotemporal scales of this repeat depredation to provide guidance to fishermen on how far to move or how long to wait to reduce the probability of repeated interactions. The risk of depredation decreased with both space and time from a previous occurrence, with the greatest benefits achieved by moving ˜400 km or waiting ˜9 d, which reduced the occurrence of depredation from 18% to 9% (a 50% reduction). Fishermen moved a median 46 km and waited 4.7 h following an observed depredation interaction, which our analysis suggests is unlikely to lead to large reductions in risk. Satellite-tagged pelagic false killer whales moved up to 75 km in 4 h and 335 km in 24 h, suggesting that they can likely keep pace with longline vessels for at least four hours and likely longer. We recommend fishermen avoid areas of known depredation or bycatch by moving as far and as quickly as practical, especially within a day or two of the depredation or bycatch event. We also encourage captains to communicate depredation and bycatch occurrence to enable other vessels to similarly avoid high-risk areas.
Data Envelopment Analysis and Panel Regression in Analysing Technical Efficiency and Its Determinants of the Palm Oil Products-Based Manufacturing Subsector

This study measures the technical efficiency score of the manufacturing sector of the palm oil products-based subsector in Malaysia and investigates the major determinants that influence efficiency. Based on the Industrial Manufacturing Survey, this study explores the data from 2000 to 2015 over sixteen years, with a total of eleven subsectors of palm oil products-based. The first stage of the study is carried out with a data envelopment analysis (DEA) to calculate the technical efficiency score, which is considered a dependent variable. The second stage of the study uses a panel regression model to examine the determinants of efficiency comprise the variable capital-labour ratio, research and development, information communication technology, training, and skilled labour. The study findings show that most of the palm oil products-based subsectors are not operating efficiently. Skilled labour, technical and supervisory, and professional is one of the main determinants contributing to the efficiency level. The variable capital-labour ratio though significant, but harms the efficiency level. The moderating effects show that skilled technical and supervisory workers relatively affect the food industry’s efficiency larger than the non-food industry. Therefore, the industry still has room to improve efficiency by utilising input efficiently. Moreover, the efforts involve organisational management, equipped appropriate technology and related factors that will improve efficiency, increase productivity and competitiveness of palm oil products-based industries.
COMPARISON OF CONVENTIONAL AND Vibrio fischeri BIOASSAYS FOR THE ASSESSMENT OF MUNICIPAL WASTEWATER TOXICITY

The bioassay based on the bioluminescence inhibition of Vibrio fischeri has been successfully applied to test municipal wastewater toxicity, in fact, the ISO 11348-3 standard was especially issued to prescribe the application of this test in wastewater toxicity assessment. However, this protocol cannot take into consideration virtual toxicity caused by turbidity or color of the sample tested. On the contrary, the kinetic version of the V. fischeri assay (ISO 21338:2010: Water quality Kinetic determination of the inhibitory effects of sediment, other solids and colored samples on the light emission of Vibrio fischeri /kinetic luminescent bacteria test/) was developed to mitigate the drawbacks of the original protocol. Municipal wastewaters are often turbid/colored. Two versions of the V. fischeri bioluminescence inhibition assays were applied for municipal wastewater samples, before and after filtering. In most cases, the traditional version of the bioassay detected higher toxicity than the kinetic version, most possibly implying that reduced light emittance caused by solid particles and/or color also contributed to toxicity readings.