Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization of Palm Oil Mill Effluent (POME) from the Residual Biogas Power Plant for Microalgae Production as Raw Material of Biodiesel

Palm Oil Mill Effluent (POME) from the residue of biogas power plants generated by the Palm Oil Mill as the waste is still a problem in the processing environment and the environmental impact. However, POME waste still contains nutrients which are essential to the growth of microalgae as a growing medium. Utilization of POME can improve the growth of microalgae Botryococcus braunii compared to conventional media. By doing this project, the cost used by the palm oil mill waste management can be reduced and even be able to provide profits for palm oil mill and also can reduce the negative impact on the environment. The study was conducted at the Microalgae Laboratory of SBRC - IPB in September-Oktober 2018. Botryococcus braunii was cultivated in 1,000 ml erlenmeyer compared to standard medium as the control solution of 0.1% POME from the residue biogas power plants. Observation of Botryococcus braunii growth curve has carried out in accordance. Studies were arranged in CRD, with three replication. The results showed that the rate of Botryococcus braunii growth in POME is higher than the control for 2.7x106 sel/ml. And lipid extraction using osmotic shock method with HCl and CH3COONa as osmotic agents in concentrations of 0.1; 0.3; 0.5 M, the highest yield of 0.108 g was obtained on the osmotic agent CH3COONa 0.3 M.
Interactive comment on “Chemical Composition and Hydrolysis of Organic Nitrate Aerosol Formed from Hydroxyl and Nitrate Radical Oxidation of α-pinene and β-pinene” by

This manuscript describes laboratory experiments that aim to investigate the efficiency of nitrate hydrolysis reactions on secondary organic aerosol (SOA) in order to order to try to reconcile existing discrepancies between previous bulk laboratory experiments and laboratory experiments that directly study SOA. Specifically, the work uses both High Resolution Time-of-Flight Aerosol Mass Spectrometry (HR-ToF-AMS) and Filter Inlet for Gases and Aerosols/Chemical Ionization Mass Spectrometry (FIGAEROCIMS) techniques in order to track the evolution of alphaand beta-pinene-derived SOA
Adaptive Attention Annotation Model: Optimizing the Prediction Path through Dependency Fusion

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.
A Case Study of the the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction 0

plosive cyclogenesis. Most studies focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the overforecasting error (Le-, the prediction of an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of a.ll models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on o p erational weather forecasting. Therefore, it is important to understand and reduce the predictions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 W a cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hF'a in one day. The cyclone is not verified in the a d yses and is thus a case of severe over-forecasting. By assimilating data from the Atmospheric Infrared Sounder (AIRS) mounted on the NASA's Aqua Spacecrdt, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed. Large erras h xy~eric3-l weather prediction are often associated with ex-
Determinants of adoption of improved sorghum package in agro-pastoral households of Somali Region of Ethiopia: A gender perspective

The study analysed determinants of adoption of improved sorghum package between male-headed and female-headed households. The specific objectives were to analyse adoption differentials of improved sorghum package, and to identify factors that affect adoption decision of improved sorghum package by male and female-headed households. Purposeful sampling method was employed to select Woreda of sorghum growers with greater number of female-headed households with the aim to involve the required number for the analysis. A multi-stage stratified random sampling technique was used to select four PAs out of 23 PAs in the Woreda and households to be interviewed were derived proportionally to the size of the number of household in each PAs. Cross-sectional research design was applied to collect data from a total of 180 respondents in this study whereby 90 were female-headed and 90 were male-headed households. Pre-tested structured interview questionnaire was used for collecting the essential quantitative data. The Logistic Regression model results revealed that the adoption of improved sorghum package is biased by gender, where Female-Headed Households adopt the package relatively less. Regarding factors affecting adoption decision, non-farm activity had a significant and positive influence on the adoption decision of improved sorghum package, whereas distance to market had a significant and negative influence on the adoption decision for Male-Headed Household. Extension contact, family active labour force, attitude toward sorghum package had a significant and positive influence on adoption decision of Female Headed Household, whereas cosmopoliteness influences negatively. Therefore, policy should address gender disparities in extension services and access to resources that exist because of socio-cultural and institutional factors limiting the adoption of technologies for Female Headed Household. Thus, enhancing efficient delivery of extension with due consideration of the participation of female headed household would improve the livelihood of the households. Key words: Adoption, gender, agro-pastoral, sorghum package, Somali Region.
EGFRvIII Antibody Conjugated Iron Oxide Nanoparticles for MRI Guided Convection-Enhanced Delivery and Targeted Therapy of Glioblastoma

The magnetic nanoparticle has emerged as a potential multifunctional clinical tool that can provide cancer cell detection by magnetic resonance imaging (MRI) contrast enhancement as well as targeted cancer cell therapy. A major barrier in the use of nanotechnology for brain tumor applications is the difficulty in delivering nanoparticles to intracranial tumors. Iron oxide nanoparticles (IONPs; 10 nm in core size) conjugated to a purified antibody that selectively binds to the epidermal growth factor receptor (EGFR) deletion mutant (EGFRvIII) present on human glioblastoma multiforme (GBM) cells, were used for therapeutic targeting and MRI contrast enhancement of experimental glioblastoma both in vitro and in vivo after convection-enhanced delivery (CED). A significant decrease in glioblastoma cell survival was observed after nanoparticle treatment and no toxicity was observed with treatment of human astrocytes ( P<0.001 ). Lower EGFR phosphorylation was found in glioblastoma cells after EGFRvIIIAb-IONP treatment. Apoptosis was determined to be the mode of cell death after treatment of GBM cells and glioblastoma stem cell (GSC)-containing neurospheres with EGFRvIIIAb-IONPs. MRI-guided CED of EGFRvIIIAb-IONPs allowed for the initial distribution of magnetic nanoparticles within or adjacent to intracranial human xenograft tumors and continued dispersion days later. A significant increase in animal survival was found after CED of magnetic nanoparticles ( P<0.01 ) in mice implanted with highly tumorigenic glioblastoma xenografts (U87 Δ EGFRvIII). IONPs conjugated to an antibody specific to the EGFRvIII deletion mutant constitutively expressed by human glioblastoma tumors can provide selective MRI contrast enhancement of tumor cells and targeted therapy of infiltrative glioblastoma cells after CED.
A Laboratory Study on Formation of Oil-SPM Aggregates Using the NIST Standard Reference Material 1941b

Aggregation between suspended particulate matter (SPM) and oil droplets, which leads to the formation of Oil-SPM Aggregates (OSA), is widely acknowledged as a natural process that enhances dispersion of spilled oil in aquatic environments. A new laboratory study was performed to investigate the formation of OSA using the Standard Reference Material 1941b. The experiments were performed using Arabian Medium, Alaska North Slope and South Louisiana crudes. Sediment concentrations varied from 25 to 300 mg/L. A new procedure was developed to separate the OSAs from the bulk water, measure the oil trapped in OSAs by GC-FID analysis, and isolate the sediment trapped in sinking OSAs for gravimetric quantification. Results showed that this method is appropriate for measuring small amounts (less than 2 mg in this study) of oil trapped in OSAs and does not overestimate the mass of oil trapped in large OSAs as observed in a previous method based on image analysis. For the three oils, results showed also that the percentage of oil trapped in sinking OSAs increases rapidly from about 0.5 % to about 30 % of the initial oil mass when sediment concentration increases from 25 to 100 mg/l. Further increase of sediment concentration to 300 mg/l showed slower increase of oil trapping to reach about 40 to50 % depending on the type of oil. Measurements of the mass of sediment trapped in OSAs showed that the highest trapping efficiency is obtained when the sediment to oil ratio is close to 0.5. This value decreases exponentially with oil density.
Multiple Resource Management and Burst Time Prediction using Deep Reinforcement Learning

—Resource management and job scheduling are two problems that go hand-in-hand and the solutions to which are primarily dependent on the nature of workload. With increasing demand to automate the entire process from allocating resources to scheduling jobs efficiently, deep reinforcement learning techniques have been brought into the picture which adapt to the environment and learn from experience. In this paper, we present SchedQRM which classifies burst time of jobs based on their signature and employs Deep Q-Network algorithm to find an optimal solution for any arbitrary job set. We also evaluate our proposed work against state-of-the-art heuristics to show the efficacy of our approach.
How Corporate Environmental Ethics (Non-Financial Information) influence Individual InvestorTMs Trading Behavior?

This study by using structural equation modeling empirically determine thesignificant effect of corporate environmental ethics on trading behavior via incorporating the mediating role of corporate reputation and CSR belief. The cross-sectional data was collected from multiple respondents who remain engage in stock purchasing and selling at Pakistan Stock Exchange. The convenient sampling technique was used to determine the appropriate sample size. The empirical results of this study demonstrate that corporate environmental ethics of organizations remain positively and significantly associated with perceived corporate reputation and CSR belief. The statistical analysis also indicates that corporate environmental ethics significantly impacts individual investor’s trading behavior. The findings of the study suggest that corporate environmental ethics not only affect investor’strading behavior, but also spur perceived corporate reputation. Perceived corporate reputation postulates competitive edge and significantly affects the decision making behavior of investor’s. The findings of the study advocate that companies listed on Pakistan stock exchange should pay more attention to corporate environmental ethics because it is prudent to determine the level of corporate reputation and trading behavior of individual investor.
Performance test of network devices

Selection of components in contemporary computer networks is extremely important, not only because of the project budget but also because of the network performance. Therefore, it is necessary to perform tests before deploying devices. In this paper the authors show that the performance of test network devices is dependent on the nature of network traffic including its statistical properties. Also a new hybrid model of the system for testing the network device based on the q-additivity of traffic flows was proposed.
Public Policy for Academic Entrepreneurship: A Review and Critical Discussion

This article provides a critical review and discussion of current literature on technology transfer, incubators, and academic entrepreneurship. Drawing upon the notion of robustness in social systems and public choice theory, we review, code, and taxonomize 166 studies to assess the likelihood that these initiatives will generate innovation and economic growth. We find that academic entrepreneurship initiatives are characterized by conflicting goals, weak incentive structures for universities and academics, and are contextually dependent upon factors such as university strength. Our results suggest that there are critical boundary conditions that are unlikely to be fulfilled when universities and policymakers enact policies to support academic entrepreneurship initiatives. Policymakers therefore need to be cautious in the potential design of such initiatives. We discuss how technology transfer from universities might be better achieved through alternative mechanisms such as contract research, licensing, consulting and increased labor mobility among
Security Engineering for Control System

High availability is required in control systems that support critical infrastructures. Therefore, they must be protected from a growing number of cyber-attacks. However, without appropriate consideration of the requirements and operational conditions specific to control systems, engineering for security countermeasures may cause system failures. Meanwhile, Yokogawa has established its own best practices for security countermeasures based on its wealth of experience in providing control systems to customers over many years and through knowledge obtained through its involvement in international and governmental standardization. Yokogawa can offer proper security engineering based on these best practices. This paper introduces Yokogawa’s security concept for control systems and the related engineering.
Poly[nickel(II)- 2 -benzene-1,4-dicarboxylato- 2 -piperazine]

In the title coordination polymer, [Ni(C8H4O4)(C4H10N2)]n, the Ni(II) atom is situated on a C2 axis and is six-coordinated by two N atoms from two piperazine (pip) mol­ecules, four O atoms from two bidentate and chelating terephthalate (tp) anions, forming a distorted octa­hedron. Both pip and tp mol­ecules, which are centrosymmetric, bridge adjacent Ni(II) atoms, forming a three-dimensional network.
Pipelined architecture for sparse DNNs

Deep neural networks (DNNs) are increasing their presence in a wide range of applications, and their computationally intensive and memory-demanding nature poses challenges, especially for embedded systems. Pruning techniques turn DNN models into sparse by setting most weights to zero, offering optimization opportunities if specific support is included. We propose a novel pipelined architecture for DNNs that avoids all useless operations during the inference process. It has been implemented in a field-programmable gate array (FPGA), and the performance, energy efficiency, and area have been characterized. Exploiting sparsity yields remarkable speedups but also produces area overheads. We have evaluated this tradeoff in order to identify in which scenarios it is better to use that area to exploit sparsity, or to include more computational resources in a conventional DNN architecture. We have also explored different arithmetic bitwidths. Our sparse architecture is clearly superior on 32-bit arithmetic or highly sparse networks. However, on 8-bit arithmetic or networks with low sparsity it is more profitable to deploy a dense architecture with more arithmetic resources than including support for sparsity. We consider that FPGAs are the natural target for DNN sparse accelerators since they can be loaded at run-time with the best-fitting accelerator.
1-Gb/s 80-dBΩ fully differential CMOS transimpedance amplifier in multichip on oxide technology for optical interconnects

A 1-Gb/s differential transimpedance amplifier (TIA) is realized in a 0.25-/spl mu/m standard CMOS technology, incorporating the regulated cascode input configuration. The TIA chip is then integrated with a p-i-n photodiode on an oxidized phosphorous-silicon (OPS) substrate by employing the multichip-on-oxide (MCO) technology. The MCO TIA demonstrates 80-dB/spl Omega/ transimpedance gain, 670-MHz bandwidth for 1-pF photodiode capacitance, 0.54-/spl mu/A average input noise current, -17-dBm sensitivity for 10/sup -12/ bit-error rate (BER), and 27-mW power dissipation from a single 2.5-V supply. It also shows negligible switching noise effect from an embedded VCO on the OPS substrate. Furthermore, a four-channel MCO TIA array is implemented for optical interconnects, resulting in less than -40-dB crosstalk between adjacent channels.
P1.9 CHARACTERIZING AND UNDERSTANDING THE DIFFERENCES BETWEEN GOES WF_ABBA AND MODIS FIRE PRODUCTS AND IMPLICATIONS FOR DATA ASSIMILATION

Satellite remote sensing provides automated, near real-time locations and characteristics of active fires. In addition to the challenges of detection and parameterization of fires, each satellite fire product provides different information about fire activity. Diagnostic and prognostic applications, particularly in climate and air quality modeling, benefit from the use of satellite fire products where it is important to identify emission sources to model total emissions and emission transport. Disagreement among fire products causes confusion in the user community. With a better understanding of fire product similarities and differences data fusion can provide a merged fire product that combines the strengths of each fire product to provide more information than one given fire product could provide. One of the largest problems the modeling community faces is the assimilation of active fire products. To produce an accurate multi-satellite fire product it is necessary to discriminate between fires detected by multiple satellites and fires detected by only one satellite. Some differences are expected due to orbit, instrument, and algorithm differences; however, other differences can be attributed to fire characteristics. To improve user confidence, this study employs new methods to identify collocated fire pixels and applies statistical tools to characterize and better understand the differences between the Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) and MODerate resolution Imaging Spectroradiometer (MODIS) fire products for enhanced applications in model assimilation.
Numerical Modelling of Axissymetrical Ferrite-Core Probes over Planar Specimens Using a Coupled FIT/Semi-Analytical Formulation

A hybrid approach coupling the finite integration technique (FIT) with a semi-analytical model is presented for the calculation of the eddy-current interaction between axissymmetrical ferrite-core probes and planar multi-layered conducting and/or ferromagnetic specimens. The initial 3D problem is decomposed in this way into two simpler ones, each one being symmetrical in its proper frame of reference. The corresponding exploitation of the symmetries of the involved objects in combination with a modal based formulation of the classical FIT discretization scheme allows us to avoid the 3D meshing of the probe-specimen ensemble and hence to reduce the size of the numerical problem to be solved.
Learning Visually Grounded Representations with Sketches

We test whether visually grounded meaning representations for words can be improved by grounding to sketches rather than to natural images. Intuitively, sketches encode higher-level abstract representations of the concepts to which words refer. We test empirically whether such abstractions are beneficial for the purposes of grounded representation learning. We evaluate our representations in terms of correlations with human inferences about the semantic and visual similarity between concepts. Our results suggest that grounding to sketches yields better representations than does grounding to other visual representations.
Microencapsulation and Its Uses in Food Science and Technology: A Review

Microencapsulation is a group of technologies aiming to produce small particles called microcapsules that can be released at a specific speed under certain conditions. Microencapsulation technology is used in the pharmaceutical, agrochemical, and food industries; however, microcapsule production is most challenging for applications in the food industry owing to the high costs of the technique, which may make the final product too expensive. Common methods for microencapsulation include spray-drying and coacervation, and different wall materials and filling materials can be used for both techniques. In this review, we summarize current methodologies used for microencapsulation, with a focus on applications in the food industry.
(1 E ,4 E )-1,5-Bis(2-chlorophenyl)penta-1,4-dien-3-one

The title compound, C17H12Cl2O, crystallizes in a centrosymmetric space group. The dihedral angle between the two benzene rings is 24.7 (1)°. The crystal packing is characterized by non-classical C—H.O and C—H.Cl inter- and intramolecular hydrogen bonds.