Pinusong sa Panahon ng “Veerus”: Ang Tula sa Internet Bilang Kontra-Pusong

Naglalayong mailantad ng papel na ito ang mga tula sa internet na may katangiang pinusong at maitanghal ang mga ito bilang kontra-pusong sa panahon ng “veerus” o panahon ng pandemya sa ilalim ng pamamahala ni Duterte, na kinikilala rin bilang “pinunong pusong” (sovereign trickster) ayon kay Vicente Rafael. Gamit ang teoryang carnivalesque ni Mikhail Bahktin, binasa ang mga tula ng makatang pusong sa layuning mailantad ang gawaing pagbabaligtad at pagtumba ng kaayusan bilang politikal na proyekto ng pagbalikwas sa diskurso ng “veerus” na siyang kinokonstrak ng “pinunong pusong.” Sa tradisyonal na pagbasa ng pinusong, ipinalalabas na ito ay karaniwang gawaing pangungutya at pagbabaligtad ng mababang uri ng kaayusang pumapabor sa naghaharing uri. Subalit lumalabas na ang mga tulang isinulat bilang pinusong ay maaaring hindi limitado sa artikulasyon ng mababang uri sa dahilang ang mga ito ay markado ng pagiging edukado at pagkakaroon ng modernong rasyonalidad ng makatang sumulat. Batay sa naunang konstruksiyon ng “pinunong pusong,” ang mga binasang tula ay maituturing nang kontra-pusong at maituturing nang taliwas sa tradisyonal na konsepto ng pusong sa kabila ng pagpapatuloy pa rin ng pinusong na praktis ng pagtatanghal ng mali, ng pangit, ng madugo, ng bastos, at ng pagpapalusot.unang ipinasa: 29 Setyembre 2020tinanggap para sa publikasyon: 25 Nobyembre 202

Profile of Volatile Organic Compounds (VOCs) from Cold-Processed and Heat-Treated Virgin Coconut Oil (VCO) Samples

Virgin coconut oil (VCO) can be prepared with or without heat. Fermentation and centrifuge processes can be done without the use of heat (cold process), while expelling involves heat due to friction. Volatile organic compounds (VOCs) from VCO samples prepared using these three methods were collected using solid phase microextraction (SPME) and analyzed using gas chromatography–mass spectrometry (GC-MS). Twenty-seven VCO samples from nine VCO producers were analyzed. The VOCs from refined, bleached, and deodorized coconut oil (RBDCO) were also obtained for comparison. Fourteen compounds were found to be common in more than 80% of the VCO samples analyzed. These included: Acetic acid; C6, C8, C10, C12, and C14 fatty acids, and their corresponding delta-lactones; and C8, C10 and C12 ethyl carboxylates. Fourteen minor VOCs were likewise detected which can be grouped into five types: Carboxylic acids (formic acid, butanoic acid, benzoic acid, and pentadecanoic acid), ketones (acetoin, 2-heptanone), an alcohol (ethanol), aldehydes (acetaldehyde, hexanal, benzaldehyde), esters (ethyl acetate, methyl tetradecanoate), and hydrocarbons (n-hexane and toluene). Five pyrazines were detected in expeller VCO. Various hydrocarbons from C5 to C14 were noted to be higher in old RBDCO and VCO samples. There were variations in the VOCs within each VCO process as each producer used different processing times, temperatures, and drying procedures. Principal components analysis (PCA) was able to group the samples according to the process used, but there were overlaps which may be due to variations in the specific procedures used by the manufacturers. These results may help VCO manufacturers control their production processes

High Resolution Mass Spectrometry of Polyfluorinated Polyether-Based Formulation

High resolution mass spectrometry (HRMS) was successfully applied to elucidate the structure of a polyfluorinated polyether (PFPE)-based formulation. The mass spectrum generated from direct injection into the MS was examined by identifying the different repeating units manually and with the aid of an instrument data processor. Highly accurate mass spectral data enabled the calculation of higher-order mass defects. The different plots of MW and the nth-order mass defects (up to n = 3) could aid in assessing the structure of the different repeating units and estimating their absolute and relative number per molecule. The three major repeating units were -C2H4O-, -C2F4O-, and -CF2O-. Tandem MS was used to identify the end groups that appeared to be phosphates, as well as the possible distribution of the repeating units. Reversed-phase HPLC separated of the polymer molecules on the basis of number of nonpolar repeating units. The elucidated structure resembles the structure in the published manufacturer technical data. This analytical approach to the characterization of a PFPE-based formulation can serve as a guide in analyzing not just other PFPE-based formulations but also other fluorinated and non-fluorinated polymers. The information from MS is essential in studying the physico-chemical properties of PFPEs and can help in assessing the risks they pose to the environment and to human health

Quality characteristics of virgin coconut oil:Comparisons with refined coconut oil

Virgin coconut oil (VCO) is a vegetable oil that is extracted from fresh coconut meat and is processed using only physical and other natural means. VCO was compared to refined, bleached, and deodorized coconut oil (RCO) using standard quality parameters, 31 P nuclear magnetic resonance (NMR) spectroscopy, and headspace solid-phase micro - extraction/gas chromatography mass spectrometry (SPME/GCMS). VCO tends to have higher free fatty acids (FFAs), moisture, and volatile matter and lower peroxide value than RCO. However, the range of values overlap and no single standard parameter alone can be 31 used to differentiate VCO from RCO. Using 31P NMR, VCO and RCO can be distinguished in terms of the total amount of diglycerides: VCO showed an average content (w/w %) of 1.55, whereas RCO gave an average of 4.10. There was no overlap in the values found for individual VCO and RCO samples. There are four common methods of producing VCO: expeller (EXP), centrifuge (CEN), and fermentation with and without heat. VCO products prepared using these four methods could not be differentiated using standard quality parameters. Sensory analysis showed that VCO produced by fermentation (with and without heat) could be distinguished from those produced using the EXP and CEN methods; this sensory differentiation correlated with the higher levels of acetic acid and octanoic acid in the VCO produced by fermentation. Studies on physicochemical deterioration of VCO showed that VCO is stable to chemical and photochemical oxidation and hydrolysis. VCO is most susceptible to microbial attack, which leads to the formation of various organic acids, in particular, lactic acid. However, at moisture levels below 0.06 %, microbial action is significantly lessened

SEAFDEC/AQD stock enhancement initiatives: release strategies

The Aquaculture Department of the Southeast Asian Fisheries Development Center (SEAFDEC/AQD) started its Stock Enhancement Program more than a decade ago with the first stock enhancement initiative on the mud crab Scylla spp. funded by the European Commission. This was followed by another stock enhancement program in 2005 supported by the Government of Japan Trust Fund. In preparation for its implementation, a Regional Technical Consultation on Stock Enhancement of Species Under International Concern was convened in Iloilo City, Philippines in July 2005 to identify species for stock enhancement. During the meeting, seahorses Hippocampus spp., giant clam Tridacna gigas, abalone Haliotis asinina, and sea cucumbers Holothuria spp. were among the priority species for stock enhancement work. Stock enhancement, restocking and ranching are management approaches involving the release of wild or hatchery-bred organisms to enhance, conserve or restore fisheries. This paper reports SEAFDEC/AQD release activities and some of the release strategies that have been established for mud crabs, giant clams and abalone

Physico-Chemical and Microbiological Parameters in the Deterioration of Virgin Coconut Oil

The deterioration of virgin coconut oil (VCO) due to physico-chemical oxidation and hydrolysis and microbiological processes was studied. The physico-chemical oxidation of VCO in the air at room temperature was negligible. Oxidation of VCO was observed only in the presence of air, UV radiation, ferric ion (Fe3+), and high free fatty acid (FFA) content. Chemical hydrolysis was performed at varying moisture levels and temperatures. The rate of hydrolysis to produce FFAs was measured using 31P NMR under conditions of saturated water (0.22%) and 80°C was found to be 0.066 µmol/g-hr (expressed as lauric acid). At 0.084% moisture and 80°C, the rate of FFA formation was found to be 0.008 µmol/g-hr. The microbial decomposition of VCO was determined after four days of incubation at 37°C. At low moisture levels (\u3c0.06%), VCO was stable to microbial decomposition. However, at higher moisture levels, there was an increase in the formation of organic acids, in particular, lactic acid, dodecanoic acid, succinic acid, acetic acid, and fumaric acid, indicating that microbial action had occurred. The most important conditions that influence the physicochemical and microbial degradation of VCO are moisture, temperature, and the presence of microorganisms. These degradation processes can be minimized if the moisture level is maintained below 0.06%

Automatic assessment of oral reading fluency from children\u27s read speech in the Filipino language

With the end view of helping the Philippine education system in its literacy initiatives, this study aims to develop methods for automatic assessment of oral reading fluency from children\u27s read speech in the Filipino language. Thus, this study seeks to design methods of automatically extracting and analyzing prosodic features of children\u27s read speech in Filipino. To achieve this, the four-fold set of research activities was conducted to describe an automated oral reading fluency assessment system. It consisted of 1) building a children\u27s Filipino speech corpus, 2) designing methods of extracting and analyzing prosodic features, 3) developing methods of automatically assessing oral reading fluency, and 4) evaluating the performance of developed methods. The dataset consisted of 192 audio files totaling 11 hours, 48 minutes, and 13 seconds. The audio files were recordings of children ages 6 to 11 years reading grade-appropriate passages in the Filipino language. Human raters manually annotated the files as fluent or nonfluent; and as independent, instructional, and frustration levels. Audio and prosodic features were extracted and used as predictor variables in the machine learning training and testing. The machine learning classification methods produced results indicating that the SVM had validation accuracies of 81.18% and 87.71% for the three-level fluency scheme and two-level fluency scheme, respectively. The predictor variables used for these classifications were different. For the three-level scheme, the variables were DSP- and ASR-computed speech rate and Levenshtein distance, while for the two-level scheme, they were total duration, Levenshtein distance, out-of-vocabulary words, DSP-computed articulation rate, and ASR-computed speech rate. The Mel-frequency and gammatone cepstrum coefficients, spectral audio, and wavelet features did not provide significant prediction performance results. On the other hand, the LSTM deep learning method resulted in validation accuracies of 55.08% and 79.61% for the three- and two-level fluency schemes, respectively. To further improve the prediction accuracy, it is recommended that more predictor features be identified, such as other types of reading miscues and pauses features. Also, more reading data may be gathered to balance the distribution of fluency classes in the dataset and to make deep-learning methods discover robust predictor features and improve performance. This study is relevant in addressing the issue of poor reading performance among Filipino children. The study has created a children\u27s read speech corpus in Filipino language, which will eventually be a part of a larger dataset aimed at addressing the limited availability of children\u27s Filipino speech corpus. The study has identified relevant and non-relevant predictor features that can be used to automatically classify oral reading fluency. These features were used as inputs to develop fluency classification methods. The speech corpus, fluency predictor features, and classification techniques based on DSP- and ASR-based feature extraction developed in this study will form as a framework for building an automated oral reading fluency assessment system