Strategic Responses and Organizational Adaptations of Some Manufacturing Companies During the Community Quarantine Due to COVID-19 Pandemic

The COVID-19 pandemic crisis was abrupt, worldwide in scope, and its effect on individuals, organizations, and economies was devastating. Such crisis and emergency need appropriate and timely response. This paper intends to know how business firms responded to the challenges posed by the COVID-19 pandemic. Specifically, it aims to determine the strategies adopted by manufacturing companies, to mitigate the adverse impact of quarantine and lockdown. In view of this objective, asurvey was conducted involving seventeen companies, operating in the Province of Cavite, which is a home for eight special economic zones. The study aims to gather information on how business organizations in the provincial level survived the crisis, and hopefully, use it as initial baseline data for formulating contingency plans for future occurrence of pandemics and healt h crises. The result showed that 71% of the companies adopted multiple strategies to alleviate the negative effects of thepandemic. Ninety-four percent (94%) of the firms persevered by implementing reduced workweek and scaled-down or “stayin” production operation; 47% enforced retrenchment in the form of reduction in cost, assets, product lines, and overhead; 59% introduced innovation in production processes to adapt to the pandemic situation; and 35% opted to discontinue the production of less essential products. The result implies that business resiliency depends on the readiness of the organization to handle acrisis and production activities can continue during pandemic without compromising the minimum health standards, provided, appropriate strategies and adaptation plans are implemented. In other words, companies need not shut down during lockdown

Technical-Vocational Livelihood Education: Emerging Trends in Contextualised Mathematics Teaching

Technical-Vocational Livelihood Education (TVLE) Strategies and Indicators (S&Is) are the strategic procedures needed to come up with a well-informed contextualised learning instruction. This study is aimed at exploring the trends in Technical-Vocational Livelihood Education. The focus of this study is on soliciting relevant strategies and indicators (S&I) that can be utilised to develop a contextualised mathematics teaching module. S&Is in this study are consolidated from various experts in the field of curriculum contextualisation who were purposively selected from various regions representing the DepEd Manila, DepEd Mindoro, Marinduque, Romblon, and Palawan (MIMAROPA), DepEd Bicol region (Region 5), and DepEd Central Visayas (Region 7) recommended by the Department of Education (DepEd) Manila. Formal interviews and coding of consolidated experts’ experiences have passed through a qualitative thematic analysis to obtain a profound understanding of the strategies and indicators. After a thorough investigation of the information gathered, related studies, and theoretical reviews, the study resulted in the seven (7) stages of a contextualised mathematics teaching module such as 1) Planning, 2) Assessment of the curriculum guide and resources, 3) Collaboration and Consultative Meeting, 4) Crafting and Developing of the Contextualise Learning Modules/Lessons, 5) Implementation, 6) Monitoring, and 7) Evaluation and feedback. The first four (4) stages are the developmental phase cons Planning, Assessment, Collaboration, and Crafting of the working module (PACC). While, the remaining three stages to implement, monitor, and conducts of evaluation and feedback are on the validation phase. As module, the contextualised mathematics teaching can be utilised as a training guide for teachers in Technical-Vocational Livelihood Education strands of the K-12 curriculum. Further research may be conducted to validate the most appropriate modular approach in teaching specific subjects

Optimization of CO2 Laser Cutting Parameters Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Laser cutting is a manufacturing technology that uses laser light to cut almost any materials. This type of cutting technology has been applied in many industrial applications. Problems seen with a laser is the cutting efficiency and the quality wherein these two parameters are both affected by the laser power and its process speed. This study presents the modelling and simulation of an intelligent system for predicting and optimising the process parameters of CO2 laser cutting. The developed model was trained and tested using actual data gathered from actual laser cut runs. For the system parameters, two inputs were used: the type of material used and the material thickness (mm). For the desired response, the output is the process speed or cutting rate (mm/min). Adaptive neuro-fuzzy inference system (ANFIS) was the tool used to model the optimisation cutting process. Moreover, grid partition (GP) and subtractive clustering were both used in designing the fuzzy inference system (FIS). Among the training models used, GP Gaussian bell membership function (Gbellmf) provided the highest performance with an accuracy of 99.66%

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Application of computational intelligence in plant growth modelling

Nowadays, systems are being developed intelligently through the use of computational intelligence (CI). The central scientific goal of CI is to understand the principles that make intelligent behavior possible in natural or artificial systems. The growth environment of plants affects their survival, development, productivity and quality. Therefore, an understanding of the different balances of these different types of factors is necessary to allow a precise analysis of the plant condition in different growth environments. Crop growth is under a complex system that many variables are contributing to it. Variables in this system are strongly interdependent and this makes it difficult to know exactly which inputs contribute to an observed output and its contribution extent. With this, there is a need to develop an intelligent model that is capable of filtering noise and capable to come up with solution even though there is a limitation on its parameters. This study is focused on modelling the related factors for the crop growth of lettuce using various computational intelligence such as artificial neural network, genetic algorithm and adaptive neuro-fuzzy inference system. The pre-harvest factors such as temperature, light intensity and carbon dioxide are considered as input in modelling the crop growth. Also, a vision system is used to obtain the image of the lettuce for the quality assessment and crop stage determination. The canopy measurement which is related to the crop stage and yield is done on this study

PARAMETER EXTRACTION OF OPTOELECTRONIC pH SENSOR BASED ON THE HUE ABSORBANCE OFA pH TEST STRIP

Optical pH measurement commonly uses a strip of paper with embedded indicator. A pH test strip which has four (4) test pads changes its color when it is dipped in a sample solution. A Bogen universal indicator solution is used in one of the study to cause a color change in a sample depending on the pH of the sample. It is combined with a white light source and CMOS optical sensor chip to measure pH with color change as an input. The output voltage of the CMOS photodetector will give the equivalent pH value. This sensor can determine the pH of the sample from pH 1 to 9 in real-time. Aside from using Bogen universal indicator, paper-based indicator like test strip has also been introduced that shows color change when it is dipped in the solution. To determine the pH value of the sample, the color change of the test strip is compared to a color chart. A schematic diagram of the absorption-measuring optical module is developed. This is composed of tri-chromatic LED, photodiodes and polymer light guide. This device is able to detect the color change by measuring the optical absorbance of the urine test strip. The color change is analyzed to determine the amount of glucose, protein and red blood cells. The purpose of this study is to extract the parameter of an optoelectronic pH sensor based on the hue absorbance of the pH test strip and be implemented on Simulation Program with Integrated Circuit Emphasis (SPICE). Through experiments using a devised sensor module, highest linearity is obtained when the ILED is 20 mA. The sensitivity of the device at pad I is 0.4217 mV/pH with a correlation coefficient of 0.8177, pad 2 is 0.3667 V/pH with a correlation coefficient of 0.9597, pad 3 is 0.2659 V/pH with a correlation coefficient of 0.0.9923, and pad 4 is 0.0347 V/pH with a correlation coefficient of 0.9948. It outputs the RGB Hue levels (in voltage) of the pH test strip. Optoelectric characteristics of the pH sensor are described by the result acquired in the experiments on parameter extraction. The transconductance of the device is derived based on the phototransistor current and pH-dependent voltage. The extracted parameters used on the equivalent circuit of the optoelectronic pH sensor are simulated on SPICE. The percent error between the responses on experimental and simulation of the test pads 1, 2, 3 and 4 are 10.28%, 0.01%, 0.34% and 0.86% respectively. Based on these results, an optoelectronic pH sensor model is developed

Implementation of a low-power wireless sensor network for smart farm applications

In the Philippines, The concept of Internet of Things(IoT) has been employed already in various commercial and industrial applications either through mobile or WiFi network communications. Though IoT has experienced a blooming development these years, still, the problem of slow and expensive Internet services hinder its usage to a lot of applications. In this study, a wireless sensor network (WSN) setup using low-power wide area network (LPWAN) is being proposed to address the problem of a reliable wireless communication system. This research will make use of a smart farm setup as the application platform. The study will also monitor some of its environmental parameters such as temperature, soil moisture, relative humidity, etc. Moreover, the system will be capable of controlling some actuators in the farm such as the water pump and solenoid valves. © 2018 IEEE

Color space analysis using KNN for lettuce crop stages identification in smart farm setup

Advancing technologies are being done in improvement and enhancement of the smart farming all over the world. The growth of the plants is being monitored through the vision system and image processing is done to identify their growth stages. This is important since the amount of light, temperature and water varies at each stage. One of the challenges in the image processing is the selection of the color space that will be appropriate for a particular setup. In this study, K-nearest neighboring is used in the image segmentation for the RGB, HSV, CIELab, and YCbCr color spaces. The specificity and sensitivity of each color spaces were computed and compared. Based on the result obtained, CIELab color space is the best color space to be used in the identification of the growth stage of the lettuce. © 2018 IEEE