A Modified Balcik Last Mile Distribution Model for Relief Operations Using Open Road Networks

The last mile in disaster relief distribution chain is the delivery of goods from a central warehouse to the evacuation centers assigned for a given area. Its effectiveness relies on the proper allocation of each kind of relief good amongst the demand areas on a given frequency. Because these operations involve a limited supply of relief goods, vehicles, and time, it is important to optimize these operations to satisfy as much demand as possible. The study aims to create a linear programming model which provides a set of recommendations on how the current disaster relief supply chain may be carried out, specifically on how distribution operations allocate supplies among demand nodes as well as the routes taken in a day. The areas visited per day would depend on the capacity of the vehicle fleet as well as on the routes that can be used. This linear programming model will use Balcik’s last mile distribution model, while modifying it for the relief operations in the Philippines. The model minimizes routing costs as well as penalty costs for unsatisfied demands. Map data is used for determining routes and historical data from previous disasters are used to determine the supply and demand for relief goods while providing a benchmark for results. The model produces recommendations for (1) Demand node schedule, (2) Best route for schedule, (3) Relief good allocation, and (4) Operational costs. It also provides the computational backbone for relief distribution decisions in the Philippines, allowing for more optimal operations in the future

Women on Boards of Philippine Corporations: Quantitative Explorations

This inductive study explored the likelihood and correlates of gender diversity in corporate boards in the Philippines. The improvement of gender diversity on boards is of advocacy and policy interest as the country emerges to middle-high income status. Logistic regression analyses from individuals\u27 (in a directors\u27 talent pool) responses to an online survey showed that females had a likely odds of 0.10 to be on the boards, compared to males. For every one female getting onto boards, 9 would be unable to. Females with advanced degrees were 7x likely to be on boards than female and male counterparts. The odds of a board seat is significantly likely for individuals in some industries compared to a referent industry (government). At the firm level, controlling other variables in the model, as the size of boards are increased by a unit, the odds of having a woman on board increase 1.3 times. This implies that the likelihood of having a woman board of director rises if the size of boards is raised by a third. Corroboration from text mining technique applied to survey responses showed strong correlation across academic degrees (both bachelor\u27s and advanced), industry, and job title; pointing that having more women in C-roles increase the odds of increasing their numbers on corporate boards. Gender diversity on boards have been studied largely from the developed economy lens and/or international comparisons. These quantitative explorations showed pathways that can advance not only understanding and support for extant theories (human capital, resource dependence), but also point to further work (institutional, industry) that can provide levers for policy and advocacy, for countries with similar challenges

Understanding the Behavior of Filipino Twitter Users during Disaster

The Philippines is a country that frequentlyexperiences disasters, such as typhoons. During these events,many citizens spread information and communicate with eachother through social media like Twitter. This study aims to takeadvantage of that fact by analyzing the data from social media toget some insights on the situation. Specifically, this paper studiesthe behavior of Filipinos on Twitter during a disaster, and tries tosee the differences between participants, or the direct victims ofthe disaster, and observers. The study used Latent DirichletAllocation and Principal Component Analysis to extract thedifferent topics discussed during a disaster, and found out whichtopics participants are more likely to talk about. Results also showwhich topics are more likely to be retweeted, which languageparticipants in disaster use more often, and what emotions arepresent in the disaster-time tweets of Filipinos

Assessing the performance of maternity care in Europe: A critical exploration of tools and indicators

Background: This paper critically reviews published tools and indicators currently used to measure maternity care performance within Europe, focusing particularly on whether and how current approaches enable systematic appraisal of processes of minimal (or non-) intervention in support of physiological or "normal birth". The work formed part of COST Actions IS0907: "Childbirth Cultures, Concerns, and Consequences: Creating a dynamic EU framework for optimal maternity care" (2011-2014) and IS1405: Building Intrapartum Research Through Health - an interdisciplinary whole system approach to understanding and contextualising physiological labour and birth (BIRTH) (2014-). The Actions included the sharing of country experiences with the aim of promoting salutogenic approaches to maternity care. Methods: A structured literature search was conducted of material published between 2005 and 2013, incorporating research databases, published documents in english in peer-reviewed international journals and indicator databases which measured aspects of health care at a national and pan-national level. Given its emergence from two COST Actions the work, inevitably, focused on Europe, but findings may be relevant to other countries and regions. Results: A total of 388 indicators were identified, as well as seven tools specifically designed for capturing aspects of maternity care. Intrapartum care was the most frequently measured feature, through the application of process and outcome indicators. Postnatal and neonatal care of mother and baby were the least appraised areas. An over-riding focus on the quantification of technical intervention and adverse or undesirable outcomes was identified. Vaginal birth (no instruments) was occasionally cited as an indicator; besides this measurement few of the 388 indicators were found to be assessing non-intervention or "good" or positive outcomes more generally. Conclusions: The tools and indicators identified largely enable measurement of technical interventions and undesirable health (or pathological medical) outcomes. A physiological birth generally necessitates few, or no, interventions, yet most of the indicators presently applied fail to capture (a) this phenomenon, and (b) the relationship between different forms and processes of care, mode of birth and good or positive outcomes. A need was identified for indicators which capture non-intervention, reflecting the reality that most births are low-risk, requiring few, if any, technical medical procedures

Cardiovascular Risk Factors Associated With Venous Thromboembolism.

IMPORTANCE: It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). OBJECTIVE: To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. DESIGN, SETTING, AND PARTICIPANTS: This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. EXPOSURES: A panel of several established cardiovascular risk factors. MAIN OUTCOMES AND MEASURES: Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). RESULTS: Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. CONCLUSIONS AND RELEVANCE: Older age, smoking, and adiposity were consistently associated with higher VTE risk.This research has been conducted using the UK Biobank resource under Application Number 26865. This work was supported by underpinning grants from the UK Medical Research Council (grant G0800270), the British Heart Foundation (grant SP/09/002), the British Heart Foundation Cambridge Cardiovascular Centre of Excellence, UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (grant 268834), the European Commission Framework Programme 7 (grant HEALTH-F2-2012-279233), and Health Data Research UK. Dr Danesh holds a British Heart Foundation Personal Chair and a National Institute for Health Research Senior Investigator Award

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Towards a bilingual sentiment analysis model for English and Filipino

There is an opportunity to learn and understand how Filipinos think, behave and react online, especially in responding to significant events. Resources, such as lexicons and corpora or a combination in a target language, as well as selection machine learning classifiers may be used to address this opportunity. However, there is little work on bilingual conversations. Filipino Tweets provide a rich source of data for building corpora and model for this kind of classification as it is composed of a mixture of English and mostly Filipino terms. This study looked into building bilingual sentiment analysis models for classifying bilingual English and Filipino disaster tweets. The study applied a supervised learning approach for subjective and sentiment models using Support Vector Machine (SVM), Na?ve Bayes, and K-Nearest Neighbor (K-NN) and bilingual English and Filipino lexicon, corpora and a combination in fixed distribution sets, in creating bilingual English and Filipino sentiment analysis models. Accuracy, precision, recall and F-measure were used to evaluate the performance of the models. Each of the resulting models were further evaluated against manually annotated corpora of tweets to determine its performance and reliability. For the bilingual subjective classification model, performance was highest in Nave Bayes, using the combination of lexicon and corpora, at 95% objective-5% subjective imbalanced distribution, with F measure of 73.53%. Similarly, the bilingual sentiment classification model performed highest in Na?ve Bayes, using the combination of lexicon and corpora, at 95% positive-5% negative, with F measure of 72.41%. The study showed that for English-Filipino sentiments, bilingual classification works best with an imbalanced distribution scheme and combination of lexicon and corpora data sets. PCA was performed further on the resulting positive and negative sentiments to obtain manifest constructs on sentiments. Results showed a promising possibility of extending the bilingual sentiment classification model further to include specific positive and negative emotions

Development of an Automatic Document to Digital Record Association Feature for a Cloud-Based Accounting Information System

Documents such as contracts; receipts; and sales invoices are proofs of transactions generated by various functions of business organizations. Though some organizations have initiatives to digitize paper-based proof of transactions; their business processes do not remove paper trails entirely. Organizations normally scan business document transactions; manually classify digitized documents; and associate digitized documents to digital records in a database management system. Hence; the digitization process introduced more work rather than efficiency. This study seeks to eliminate the additional work brought about by the document digitization process. It specifically looks at the application of image enhancing techniques and open-source Optical Character Recognition (OCR) technology to automatically classify and associate business documents to digital records in a database management system. The study presents how an alternative document digitizer and image enhancing feature is integrated into an accounting information system to facilitate the automatic classification and association of digitized documents to specific database records. The application of image cropping and grayscale color processing image enhancing techniques contributed to achieving an average of 90% level of confidence in extracting field labels while 91.5% level of confidence in extracting field values in business documents

The Integration of a Modified Balcik Last Mile Distribution Model Using Open Road Networks Into a Relief Operations Management Information System

The last mile in a disaster relief distribution chain is the delivery of goods from a central warehouse to the evacuation centers assigned for a given area. Its effectiveness relies on the proper allocation of each kind of relief good amongst the demand areas based on a given schedule. Because these operations involve a limited supply of relief goods, vehicles, and time, it is important to find ways to have more data-driven operations to satisfy as much demand as possible. There are various ways to model relief operations. One of them is Balcik\u27s Last Mile Distribution Model, which uses linear programming to minimize routing costs as well as penalty costs for unsatisfied demands. The model provides an allocation of each kind of relief good to the demand areas visited per day. The areas visited per day would depend on the capacity of the vehicle fleet as well as on the routes that can be used. Map data used for determining routes and historical data from previous disasters are used to determine the supply and demand for relief goods while providing a benchmark for results. The study compares Balcik\u27s Last Mile Distribution Model with other programming models intended for relief distribution to see how this is most applicable in Philippine relief scenarios. The said model is modified to fit the relief operations in the Philippines, specifically in Marikina City, specifically by changing the item types that the Balcik model would read. The model is integrated into a relief operations management information system, which will also be modified to better suit the usability needs of relief practitioners. The result is an allocation of relief goods for each evacuation area, a schedule for relief operations, as well as a visualization of the route to be used. The model provides the computational backbone for relief distribution decisions in the Philippines, allowing for more data-driven operations in the future