Branding strategies for service firms- a study on the selected Internet Service Providers (ISPs) in Bangladesh

Research work has been done on how to create a brand supporting behaviour but most of the time the existing insights have generally stemmed from research with management, brand practitioner’s and even consumers’ perspectives. Very little has been done to research the employees’ perception towards internal branding and to compare the view of the management and employees’ on internal communication. The existing researches are also done on mostly in the context of the businesses in industrialize countries whereas this research would be an intent to find the internal branding practice in a service firm in a country of developing economy. This would able to identify the gap in the practice of internal branding in different socio-economical context. This research is dedicated to find out both back end and front end skilled employees’ view towards internal communication in a service firm and based on the findings attempt would be done to see whether the staffs perceive their role differently towards the brand. For conducting the research qualitative data were gathered from the qualitative survey by questioning different employee and management about the internal brand communication and the analysis was done on that. With the respondents view on the internal marketing process, the management do not have fully structured plan to implement a sound internal branding strategy. With a given economical constraint it is not always possible to practice all the aspect of management science, but from the study of the company we can see that service firms such as ISP (internet service provider) companies in weaker economical countries could increase the internal communication practice by just altering the existing inter-departmental communication monitored by innovative senior management, co-ordination of HRM and Marketing with input from engineers

Evaluation of Nutrition Surveys in Flood-affected Areas of Pakistan: Seeing the Unseen!

In 2010 Pakistan experienced the worst floods recorded in its history; millions of people were affected and thousands lost their lives. Nutrition assessment surveys led by UNICEF were conducted in flood?affected areas of Punjab and Sindh provinces to assess the nutrition status of children between 6–59 months while Aga Khan University (AKU) undertook a parallel assessment including micronutrient status in their project areas within Balochistan, Sindh and Punjab. Standardised Monitoring and Assessment of Relief and Transition (SMART) methodology was used. 881 children from Sindh, 1,143 from Punjab and 817 from AKU project areas were measured for anthropometry and their households were interviewed. The findings indicated that while immediate life?saving interventions were essential, there was also an urgent need to address chronic malnutrition. Through high?level dissemination of the survey results, treatment and prevention of malnutrition has become a priority for the provincial and federal government in Pakistan and for donors

A review of 2.45 GHz microstrip patch antennas for wireless applications

Recently, microstrip patch antennas have become popular. Due to their ubiquity, these antennas have more uses every day. In this research paper, a 2.45 GHz microstrip patch antenna has been reviewed and analyzed. Different substrate materials have been used to make these antennas, and their thickness is different. Various antennas are designed based on the application, such as rectangular, square, triangle, ring, donut, and dipole. Other types of software were used to design the antenna, including CST, HFSS, MATLAB, ADS, and FEKO. Microstrip patch antenna design is a relatively new field of study for wireless applications. Several devices are linked to send or receive radio waves using a single antenna. Antennas designed for 2.45 GHz are used in various wireless communication systems, including television broadcasts, microwave ovens, mobile phones, wireless local area network (WLAN), Bluetooth, global positioning system (GPS), and two-way radios. This article looks at the geometric structures of antennas, including their many parameters and materials and the many different shapes they can take. In addition, the substrate materials, the loss tangent, the thickness, the return loss, the bandwidth, the voltage standing wave ratio (VSWR), the gain, and the directivity of previous articles will also be discussed

For wireless LAN application, microstrip patch antenna design in S-band

This article presents a 3.5 GHz rectangular microstrip patch antenna (RMPA) designed, studied, and analyzed for wireless LAN applications. Using Fr-4 as substrate material, whose dielectric permittivity is 4.3, patch thickness is 1.65 mm, and loss tangent is 0.025. A feeding line with an impedance of 50 Ω is utilized to supply the antenna with power. Computer simulation technology (CST) software has been used to design the antenna and origin pro software has been used to display the resulting figures from the simulation. The antenna simulation showed that the return loss is -56.82 dB; the directivity gain is 6.02 dBi, the bandwidth is 0.148 GHz, and the voltage standing wave ratio (VSWR) is 1.0028. The paper aims to increase the return loss, develop a standard VSWR, increase the directivity gain of the antenna, and improve the antenna bandwidth. The results of the proposed antenna were much better than previously published papers, which were suitable for wireless applications. This proposed antenna can be used for future wireless LAN applications

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk outcome pairs, and new data on risk exposure levels and risk outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017,34.1 million (95% uncertainty interval [UI] 33.3-35.0) deaths and 121 billion (144-1.28) DALYs were attributable to GBD risk factors. Globally, 61.0% (59.6-62.4) of deaths and 48.3% (46.3-50.2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10.4 million (9.39-11.5) deaths and 218 million (198-237) DALYs, followed by smoking (7.10 million [6.83-7.37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6.53 million [5.23-8.23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4.72 million [2.99-6.70] deaths and 148 million [98.6-202] DALYs), and short gestation for birthweight (1.43 million [1.36-1.51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4.9% (3.3-6.5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23.5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18.6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

The effect of prenatal balanced energy and protein supplementation on gestational weight gain: An individual participant data meta-analysis in low- and middle-income countries

Background AU Understanding: Please confirm that all heading levels are represented correctly the effects of balanced energy and protein (BEP): supplements on gestational weight gain (GWG) and how the effects differ depending on maternal characteristics and the nutritional composition of the supplements will inform the implementation of prenatal BEP interventions. Methods and findings Individual participant data from 11 randomized controlled trials of prenatal BEP supplements (N = 12,549, with 5,693 in the BEP arm and 6,856 in the comparison arm) in low- and middle-income countries were used. The primary outcomes included GWG adequacy (%) and the estimated total GWG at delivery as continuous outcomes, and severely inadequate (125% adequacy) as binary outcomes; all variables were calculated based on the Institute of Medicine recommendations. Linear and log-binomial models were used to estimate study-specific mean differences or risk ratios (RRs), respectively, with 95% confidence intervals (CIs) of the effects of prenatal BEP on the GWG outcomes. The study-specific estimates were pooled using meta-analyses. Subgroup analyses were conducted by individual characteristics. Subgroup analyses and meta-regression were conducted for study-level characteristics. Compared to the comparison group, prenatal BEP led to a 6% greater GWG percent adequacy (95% CI: 2.18, 9.56; p = 0.002), a 0.59 kg greater estimated total GWG at delivery (95% CI, 0.12, 1.05; p = 0.014), a 10% lower risk of severely inadequate GWG (RR: 0.90; 95% CI: 0.83, 0.99; p = 0.025), and a 7% lower risk of inadequate GWG (RR: 0.93; 95% CI: 0.89, 0.97; p = 0.001). The effects of prenatal BEP on GWG outcomes were stronger in studies with a targeted approach, where BEP supplements were provided to participants in the intervention arm under specific criteria such as low body mass index or low GWG, compared to studies with an untargeted approach, where BEP supplements were provided to all participants allocated to the intervention arm. Conclusions Prenatal BEP supplements are effective in increasing GWG and reducing the risk of inadequate weight gain during pregnancy. BEP supplementation targeted toward pregnant women with undernutrition may be a promising approach to delivering the supplements

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa.

BACKGROUND: Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature. METHODS: A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes. RESULTS: With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature. CONCLUSIONS: In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs

Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation.

BACKGROUND: Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision. METHODS: Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard). RESULTS: Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%. CONCLUSION: Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately