THE NEXUS between TRADE LIBERALIZATION and POVERTY: A DISAGGREGATED ANALYSIS

Decades after trade liberalization, poverty among the developing countries has continued to rise as their economies adjust to fierce and increasing external competition. This study investigates the impact of the 1988 trade reforms on poverty in Pakistan using micro-level data from 1990 to 2005. Using the feasible generalized least squares (FGLS) model in panel setting to address the problem of heteroskedasticity, we explored the impact of trade liberalization on the poverty level in Pakistan. The findings reveal that reductions in import tariffs have increased Pakistan s level of poverty. Our findings are robust and impervious to different poverty measures (i.e., headcount ratio, poverty gap, and squared poverty gap). Also, it was observed that lagged trade policies are associated with poverty in Pakistan. The rise in poverty may be attributed to the lower participation of the poor in external markets. It is recommended that policymakers enact policies that will encourage poor people to participate in external markets if they wish to benefit from trade liberalization. © 2022 AESS Publications. All Rights Reserved

Impact of diabetes continuing education on health care professionals’ attitudes towards diabetes care in a Yemeni city

Purpose: To evaluate the impact of a continuing education (CE) program on the attitudes of health care professionals (HCPs) towards diabetes care in Yemen.Methods: A pre- and post-intervention study was carried out in Mukalla City, Hadramout, Yemen and was offered to all physicians, pharmacists, and nurses registered in the Health Office in the Mukalla City. The HCPs were invited to attend a CE program. All participants filled out a questionnaire before the intervention (pre-test) that measured the attitudes of the participants towards diabetes. An interventional program was given in the form of a seminar, and participants were requested to complete the same questionnaire after the seminar.Results: A total of 73 HCPs attended the CE, including 19 pharmacists (26 %), 37 physicians (50.7 %), and 17 (23.3 %) nurses. The pre- and post-intervention changes in the questionnaire responses were significant only for attitude toward the values of blood glucose levels (p = 0.009) and attitude toward autonomy of diabetes patients (p = 0.023).Conclusion: HCPs in Mukalla City have positive attitudes toward diabetes. Physicians were more aware of the sequelae of diabetes than other healthcare professional groups with nurses showing the least understanding. Therefore, more emphasis should be placed upon designing education programs for diabetes specifically tailored for nurses and pharmacists.Keywords: Diabetes, Continuing education, Attitude, Health care professional

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions

Impact of magnetic field on the stability of the CMS GE1/1 GEM detector operation

The Gas Electron Multiplier (GEM) detectors of the GE1/1 station of the CMS experiment have been operated in the CMS magnetic field for the first time on the 7$^{th}$ of October 2021. During the magnetic field ramps, several discharge phenomena were observed, leading to instability in the GEM High Voltage (HV) power system. In order to reproduce the behavior, it was decided to conduct a dedicated test at the CERN North Area with the Goliath magnet, using four GE1/1 spare chambers. The test consisted in studying the characteristics of discharge events that occurred in different detector configurations and external conditions. Multiple magnetic field ramps were performed in sequence: patterns in the evolution of the discharge rates were observed with these data. The goal of this test is the understanding of the experimental conditions inducing discharges and short circuits in a GEM foil. The results of this test lead to the development of procedure for the optimal operation and performance of GEM detectors in the CMS experiment during the magnet ramps. Another important result is the estimation of the probability of short circuit generation, at 68 % confidence level, p$_{short}$$^{HV}$ $^{OFF}$ = 0.42$^{-0.35+0.94}$% with detector HV OFF and p$_{short}$$^{HV}$ $^{OFF}$ < 0.49% with the HV ON. These numbers are specific for the detectors used during this test, but they provide a first quantitative indication on the phenomenon, and a point of comparison for future studies adopting the same procedure

Triple-GEM discharge probability studies at CHARM: Simulations and experimental results

The CMS muon system in the region with 2.03<|η|<2.82 is characterized by a very harsh radiation environment which can generate hit rates up to 144 kHz/cm$^{2}$ and an integrated charge of 8 C/cm$^{2}$ over ten years of operation. In order to increase the detector performance and acceptance for physics events including muons, a new muon station (ME0) has been proposed for installation in that region. The technology proposed is Triple—Gas Electron Multiplier (Triple-GEM), which has already been qualified for the operation in the CMS muon system. However, an additional set of studies focused on the discharge probability is necessary for the ME0 station, because of the large radiation environment mentioned above. A test was carried out in 2017 at the Cern High energy AcceleRator Mixed (CHARM) facility, with the aim of giving an estimation of the discharge probability of Triple-GEM detectors in a very intense radiation field environment, similar to the one of the CMS muon system. A dedicated standalone Geant4 simulation was performed simultaneously, to evaluate the behavior expected in the detector exposed to the CHARM field. The geometry of the detector has been carefully reproduced, as well as the background field present in the facility. This paper presents the results obtained from the Geant4 simulation, in terms of sensitivity of the detector to the CHARM environment, together with the analysis of the energy deposited in the gaps and of the processes developed inside the detector. The discharge probability test performed at CHARM will be presented, with a complete discussion of the results obtained, which turn out to be consistent with measurements performed by other groups

Benchmarking LHC background particle simulation with the CMS triple-GEM detector

In 2018, a system of large-size triple-GEM demonstrator chambers was installed in the CMS experiment at CERN\u27s Large Hadron Collider (LHC). The demonstrator\u27s design mimicks that of the final detector, installed for Run-3. A successful Monte Carlo (MC) simulation of the collision-induced background hit rate in this system in proton-proton collisions at 13 TeV is presented. The MC predictions are compared to CMS measurements recorded at an instantaneous luminosity of 1.5 ×10$^{34}$ cm$^{-2}$ s$^{-1}$. The simulation framework uses a combination of the FLUKA and GEANT4 packages. FLUKA simulates the radiation environment around the GE1/1 chambers. The particle flux by FLUKA covers energy spectra ranging from 10$^{-11}$ to 10$^{4}$ MeV for neutrons, 10$^{-3}$ to 10$^{4}$ MeV for γ\u27s, 10$^{-2}$ to 10$^{4}$ MeV for e$^{±}$, and 10$^{-1}$ to 10$^{4}$ MeV for charged hadrons. GEANT4 provides an estimate of the detector response (sensitivity) based on an accurate description of the detector geometry, the material composition, and the interaction of particles with the detector layers. The detector hit rate, as obtained from the simulation using FLUKA and GEANT4, is estimated as a function of the perpendicular distance from the beam line and agrees with data within the assigned uncertainties in the range 13.7-14.5%. This simulation framework can be used to obtain a reliable estimate of the background rates expected at the High Luminosity LHC

Detector Control System for the GE1/1 slice test

Gas Electron Multiplier (GEM) technology, in particular triple-GEM, was selected for the upgrade of the CMS endcap muon system following several years of intense effort on R&D. The triple-GEM chambers (GE1/1) are being installed at station 1 during the second long shutdown with the goal of reducing the Level-1 muon trigger rate and improving the tracking performance in the harsh radiation environment foreseen in the future LHC operation [1]. A first installation of a demonstrator system started at the beginning of 2017: 10 triple-GEM detectors were installed in the CMS muon system with the aim of gaining operational experience and demonstrating the integration of the GE1/1 system into the trigger. In this context, a dedicated Detector Control System (DCS) has been developed, to control and monitor the detectors installed and integrating them into the CMS operation. This paper presents the slice test DCS, describing in detail the different parts of the system and their implementation