Practical Tool Research Articles

Energy Conservation for Mobile Cellular Systems through Site Optimization (Base Transceivers Station Optimization)

Mobile communication systems have cemented their position as a vital and practical tool for communication that supports productive corporate operations. The energy use of wireless access networks has grown to be a significant economic and environmental problem, the potential for utilizing the conserved energy was highlighted along with techniques that may boost energy efficiency in this paper's investigation of the energy consumption of base transceivers stations (BTS). Analysis of the data reveals that by optimizing 60 BTSs, the energy consumption decreases by 36.83% with 127.4MWh of monthly reserve. This is equivalent to powering 35 additional BTSs with a demand of 3.64224MWh or 69 homes for a daily power supply of 10 hours with a demand of 1.8452MWh and 440 homes with a total demand of 289.8KWh.

Design and implementation of a cost‐effective practical single‐phase power quality analyzer using pyboard microcontroller and python‐to‐python interface

AbstractThis work presents the design, development, and performance evaluation of a cost‐effective and practical single‐phase power quality analyzer (SPPQA) using Pyboard microcontroller and Python‐to‐Python interface. The SPPQA offers comprehensive measurements, including voltage/current harmonics up to the seventh order, frequency, voltage and current root mean square values, total power, active/reactive power, power factor, voltage and current crest factor, as well as voltage/current total harmonic distortion. To ensure the accuracy of the SPPQA, a testing procedure was conducted using a test bench equipped with a non‐linear load and a 220‐V power source operating at a frequency of 50 Hz. The obtained results, such as and , are compared with measurements obtained from classical approaches and devices, validating the authenticity and reliability of the SPPQA's performance. The SPPQA demonstrates considerable accuracy and reliability in its measurements, establishing it as an efficient and practical tool for power quality assessment. The ability to measure the seven harmonics of voltage and current, alongside other critical parameters, positions the SPPQA as a practical asset in the academic, industrial, and commercial sectors. The affordability and practicality of the SPPQA make it practical in both academic research and practical scenarios. Utilizers can gain deeper information into power quality through its measurements and harmonics analysis, while also enhancing industrial processes and ensuring a reliable power supply in commercial settings.

Migrant Pathology Screening in the Pediatric Population: A Five-Year Retrospective Study From a Level II Hospital.

The migrant population residing in Portugal has been growing. In 2015, the pediatrics department at ProfessorDoutor Fernando Fonseca Hospital,a level II hospital, implemented a screening for endemic pathologies in asymptomatic migrant children to enable their timely diagnosis and treatment. This study aimed to identify and characterize the main findings in the migrant pathology screening. This was a retrospective and descriptive study of asymptomatic children and adolescents who underwent opportunistic screening for migrant pathology in a hospital setting between January 2016 and April 2021. Data analysis was performed using Microsoft Excel®. A total of 256 individuals were included in the study; 53.5%(137/256) were female, with a median age of eight years and two months (minimum five months; maximum 17 years and 10 months). The majority of the participants were from Guinea-Bissau (29.7%, 76/256), Angola (19.1%, 49/256) and Cape Verde (12.1%, 31/256) and had been residents in Portugal for a median time of five months (minimum two days; maximum three years and five months). A total of 42.6% (109/256) participants did not have the Portuguese vaccination scheduleupdated. Screening was carried out in an outpatient setting in 71.9% (184/256) of individuals. A total of 38.7% (99/256) presented screening alterations, including 65 anemia cases (18 caused by iron deficiency and one by sickle cell anemia), 5 cases of tuberculosis infection and 1 case of pulmonary tuberculosis, 1 of human immunodeficiency virus infection, 3 hepatitis B virus infection cases, 20 of parasitic infections and 2 cases of female genital mutilations. The revised migrant pathology screening protocol enabled the detection of diseases with a significant impact on the health of individual children and adolescents. This protocol serves as a practical tool for accurately monitoring the health status of this population.

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models.

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Predictive value of systemic immune-inflammation index in the high-grade subtypes components of small-sized lung adenocarcinoma

BackgroundIdentification of micropapillary and solid subtypes components in small-sized (≤ 2 cm) lung adenocarcinoma plays a crucial role in determining optimal surgical procedures. This study aims to propose a straightforward prediction method utilizing preoperative available indicators.MethodsFrom January 2019 to July 2022, 341 consecutive patients with small-sized lung adenocarcinoma who underwent curative resection in thoracic surgery department of Xuanwu Hospital, Capital Medical University were retrospectively analyzed. The patients were divided into two groups based on whether solid or micropapillary components ≥ 5% or not (S/MP5+ and S/MP5-). Univariate analysis and multivariate logistic regression analysis were utilized to identify independent predictors of S/MP5+. Then a nomogram was constructed to intuitively show the results. Finally, the calibration curve with a 1000 bootstrap resampling and the receiver operating characteristic (ROC) curve were depicted to evaluate its performance.ResultsAccording to postoperative pathological results, 79 (23.2%) patients were confirmed as S/MP5+ while 262 (76.8%) patients were S/MP5-. Based on multivariate analysis, maximum diameter (p = 0.010), consolidation tumor ratio (CTR) (p < 0.001) and systemic immune-inflammation index (SII) (p < 0.001) were identified as three independent risk factors and incorporated into the nomogram. The calibration curve showed good concordance between the predicted and actual probability of S/MP5+. Besides, the model showed certain discrimination, with an area under ROC curve of 0.893.ConclusionsThe model constructed based on SII is a practical tool to predict high-grade subtypes components of small-sized lung adenocarcinoma preoperatively and contribute to determine the optimal surgical approach.

Developing a Practical Tool for Predicting Wound Healing Outcomes of Patients with Diabetic Forefoot Ulcers: Focus on Vasculopathy and Infection.

To develop a preliminary risk scoring system to predict the prognosis of patients with diabetic forefoot ulcers based on the severity of vasculopathy and infection, which are the major risk factors for amputation. Forefoot was defined as the distal part of the foot composed of the metatarsal bones and phalanges and associated soft tissue structures. The degree of vasculopathy was graded as V0, V1, or V2 according to transcutaneous partial oxygen tension values and toe pressure. The degree of infection was graded as I0, I1, or I2 according to tissue and bone biopsy culture results. The risk scores were calculated by adding the scores for the degree of vasculopathy and infection and ranged from 0 to 4. Wound healing outcomes were graded as healed without amputation, minor amputation, or major amputation. The authors evaluated wound healing outcomes according to risk scores. As the risk score increased, the proportion of patients who underwent both major and minor amputations increased (P < .001). In the multivariate logistic analysis, the odds ratios of amputation also increased as the risk score increased. Patients with a risk score of 4 were 75- and 19-fold more likely to undergo major and minor amputations, respectively, than patients with a risk score of 0 (P = .006 and P < .001). The risk score can be used as an indicator to predict the probability of amputation in patients with diabetic forefoot ulcers.

Racial and ethnic differences in the receipt of metabolic syndrome risk factor screening and treatment among individuals with prostate cancer treated with androgen deprivation therapy.

29 Background: Racial and ethnic health disparities in prostate cancer screening, and treatment constitute the largest of all cancer disparities. Androgen deprivation therapy (ADT) is widely used in the treatment of localized or metastatic prostate cancer. However, ADT is associated with toxic cardiovascular and metabolic adverse events that may occur as early as 6 months of therapy initiation. This study aimed to assess racial/ethnic differences and longitudinal changes in metabolic syndrome risk factor (MSRF) screening/treatment among patients with prostate cancer treated with ADT at a university-affiliated comprehensive cancer center in the southwestern U.S. between 2010-2021. We also aimed to identify patient and healthcare provider factors that influence MSRF screening among study sample. Methods: A retrospective observational cohort study of 803 patients treated with ADT for at least six months at the cancer center was conducted. Patients were followed three months pre- to 12 months post-ADT initiation to evaluate MSRF screening/treatment. Determination of MSRF screening was based on primary care provider referral for MSRF screening/treatment or receipt of blood glucose, lipid profile, and blood pressure screening within six months post-treatment with ADT. Patients were considered treated for MSRF if they were started or continued therapy (within six months of ADT initiation) with science advisory guideline recommended treatments. Results: Guideline-concordant MSRF screening rate approached 23.5%; MSRF treatment rate was 76.9%. A higher treatment rate in MSRF treatment was found for NHW compared to Hispanic men ( p=0.02). We found a significant difference in the proportion of patients receiving MSRF screening across all racial/ethnic groups ( p=0.03). MSRF screening rates from 13.9% to 35.6% were observed over the 10-year data collection period. Patients with dyslipidemia at baseline had higher odds of having MSRF screening than patients without dyslipidemia ( p&lt;0.0001). Oncologists with &gt;20 years of experience had higher odds of providing MSRF screening compared with &lt;10 years of experience ( p=0.006). Conclusions: Minority populations had significantly lower odds of having MSRF screening than NHW patients after adjusting for clinical and socio-economic variables. Closer clinical attention and education, as well as the development and implementation of innovative practice tools and interventions to optimize MSRF screening and treatment are warranted to mitigate the harmful adverse effects of ADT in patients with prostate cancer.

Impeller optimization using a machine learning-based algorithm with dynamic sampling method and flow analysis for an axial flow pump

Design optimization for widely used axial flow pumps presents a formidable challenge due to the significant impact of numerous parameters associated with impeller geometry on hydraulic performance. The expansive design space raises concerns about the cost and time implications of the optimization process. This paper introduces a machine learning-based algorithm with a dynamic sampling approach to enhance the hydraulic performance of axial flow pumps. The focus is on an axial flow pump designed for China’s South-to-North Water Diversion Project. Optimization involves selecting 15 design variables governing impeller geometry, considering meridional shape and mean blade profiles. The optimization process predicts hydraulic performance using CFD methods, with a primary objective of maximizing efficiency at the axial flow pump’s design point while maintaining pump head around the design value. The results indicate that the proposed machine learning-based algorithm exhibits commendable convergence, delivering a notable improvement in performance. For instance, the optimized axial flow pump displays 2% efficiency increase compared to the initial design. Further analysis employing concepts like entropy generation rate and boundary vorticity flux reveals that the optimized pump has more uniform flow near the pressure side of the impeller blade. Additionally, design optimization effectively suppresses flow separation at the blade trailing edge near the impeller hub. This study offers valuable insights and a practical tool for the design optimization of axial flow pumps.

Authentic spatial vulnerability assessment for evacuation shelters in disaster planning: a case study of Tubay, Agusan Del Norte, Philippines

Authenticity plays a pivotal role in addressing the primary challenges encountered in disaster planning, particularly when it comes to assessing vulnerability and evaluating the spatial distribution of shelter demand and resources. Ensuring the accuracy and reliability of data is essential for effectively managing available resources at each evacuation center, as well as for accommodating evacuees and ensuring the safety of both staff and refugees. In real-life circumstances, the authenticity of the information gathered becomes even more critical, as an imbalance in shelter and population distribution often hinders access to evacuation shelters. In this paper, the researchers introduce a spatial assessment technique that not only analyzes vulnerability but also authentically assesses the spatial distribution of shelter demand and supplies, thereby enhancing the overall effectiveness of disaster planning in Tubay, Agusan del Norte, Philippines. The study focuses on Tubay, Agusan del Norte, Philippines, taking into account spatial accessibility. The researchers found that out of the 33 shelters assessed, 21 are feasible locations for evacuation centers, and approximately 51% of the total population points are vulnerable to flooding. Utilizing GIS maps, the results reveal a significant decrease in overall shelter capacity to 64.26%. The study's findings emphasize the critical need for evaluating the relationship between vulnerable shelters and the demand for resources at each location. This information serves as a practical tool, offering flexibility in terms of data availability for decision-makers, enabling them to investigate areas, and providing a valuable reference for developing and improving emergency management decisions and strategies.

Utilization of and perceived need for simulators in clinical electrophysiology: results from an EHRA physician survey.

Simulator training has been recently introduced in electrophysiology (EP) programmes in order to improve catheter manipulation skills without complication risks. The aim of this study is to survey the current use of EP simulators and the perceived need for these tools in clinical training and practice. A 20-item online questionnaire developed by the Scientific Initiatives Committee of the European Heart Rhythm Association (EHRA) in collaboration with EHRA Digital Committee was disseminated through the EHRA Scientific Research Network members, national EP groups, and social media platforms. Seventy-four respondents from 22 countries (73% males; 50% under 40 years old) completed the survey. Despite being perceived as useful among EP professionals (81%), EP simulators are rarely a part of the institutional cardiology training programme (20%) and only 18% of the respondents have an EP simulator at their institution. When available, simulators are mainly used in EP to train transseptal puncture, ablation, and mapping, followed by device implantation (cardiac resynchronization therapy [CRT], leadless, and conduction system pacing [CSP]). Almost all respondents (96%) believe that simulator programmes should be a part of the routine institutional EP training, hopefully developed by EHRA, in order to improve the efficacy and safety of EP procedures and in particular CSP 58%, CRT 42%, leadless pacing 38%, or complex arrhythmia ablations (VT 58%, PVI 45%, and PVC 42%). This current EHRA survey identified a perceived need but a lack of institutional simulator programme access for electrophysiologists who could benefit from it in order to speed up the learning curve process and reduce complications of complex EP procedures.

Examining the Intersection of Strategic Thinking and Continuous Process Improvement Through the Lens of Military Medical Education to Build a Novel Model.

In 2020, the U.S. Air Force School of Aerospace Medicine hired its first civilian dean since its founding in 1918, tasked with building the school's first strategic plan to modernize and improve the institution. Using a combination of military continuous process improvement and academic strategic thinking, the dean produced a highly successful strategic plan. However, its resource-heavy and time-consuming methodology made it difficult to replicate. This study aimed to create a novel and streamlined strategic planning model that combined best practices from continuous process improvement and strategic thinking without redundancy. A qualitative descriptive case study was used to analyze the detailed efforts through content analysis of 150 pages of documentation. A hybrid approach to coding uncovered 44 deductive codes and 5 inductive codes from 10 strategic tools. Results indicated a converging relationship between all strategic processes tested-strategic planning, strategic thinking, and continuous process improvement and their associated tools. A five-step model called the Triple "O" OODA Loop was created, combining best practice tools from strategic planning (purpose trident and SWOT analysis or strengths, weaknesses, opportunities, and threats), strategic thinking (Hot Spots scale and GOST framework or goals, objectives, strategies, and tactics) and continuous process improvement (phases 6-8).

Safety of medical personnel when using personal protective equipment during the liquidation of a biological and social emergency

Relevance. According to scientific publications, preventive measures undertaken amid the COVID-19 pandemic failed to did reliably protect healthcare workers from the new coronavirus infection. The use of personal protective equipment (PPE) that protected against biological factors failed to protect HCWs from becoming infected and in most cases contributed to the functional disorders and impaired health. The objective is to provide evidence-based justification of preventive measures aimed at increasing healthcare workers’ (HCW) safety by using PPE during elimination of biological and social emergencies (based on the case of the COVID-19 pandemic).Methods. The study design included four consecutive stages: occupational morbidity assessment for COVID-19, assessment of HCWs’ working conditions; assessment of HCWs’ safety when using PPE sets in real setting and simulation, and justification of preventive measures to improve safety of HCWs.Results and discussion. The first year of the COVID-19 pandemic saw the incidence of 30.4 % among all HCWs working at the Military Medical Academy, suggesting a high risk of infection transmission. Morbidity was utmost among HCWs specializing in infections, followed by a significant number of HCWs who do not work in contaminated areas on a daily basis (surgeons, urologists, traumatologists). In addition to high incidence rate, the new coronavirus infection pandemic demanded enforcement of specific measures associated with the use of PPE at increased physical stress, including amid elevated ambient temperature. A sociological survey and experimental data allowed to assess the risk of functional and heat-injury related health disorders, as well as injuries in HCWs using PPEs. In order to reinforce occupational safety and minimize occupational risks of health deterioration in HCWs wearing PPEs at work, the research results allowed to develop a two-stage preventive action plan: stage 1 includes identification of dangerous factors, whereas stage 2 focuses on preventive measures to eliminate negative impact of hazards and associated risks.Conclusion. A set of preventive measures developed for the COVID-19 pandemic suggests practical tools to reinforce occupational safety for HCWs who use PPEs to deliver biological and social emergency response and elimination.

Exploring the facilitators and barriers in opioid deprescribing for non-cancer pain treatment experienced by general practitioners: A qualitative study.

Guidelines recommend opioid deprescribing in patients on long-term opioid treatment for chronic non-cancer pain. This study aims to explore facilitators and barriers in opioid deprescribing among general practitioners in the Netherlands. In addition, this study aims to identify possibilities for improvement regarding opioid deprescribing in primary care. Focus group discussions with Dutch General practitioners were held by two skilled moderators. The focus group discussions were transcribed verbatim and analysed using MAXQDA software. Three independent reviewers searched for overarching themes using thematic analysis with an inductive approach. Discussions were organized until data saturation was reached. Twenty-two general practitioners participated in four focus group discussions. Five main themes emerged from the data: (1) patient-centred care; (2) ensuring proper pain management (3) dilemmas and hardships in dealing with opioid use disorder; (4) the competency gap; (5) needs and possibilities to improve opioid deprescribing in primary care. The first theme addresses the main facilitators in opioid tapering. The following three themes emerged as main barriers in opioid deprescribing. The fifth theme identified possibilities for change. This study indicates the importance of intrinsic motivation and a tailored approach to deprescribe opioids in patients with chronic pain on long-term opioid treatment. Identified barriers include struggles in pain management, challenges caused by opioid use disorder, insufficient capacities such as time constraints and lack of skills. Recommendations for improvement involve enhanced collaboration with healthcare professionals in primary and secondary care, provision of practical tools and assurance of sufficient time. This focus group study among 22 Dutch general practitioners elucidates the complexities of opioid deprescribing and reveals pivotal themes such as patient-centred care, pain management challenges, and competency gaps. The findings underscore the crucial role of intrinsic motivation and that of a tailored approach in opioid deprescribing, while demonstrating how a lack in effective pain treatments, practical capacities and challenges caused by opioid dependence, impede opioid deprescribing. By uncovering these complexities, this study aims to inform future deprescribing strategies.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients’ survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.

Development and validation of prediction model for older adults with cognitive frailty

ObjectiveThis study sought to develop and validate a 6-year risk prediction model in older adults with cognitive frailty (CF).MethodsIn the secondary analysis of Chinese Longitudinal Healthy Longevity Survey (CLHLS), participants from the 2011–2018 cohort were included to develop the prediction model. The CF was assessed by the Chinese version of Mini-Mental State Exam (CMMSE) and the modified Fried criteria. The stepwise regression was used to select predictors, and the logistic regression analysis was conducted to construct the model. The model was externally validated using the temporal validation method via the 2005–2011 cohort. The discrimination was measured by the area under the curve (AUC), and the calibration was measured by the calibration plot. A nomogram was conducted to vividly present the prediction model.ResultsThe development dataset included 2420 participants aged 60 years or above, and 243 participants suffered from CF during a median follow-up period of 6.91 years (interquartile range 5.47–7.10 years). Six predictors, namely, age, sex, residence, body mass index (BMI), exercise, and physical disability, were finally used to develop the model. The model performed well with the AUC of 0.830 and 0.840 in the development and external validation datasets, respectively.ConclusionThe study could provide a practical tool to identify older adults with a high risk of CF early. Furthermore, targeting modifiable factors could prevent about half of the new-onset CF during a 6-year follow-up.

Image-guided programming deep brain stimulation improves clinical outcomes in patients with Parkinson’s disease

Deep brain stimulation (DBS) is an effective treatment for patients with Parkinson’s disease (PD). However, some patients may not respond optimally to clinical programming adjustments. Advances in DBS technology have led to more complex and time-consuming programming. Image-guided programming (IGP) could optimize and improve programming leading to better clinical outcomes in patients for whom DBS programming is not ideal due to sub-optimal response. We conducted a prospective single-center study including 31 PD patients with subthalamic nucleus (STN) DBS and suboptimal responses refractory to clinical programming. Programming settings were adjusted according to the volumetric reconstruction of the stimulation field using commercial postoperative imaging software. Clinical outcomes were assessed at baseline and at 3-month follow-up after IGP, using motor and quality of life (QoL) scales. Additionally, between these two assessment points, follow-up visits for fine-tuning amplitude intensity and medication were conducted at weeks 2, 4, 6, and 9. After IGP, twenty-six patients (83.9%) experienced motor and QoL improvements, with 25.8% feeling much better and 38.7% feeling moderately better according to the patient global impression scale. Five patients (16.1%) had no clinical or QoL changes after IGP. The MDS-UPDRS III motor scale showed a 21.9% improvement and the DBS-IS global score improved by 41.5%. IGP optimizes STN-DBS therapy for PD patients who are experiencing suboptimal clinical outcomes. These findings support using IGP as a standard tool in clinical practice, which could save programming time and improve patients’ QoL.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code

BackgroundThe rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings.MethodsWe present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator’s descriptive accuracy.ResultsIn this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8–22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65–5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator.ConclusionsThe emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

Evaluation of potential association of metabolic syndrome in obese and non-obese PCOS women

BackgroundPolycystic ovary syndrome is an escalating endocrinal and reproductive disorder among women of reproductive age and is considered the foremost health burden. Therefore, the present study is an attempt to estimate the potential association of metabolic syndrome in obese and non-obese PCOS women. A total of 250 PCOS women ranging in age from 18 to 45 years and living in the Chandigarh Capital Region were selected from the OPD, PGIMER, Chandigarh, (North India). Rotterdam Criteria (2003) was employed to diagnose polycystic ovary syndrome among women.ResultsThe prevalence of metabolic syndrome (MS) as assessed by the National Cholesterol Education Program Adult Treatment Panel (NCEP ATP III) was found to be 30% (4% in non-obese and 26% in obese) in PCOS women. Waist circumference > 88 cm is the most prevalent feature in PCOS women (67.2%) followed by HDL < 50 mg/dL (56.8%). The multivariate logistic regression analysis exhibited that visceral adiposity index (VAI) was a significant determinant of metabolic syndrome in obese (OR = 1.17, p < 0.01) and non-obese (OR = 1.2, p < 0.01) PCOS women. Results of ROC analysis further established visceral adiposity index as a potential determinant in metabolic syndrome in both the groups of PCOS women. In obese PCOS women, the W/H ratio also depicted accuracy in predicting metabolic syndrome risk.ConclusionsVisceral adiposity index is a significant tool to assess the metabolic syndrome in both groups, i.e., obese and non-obese. However, the waist/hip ratio can be considered as a predictive tool in obese women only. Thus, it could be used as a significant and inexpensive tool in clinical practices for early detection of metabolic syndrome in PCOS women.

Detail Explanation of Coordinate Transformation Procedures Used in Geodesy and Geoinformatics on the Example of the Territory of the Republic of Croatia

With strong technology improvement, especially GNSS, coordinate transformation has become an essential tool in everyday practice. For this reason, it is beneficial to have a good understanding of the mathematical process of coordinate transformation, as well as the method for calculating transformation parameters. The purpose of this article is to provide a detailed explanation of the coordinate transformation procedure and the calculation of transformation parameters for seven transformation models: 3-parameter, 5-parameter standard and abridged, 7-parameter, 8-parameter, 9-parameter, and 12-parameter. For each transformation model, a basic transformation equation, a procedure for calculating transformation parameters, inverse expression for reversible transformation, as well as characteristics of each model are provided. As an addition, for each model, a numerical example of calculating transformation parameters for the territory of the Republic of Croatia is provided. Additionally, for the example of the 7-parameter transformation, importance of reversible transformation, rotation convention, as well as the form of the rotation matrix used during transformation are demonstrated.

Modified Chinese disabilities of arm, shoulder and hand tool: Validity and reliability for upper extremity injuries

DesignClinimetric evaluation study. IntroductionThe Chinese Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire has necessitated the development of a revised version to the specific needs of individuals with upper extremity injuries with the progress of times and lifestyle changes. Purpose of the studyThis research aimed to evaluate the reliability and validity of Modified Chinese Disability of Arm, Shoulder and Hand (MC-DASH) questionnaire in individuals with upper extremity injuries. MethodsOne hundred and one individuals with upper extremity injuries (UEI) were recruited. The function of upper extremity was measured using the electronic version of MC-DASH, and compared against the Chinese Disability of Arm, Shoulder and Hand. The MC-DASH was reassessed within three days in all individuals. We investigated the internal consistency, test-retest reliability, content validity, criterion validity, and construct validity of MC-DASH. ResultsThe internal consistency was deemed sufficient, as indicated by a Cronbach's alpha of 0.986 and an intraclass correlation coefficient of 0.957. Moreover, the mean total scores of MC-DASH on the first-test and retest were 37.86 and 38.19, respectively (ICC: 0.957, 95 %CI: 0.937–0.971, p < 0.001). Furthermore, the MC-DASH version exhibited satisfactory content validity evidenced by its strong correlation (R= 0.903, p < 0.001) with the Chinese DASH. Three major influencing factors were identified from 37 items. The cumulative variance contribution rate of the MC-DASH questionnaire was 75.76 %, confirming its construct validity. ConclusionThe Modified Chinese Disability of Arm, Shoulder and Hand questionnaire has been shown to be a valid, reliable, and practical tool for use in patients with upper extremity injuries.