FRAX calculation with and without bone mineral density for assessment of osteoporotic fracture risk in patients of rheumatic disease: a cross-sectional study

To compare fracture risk assessment (FRAX) calculation with and without bone mineral density (BMD) in predicting 10-year probability of hip and major osteoporotic fracture in patients of rheumatic diseases.

Methodology

A cross-sectional was conducted at outpatient Department of Rheumatology. Eighty-one Patients of more than 40 years of age having either sex. Diagnosed case of Rheumatic diseases were according to American College of Rheumatology (ACR) /European Alliance of Associations for Rheumatology (EULAR) criteria were included in our study. FRAX score without BMD was calculated and information was recorded in proforma. These patients were advised dual energy X-ray absorptiometry Scan and after that FRAX with BMD was calculated, after which comparison between result of two scores was made. The data were analyzed by SPSS software version 24. Effect modifiers were controlled by stratification. Post-stratification χ² test were applied. P value less than 0.05 was considered as significant.

Results

This study consisted of 63 participants, who were assessed for osteoporotic risk fracture, with and without BMD. Data analysis revealed a significant association between the type of fracture and age (p value=0.009), previous fracture (p value=0.25), parent fractured hip (p values) and treatment with bone mineral dismissal. There was no statistically significant association seen of fractures with bone deterioration with sex, weight, height, or current smoking.

Conclusion

FRAX may be crucial in rural areas where dual energy X-ray absorptiometry scanning is not available since it is a readily available instrument. FRAX is a useful substitute for estimating osteoporosis risk when funds are scarce. Given the possible effect it will have on healthcare costs, this is extremely pertinent.

Modeling Strategies for Crude Oil-Induced Fouling in Heat Exchangers: A Review

Semi-empirical fouling models have proven more effective in predicting the fouling behavior of crude oils in heat exchangers. These models have aided refineries in optimizing operating conditions to minimize or eliminate fouling in preheat exchangers. Despite their complexity, the models continue to improve in approximating real behavior by taking into account previously neglected aspects. This paper summarizes these findings from various studies along with highlighting different factors which were considered to enhance the predictability of the models. A critical analysis is presented to emphasize that activation energy in the deposition term varies depending on the physical processes involved and may not conform to the precise definition of activation energy. Two primary modeling approaches for crude oil fouling have emerged, i.e., deterministic and threshold models. Threshold models have gained more attention due to their fewer adjustable parameters. The stability or compatibility of crude oils has a substantial impact on asphaltene deposition, which is a major contributor to fouling. However, incorporating this factor into fouling models has received little attention. The inclusion of parameters for inorganic fraction and ageing has increased predictability by accurately estimating the fouling thickness. The use of CFD to analyze fouling mechanisms is promising, particularly for complex geometries. The dynamic and moving boundary modeling approach has potential to broaden the applicability of fouling models.

Inquiring the inter-relationships amongst grain-filling, grain-yield, and grain-quality of Japonica rice at high latitudes of China

The widespread impacts of projected global and regional climate change on rice yield have been investigated by different indirect approaches utilizing various simulation models. However, direct approaches to assess the impacts of climatic variabilities on rice growth and development may provide more reliable evidence to evaluate the effects of climate change on rice productivity. Climate change has substantially impacted rice production in the mid-high latitudes of China, especially in Northeast China (NEC). Climatic variabilities occurring in NEC since the 1970s have resulted in an obvious warming trend, which made this region one of the three major rice-growing regions in China. However, the projections of future climate change have indicated the likelihood of more abrupt and irregular climatic changes, posing threats to rice sustainability in this region. Hence, understanding the self-adaptability and identifying adjustive measures to climate variability in high latitudes has practical significance for establishing a sustainable rice system to sustain future food security in China. A well-managed field study under randomized complete block design (RCBD) was conducted in 2017 and 2018 at two study sites in Harbin and Qiqihar, located in Heilongjiang province in NEC. Four different cultivars were evaluated: Longdao-18, Longdao-21 (longer growth duration), Longjing-21, and Suijing-18 (shorter growth duration) to assess the inter-relationships among grain-filling parameters, grain yield and yield components, and grain quality attributes. To better compare the adaptability mechanisms between grain-filling and yield components, the filling phase was divided into three sub-phases (start, middle, and late). The current study evaluated the formation and accumulation of the assimilates in superior and inferior grains during grain-filling, mainly in the middle sub-phase, which accounted for 59.60% of the yield. The grain yields for Suijing-18, Longjing-21, Longdao-21, and Longdao-18 were 8.02%, 12.78%, 17.19%, and 20.53% higher in Harbin than those in Qiqihar, respectively in 2017, with a similar trend observed in 2018. At Harbin, a higher number of productive tillers was noticed in Suijing-18, with averages of 17 and 15 in 2017 and 2018, respectively. The grain-filling parameters of yield analysis showed that the filling duration in Harbin was conducive to increased yield but the low dry weight of inferior grains was a main factor limiting the yield in Qiqihar. The average protein content values in Harbin were significantly higher (8.54% and 9.13%) than those in Qiqihar (8.34% and 9.14%) in 2017 and 2018, respectively. The amylose content was significantly higher in Harbin (20.03% and 22.27%) than those in Qiqihar (14.44% and 14.67%) in 2017 and 2018, respectively. The chalkiness percentage was higher in Qiqihar, indicating that Harbin produced good quality rice. This study provides more direct evidence of the relative changes in rice grain yield due to changes in grain-filling associated with relative changes in environmental components. These self-adaptability mechanisms to climatic variability and the inter-relationships between grain-filling and grain yield underscore the urgent to investigate and explore measures to improve Japonica rice sustainability, with better adaptation to increasing climatic variabilities. These findings may also be a reference for other global rice regions at high latitudes in addressing the impacts of climate change on future rice sustainability.

Enhancing healthcare supply chain resilience: decision-making in a fuzzy environment

Purpose

Resilience is a fundamental component of healthcare supply chains, as the quality and endurance of human life are dependent on them. However, there are numerous resilience-building measures, and there is a need for prioritization of those strategies. This research study aims to prioritize resilience strategies for healthcare supply chains while considering the risks that most severe, probable to occur and have the lengthiest periods of recovery.

Design/methodology/approach

This research study has used multi-criteria decision-making (MCDM) techniques for analysis. Initially, the criteria for prioritization of risks, i.e. severity, probability of occurrence and recovery time were assigned with importance weights through the fuzzy analytical hierarchy process (AHP). Then, these weights were used in the fuzzy technique for order preference by similarity to ideal solution (TOPIS) analysis for prioritization of risks. Subsequently, the identified risks were used for highlighting the appropriate resilience strategies through the fuzzy quality function deployment (QFD) technique.

Findings

Results indicate that Industry 4.0, multiple sourcing, risk awareness, agility and global diversification of suppliers, markets and operations are the most significant resilience strategies.

Research limitations/implications

This study's limitation is that it is conducted in a general perspective, rather than reducing the context to a developing or developed country. Different areas have variable market factors, due to which potential risks occur in a different form. Moreover, resilience strategies work differently in different environments. Therefore, for future endeavors, the studies should be carried out in a limited context.

Originality/value

This research study proposes a novel MCDM-based approach for ranking resilience strategies, in light of the most probable, severe and long-lasting risks. In addition, this approach has been employed for the enhancement of resilience in healthcare supply chains.

Optimization of CNN through Novel Training Strategy for Visual Classification Problems

The convolution neural network (CNN) has achieved state-of-the-art performance in many computer vision applications e.g., classification, recognition, detection, etc. However, the global optimization of CNN training is still a problem. Fast classification and training play a key role in the development of the CNN. We hypothesize that the smoother and optimized the training of a CNN goes, the more efficient the end result becomes. Therefore, in this paper, we implement a modified resilient backpropagation (MRPROP) algorithm to improve the convergence and efficiency of CNN training. Particularly, a tolerant band is introduced to avoid network overtraining, which is incorporated with the global best concept for weight updating criteria to allow the training algorithm of the CNN to optimize its weights more swiftly and precisely. For comparison, we present and analyze four different training algorithms for CNN along with MRPROP, i.e., resilient backpropagation (RPROP), Levenberg–Marquardt (LM), conjugate gradient (CG), and gradient descent with momentum (GDM). Experimental results showcase the merit of the proposed approach on a public face and skin dataset.

Large scale model predictions on the effect of GDL thermal conductivity and porosity on PEM fuel cell performance

<p class="PaperAbstract">The performance of proton exchange membrane (PEM) fuel cell majorly relies on properties of gas diffusion layer (GDL) which supports heat and mass transfer across the membrane electrode assembly. A novel approach is adopted in this work to analyze the activity of GDL during fuel cell operation on a large-scale model. The model with mesh size of 1.3 million computational cells for 50 cm² active area was simulated by parallel computing technique via computer cluster. Grid independence study showed less than 5% deviation in criterion parameter as mesh size was increased to 1.8 million cells. Good approximation was achieved as model was validated with the experimental data for Pt loading of 1 mg/cm². The results showed that GDL with higher thermal conductivity prevented PEM from drying and led to improved protonic conduction. GDL with higher porosity enhanced the reaction but resulted in low output voltage which demonstrated the effect of contact resistance. In addition, the compressive force reduced the porosity under the rib regions which resulted in lower gas diffusion and heat and water accumulation.</p>

Proteinuria in patients with sleep apnea

BACKGROUND

Proteinuria severe enough to be in the nephrotic range has been noted on occasion in patients with obstructive sleep apnea (OSA), but it is not known what factors are related to the severity of proteinuria in these patients. This study was conducted to determine if the severity of proteinuria is related to the number of apneas.

METHODS

The level of proteinuria was determined by the dipstick method in 407 patients in whom OSA had been diagnosed. The apnea-hypopnea index (AHI) was calculated in each patient after all-night polysomnography. Sleep apnea was defined as the presence of at least 30 apneas during a 6- to 8-hour monitoring period and an AHI greater than 15.

RESULTS

The apnea-hypopnea index in patients without proteinuria was similar to that of patients with 1+ to 3+ proteinuria. However, the AHI was significantly greater in the 9 patients with 4+ proteinuria.

CONCLUSIONS

While severe proteinuria in OSA occurs in patients with a higher rate of sleep apneas, the lesser degree of proteinuria cannot be explained by AHI alone. Other factors that determine the severity of the disease, such as hypoxemia, merit further investigation.