DXA technology is widely available today in many regions of the world. There is a growing realization of the value of DXA not only for osteoporosis management but also for sports medicine, sarcopenia, and the assessment of cardiovascular disease and mortality. Such features may be of particular interest for populations with a greater risk of these outcomes such as those with diabetes mellitus or rheumatoid arthritis. Recent systematic reviews and meta-analyses show DXA can robustly predict fractures, cardiovascular disease, dementia and mortality. Rheumatoid arthritis (RA) is a chronic inflammatory disease affecting multiple organs including synovial joints, bone and other tissues. People suffering from RA have a greater propensity to osteoporotic fracture, cardiovascular disease, infection and premature death, which is well recognised. RA is the only unique disease included in some fracture risk algorithms such as FRAX, and so RA patients are often referred for a DXA scan to evaluate their risk of osteoporosis. We have previously shown vertebral fractures, aortic calcification and cardiovascular disease are prevalent in our RA population, with strong association. In this paper we performed a scoping review of published literature in Medline and Embase to better understand the current status of DXA and cardiovascular disease in RA populations. 822 papers were identified in an initial search of which 7 papers reflecting 2,038 RA patients from 7 different countries were included. Study design included 4 cross-sectional, 2 longitudinal and 1 case-control. All included associations with various cardiovascular measures, while only 1 included clinical events as an outcome. Our results suggest this is an area which remains relatively unexplored but has substantial important clinical potential.

BACKGROUND:Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS:We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS:2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS:Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.

In medical settings, a significant disparity often exists between the rates of positive and negative health outcomes, exemplified by diseases such as cancer and instances of bone fractures. Predictive modeling for these outcomes can be adversely affected by data imbalance. Sample ratio imbalance is still a challenging and critical issue. While many existing studies have explored the imbalance between majority and minority class samples, the internal imbalance within minority class samples has not yet been well addressed. This study proposed and developed a novel algorithm, called Multi-label Random Undersampling and Synthetic Minority Oversampling Technique (ML-RUSMOTE), to address such complex multilabel sample imbalances. We then developed multi-label predictive models by combining the proposed ML-RUSMOTE algorithm with three representative classifiers: Binary Relevance, Multi-label k-Nearest Neighbors, and Multi-label Deep Neural Networks, respectively. The approach was benchmarked against various traditional methods for handling multi-label imbalances. We utilized a substantial cohort of 6,374 patients from three hospital centers in Western Ireland for model validation. Our findings demonstrate that the proposed ML-RUSMOTE algorithm, particularly when integrated with deep learning techniques, significantly outperforms conventional methods in managing multi-label imbalances. Promisingly, the proposed approach can help address common imbalance issues in disease risk predictions, particularly for those patient subgroups whose numbers or outcomes are underrepresented.

The demand for products in the retail industry is often characterized by intermittent and volatile patterns. Specifically, at the SKU level, the demand exhibits intermittent and lumpy behavior, which presents challenges for accurate forecasting due to demand fluctuations and interval uncertainty. This study proposes an exponential smoothing (ES)-based model, called ES-C-T, that incorporates contemporaneous and temporal aggregation to forecast intermittent and lumpy retail demand. The ES-C-T model consists of three stages. Firstly, the ES model predicts the sales of each product by aggregating contemporaneously across all stores, and the resulting predictions are allocated to the SKU level using a weight vector. Secondly, the ES model forecasts the sales of each SKU by aggregating temporally within each store. Finally, the ES-C-T model derives a weight coefficient based on the predictions from the first and second stages, adjusts the initial predictions, and utilizes this as the final forecast. To validate the effectiveness of the ES-C-T model, sales data from a large Chinese convenience store chain are employed. The results demonstrate that the proposed ES-C-T model outperforms the benchmark model in terms of MAE, RMSE, and WRMSSE, effectively predicting retail sales for intermittent and lumpy demand.

To gain a deeper understanding of the user experience in university libraries, an alternative channel for collecting user feedback was explored utilizing a prominent Chinese Question and Answer (Q&A) platform, Zhihu. A dataset consisting of 11 questions and 12,647 valid answers related to the user experience of university libraries on the Zhihu platform was collected. To analyze the collected user comments (answers) quantitatively, various techniques including word frequency analysis, semantic network analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis were used. Findings revealed that factors influencing user experience can be categorized into six main groups: university life and future planning, choice and efficiency of study spaces, library resource management and staff behavior, seat usage behavior, noise issues, and the behavior of other users. Sentiment analysis revealed a mix of emotions in user experience. Positive experiences stemmed from quality learning environments and personal development support, while negative experiences were primarily caused by noise, seat scarcity, management issues, and other users' behavior. These varied emotional responses and suggested targeted improvements were explored. Findings could contribute to a deeper understanding of the user experience in university libraries and offer practical insights for improving library services.

Objectives RA is a chronic disabling disease affecting 0.5-1% of adults worldwide. People with RA have a greater prevalence of multimorbidity, particularly osteoporosis and associated fractures. Recent studies suggest that fracture risk is related to both non-RA and RA factors, whose importance is heterogeneous across studies. This study seeks to compare baseline demographic and DXA data across three cohorts: healthy controls, RA patients and a non-RA cohort with major risk factors and/or prior major osteoporotic fracture (MOF).Methods This is a cross-sectional study using data collected from three DXA centres in the west of Ireland from January 2000 to November 2018.Results Data were available for 30 503 subjects who met our inclusion criteria: 9539 (31.3%) healthy controls, 1797 (5.9%) with RA and 19 167 (62.8%) others. Although age, BMI and BMD were similar between healthy controls, the RA cohort and the other cohort, 289 (16.1%) RA patients and 5419 (28.3%) of the non-RA cohort had prior MOF. In the RA and non-RA cohorts, patients with previous MOF were significantly older and had significantly lower BMD at the femoral neck, total hip and spine.Conclusion Although age, BMI and BMD were similar between a healthy control cohort and RA patients and others with major fracture risk factors, those with a previous MOF were older and had significantly lower BMD at all three measured skeletal sites. Further studies are needed to address the importance of these and other factors for identifying those RA patients most likely to experience fractures. What does this mean for patients?Rheumatoid arthritis (RA) is a disabling disease affecting millions of people worldwide. This disease causes pain, disability and other problems. RA affects not only joints (e.g. hip, knee, wrist), but also the bones, lungs, eyes and other body tissues. International studies show that people with RA are almost three times as likely to break a bone as the general population. Experts conclude that this is because of bone loss from the inflammation in RA. We measure bone mineral density (BMD) to manage osteoporosis in clinical practice with a test known as a DXA scan. People with lower BMD are more likely to break bones, causing further suffering and illness. In this study, we found that Irish patients with RA are much more likely to have fractures. An especially interesting finding in our study is that although the RA patients had similar age and BMD to healthy controls, far more of them had fractures. However, those with broken bones had lower BMD than those without, whether they had RA or not. Our findings suggest that more research is needed to gain a better understanding of why RA patients are prone to fractures, in order to prevent fracture occurrence in future.

ABSTRACT Osteoporosis is a common disease that has a significant impact on patients, healthcare systems, and society. World Health Organization (WHO) diagnostic criteria for postmenopausal women were established in 1994 to diagnose low bone mass (osteopenia) and osteoporosis using dual‐energy X‐ray absorptiometry (DXA)‐measured bone mineral density (BMD) to help understand the epidemiology of osteoporosis, and identify those at risk for fracture. These criteria may also apply to men ≥50 years, perimenopausal women, and people of different ethnicity. The DXA Health Informatics Prediction (HIP) project is an established convenience cohort of more than 36,000 patients who had a DXA scan to explore the epidemiology of osteoporosis and its management in the Republic of Ireland where the prevalence of osteoporosis remains unknown. In this article we compare the prevalence of a DXA classification low bone mass (T‐score < −1.0) and of osteoporosis (T‐score ≤ −2.5) among adults aged ≥40 years without major risk factors or fractures, with one or more major risk factors, and with one or more major osteoporotic fractures. A total of 33,344 subjects met our study inclusion criteria, including 28,933 (86.8%) women; 9362 had no fractures or major risk factors, 14,932 had one or more major clinical risk factors, and 9050 had one or more major osteoporotic fractures. The prevalence of low bone mass and osteoporosis increased significantly with age overall. The prevalence of low bone mass and osteoporosis was significantly greater among men and women with major osteoporotic fractures than healthy controls or those with clinical risk factors. Applying our results to the national population census figure of 5,123,536 in 2022 we estimate between 1,039,348 and 1,240,807 men and women aged ≥50 years have low bone mass, whereas between 308,474 and 498,104 have osteoporosis. These data are important for the diagnosis of osteoporosis in clinical practice, and national policy to reduce the illness burden of osteoporosis. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Summary Appropriate use of FRAX reduces the number of people requiring DXA scans, while contemporaneously determining those most at risk. We compared the results of FRAX with and without inclusion of BMD. It suggests clinicians to carefully consider the importance of BMD inclusion in fracture risk estimation or interpretation in individual patients. Purpose FRAX is a widely accepted tool to estimate the 10-year risk of hip and major osteoporotic fracture in adults. Prior calibration studies suggest this works similarly with or without the inclusion of bone mineral density (BMD). The purpose of the study is to compare within-subject differences between FRAX estimations derived using DXA and Web software with and without the inclusion of BMD. Method A convenience cohort was used for this cross-sectional study, consisting of 1254 men and women aged between 40 and 90 years who had a DXA scan and complete validated data available for analysis. FRAX 10-year estimations for hip and major osteoporotic fracture were calculated using DXA software (DXA-FRAX) and the Web tool (Web-FRAX), with and without BMD. Agreements between estimates within each individual subject were examined using Bland–Altman plots. We performed exploratory analyses of the characteristics of those with very discordant results. Results Overall median DXA-FRAX and Web-FRAX 10-year hip and major osteoporotic fracture risk estimations which include BMD are very similar: 2.9% vs . 2.8% and 11.0% vs . 11% respectively. However, both are significantly lower than those obtained without BMD: 4.9% and 14% respectively, P < 0.001. Within-subject differences between hip fracture estimates with and without BMD were < 3% in 57% of cases, between 3 and 6% in 19% of cases, and > 6% in 24% of cases, while for major osteoporotic fractures such differences are < 10% in 82% of cases, between 10 and 20% in 15% of cases, and > 20% in 3% of cases. Conclusions Although there is excellent agreement between the Web-FRAX and DXA-FRAX tools when BMD is incorporated, sometimes there are very large differences for individuals between results obtained with and without BMD. Clinicians should carefully consider the importance of BMD inclusion in FRAX estimations when assessing individual patients.

Many forecasting techniques have been applied to sales forecasts in the retail industry. However, no one prediction model is applicable to all cases. For demand forecasting of the same item, the different results of prediction models often confuse retailers. For large retail companies with a wide variety of products, it is difficult to find a suitable prediction model for each item. This study aims to propose a dynamic model selection approach that combines individual selection and combination forecasts based on both the demand patterns and the out-of-sample performance for each item. Firstly, based on both metrics of the squared coefficient of variation (CV2) and the average inter-demand interval (ADI), we divide the demand patterns of items into four types: smooth, intermittent, erratic, and lumpy. Secondly, we select nine classical forecasting methods in the M-Competitions to build a pool of models. Thirdly, we design two dynamic weighting strategies to determine the final prediction, namely DWS-A and DWS-B. Finally, we verify the effectiveness of this approach by using two large datasets from an offline retailer and an online retailer in China. The empirical results show that these two strategies can effectively improve the accuracy of demand forecasting. The DWS-A method is suitable for items with the demand patterns of intermittent and lumpy, while the DWS-B method is suitable for items with the demand patterns of smooth and erratic.

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.

Osteoporosis is an important global health problem resulting in fragility fractures. The vertebrae are the commonest site of fracture resulting in extreme illness burden, and having the highest associated mortality. International studies show that vertebral fractures (VF) increase in prevalence with age, similarly in men and women, but differ across different regions of the world. Ireland has one of the highest rates of hip fracture in the world but data on vertebral fractures are limited. In this study we examined the prevalence of VF and associated major risk factors, using a sample of subjects who underwent vertebral fracture assessment (VFA) performed on 2 dual-energy X-ray absorptiometry (DXA) machines. A total of 1296 subjects aged 40 years and older had a valid VFA report and DXA information available, including 254 men and 1042 women. Subjects had a mean age of 70 years, 805 (62%) had prior fractures, mean spine T-score was − 1.4 and mean total hip T-scores was − 1.2, while mean FRAX scores were 15.4% and 4.8% for major osteoporotic fracture and hip fracture, respectively. Although 95 (7%) had a known VF prior to scanning, 283 (22%) patients had at least 1 VF on their scan: 161 had 1, 61 had 2, and 61 had 3 or more. The prevalence of VF increased with age from 11.5% in those aged 40–49 years to > 33% among those aged ≥ 80 years. Both men and women with VF had significantly lower BMD at each measured site, and significantly higher FRAX scores, P < 0.01. These data suggest VF are common in high risk populations, particularly older men and women with low BMD, previous fractures, and at high risk of fracture. Urgent attention is needed to examine effective ways to identify those at risk and to reduce the burden of VF.

A major impediment to crowdfunding is the unobservable actions initiated by an entrepreneur, which generally results in moral hazard. This study aims to demonstrate how an online intermediary, functioning as a crowdfunding platform, can reduce the moral hazard problem by designing a cash deposit mechanism. A model that illustrates the relationship among a crowdfunding platform, an entrepreneur and various consumers is built. With crowdfunding, an entrepreneur can ask a large audience to back up his/her project with money and then reward each funder with a product. The quality of the product depends on the combined effort of the entrepreneur and pure luck. Our results show that for projects with a high marginal cost of effort and low difference in quality, the crowdfunding platform tends to set a high compensation ratio and implement strict control of the starting capital. The crowdfunding platform prefers projects with a low marginal cost of effort and high difference in quality due to the higher amount of financing. The major findings indicate that crowdfunding platforms can reduce moral hazard by setting up a cash deposit mechanism. However, this mechanism exhibits limitations in projects with high fixed costs, that is, the effort cannot reach the constrained first-best level. For projects with a high marginal cost of effort or low difference in quality, this mechanism will reduce social welfare.

Many algorithms have been developed and publicised over the past 2 decades for identifying those most likely to have osteoporosis or low BMD, or at increased risk of fragility fracture. The Osteoporosis Self-assessment Tool index (OSTi) is one of the oldest, simplest, and widely used for identifying men and women with low BMD or osteoporosis. OSTi has been validated in many cohorts worldwide but large studies with robust analyses evaluating this or other algorithms in adult populations residing in the Republic of Ireland are lacking, where waiting times for public DXA facilities are long. In this study we evaluated the validity of OSTi in men and women drawn from a sampling frame of more than 36,000 patients scanned at one of 3 centres in the West of Ireland. 18,670 men and women aged 40 years and older had a baseline scan of the lumbar spine femoral neck and total hip available for analysis. 15,964 (86%) were female, 5,343 (29%) had no major clinical risk factors other than age, while 5,093 (27%) had a prior fracture. Approximately 2/3 had a T-score ≤-1.0 at one or more skeletal sites and 1/3 had a T-score ≤-1.0 at all 3 skeletal sites, while 1 in 5 had a DXA T-score ≤-2.5 at one or more skeletal sites and 5% had a T-score ≤-2.5 at all 3 sites. OSTi generally performed well in our population with area under the curve (AUC) values ranging from 0.581 to 0.881 in men and 0.701 to 0.911 in women. The performance of OSTi appeared robust across multiple sub-group analyses. AUC values were greater for women, proximal femur sites, those without prior fractures and those not taking osteoporosis medication. Optimal OSTi cut-points were '2' for men and '0' for women in our study population. OSTi is a simple and effective tool to aid identification of Irish men and women with low BMD or osteoporosis. Use of OSTi could improve the effectiveness of DXA screening programmes for older adults in Ireland.

零售国际化是国际知名零售品牌的常规发展战略,但目前中国零售企业不仅难以走出国门,甚至难以实现全国性跨区域经营.本文以物理-事理-人理(WSR)方法论为理论框架,以中国近年来罕有的快速实现了零售国际化扩张的名创优品为研究对象,探索中国零售企业实施国际化战略的影响因素和管理策略等问题.案例研究表明,名创优品在信息化加载的虚拟零售企业模型指导下,通过加盟模式创新、供应链整合能力输出和品牌输出,实现了外部门店快速扩张,在内部通过双元性创新能力完善其商业模式和运营管理,不断提升供应链和品牌价值,保证了国际化战略的成功实施以及企业的快速健康发展.这一工作弥补了当前零售国际化研究中聚焦西方企业而缺乏中国案例的不足,也间接构建了一种零售国际化战略决策模型.

The forecasting of intermittent demand is a complex task owing to demand fluctuations and interval uncertainty. Intermittent demand is essentially random demand with a high percentage of zero values. In the retail industry, there are many products which face intermittent demand and this poses a problem of inventory management. This study proposes a Markov-combined method (MCM) for forecasting intermittent demand, which takes into account the inventory status and historical sales of products. We divide the prediction process into two stages. In the first stage, the transition probabilities of the four basic states of demand and inventory are calculated. In the second stage, the corresponding and appropriate prediction method is selected according to the predicted state. Further, using two large datasets from the two biggest e-commerce companies in China, we verify our results and show that the MCM forecasts more accurately than the Single Exponential Smoothing (SES), Syntetos-Boylan Approximation (SBA), and Croston (CR) methods. The MCM can be as an alternative method for forecasting intermittent demand because it is easy to compute and typically more accurate than the classical forecasting methods.

This study examines the distribution of proximal femur bone mineral density in a cohort of healthy Irish adults. These values are similar to those of the NHANES III Caucasian cohorts, supporting international recommendations to use this reference group for calculating DXA T-scores and Z-scores in Irish adults. Bone mineral density (BMD) is widely used in the assessment and monitoring of osteoporosis. International guidelines recommend referencing proximal femur BMD measurements to NHANES III values to calculate T-scores and Z-scores, but their validity for the Irish population has not been established. In this study, we compare BMD values of healthy Irish Caucasian adults to those of Caucasian men and women in the NHANES III cohort study. Men and women without bone disease and/or major risk factors for fracture, and/or not taking osteoporosis medication who had a screening DXA scan (GE Lunar, Madison, USA) at one of 3 centres in the West of Ireland were selected for this study. We calculated the mean and standard deviation (SD) used by GE for calculating white female NHANES III T-scores at the femoral neck and total hip sites, and used these values to calculate white female T-scores for men and women across each decade in our study sample. We calculated mean white female T-scores for each decade for both Caucasian men and women in the NHANES III cohort using the published data. Finally, we plotted these results against those of our study population. In total, 6729 (18.5%) of 36,321 adults were included in our analyses, including 5923 (88%) women. The majority of the study population were aged between 40 and 89 years. Our results show that the proximal femur BMD of healthy Irish men and women is broadly similar to that of the NHANES III reference population, especially middle-aged adults. Results differ for very young and very old adults, likely reflecting the small sample size and a referral bias. Further studies of these populations and other manufacturers could help clarify these uncertainties. Our results support using the NHANES III reference population to calculate proximal femur adult T-scores and Z-scores to establish the presence or prevalence of osteoporosis in Ireland.

BACKGROUND:Identification of those at high risk before a fracture occurs is an essential part of osteoporosis management. This topic remains a significant challenge for researchers in the field, and clinicians worldwide. Although many algorithms have been developed to either identify those with a diagnosis of osteoporosis or predict their risk of fracture, concern remains regarding their accuracy and application. Scientific advances including machine learning methods are rapidly gaining appreciation as alternative techniques to develop or enhance risk assessment and current practice. Recent evidence suggests that these methods could play an important role in the assessment of osteoporosis and fracture risk. METHODS:Data used for this study included Dual-energy X-ray Absorptiometry (DXA) bone mineral density and T-scores, and multiple clinical variables drawn from a convenience cohort of adult patients scanned on one of 4 DXA machines across three hospitals in the West of Ireland between January 2000 and November 2018 (the DXA-Heath Informatics Prediction Cohort). The dataset was cleaned, validated and anonymized, and then split into an exploratory group (80%) and a development group (20%) using the stratified sampling method. We first established the validity of a simple tool, the Osteoporosis Self-assessment Tool Index (OSTi) to identify those classified as osteoporotic by the modified International Society for Clinical Densitometry DXA criteria. We then compared these results to seven machine learning techniques (MLTs): CatBoost, eXtreme Gradient Boosting, Neural network, Bagged flexible discriminant analysis, Random forest, Logistic regression and Support vector machine to enhance the discrimination of those classified as osteoporotic or not. The performance of each prediction model was measured by calculating the area under the curve (AUC) with 95% confidence interval (CI), and was compared against the OSTi. RESULTS:A cohort of 13,577 adults aged ≥40 yr at the age of their first scan was identified including 11,594 women and 1983 men. 2102 (18.13%) females and 356 (17.95%) males were identified with osteoporosis based on their lowest T-score. The OSTi performed well in our cohort in both men (AUC 0.723, 95% CI 0.659-0.788) and women (AUC 0.810, 95% CI 0.787-0.833). Four MLTs improved discrimination in both men and women, though the incremental benefit was small. eXtreme Gradient Boosting showed the most promising results: +4.5% (AUC 0.768, 95% CI 0.706-0.829) for men and +2.3% (AUC 0.833, 95% CI 0.812-0.853) for women. Similarly MLTs outperformed OSTi in sensitivity analyses-which excluded those subjects taking osteoporosis medications-though the absolute improvements differed. CONCLUSION:The OSTi retains an important role in identifying older men and women most likely to have osteoporosis by bone mineral density classification. MLTs could improve DXA detection of osteoporosis classification in older men and women. Further exploration of MLTs is warranted in other populations, and with additional data.

Purpose The purpose of the Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project is to create a large retrospective cohort of adults in Ireland to examine the validity of DXA diagnostic classification, risk assessment tools and management strategies for osteoporosis and osteoporotic fractures for our population. Participants The cohort includes 36 590 men and women aged 4–104 years who had a DXA scan between January 2000 and November 2018 at one of 3 centres in the West of Ireland. Findings to date 36 590 patients had at least 1 DXA scan, 6868 (18.77%) had 2 scans and 3823 (10.45%) had 3 or more scans. There are 364 unique medical disorders, 186 unique medications and 46 DXA variables identified and available for analysis. The cohort includes 10 349 (28.3%) individuals who underwent a screening DXA scan without a clear fracture risk factor (other than age), and 9947 (27.2%) with prevalent fractures at 1 of 44 skeletal sites. Future plans The Irish DXA HIP Project plans to assess current diagnostic classification and risk prediction algorithms for osteoporosis and fractures, identify the risk predictors for osteoporosis and develop novel, accurate and personalised risk prediction tools, by using the large multicentre longitudinal follow-up cohort. Furthermore, the dataset may be used to assess, and possibly support, multimorbidity management due to the large number of variables collected in this project.

突破传统采用历史数据训练来评估预测方法效度的局限,提出了一种基于真实情景中公开预报数据来评估预测有效性的框架.通过回顾分析一项长达五年公开发布的珠三角港口集装箱需求月度预报数据,检验了TEI@I预测方法论的有效性.实证结果表明,TEI@I预测方法论具有非常优良的预测精度和稳定性,预测效度随着预测时长的增加有所降低.同时论证了采用合理的预测方法,能够有效估计系统未来发展趋势.

互斥产品(如液体、危隆化学品等)不能混装到同一个容器中,物流企业通常使用多隔舱运输车为顾客配送多种互斥产品,合理确定装载与配送路径是提高配送效率、降低配送成本的重要手段.本文考虑互斥产品的装卸顺序约束、在途运输时间约束等,构建了以配送成本最小化为目标的互斥产品装载配送联合优化模型,设计了求解模型的改进遗传算法,算法采用蜂王进化和基于概率的边重构交叉运算,有效提高了寻优能力.本文利用Augerat提供的车辆路径问题标准测试集构造算例测试算法的运行时间和求解效果.结果显示,改进遗传算法的求解效果明显优于经典遗传算法对于小规模算例,改进的遗传算法可以得到精确最优解,对于中等规模和不超过101个顾客点的大规模算例,改进的遗传算法可以在130秒内得到近似最优解.本文的创新点在于构建了一类新的车辆路径扩展问题的数学模型并设计了求解模型的快速有效算法,为物流企业制定多类型互斥产品配送计划提供了理论依据和算法支持.