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Improving the quality of the fracture liaison service through the implementation of a structured health record

ObjectiveAccording to the best practice framework for secondary fracture prevention, all patients aged ≥50 years with a fracture should be assessed for osteoporosis within the fracture liaison service (FLS). The...

On the benefits of ex-post pricing for ride-pooling

Today’s digital ride-hailing systems experienced significant growth. In this context, ride-pooling promises more sustainable on-demand transportation by allowing customers with similar transportation requests to share a ride and, accordingly, a more efficient use of resources. In practice, mobility service providers that allow for ride-pooling offer customers an ex-ante fixed price regardless of whether the customers eventually share their rides. This can lead to lost profits for providers or to customers only participating in pooling because they gamble on a cheaper solitary ride, while an eventual pooling might decrease their utility. We study the implementation of an ex-post pricing (EPP) scheme for ride-pooling that accounts for individual customers’ pooling benefits. Within this mechanism, customers receive a maximum price-to-pay information up-front, which may decrease in the event of a profitable pooling. We study the benefit of this mechanism from a customer, fleet operator, and system perspective, and compare it to the existing ex-ante pricing (EAP) mechanism. We prove that it is individually rational and weakly dominant for a customer to participate in a pooling service with EPP, but not for a mechanism with EAP. We substantiate this analysis with complementary numerical studies, implementing a simulation environment based on real-world data that allows us to assess both mechanisms’ benefits. Our results allow to analyze the difference between EAP and EPP pricing from a holistic perspective, and thus to provide decision support for mobility service operators. We analyze in which cases an EPP mechanism Pareto dominates an EAP mechanism and show that an EPP mechanism is preferable from a mobility service provider perspective. Moreover, we show that EPP mechanisms can lead to a win-win situation for operators and customers while improving system performance and reducing emissions up to 32% compared to not pooling customers.

Mitigating casualty risks from primary fragmentation hazards

Primary fragmentation from detonation of high-explosive metal-cased munitions imposes significant risks to the safety of related personnel and the public. Barricades or other protective structures are commonly used to stop fragments and reduce casualty risks caused by detonated munitions when a sufficient safety distance cannot be guaranteed. This study aims to provide decision support for the positioning of barricades that can reasonably mitigate primary fragmentation hazards from the detonation of large calibre munitions using a probabilistic risk assessment approach. This approach enables a stochastic characterization of fragment ejections, stacking effects, fragment trajectories, human vulnerability and fragment hazard reduction by barricade. In a case study, the assessments of casualty risks and effectiveness of barricades were conducted for a single and a pallet of 155 mm projectiles. It was found that barricades with heights exceeding the height of munitions can significantly reduce the hazardous fragment densities and casualty risks beyond the barricade. The benefit of increasing the barricade height becomes marginal when it exceeds the height of munitions.

Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil

Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92% of the training data and 90% of the validation data achieved errors within 1%, and could fully reflect the target ions’ content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology.

Which computable biomedical knowledge objects will be regulated? Results of a UK workshop discussing the regulation of knowledge libraries and software as a medical device.

To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom. A briefing paper was circulated to a multi-disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1-day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions. This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level. If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as "software as a medical device." However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries.

Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?

Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.

Evaluation of Spatial Functions and Scale Effects of “Production–Living–Ecological” Space in Hainan Island

The identification, evaluation, and spatial distribution of “Production–Living–Ecological” space (PLEs) have been widely studied, but there is still little attention paid to whether their distribution characteristics will vary with scale changes. This article takes the organic whole of the PLEs composed of production space, living space, and ecological space on Hainan Island as the research object. Starting from the perspective of spatial heterogeneity, it quantitatively evaluates its spatial functions and explores the regularities of its aggregation and coordination characteristics with changes in scale, revealing the evolution of its distribution pattern with changes in scale. The results show that: (1) The distribution of PLEs in Hainan Island has obvious heterogeneity. The functional values of production and living space are distributed in a pattern of high in the south and north, low in the middle; The ecological space shows a high distribution pattern in the middle and low around it. (2) The PLEs in Hainan Island is significantly affected by scale effects. The degree of aggregation decreases as the scale increases, with the ecological space showing the most obvious downward trend, the living space showing a weaker downward trend and the production space being moderate. (3) The overall level of coupling-coordination of PLEs in Hainan Island is low, which decreases as the scale increases, with 500 m × 500 m being the peak value. The research results of this article indicate that there are scale effects in the functional distribution characteristics of PLEs, which can provide decision support for the national spatial planning at different scales.

More is better? Understanding the effects of online interactions on patients health anxiety

AbstractOnline health platforms play an important role in chronic disease management. Patients participate in online health platforms to receive and provide health‐related support from each other. However, there remains a debate about whether the influence of social interaction on patient health anxiety is linearly positive. Based on uncertainty, information overload, and the theory of motivational information management, we develop and test a model considering a potential curvilinear relationship between social interaction and health anxiety, as well as a moderating effect of health literacy. We collect patient interaction data from an online health platform based on chronic disease management in China and use text mining and econometrics to test our hypotheses. Specifically, we find an inverted U‐shaped relationship between informational provision and health anxiety. Our results also show that information receipt and emotion provision have U‐shaped relationships with health anxiety. Interestingly, health literacy can effectively alleviate the U‐shaped relationship between information receipt and health anxiety. These findings not only provide new insights into the literature on online patient interactions but also provide decision support for patients and platform managers.

Remanufacturing model selection with 3D printing

This paper investigates a two-echelon 3D printing (3DP) remanufacturing supply chain consisting of the original equipment manufacturer (OEM) and the retailer, and builds a Stackelberg game model with OEM remanufacturing and retailer remanufacturing. We use backward induction to obtain the optimal 3DP usage level, product price, and profit of the supply chain under different remanufacturing models. Through the comparison of the optimal decisions of the two remanufacturing models, we analyze the impact of the 3DP cost, sensitivity coefficient, and licensing fee on the 3DP strategy and model selection. The results show the following: First, the effect of the 3DP correlation coefficient on the price of new products varies with different remanufacturing entities. Second, when the market size variation coefficient of remanufactured products is small, the optimal 3DP usage level under the OEM remanufacturing model is higher than that under the retailer remanufacturing model. Third, the OEM’s choice of remanufacturing model is affected by the market size variation coefficient, 3DP cost and licensing fee. The research findings of this paper can provide decision support for the application of 3D printing technology in the field of remanufacturing.

Predicting phases and hardness of high entropy alloys based on machine learning

The mechanical properties of high entropy alloys are highly dependent on phase structure. Therefore, accurate prediction of the phase structure is a guide to composition selection and performance enhancement, and it saves both experimental and financial expenditure. In this study, seven classification models were selected to predict the phase structure of high-entropy alloys and six regression models were selected to predict the hardness, and it was found that the XGB model performed best in both classification and regression predictions, where in the prediction phase structure, the XGB model has an error rate of 4.5% in the 10% prediction set, 11.6% in the 20% prediction set and 6.25% in the 30% prediction set. In predicting the hardness, the RMSE and MAE evaluation indexes of the XGB model are 52.66, 26.49. The δ (atomic radius difference) feature parameter having the strongest contribution to the phase formation in the high-entropy alloys. In addition, valence electron concentration(VEC), y/n BCC (The presence or absence of BCC) and y/n lm (The presence or absence of other phases) were key features affecting the hardness. Predictive analysis of two high-entropy alloys, AlCoCrFeNi and AlCoCrFeNiTi0.5, and a characterization effect map based on the Shapley additive explanations (SHAP) framework were developed to provide decision support for hardness assessment.

The Impacts of Climate and Human Activities on Grassland Productivity Variation in China

Grassland degradation is widespread and increasing globally, which is closely related to the sustainable development of the ecosystems and the well-being of human life in pastoral areas. Quantifying the factors influencing grassland ecosystems, specifically climate change and human activities, is of great significance for grassland restoration. However, due to the unpredictability of human activities, further research is still needed to distinguish and identify the factors affecting grasslands. In this study, we examined the changes in the gross primary productivity (GPP) of grassland cover in 10 provinces (autonomous regions) of China from 2000 to 2018 and selected three representative climate factors (temperature, precipitation, solar radiation) and six factors covering socioeconomic (primary industry production and population), animal husbandry (large livestock and sheep populations), and national policies (grazing areas, rodent, and pest control) to characterize human activities; then, we quantified the effects and contribution of climate and human factors using three analysis methods (partial correlation analysis, geographical and temporal weighted regression model, and Lindeman Merenda Gold method). The results indicated that the GPP of grassland presented an obvious uptrend (4.75 g C m−2 yr−1, p < 0.05). Among the nine factors, sheep, precipitation, and temperature were the primary factors affecting grassland dynamics. Additionally, the GPP dynamics of grassland were mainly dominated by human activities in seven provinces (autonomous regions). These findings provide decision support for protecting grassland ecosystems and implementing ecological restoration policies in China.

A Computationally Efficient Benders Decomposition for Energy Systems Planning Problems with Detailed Operations and Time-Coupling Constraints

Energy systems planning models identify least-cost strategies for expansion and operation of energy systems and provide decision support for investment, planning, regulation, and policy. Most are formulated as linear programming (LP) or mixed integer linear programming (MILP) problems. Despite the relative efficiency and maturity of LP and MILP solvers, large scale problems are often intractable without abstractions that impact quality of results and generalizability of findings. We consider a macro-energy systems planning problem with detailed operations and policy constraints and formulate a computationally efficient Benders decomposition separating investments from operations and decoupling operational timesteps using budgeting variables in the master model. This novel approach enables parallelization of operational subproblems and permits modeling of relevant constraints coupling decisions across time periods (e.g., policy constraints) within a decomposed framework. Runtime scales linearly with temporal resolution; tests demonstrate substantial runtime improvement for all MILP formulations and for some LP formulations depending on problem size relative to analogous monolithic models solved with state-of-the-art commercial solvers. Our algorithm is applicable to planning problems in other domains (e.g., water, transportation networks, production processes) and can solve large-scale problems otherwise intractable. We show that the increased resolution enabled by this algorithm mitigates structural uncertainty, improving recommendation accuracy. Funding: Funding for this work was provided by the Princeton Carbon Mitigation Initiative (funded by a gift from BP) and the Princeton Zero-carbon Technology Consortium (funded by gifts from GE, Google, ClearPath, and Breakthrough Energy). Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoo.2023.0005 .

User's Cognitive-Behavior-Based Preference Access Under Disease-Specific Online Medical Inquiry Text Mining

Substantial real cases can be formed from disease-specific online medical inquiry texts. Therefore, user preferences can constitute potentially rich commercial medical value and provide decision support for medical service recommendations. It is necessary to mine user preferences in disease-specific online medical inquiry texts. However, user preferences will change with cognitive behavior decisions in the context of disease-specific inquiry texts. More importantly, the context of disease-specific online inquiry texts in which user preferences are located will affect users' cognitive behavior decisions in real time. However, the existing preference access methods have relatively low precision since they fail to consider the inherent connection between different users’ cognitive behaviors in various contexts and their preferences. We expanded the contextual perception preference model to propose a cognitive-behavior-based method to assess user preference. The contextual information (in online medical inquiry texts on various disease topics) and the impacts of user and text attributes on their cognitive behaviors were abstracted into concept models (including level, usefulness, risk, and effectiveness cognition) to obtain the mutual influences and adjusted relationships. Moreover, more accurate user cognition references were obtained within the multidimensional text space and multidimensional disease space. Based on a large-volume real-world dataset, our experiments revealed the AP@R as the evaluation standard. The method proposed in this article, compared with the contextual perception and ELPCAP algorithms, significantly improves the precision of preference prediction after the addition of cognitive behaviors, suggesting that the method can effectively mine the user cognitive behavior-preference relationships.

Sequence optimization of departure flights considering ferry scheduling

Due to uncertain factors such as weather, large-scale flight delays will occur on the ground at the airport. A reasonable and effective flight sequence can make the airport resume regular operation as soon as possible. However, limited shuttle bus resources will limit the execution of the flight sequence plan. In order to provide decision support for airport authorities, we establish an integrated optimization model for flight sequences that consider shuttle bus resources. The objective function comprehensively considers the operational losses, airline profit losses, and loss of passenger time value. Then we design a multi-chromosome genetic algorithm to solve the model according to the characteristics of the integration problem. In addition, a simulation experiment is conducted based on the data from Yunnan Kunming Changshui International Airport. The simulation experiment results show that compared with the current first-come-first-served (FCFS) strategy, the algorithm reduces the overall loss caused by delays by 7.03%. The results of the simulation example verify the effectiveness of the proposed model and algorithm.

Low-Carbon Manufacturing or Not? Equilibrium Decisions for Capital-Constrained News Vendors with Subsidy and Carbon Tax

At a time when low-carbon life has become an important global issue, the decarbonization of manufacturing enterprises cannot be delayed in the face of the government’s green policy and other objective conditions. Under these circumstances, how to arrange the production plan before and after the implication of low-carbon policies is an urgent issue to be settled, especially for the capital-constrained news vendors. To address these problems, this paper firstly builds a stylized supply chain model consisting of two different types of manufacturers (i.e., low-carbon type and traditional type) with capital constraints, then obtains equilibrium production and business strategies resorting to Stackelberg game theory, and lastly conducts an analysis of how the key factors affect manufacturers’ green transition decisions under different scenarios of carbon policy. With the study’s structure, some interesting and important results and managerial insights are derived. For example, but not limited to, it is found that: (i) compared with traditional products, the price fluctuation of low-carbon products is greater than that of traditional products with the increase in consumers’ low-carbon awareness. And consumers have more tolerance for price increases for low-carbon products. (ii) When producing low-carbon products is cost-effective, it is a wise choice to produce more low-carbon products. When producing low-carbon items is costly, producing more low-carbon products is still a dominant strategy until the expenditure difference between low-carbon and traditional ones exceeds a certain threshold. (iii) When the expenditure on low-carbon production is moderate, the manufacturer first prefers the traditional strategy, then the low-carbon one, with an increase in consumers’ low-carbon awareness. When the expenditure on low-carbon production stays at a low or high level, the low-carbon strategy always dominates the traditional one with a certain condition satisfied. This study can enrich the theory of green supply chain management and provide decision support for enterprise managers in the green transition.

A Bi-Objective Mixed-Integer Linear Programming Model for a Sustainable Agro-Food Supply Chain with Product Perishability and Environmental Considerations

Background: Agro-food supply chains possess specific characteristics due to the diverse nature of products involved and contribute to all three pillars of sustainability, making the optimal design of a sustainable agro-food supply chain a complex problem. Therefore, efficient models incorporating the unique characteristics of such chains are essential for making optimal supply chain decisions and achieving economically and environmentally sustainable agro-food supply chains that contribute to global food security. Methods: This article presents a multi-objective mixed-integer linear programing model that integrates agricultural-related strategic decisions into the tactical design of an agro-food supply chain. The model considers transportation, inventory, processing, demand fulfilment, and waste disposal decisions. It also accounts for seasonality and perishability, ensuring a comprehensive approach to sustainability. The model aims to maximize the total generated profits across the supply chain while simultaneously minimizing CO2 emissions as a measure of environmental impact. Results: By implementing the model on a sugar beet supply chain in the Netherlands, strategic crop rotation farm schedules for the crop rotation cycle and the optimum supply network decisions are obtained. Furthermore, different objectives are analyzed and the Pareto-efficient frontier is investigated to analyze the underlying trade-offs. Additionally, the model serves as a decision support tool for managers facilitating informed investment decisions in technologies that prolong product shelf life while maintaining profitability. Conclusions: The proposed multi-objective model offers a valuable framework for designing economically and environmentally sustainable agro-food supply chains. By aligning with sustainability goals and providing decision support, this research contributes to enhancing global food security and promoting sustainable resource utilization.

Post-Earthquake Scheduling of Rescuers: A Method Considering Multiple Disaster Areas and Rescuer Collaboration

Reasonable and efficient scheduling of rescuers plays a crucial role in earthquake emergency relief, which can effectively reduce disaster losses and promote social stability and sustainable development. Due to the suddenness of disasters, the urgency of time, and the complexity of rescue efforts, scheduling of rescuers often involves multiple disaster areas, multiple departure areas, and diverse rescue tasks. However, most existing studies have paid little attention to the scheduling problem of rescuers considering multiple disaster areas, multiple departure areas, and multiple rescue tasks and have not comprehensively considered the collaboration of rescuers and task requirements. Thus, how to reasonably dispatch rescuers to disaster areas by considering the collaboration of rescuers and task needs is a noteworthy research problem. The objective of this paper is to propose a method considering multiple disaster areas and the collaboration of rescuers to solve the scheduling problem of rescuers after earthquakes. Firstly, the collaborative degrees of rescuers are calculated according to the collaborative performance among rescuers concerning collaborative feature indicators. Secondly, according to the performance of rescuers concerning professional skill evaluation indicators, the professional abilities of rescuers are calculated, and the comprehensive performance indicators for rescuers are obtained by aggregating the collaborative degrees and the professional abilities of rescuers. Thirdly, the time satisfaction degrees are calculated based on the times taken by rescuers from different departure areas to disaster areas and the time requirements of disaster areas. Then, the time satisfaction degrees and the comprehensive performance of rescuers are aggregated to obtain the comprehensive matching degrees. Furthermore, a rescuer scheduling model for earthquake emergency rescue is constructed to maximize the comprehensive matching degrees between rescuers and rescue tasks, and the optimal scheduling scheme is determined by solving the model. Finally, a case study and comparative analyses are presented to verify the rationality and feasibility of the proposed method. The results show that the proposed method can reasonably assign rescuers to quickly respond to the needs of rescue tasks in disaster areas, and is better than the other two methods in terms of rescue comprehensive capability evaluation. The proposed method can provide decision support for solving the post-earthquake scheduling problem of rescuers and help to improve the emergency response ability for large-scale geological disaster events.

PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING UNTUK MEMILIH JURUSAN PADA KAMPUS NEGERI

Students in grade twelfth high school must be able to determine their individual student interests to be able to continue further studies at university. But there are still many students who have not been able to determine their specialization even though they have sufficient understanding and knowledge of learning during high school. This study aims to apply simple additive weighting and calculation of the tightness capacity of each specialization major on a public campus in making a Decision Support System in determining student majors in the Angkasa High School environment. By paying attention to the weight and each criterion for the specialization major, then calculate with the tight capacity of the specialization department on each state campus so that the results issued by the calculation have provided decision support for students to determine specialization and state campuses more precisely. The decision support system uses the Kotlin programming language and Android-based NoSQL MongoDB database. The results of the manual calculation of the Simple Additive Weighting and Opportunity method with a decision support system reach 99% according.

Application of copula-based Bayesian network method to water leakage risk analysis in cross river tunnel of Wuhan Rail Transit Line 3

Water leakage typically infiltrates through bolt holes and segment joints, posing a threat to the stability of tunnel structures. In this paper, we develop a copula-based Bayesian network (CBN) method for dependency modeling of complex systems to conduct risk analysis and management of water leakage in operational tunnels. Thirteen potential factors are used to construct the water leakage risk system, and the permeate water volume is selected as the observation index of water leakage risk. An application to a realistic cross-river tunnel of Wuhan Rail Transit Line 3 is implemented to verify the effectiveness of the developed model. Through correlation analysis, the bolt failure rate and seal breakage and aging rate are diagnosed as critical factors with the greatest impact on the water leakage risk. Forward and backward reasoning enables the model to be dynamically updated to provide decision support for water leakage risk management. This research contributes to the following: (a) expressing the dependence of the factors affecting water leakage; (b) solving the problem of modeling uncertainty and information fusion; and (c) updating the model to realize the safety control of tunnel water leakage.

Energy vulnerability in Mediterranean countries: A latent class analysis approach

The need to protect vulnerable consumers is a priority for the European Commission but defining who is energy vulnerable remains difficult. Thus, in this paper, we provide an overview of energy poverty in 2019 for five Mediterranean countries (Greece, Spain, Italy, France and Portugal) using the EU-SILC database. We overcome a methodological difficulty inherent in pan-European research by developing the same tool, LCA (Latent Class Analysis) for characterizing energy poverty in all countries. Then we provide decision support in terms of energy poverty policy, without arbitrarily defining a binary tipping point. Indeed, we identify three groups with different needs: energy sufficient households, energy-poor households and more importantly, we highlight a third group which we refer to as energy vulnerable, who can easily fall below the radar of policy makers because they are not initially defined as precarious.