Recursive Model Research Articles

Strategy of user-driven re-design for inclusivity: Schuster Park in Rome

The paper presents the results of a research carried out by the Department of Architecture of Roma Tre University about user-driven re-design of public spaces, with the aim of transforming them into accessible and inclusive public spaces. Activities are related to the case of Schuster Park in Rome, whose re-functionalisation in terms of accessibility and inclusiveness is currently being planned. With this target, the research team, during the support phase for a preliminary feasibility study, experimented with a recursive participatory model, which can also be applied to other contexts related to public spaces.

Boosting Engineering Optimization With a Novel Recursive Transfer Bifidelity Surrogate Modeling

Abstract In the engineering optimization, there often exist the multiple sources of information with different fidelity levels. In general, low-fidelity (LF) information is usually more accessible than high-fidelity (HF) information, while the latter is usually more accurate than the former. Thus, to capitalize on the advantages of this information, this study proposes a novel recursive transfer bifidelity surrogate modeling to fuse information from HF and LF levels. First, the selection method of optimal scale factor is proposed for constructing bifidelity surrogate model. Then, a recursive method is developed to further improve its performance. The efficacy of the proposed model is comprehensively evaluated using numerical problems and an engineering example. Comparative analysis with some surrogate models (five multifidelity and a single-fidelity surrogate models) demonstrates the superior prediction accuracy and robustness of the proposed model. Additionally, the impact of varying cost ratios and combinations of HF and LF samples on the performance of the proposed model is also investigated, yielding consistent results. Overall, the proposed model has superior performance and holds potential for practical applications in engineering design optimization problems.

A dynamic discrete choice modelling approach for forward-looking travel mode choices

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.

Forecasting the climate-conflict risk in Africa along climate-related scenarios and multiple socio-economic drivers

This study investigates how climate change might impact economic development in the future through its effects on violence, addressing the gap in research on long-term conflict risk assessment. Using geocoded data (1°resolution) on climate and socio-economic indicators covering 1990-2050, we employ a forecasting recursive model to examine the probability and intensity of different types of conflict, under various socio-economic and climate scenarios. Our analysis reveals that climate change has both direct and indirect effects on violence, highlighting the key role of the agricultural channel, the spillover across neighbouring areas and the socio-economic context. These findings offer new insights into adaptation strategy and provide implications for the need to jointly account for the complex interactions between climate conditions, socio-economic factors, and conflict dynamics.

A Study on the Key Factors for the Sustainable Development of Shared Mobility Based on TDM Theory: The Case Study from China

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the “soft strategy” and “hard strategy” in the framework of TDM are determined. In the soft strategy, we should focus on “shared mobility education” (shared mobility education, shared mobility publicity and shared mobility “environment” information) and “community organization” (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on “traffic planning and measures”, “dedicated lanes”, “parking facilities”, and “financial subsidies”. (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations.

512 Divergence of the sow vaginal microbiome based on fertility status

Abstract The need for alternative biomarkers of reproductive potential in the pork industry is apparent as the current selection approach for sow fertility is not keeping up with the improvement of other production-related traits. To date, the vaginal microbiome has been overlooked as a source of potential biomarkers of sow fertility status. We aimed to elucidate a possible divergence of the vaginal microbiome between sows of differing fertility status and identify possible microbial biomarkers that classify the fertility potential of a sow. Microbial DNA was extracted from vaginal microbiome samples originating from highly reproductive sows (HRP, n = 41) with the number of piglets born alive ≥ 13 or infertile sows (INF, n = 22), that failed to become pregnant after two rounds of consecutive artificial insemination. Our 16S rRNA sequencing results revealed significant (P < 0.05) beta diversity divergence at the family and genus taxonomic levels between HRP and INF vaginal communities. INF vaginal communities displayed significantly greater (P < 0.05) alpha diversity at both the family and genus levels and had a significantly (P < 0.05) greater number of unique OTUs identified at the genus level. The composition of the vaginal microbiome diverges between HRP and INF sows, with INF sows experiencing a disruption of their communities via the colonization of opportunistic pathogens reducing the abundance of normal microbiota seen in HRP. The genera Streptococcus, Lachnospiraceae XPB1014 group, Staphylococcus and Treponema were selected to test as potential biomarkers due to their univariate significance, contribution to partial least squares discriminant analysis (PLS-DA) and selection in a recursive feature elimination model (RFE). The resultant support-vector machine model was diagnostic (achieving an ROC-AUC = 0.86), supporting the selected biomarkers as diagnostic markers for sow fertility status. Overall, there was an apparent shift from a normal vaginal microbiota in HRP sows to a dysbiotic vaginal microbiota in INF sows dominated by potentially pathogenic, more anaerobic and short-chain fatty acid (SCFA)-producing microbiota that may play damaging and pro-inflammatory roles in the vaginal canal. Lactobacillus, known to be the dominant genus in a majority of healthy human vaginal microbiomes, was not the dominating genus in either INF or HRP vaginal microbiomes, suggesting the uniqueness of pig vaginal microbiomes. This also emphasizes the need for pig-specific vaginal microbiome research. Investigations regarding the application of the potential vaginal microbial biomarkers identified in this study to a large, novel population of sow and gilt vaginal microbiome samples to test their on-farm accuracy are underway. In addition, the mechanistic and physiologic role of SCFAs and the bacteria that produce them in the sow vaginal microbiome should be further investigated to understand how they may influence or be a product of fertility status.

Dynamic modeling and trajectory optimization for the rigid-flexible coupled spacecraft with the free-floating manipulator and solar panels

The rigid-flexible coupled spacecraft, composed of flexible solar panels and a multilink manipulator, has gained prominence in on-orbit servicing due to rapid advancements in space technology. However, the intricate effects of rigid-flexible coupling pose significant challenges for dynamic modeling, trajectory planning, and control. This paper aims to develop general dynamic approaches for modeling and trajectory planning in such spacecraft, considering large deformations. The main distinguishing feature is the use of the referenced nodal coordinate formulation to accurately describe the large-deformed solar panels rather than directly treating them as disturbance for the free-floating system. Additionally, the common recursive model for the multilink manipulator is integrated into the same framework. The modal reduction method with modal derivatives techniques is employed to address geometric nonlinearity resulting from large deformations. Polynomial trajectory parameters with different performance characteristics are obtained by defining various optimization objectives. The coupling analysis is conducted based on an accurate reduced-order dynamic model, the results of which can be used for designing manipulator tasks. Coupling values are defined as the objective for trajectory optimization, offering advantages such as insensitivity to dynamic model accuracy and a fast optimization process. After validating the accuracy of the proposed dynamic model through simulations, the performances of different optimized trajectories are demonstrated using numerical examples.

Simulating farm structural change dynamics in Thessaly (Greece) using a recursive programming model

Although the policy impacts on farms accumulate year by year, most farm decision models focus on short-term decisions, evaluating policies based on snapshots. Structural changes are gradually built; therefore, farm decision models should consider the sequences within the period under study. Multiyear data from the arable sector in Thessaly, Greece, have fed a newly developed recursive programming model mainly to simulate farm structural change dynamics. The proposed model incorporates new evidence on the strategic decision of arable crop farms regarding their remaining in the production system and farm expansion. Results reveal an evident gradual farmland concentration in relatively large farms, accompanied by a gradual expansion of the most profitable cropping activities, verifying the real-world survival strategy of farms.

Research on the Inhibitory Effect of the EU’s Carbon Border Adjustment Mechanism on Carbon Leakage

Associated with more ambitious targets for reducing emissions, the European Union (EU) plans to implement the Carbon Border Adjustment Mechanism (CBAM) fully in 2026, aiming to reduce carbon leakage and competitiveness concerns by imposing tariffs on carbon-intensive imports, which is expected to significantly impact its trade partners. Existing research has focused on CBAM’s impact on macroeconomic indicators but has insufficiently addressed its effects on global and regional carbon leakage, especially in non-EU countries like China. This research offers a detailed analysis of industry-specific leakage rates and integrates both global and regional impacts by employing the dynamic recursive GTAP-E general equilibrium model to numerically simulate CBAM’s inhibitory effect on carbon leakage under different carbon tariff scenarios, while also exploring the synergistic effects of anti-leakage policies in non-EU countries. Our simulations indicate the following: (1) CBAM effectively inhibits carbon leakage, with greater inhibition observed at higher tax rates and with the expansion of covered industries. (2) Establishing China’s domestic carbon market pricing can further reduce regional carbon leakage rates. Implementing global export carbon tax policies will significantly diminish the risk of global carbon leakage. (3) The implementation of CBAM is projected to reduce China’s total exports to the EU, though this loss will be partly offset by trade diversion effects. Carbon-intensive industries are more adversely affected in the short term, while all industries except fossil fuels face inevitable long-term negative impacts.

Welfare costs of suboptimal retiree decisions.

We make a novel investigation of welfare costs associated with various suboptimal decisions made by retirees, both analytically and numerically. We utilize a unique framework that incorporates recursive utility with housing, and also encompasses expected utility and recursive utility without housing as special cases. Our findings indicate that under-investment in stocks incurs lower welfare costs compared to an equivalent over-investment. Suboptimal allocations in bond holdings result in higher costs than similar misallocations in stocks. Choosing not to participate in the stock market is less detrimental than avoiding the bond market. Should retirees opt to simplify their decision-making by investing solely in one type of asset, it is less costly for them to invest exclusively in bonds. Overconsumption of housing is less costly than an equivalent underconsumption. Suboptimal consumption imposes the highest welfare cost. Decisions regarding consumption, housing, and savings are found to be more crucial than the choice of how to distribute liquid savings between stocks and bonds. Additionally, recursive utility model better captures retirees' preference for bonds over stocks than expected utility model. Our results, which are consistent across various parameter settings, provide valuable insights for academics and policymakers aiming to enhance retiree welfare.

Health insurance system fragmentation and COVID-19 mortality: Evidence from Peru.

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer's network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance.

Adaptive model predictive control scheme for partially internal thermally coupled air separation column

Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and cost-effective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the operating stability of this technology. This article, therefore, proposes an adaptive model predictive control (AMPC) scheme for the P-ITCASC process. The controller incorporates an auto-regressive and exogenous inputs (ARX) model, a linear time-varying Kalman filter, and a recursive polynomial model estimator (RPME) algorithm. Within RPME, ARX polynomial models are identified and utilized to estimate the time-varying parameters and update the prediction model of the process. The process states are observed through the Kalman filter, and the constrained receding horizon optimization problem is solved using quadratic programming. This control scheme ensures the closed-loop system's feasibility and stability in the presence of output/input constraints. An adaptive generic model control, model predictive control, and adaptive internal model control schemes were also designed for benchmarking study. Numerical simulations show that AMPC is more efficient in handling nonlinear dynamics and maintaining product concentration to desired set points compared to other control schemes.

Does local non-farm income moderate the intensity of climate change induced migration in India?

While the impact of climate change on migration is well documented in the literature, very little is known about the moderating impact of local non-farm income on climate change induced migration. To fill this gap in the literature, the paper combines real time weather data provided by Indian Meteorological Department with a very rich household data on Indian states belonging to the eastern and semi-arid tropic regions collected by ICRISAT to investigate whether farmers always resort to migration or take it up as a second-best option only after they fail to diversify locally on the face of climate change induced crop loss. Using recursive econometric model, it is observed that households first try to diversify their income by engaging in local non-farm activities and availability of local non-farm income further moderates the effect of climate change induced yield loss on migration. Higher rural out migration may adversely impact the agricultural sector and affect sustainable urbanization, Also, decision to migrate is often challenging as it necessitates leaving behind social and community networks. Therefore, our findings offer valuable insights and discusses important policy recommendations towards improving the access to local non-farm activities.

Analysis of the periodicity of movements of mechanisms with elastic links of variable length

Abstract Elastic dynamic systems with open kinematic chains are effectively described by a recursive model of the generalized Newton–Euler method with the provision of computational algorithms with a degree of complexity proportional to the dimension of these systems, i.e. O(n). Such systems, in particular, are elastic manipulators, some of which are discussed in this article. In the case when elastic dynamical systems have closed kinematic circuits, the application of a numerical analysis strategy without reversing the mass matrix is extremely difficult. The analysis of the simplest type of elastic mechanisms with closed circuits, such as a crank–slide mechanism and a four–pin mechanism with elastic connecting rods, revealed a peculiar picture of the periodicity of the functions of elastic movements of connecting rods, which differs significantly from the periods of operation of the mechanisms themselves. In this article, the properties of the periodicity (or pseudo-periodicity) of the elastic displacement function of mechanisms with elastic links of variable length are investigated using examples of numerical dynamic analysis of crank–rocker mechanisms.

Hiddenly degrading performance prognosis based on recursive model incorporating double-layer rolling optimization strategy

An integrated and adaptive prognostic framework is proposed for hiddenly degrading actuator in the closed-loop system. Furthermore, the dual influence of degradation progress and operation time is considered in the established augmented model. It is important to obtain accurate and real-time distribution of degradation information under different degradation progress. Firstly, the expectation and variance of hidden index, unknown operation time parameter and degradation progress parameter are evaluated by designing a double-layer rolling optimization strategy. Then, the uncertainty distribution of degradation model is iteratively derived by employing Bayesian theorem. Finally, the distribution of remaining useful life (RUL) is calculated based on available distribution of degradation model and states. The effectiveness of proposed algorithm is demonstrated by a mold model of continuous casting driven by servo motor.

Agricultural extension and farm entrepreneurship: evidence from small-scale farmers of high-density plantations in Kashmir

ABSTRACT Purpose The study examines the transformative trends in agriculture by analysing the adoption patterns of high-density plantations (HDP) among small-scale apple farmers in Kashmir. It explores the factors shaping the adoption process and the role played by extension services in fostering this adoption. Design Employing a mixed-method approach, this study investigates how extension services influence the adoption of high-density farming and farm entrepreneurship. Utilising cross-sectional data from 160 small-scale farmer households, the research employs a recursive bivariate probit regression model to unveil the relationship between extension services and the adoption of high-density farming. Findings The findings reveal a crucial link between small-scale farmers’ adoption decisions and their access to agricultural extension services. Land size, credit, and subsidies play key roles in influencing both HDP adoption and extension service accessibility, highlighting the interconnectedness of financial support in promoting sustainable agriculture. Furthermore, early adopters showcase innovative behaviour driven by production advantages and sustainability concerns. Practical implications It underscores the need for targeted interventions to promote sustainable agricultural practices and enhance extension service accessibility. Recognising the positive impact of credit and subsidy availability on both HDP adoption and access to extension services emphasises the interconnected role of financial support in driving sustainable practices. Theoretical implications The paper integrates theoretical frameworks from agricultural adoption, extension and entrepreneurship while contributing a more comprehensive understanding of sustainable and transformed food systems. Originality This study develops a crucial framework that underscores the significance of public extension services for small-scale farmers in the Himalayan regions. Additionally, it enhances understanding of the potential benefits of high-density farming and farm entrepreneurship for sustainable agriculture and transforming food systems for rural development globally.

Unreliability tracing of power systems with reservoir hydropower based on a temporal recursive model

AbstractPower system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model.

Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects

Machine learning algorithms can improve the time series data analysis as compared to the traditional methods such as moving averages or auto-regressive approaches. This advancement has helped to unlock several challenging problems since machine learning not only helps to forecast the overall trend of the data, but it also helps to keep the historical track of changes in factors, influencing this trend. These predictions play a pivotal role in almost all areas of research where the observations are time dependent, such as problems ranging from challenges of finance to public health, environmental and climate change challenges. A key challenge of these domains is the higher number of attributes and predictors since managing and manipulating data from many attributes is itself a significant challenge for future forecasting. Addressing these challenges is possible with Recursive Long Short-Term Memory models. The application of such models is crucial, and their efficacy is further amplified when considering transfer learning. During this research, a detailed and comprehensive description of such models is addressed. Practical application is illustrated through an example, emphasizing that these models, when transferred to complex and large datasets using transfer learning, hold great promise.

Recursive two-stage evaluation model for dynamic decision making under ambiguity

In this paper, the two-stage-evaluation (TSE) model for decision making under ambiguity (He 2021) is extended to intertemporal setting in an axiomatic approach. The first set of axioms employed are commonly adopted for dynamic non-expected utility models in the literature. Besides these regular axioms, I also assume dynamic consistency and conditions which deliver a static TSE for consumption plans only pay non-zero consequences for one period. It is shown that these axioms hold if and only if these exists a recursively defined evaluation utility model representing decision maker (DM)’s preferences over consumption plans conditional on arriving at any node in an event tree. Such a recursive form implies that one can apply dynamic programming technique (rolling back the decision tree) to solve a dynamic decision making problem under TSE model. It can be shown that the solution for the recursively defined dynamic TSE model exists uniquely. Due to the “small domain” setup, the agent is short-sighted in the sense that they only process subjective probabilities over events defined on one period uncertainty over next period states, which differs from “far-sighted” assumption in most extant models that assume subjective probabilities exist over events defined on multiple periods. It is shown that under some extra conditions, our DM applies Bayes’ rule “updating’ her subjective beliefs.

Prognostic value of tissue bridges in cervical spinal cord injury: a longitudinal, multicentre, retrospective cohort study

The accuracy of prognostication in patients with cervical spinal cord injury (SCI) needs to be improved. We aimed to explore the prognostic value of preserved spinal tissue bridges-injury-spared neural tissue adjacent to the lesion-for prediction of sensorimotor recovery in a large, multicentre cohort of people with SCI. For this longitudinal study, we included patients with acute cervical SCI (vertebrae C1-C7) admitted to one of three trauma or rehabilitation centres: Murnau, Germany (March 18, 2010-March 1, 2021); Zurich, Switzerland (May 12, 2002-March 2, 2019); and Denver, CO, USA (Jan 12, 2010-Feb 16, 2017). Patients were clinically assessed at admission (baseline), at discharge (3 months), and at 12 months post SCI. Midsagittal tissue bridges were quantified from T2-weighted images assessed at 3-4 weeks post SCI. Fractional regression and unbiased recursive partitioning models, adjusted for age, sex, centre, and neurological level of injury, were used to assess associations between tissue bridge width and baseline-adjusted total motor score, pinprick score, and light touch scores at 3 months and 12 months. Patients were stratified into subgroups according to whether they showed better or worse predicted recovery. The cohort included 227 patients: 93 patients from Murnau (22 [24%] female); 43 patients from Zurich (four [9%] female); and 91 patients from Denver (14 [15%] female). 136 of these participants (from Murnau and Zurich) were followed up for up to 12 months. At 3 months, per preserved 1 mm of tissue bridge at baseline, patients recovered a mean of 9·3% (SD 0·9) of maximal total motor score (95% CI 7·5-11.2), 8·6% (0·8) of maximal pinprick score (7·0-10·1), and 10·9% (0·8) of maximal light touch score (9·4-12·5). At 12 months post SCI, per preserved 1 mm of tissue bridge at baseline, patients recovered a mean of 10·9% (1·3) of maximal total motor score (8·4-13·4), 5·7% (1·3) of maximal pinprick score (3·3-8·2), and 6·9% (1·4) of maximal light touch score (4·1-9·7). Partitioning models identified a tissue bridge cutoff width of 2·0 mm to be indicative of higher or lower 3-month total motor, pinprick, and light touch scores, and a cutoff of 4·0 mm to be indicative of higher and lower 12-month scores. Compared with models that contained clinical predictors only, models additionally including tissue bridges had significantly improved prediction accuracy across all three centres. Tissue bridges, measured in the first few weeks after SCI, are associated with short-term and long-term clinical improvement. Thus, tissue bridges could potentially be used to guide rehabilitation decision making and to stratify patients into more homogeneous subgroups of recovery in regenerative and neuroprotective clinical trials. Wings for Life, International Foundation for Research in Paraplegia, EU project Horizon 2020 (NISCI grant), and ERA-NET NEURON.