Individual Constraints Research Articles

The Impact of the Gestational Period and Weight on Individual, Task and Environmental Constraints as Related to Perceived Confidence when Completing Ambulatory Tasks in Pregnant Women

Background: Pregnancy and the physical, sensory, perceptual, and motor changes that coincide with pregnancy can affect a person’s ability to perform basic activities of daily living (ADL’s) that involve ambulation. Previous research suggested that weight and gestational age (trimester) are factors that impact woman’s ability to ambulate through daily tasks, however, these issues require further investigation. Newell's model of constraints was used as a framework to shape the questionnaire investigating the ability to perform different ambulatory activities across various constraints. Thus, the purpose of this study was to examine whether variables such as pre-pregnancy weight (under vs. over 155 pounds) and trimester (before vs. after 26 weeks) had an impact on the ability and perception to perform different tasks requiring ambulation. Methods: Twenty pregnant or postpartum women (M = 29.7 years; SD = 3.6) were recruited and asked to complete a 15-item online survey exploring their abilities to complete a variety of daily tasks involving balance. Also, open ended questions were posed to gain qualitative insight into their experiences. A series of independent samples T-tests were used to analyse the data. Findings: The results revealed predominantly no statistical differences between the levels of the variables manipulated. At the descriptive level the women indicated that they were relatively comfortable performing the different tasks. However, the qualitative responses suggested that although they were capable, they expended a substantial amount of energy to avoid falling, resulting in fatigue and soreness. Also, they experienced overall mental anguish and feelings of being overwhelmed when performing even simple tasks involving gait. Conclusion: Overall, it was surprising that weight and gestational age, which intuitively should have an impact, did not have a more pronounced effect on the women’s confidence in performing ambulation tasks. Nevertheless, qualitative responses confirmed the challenging effects of pregnancy on the way women engage in activities of daily living.

Micro-scale transformations in sustainability practices: Insights from new migrant populations in growing urban settlements

Development that is inclusive and sustainable requires significant social and environmental transformations from current trajectories, building on demographic realities such as changing profiles of populations, and increased levels of mobility. Migration is a major driving force of urbanisation in all global regions, partly facilitated through emerging technologies and declining costs of movement and communication. Social transformations associated with increased migration are highly uneven but include shifts in the location of economic activities, major urban growth, and changing individual incentives and social constraints on sustainability trajectories. Yet, there is limited empirical evidence on how observed population movements can both challenge and promote sustainable transformations. This paper examines how migration transforms places and societies, by providing new evidence on the behaviours and practices of individuals who are part of such transformations as they assimilate, converge or remain distinctive to prior populations. Focusing on individuals in rapidly expanding cities in the Global South, this study uses new biographical life-history survey data from Accra, Ghana, to examine the barriers and enablers of sustainability practices among diverse types of migrants and a sample of non-migrants. The study uses data from 1,163 individuals: international migrants from the West African sub-region (559), internal migrants (299), and non-migrants (305) in Accra. The findings show that sustainability practices established before migration are predictors of current sustainability practices, including proactive recycling, conservation activities, and choice of mode of transportation, but that there is some convergence between behaviours, reflecting assimilation, place attachment and other factors. Internal migrants in Accra exhibit stronger sustainability practices than international migrants. Individual levels of poverty, poor infrastructural development, and perceptions about life satisfaction in the neighbourhood negatively affect sustainability practices among all respondents. These results suggest that poverty and social exclusion are critical to addressing sustainability issues in urban contexts. It is important for policy makers to address issues of urban poverty, cumulative deprivation, and inequality as strong barriers to the adoption of sustainability practices in urban areas.

Parental perception of motor, language, and literacy skills development in school-aged autistic children

Emerging evidence suggests that motor and language delays are prevalent developmental concerns in young autistic children. Nonetheless, it is not well understood how these co-occurring conditions of autism spectrum disorder (ASD) relate to each other during the later childhood of autistic children. This study aimed to elucidate influential factors of motor and language/literacy development in school-aged autistic children from parental perceptions using in-depth semi-structured interviews. Ten mothers (Mage 37.5 ± 5.9) of autistic children (Mage 9.6 ± 1.6) participated in the individual interviews via video conference or phone calls with a particular focus on individual characteristics, parental involvement, and learning environment for motor and language/literacy development in their child. Open and axial coding was conducted, and salient themes were discussed until consensus was reached. The identified themes were categorized into two focal points, including 1) general parental attitudes toward their child’s learning and development and 2) parental self-efficacy manifesting in response to individual variations (e.g. difficulty with fine motor and coordinated skills and unique autistic traits) and environmental constraints (e.g. lack of resources and limited systematic support for sustainable care) that affect their child’s fundamental skills development. Concept maps were sequentially generated to visualize the connections among influential factors for both typical and delayed motor and language/literacy development in autistic children. Further studies with objective skills assessments and triangulated multi-informant data from other key stakeholders of ASD are warranted to delineate the association between motor, language, and literacy development in school-aged autistic children.

Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

This paper investigates an energy management problem in the microgrid by scheduling heating ventilation air conditioning (HVAC) and battery energy storage system (BESS) with a distributed algorithm. A multi-layer energy management architecture is presented at a system-level to co-optimize the HVAC-BESS by taking into account solar energy forecasts. A surplus-based consensus algorithm is proposed to solve the optimization problem, where the local power mismatch is introduced as a surplus term, and the HVAC-BESS can thus be co-scheduled to maximize renewable energy efficiency at the peak generation time. A set of the convex cost functions are formulated to minimize the HVAC's user dissatisfaction degree and alleviate power loss during the BESS operation. The goal is to collectively minimize the total energy cost in a distributed manner, subject to individual load constraints and power balance constraints. It is theoretically proved that a global convergence of the proposed algorithm is achieved provided that the directed network is strongly connected. The results from a number of case studies are promising, demonstrating the effectiveness and robustness of the algorithm under practical scenarios.

Effectiveness of Applying Anti-Aging Cream with Lemon Peel Extract (Citrus Limon) in Inhibiting the Skin Aging Process of Wistar Rats (Rattus Norvegicus) Exposed to Ultra Violet-B Rays

Aging is a gradual physiological change in an organism that results in senescence or a reduction in biological function and the ability of the organism to respond to metabolic stress. Aging occurs in cells, organs, or the entire organism over time; the study of the aging process seeks to understand and regulate all elements that contribute to an individual's life constraints. The predominant acute consequence of UV radiation on normal human skin is sunburn-induced inflammation (erythema). The overall goal of this study is to examine and test the efficacy of applying an anti-aging cream containing lemon peel extract (Citrus limon) in preventing the aging process of mouse skin. Wistar strain (Rattus norvegicus) exposed to ultraviolet-B light. The specific goal of this study is to investigate the efficacy of applying lemon peel extract (Citrus limon) cream at 5%, 10%, and 15% concentrations in preventing the aging process of rat skin exposed to UV-B radiation.

Different pedagogical approaches to motor imagery both demonstrate individualized movement patterns to achieve improved performance outcomes when learning a complex motor skill.

Cognitive training techniques such as motor imagery (MI)-cognitive simulation of movement, has been found to successfully facilitate skill acquisition. The MI literature emphasizes the need to accurately imitate key elements of motor execution to facilitate improved performance outcomes. However, there is a scarcity of MI research investigating how contemporary approaches to motor learning, such as nonlinear pedagogy (NLP), can be integrated into MI practice. Grounded in an ecological dynamics approach to human movement, NLP proposes that skilled action is an emergent process that results from continuous interactions between perceptual information of the environment and movement. This emergent process can be facilitated by the manipulation of key task constraints that aim to encourage learners to explore movement solutions that satisfy individual constraints (e.g., height and weight) and achieve successful performance outcomes. The aim of the present study was to explore the application of a NLP approach to MI approach for skill acquisition. Fourteen weightlifting beginners (two female and 12 male) participated in a 4-week intervention involving either NLP (i.e. analogy-based instructions and manipulation of task constraints) or a linear pedagogy (LP; prescriptive instructions of optimal technique, repetition of same movement form) to learn a complex weightlifting derivative. Performance accuracy, movement criterion (barbell trajectory type), kinematic data, and quantity of exploration/exploitation were measured pre-mid-post intervention. No significant differences (p = .438) were observed in the amount of exploration between LP (EER = 0.41) and NLP (EER = 0.26) conditions. Equivalent changes in rearward displacement (R×D) were observed with no significant differences between conditions for technique assessments 1, 2, or 3 (p = .13 - .67). Both NLP and LP conditions were found to primarily demonstrate 'sub-optimal' type 3 barbell trajectories (NLP = 72%; LP = 54%). These results suggest that MI instructions prescribing a specific movement form (i.e., LP condition) are ineffective in restricting available movements to a prescribed technique but rather the inherent task constraints appear to 'force' learners to explore alternative movement solutions to achieve successful performance outcomes. Although MI instructions prescribing specific techniques have previously supported improved skill development, the current findings indicate that learners may self-organise their movements regardless of MI instructions to satisfy individual and task constraints while achieving improved performance. Therefore, it may be beneficial to consider scripts that are more outcome focused and incorporate task constraints to facilitate learners' inherent exploration of individual task solutions.

Individual and collective objectives in an energy community with network constraints

The issue at play is to facilitate the deployment of renewable energy sources by coordinating privately owned batteries to also support local power systems. Battery systems most often exclusively answer the individual objective of their owner (e.g., a reduction in the energy bill). This study intends to explore whether both paradigms, (i) minimizing individual costs and (ii) mitigating network constraints, are achievable for individual battery systems, and if not, what trade-offs are available. Most studies in the literature take a subjective stand in their valuation of individual costs versus grid constraints. A set of Pareto optimal solutions is explored to provide a full picture of the aforementioned trade-off. As such, a novel Multi-Objective Optimal Power Flow (MOOPF) algorithm is developed to solve a convexified battery control problem. Results show the ability of the proposed methodology to provide a frame of reference to compare online battery controllers, e.g., if a controller can exploit synergies between individual and collective objectives or not. Further, and for the proposed scenario, local energy communities provide a strictly better set of Pareto optimal solutions than individually operated systems.

The future of working from home: a mixed-methods study with IT professionals to learn from enforced working from home

PurposePositive experiences with working from home (WFH) during the Corona pandemic (COVID-19) have motivated many employees to continue WFH after the pandemic. However, factors influencing employees' WFH intentions against the backdrop of experiences during pandemic-induced enforced working from home (EWFH) are heterogeneous. This study investigates factors linked to information technology (IT) professionals' WFH intentions.Design/methodology/approachThis mixed-methods study with 92 IT professionals examines the effects of seven predictors for IT professionals' WFH intentions. The predictors are categorized according to the trichotomy of (1) characteristics of the worker, (2) characteristics of the workspace and (3) the work context. Structural equation modeling is used to analyze the quantitative survey data. In addition, IT professionals' responses to six open questions in which they reflect on past experiences and envision future work are examined.FindingsQuantitative results suggest that characteristics of the worker, such as segmentation preference, are influencing WFH intentions stronger than characteristics of the workspace or the work context. Furthermore, perceived productivity during EWFH and gender significantly predict WFH intentions. Contextualizing these quantitative insights, the qualitative data provides a rich yet heterogeneous list of factors why IT professionals prefer (not) to work from home.Practical implicationsReasons influencing WFH intentions vary due to individual preferences and constraints. Therefore, a differentiated organizational approach is recommended for designing future work arrangements. In addition, the findings suggest that team contracts to formalize working patterns, e.g. to agree on the needed number of physical meetings, can be helpful levers to reduce the complexity of future work that is most likely a mix of WFH and office arrangements.Originality/valueThis study extends literature reflecting on COVID-19-induced changes, specifically the emerging debate about why employees want to continue WFH. It is crucial for researchers and practitioners to understand which factors influence IT professionals' WFH intentions and how they impact the design and implementation of future hybrid work arrangements.

An Engineering Alternative to Lockdown During COVID-19 and Other Airborne Infectious Disease Pandemics: Feasibility Study.

Now and in the future, airborne diseases such as COVID-19 could become uncontrollable and lead the world into lockdowns. Finding alternatives to lockdowns, which limit individual freedoms and cause enormous economic losses, is critical. The purpose of this study was to assess the feasibility of achieving a society or a nation that does not require lockdown during a pandemic due to airborne infectious diseases through the mass production and distribution of high-performance, low-cost, and comfortable powered air purifying respirators (PAPRs). The feasibility of a social system using PAPR as an alternative to lockdown was examined from the following perspectives: first, what PAPRs can do as an alternative to lockdown; second, how to operate a social system utilizing PAPR; third, directions of improvement of PAPR as an alternative to lockdown; and finally, balancing between efficiency of infection control and personal freedom through the use of Internet of Things (IoT). PAPR was shown to be a possible alternative to lockdown through the reduction of airborne and droplet transmissions and through a temporary reduction of infection probability per contact. A social system in which individual constraints imposed by lockdown are replaced by PAPRs was proposed, and an example of its operation is presented in this paper. For example, the government determines the type and intensity of the lockdown and activates it. At that time, the government will also indicate how PAPR can be substituted for the different activity and movement restrictions imposed during a lockdown, for example, a curfew order may be replaced with the permission to go outside if wearing a PAPR. The following 7 points were raised as directions for improvement of PAPR as an alternative method to lockdown: flow optimization, precise differential pressure control, design improvement, maintenance method, variation development such as booth type, information terminal function, and performance evaluation method. In order to achieve the effectiveness and efficiency in controlling the spread of infection and the individual freedom at a high level in a social system that uses PAPRs as an alternative to lockdown, it was considered effective to develop a PAPR wearing rate network management system utilizing IoT. This study shows that using PAPR with infection control ability and with less economic and social damage as an alternative to nationwide lockdown is possible during a pandemic due to airborne infectious diseases. Further, the efficiency of the government's infection control and each citizen's freedom can be balanced by using the PAPR wearing rate network management system utilizing an IoT system.

Functionalized TMC and ε-CL elastomers with shape memory and self-healing properties.

Introduction: Smart elastomers, which possess self-healing and shape memory capabilities, have immense potential in the field of biomedical applications. Polycarbonates and polyesters have gained widespread interest due to their remarkable biocompatibility over the last century. Nevertheless, the lack of functional versatility in conventional polyesters and polycarbonates means that they fall short of meeting the ever-evolving demands of the future. Methods: This paper introduced a new smart elastomer, named mPEG43-b-(PMBC-co-PCL)n, developed from polyester and polycarbonate blends, that possessed shape memory and self-heal capabilities via a physical crosslinking system. Results: The material demonstrated a significant tensile strength of 0.38MPa and a tensile ratio of 1155.6%, highlighting its favorable mechanical properties. In addition, a conspicuous shape retrieval rate of 93% was showcased within 32.5seconds at 37°C. Remarkably, the affected area could be repaired proficiently with no irritation experienced during 6h at room temperature, which was indicative of an admirable repair percentage of 87.6%. Furthermore, these features could be precisely modified by altering the proportion of MBC and ε-CL to suit individual constraints. Discussion: This innovative elastomer with exceptional shape memory and self-heal capabilities provides a solid basis and promising potential for the development of self-contracting intelligent surgical sutures in the biomedical field.

Climate vs Energy Security: Quantifying the Trade-offs of BECCS Deployment and Overcoming Opportunity Costs on Set-Aside Land.

Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14-36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year-1 from the atmosphere, deliver up to 16 Tg CE year-1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.

Multi-Agent Learning of Asset Maintenance Plans through Localised Subnetworks

Maintenance planning of networked multi-asset systems is a complex problem due to the inherent individual and collective asset constraints and dynamics as well as the size of the system and interdependencies among assets. Although multi-asset systems have been studied numerous times in the past decades, maintenance planning implications of the system’s network characteristics have been barely analysed. Likewise, solutions that consider the network perspective suffer from scalability issues as a network-wide observability is assumed. This paper proposes a network maintenance planning approach based on the decomposition of the multi-asset network into fixed-size localised subnetworks. The overall network maintenance plan is produced by aggregating the subnetwork maintenance plans, which are computed independently via a multi-agent deep reinforcement learning (MARL) algorithm. The results are evaluated against a network-wide approach as well as the commonly-used individual approach. The paper also introduces a systematic approach to integrate the MARL resulting policy in a multi-asset agent-based model. Simulation results of several random asset networks and a large nationwide network infrastructure show that, although a network-wide approach outperforms, on average, other approaches considered, the localised subnetworks approach, provides an acceptable alternative in networks with small-world properties, without the need of a network-wide view.

Analysis of Accessible Digital Musical Instruments through the lens of disability models: a case study with instruments targeting d/Deaf people

Music educators and researchers have grown increasingly aware of the need for traditional musical practices to promote inclusive music for disabled people. Inclusive music participation has been addressed by Accessible Digital Musical Instruments (ADMIs), which welcome different ways of playing and perceiving music, with considerable impact on music-making for disabled people. ADMIs offer exciting possibilities for instrument design to consider and incorporate individual constraints (e.g., missing arm, low vision, hearing loss, etc.) more than traditional acoustic instruments, whose generally fixed design allows little room for disabled musicians inclusivity. Relatively few works discuss ADMIs in the context of disability studies, and no work has investigated the impact of different disability models in the process of designing inclusive music technology. This paper proposes criteria to classify ADMIs according to the medical, social, and cultural models of disability, then applies these criteria to evaluate eleven ADMIs targeting d/Deaf people. This analysis allows us to reflect on the design of ADMIs from different perspectives of disability, giving insights for future projects and deepening our understanding of medical, social, and cultural aspects of accessible music technology.

Exploring low‐income, Black fathers' strengths and barriers to positive change using qualitative methods

AbstractObjectiveThe goal was to explore low‐income, Black fathers' personal strengths and barriers to engaged fatherhood, healthy relationships, and economic stability.BackgroundFatherhood programs often rely on case management and education to promote positive fathering, healthy relationships, and economic stability. However, methods should be tailored to fit the strengths and needs of participating fathers. Low‐income, Black fathers face additional challenges related to systemic discrimination and structural violence that can hinder their success. To effectively serve this group, research is required to understand their relevant strengths and barriers.MethodsSemistructured focus groups explored fathers' personal strengths and barriers specific to the three areas of interest with fatherhood program staff (n = 8) and enrolled fathers (n = 26). Thematic analysis was conducted then systematically vetted by community scholars (previously enrolled or graduated fathers).ResultsFathering strengths included empathy, listening skills, and active family participation. Barriers included limited custody or access, individual constraints, and unmet social needs. Participants contextualized these findings with structural issues related to coparenting and the legal system (e.g., biased court systems). Men's relationship strengths included mutual respect and teamwork. Barriers included past trauma, lack of healthy relationship models, and relationship strain. Economic stability strengths included financial literacy and motivation to provide, whereas barriers included legal history, financial obligations, and limited resources.ConclusionThese findings highlight key individual‐level strengths and barriers grounded in fathers' intersectional identities as predominantly low‐income, Black men.ImplicationsPrograms serving low‐income, Black fathers should consider tailoring their programming to the lived experiences of participants to navigate these critical barriers and amplify assets for success.

Evolutionary Game Analysis of Open Financing Models for Small and Medium-Sized Internet Education Enterprises

The open financing models combine traditional financing and network financing, which plays an important role in alleviating the financing constraints of small and medium-sized education enterprises and is conducive to promoting the growth of small and medium-sized education enterprises. Based on the systematic review of evolutionary game theory, the characteristics of evolutionary game theory and its differences with game theory, this paper first constructs the game process analysis of the open financing mode of small and medium-sized Internet education enterprises, and proposes the stabilization strategies of the participants through the model analysis. On this basis, the financing model is improved by introducing the constraints of individuals, loan companies, government and intermediaries, and the open financing game process is theoretically analyzed and mathematically derived. The conclusions of the study provide an important reference for promoting the open financing model of small and medium-sized Internet education enterprises.

Enhancing Federated Learning With Spectrum Allocation Optimization and Device Selection

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile devices to collect model training data. Under such circumstances, FL is expected to meet stringent training latency requirements in the face of limited resources such as demand for wireless bandwidth, power consumption, and computation constraints of participating devices. Due to practical considerations, FL selects a portion of devices to participate in the model training process at each iteration. Therefore, the tasks of efficient resource management and device selection will have a significant impact on the practical uses of FL. In this paper, we propose a spectrum allocation optimization mechanism for enhancing FL over a wireless mobile network. Specifically, the proposed spectrum allocation optimization mechanism minimizes the time delay of FL while considering the energy consumption of individual participating devices; thus ensuring that all the participating devices have sufficient resources to train their local models. In this connection, to ensure fast convergence of FL, a robust device selection is also proposed to help FL reach convergence swiftly, especially when the local datasets of the devices are not independent and identically distributed (non-iid). Experimental results show that (1) the proposed spectrum allocation optimization method optimizes time delay while satisfying the individual energy constraints; (2) the proposed device selection method enables FL to achieve the fastest convergence on non-iid datasets.

Frequent Friers: Outcomes in Patients Who Receive Multiple Courses of Lung SBRT

Stereotactic body radiation therapy (SBRT) is a definitive therapy for early-stage non-small cell lung cancer and solitary pulmonary metastases with high tolerability and excellent survival rates. With longer survival and patients developing subsequent primaries or oligometastatic disease, patients are receiving multiple repeat lung SBRT courses. This study aims to assess safety and efficacy of high-frequency SBRT (HF-SBRT) to the lung, defined as >3 courses. A retrospective review was performed of patients who received >3 courses of lung SBRT. Logistic regression was performed to identify predictors of radiation pneumonitis (RP) and worsening pulmonary function (WPF). Local control (LC) and overall survival (OS) were evaluated using the Kaplan-Meier method. Ninety-four courses of HF-SBRT to the lung were identified among 28 patients. 78% of patients received 3 SBRT courses, 12% received 4 courses, and 7.1% received >5 courses. Median follow-up was 4.4 years. Median age at time of treatment was 73 years-old; 58% males; 42% had an underlying pulmonary comorbidity; 39% prior lung surgery; 52% history of cardiac disease; 52% prior tobacco use; median ECOG 0. Median SBRT dose was 48 Gy. Median interval between courses was 5.6 months. Zero patients experienced greater than grade 2 acute CTCAE v5 toxicity. 7.2% (7) of patients developed RP at median time of 2.6 months [IQR: 1.4,7.4]; grade 1: 3 patients, grade 2: 1 patient, grade 3: 2 patients. Of patients who developed RP, 42% (3) went on to receive further SBRT without experiencing significant adverse events (AEs). History of pulmonary disease, prior lung surgery, and history of tobacco use strongly correlated with WPF but not RP (WPF p-values: <0.001, 0.003, <0.001, respectively). History of cardiac disease did not correlate with WPF or RP. Receiving bilateral lung SBRT treatment (vs unilateral) trended towards correlation with WPF (p = 0.06) and RP (p = 0.08). Time between SBRT courses did not significantly differ for those who developed RP (p = 0.62) or WPF (p = 0.42). No individual or plan sum dosimetric constraint (GTV, PTV, unilateral lung V5, lung V10, lung V20, unilateral mean lung dose, or total lung V20) significantly differed in those who experienced RP. WPF correlated with the plan sum unilateral lung V10 (p = 0.05). LC at 1-year was 100%, 1 local failure occurred at 13 months; 2-year OS was 91.7%, median OS 5.4 years. Overall, HF-SBRT was well tolerated. Development of RP did not correlate with a specific individual or plan sum dosimetric parameter, with patients receiving subsequent courses of SBRT without increased AEs. WPF increasingly occurred with subsequent SBRT courses and correlated with unilateral plan sum V10. This study suggests that in appropriately selected patients, SBRT after RP can still be provided as definitive care, while further studies should validate this and focus attention on mitigating long-term WPF in patients who receive HF-SBRT.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems.

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Cluster Control and Energy Consumption Minimization for Cooperative Prediction Based Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is a key technique for dynamically detecting available spectrum in cognitive radio networks (CRNs), which can introduce high resource demands such as energy consumption. In this paper, we propose a novel cluster-based cooperative sensing-after-prediction scheme where a learning cluster and a sensing cluster are jointly considered to perform cooperative prediction and sensing efficiently. This enables us to skip the complex physical sensing to reduce the demands when the spectrum availability can be simply predicted using cooperative prediction. Furthermore, the clustering is flexible, in order to meet different performance requirements. We then formulate two optimization problems to minimize the total number of users in the two clusters or to minimize the total energy consumption, to meet different performance requirements, while in both cases guaranteeing the system accuracy requirement and individual energy constraints. To solve the two challenging integer programming problems, the unconstrained problems are mathematically solved first by relaxing the integer variable and fixing the cluster size. Such analytical solutions serve as a foundation for solving the original optimization problems. Then, two low-complexity search algorithms are proposed to achieve the global optimum, as they can obtain the same performance with exhaustive search. Simulation results validate the accuracy of the derived analytical expressions and demonstrate that the total energy consumption and the number of users contributing to learning and sensing can be greatly reduced by applying our optimized clustered sensing-after-prediction scheme.

Distributed model predictive control for heterogeneous vehicle platoon with unknown input of leading vehicle

In this paper, a distributed model predictive control (DMPC) algorithm for a platoon of heterogeneous vehicles is proposed. The leading vehicle is allowed to be driven by a non-zero and time-varying input, rather than traveling at a constant velocity. Except for individual state and input constraints for each vehicle, all vehicles are coupled via state-coupled inter-vehicular spacing constraints and state-coupled cost functions, which maintain the unidimensional platoon formation with satisfactory transient performance. Each vehicle communicates with its neighboring vehicles, and may not know the leading vehicle’s kinetic status information. The control input of each following vehicle is computed by a local optimization problem established by each vehicle’s local information and the assumed state information from its neighbors. By designing distributed terminal control laws for following vehicles, dividing each state-coupled set into several specific subsets, and then forcing each following vehicle to optimize its state constrained in the assigned subsets, the coupled constraints and cost functions can be decoupled, and thus a distributed and parallel computing method can be adopted to compute the control inputs of all following vehicles. Based on the tailored terminal equality constraints together with the tailored terminal control laws, the recursive feasibility of local MPC optimization problems is achieved at all time steps and the asymptotic stability of each vehicle is also guaranteed. The effectiveness of the proposed DMPC method is demonstrated in simulation, and the advantage of the proposed DMPC dealing with the leading vehicle’s non-zero, inaccessible, and time-varying input is highlighted by a comparison simulation for heterogeneous vehicle platoon with a continuously changing leading vehicle velocity.