Evaluation of an E-Therapy Program for Problem Drinkers: A Pilot Study

An e-therapy program with therapist involvement for problem drinkers was evaluated in a population of 527 Dutch-speaking patients. In a pre–post design weekly alcohol consumption, alcohol-consumption-related health problems, and motivation were assessed. Although the dropout rate was high, patients showed a significant decrease of alcohol consumption and alcohol-consumption-related health complaints. Patients’ satisfaction with the e-therapy program was high. The e-therapy program proved to be feasible and attracted patients who were otherwise unlikely to seek help. A randomized controlled trial has to provide more information about reasons for dropout, effectiveness, and the population that benefits most from the e-therapy program. The study's limitations are noted.     

The application of subspace preconditioned LSQR algorithm for solving the electrocardiography inverse problem

Regularization is an effective method for the solution of ill-posed ECG inverse problems, such as computing epicardial potentials from body surface potentials. The aim of this work was to explore more robust regularization-based solutions through the application of subspace preconditioned LSQR (SP-LSQR) to the study of model-based ECG inverse problems. Here, we presented three different subspace splitting methods, i.e., SVD, wavelet transform and cosine transform schemes, to the design of the preconditioners for ill-posed problems, and to evaluate the performance of algorithms using a realistic heart-torso model simulation protocol. The results demonstrated that when compared with the LSQR, LSQR-Tik and Tik-LSQR method, the SP-LSQR produced higher efficiency and reconstructed more accurate epcicardial potential distributions. Amongst the three applied subspace splitting schemes, the SVD-based preconditioner yielded the best convergence rate and outperformed the other two in seeking the inverse solutions. Moreover, when optimized by the genetic algorithms (GA), the performances of SP-LSQR method were enhanced. The results from this investigation suggested that the SP-LSQR was a useful regularization technique for cardiac inverse problems.     

Evaluation of an assertive outreach intervention for problem families: intervention methods and early outcomes

Scand J Caring Sci; 2011; 25; 211–219
Evaluation of an assertive outreach intervention for problem families: intervention methods and early outcomes Families who experience a chronic complex of socio‐economic and psycho‐social problems are hard to reach with mainstream care. Evidence exists that the core of this problem lies in a problematic interaction between this type of family and current systems of care. The adults and children involved have needs in multiple domains, while the care system is fragmented and highlights well‐defined requests for help. To improve access to this target group, an assertive outreach intervention was implemented into the preventive child healthcare system in the Netherlands. Evaluation research was carried out to get a detailed insight into the content of this intervention. Also, early outcomes were examined. Information was gathered by interviews, attending meetings on method development, analysing registration forms and a survey on client satisfaction. Five intervention stages were identified: case finding, making contact, sustaining contact, developing a family plan and linking (arranging for services to be delivered). Practical support was used to build rapport and clear the way to the uptake of follow‐up help. The professionals delivering the intervention need a broad range of competencies to establish a working relation with the families and to link them to care and services. A good care network across professionals from various organisations must exist to provide a variety of linking options. Early outcomes indicate that professionals were able to get in touch with the families within a mean of 13.2 days. Goals of the intervention were mainly practical support (73%), starting new assistance for a child (63%) and starting new assistance for a parent (43%). Linking to care and services was attained in the majority of the cases and parents expressed satisfaction. The findings indicate that the studied intervention is a promising one. Some potent components can be indicated: the outreach approach, practical support, maximising participation of the family and building bridges between the family and (in)formal support and assistance.     

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.

Dynamic systems, represented as a set of ordinary differential equations (ODEs), are commonly used to model behaviors in scientific domains, such as gene regulation (1), biological rhythms (2), spread of disease (3), ecology (4), etc. We focus on models specified by a set of ODEsx˙(t)=dx(t)dt=f(x(t),θ,t), t∈[0,T],[1]where the vector x(t) contains the system outputs that evolve over time t and θ is the vector of model parameters to be estimated from experimental/observational data. When f is nonlinear, solving x(t) given initial conditions x(0) and θ generally requires a numerical integration method, such as Runge–Kutta.Historically, ODEs have mainly been used for conceptual or theoretical understanding rather than data fitting as experimental data were limited. Advances in experimental and data collection techniques have increased the capacity to follow dynamic systems closer to real time. Such data will generally be recorded at discrete times and subject to measurement error. Thus, we assume that we observe y(τ)=x(τ)+ϵ(τ) at a set of observation time points τ with error ϵ governed by noise level σ. Our focus here is inference of θ given y(τ), with emphasis on nonlinear f where specialized methods that exploit a linear structure (e.g., refs. 5 and 6), are not generally applicable. We shall present a coherent, statistically principled framework for dynamic system inference with the help of Gaussian processes (GPs). The key to our method is to restrict the GPs on a manifold that satisfies the ODE system: Thus, we name our method MAGI (manifold-constrained Gaussian process inference). Placing a GP on x(t) facilitates inference of θ without numerical integration, and our explicit manifold constraint is the key idea that addresses the conceptual incompatibility between the GP and the specification of the ODE model, as we shall discuss shortly when overviewing our method. We show that the resulting parameter inference is computationally efficient, statistically principled, and effective in a variety of practical scenarios. MAGI particularly works in the cases when some system component(s) is/are unobserved. To the best of our knowledge, none of the current available software packages that do not use numerical integration can analyze systems with unobserved component(s).     

Assessment of battery utilization and energy consumption in the large-scale development of urban electric vehicles

Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter–spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking.

Large-scale electrification of transport is considered an effective solution to decrease the use of petroleum-derived fuels and mitigate the urban accumulation of air pollutants. The global stock of light-duty electric vehicles (LDEVs) exceeded 7.2 million in 2019 (1, 2), and China accounted for the largest share at ∼47%, followed by Europe and the United States. To further boost the electric vehicle (EV) market, numerous jurisdictions have introduced incentives or adopted action plans: China has prolonged subsidies for EVs to 2022 (3); the European Union has provided new tax schemes for electric cars (4); and several regions in the United States and Canada, such as California and Quebec, respectively, have enacted incentives for zero-emission vehicle programs (5, 6). The International Energy Agency indicated that the global EV stock would need to increase to 140 million by 2030 (2) to achieve the goals of existing government policies [the Stated Policies Scenario (7)]. In this context, from 2019 to 2030, the global battery capacity production and electricity demand from EVs would soar ninefold to 1.5 TWh ⋅ y⁻¹ and sixfold to 550 TWh ⋅ y⁻¹, respectively (2, 8). Accordingly, the global demand for the critical metals used in EV batteries, including cobalt, lithium, manganese, and nickel, would increase by 8 to 14 times from 2019 to 2030 (2, 9).The surging demand for battery resources and energy from EVs signifies a need to reassess the real-world battery utilization and energy consumption of urban-scale EVs. Research topics on this front have focused on analyzing the supply risks of battery resources (10–12), battery recycling (13–15), sustainability (16–18), charging planning (19–21), and the impact on urban power grids (22–24). A common and indispensable input of these studies is accurate battery utilization or energy consumption of urban EVs. Meanwhile, since the environmental gains of EVs can only be measured from electricity production processes, the energy consumption of urban EVs is also an important basis for research on emissions (25–28), air pollution (29, 30), and health benefits (31, 32). However, owing to the absence of urban-scale EV operating data, most existing assessments are conducted by relying on macroscopic evaluations or the simulations that are calibrated with aggregate-level parameters (33–35). When applied to large-scale EV applications, these simplifications can lead to nonnegligible biases in the results (36) as they cannot reflect the complexity of driving trajectories and varying battery performance in EV operation. On the other hand, in small-scale EV samples, the overall levels of battery utilization and energy consumption tend to be altered by the special use behavior of individual users. This phenomenon conceals some trends that would otherwise have been evident, such as low battery utilization and seasonal changes in the energy consumption of EVs.In 2016, the National Monitoring and Management Center for New Energy Vehicles was established in China, which serves as the national big data platform for EVs. The center has the only datasets in the world that contain real-time operating data of nationwide EVs (the number of EVs in the datasets exceeded 3 million in 2020). For the EVs in some metropolitan areas in China, such as Beijing and Shanghai, the coverage of the platform can reach up to 80%. The data content primarily includes two parts: dynamic vehicular data (general vehicle status, subsystem operating data, and location data) and static information (metadata and attributes). The temporal and spatial resolutions of the dynamic vehicular data are 1 to 30 s and 1 to 10 m, respectively. This large-scale and high-precision data source of Chinese EVs, coupled with EV datasets outside China, provides unique data support for achieving the large-scale assessments in this work (see Materials and Methods).Here, we present a fact-based assessment of battery utilization and energy consumption in urban-scale EV applications to expose several issues affecting battery resources and the urban power supply. To this end, we combine four types of data: 1) EV operating data, 2) EV operational data describing the fleet types and license plate regions, 3) vehicle feature data providing the specifications of EVs, and 4) climate data providing ambient temperatures in different urban areas (see Materials and Methods). To understand the impact of regional variability, nine metropolitan areas worldwide with large EV markets (37) are selected in this work. Accordingly, we first analyze the changes in battery utilization that are affected by user behavior or limited by current battery technology. We investigate how different fleet types and climatic conditions can affect the battery utilization of urban EVs. We also display the developing trends of battery utilization in urban-scale EV groups under different directions of battery technology improvement. Then, we assess the energy consumption of urban EVs from different perspectives. We observe that in some continental climate regions, the energy consumption of EVs fluctuates greatly in different months because of temperature shifts. These fluctuations and step changes are unfavorable as they can greatly amplify the original daily energy demand of EVs, especially in urban-scale EVs. We show the extent to which this problem can be addressed as EV technology improves. The results demonstrate how often-ignored changes in the battery utilization and energy consumption of urban EVs could affect the resource efficiency of EV batteries and urban power supply.     

机动车尾气对大鼠气道高反应性的影响

目的 :本研究旨在观察机动车尾气对大鼠气道高反应性的影响。方法 :取30只SD大鼠,随机分5组:正常对照组;吸入尾气2天组;吸入尾气4天组;吸入尾气6天组;吸入尾气8天组,每组6只。采用小动物呼吸机,肺组织切片PAS和HE染色,检测各组大鼠呼吸道反应性,肺组织杯状细胞化生指数、上皮粘液储备指数和炎症细胞数量。结果 :随着尾气吸入时间的延长,大鼠出现越来越明显的气道高反应性,同时,呼吸道粘液分泌量显著增加,气道炎症明显加重。结论 :吸入机动车尾气能引发并加重气道高反应性。     

PBL教学法在社区护士高血压病延续护理培训中的应用

【】 目的 探讨将PBL教学法在社区护士高血压病延续护理培训中的应用效果。方法 随机选取宝山区6家社区医院的42名护士为观察组, 42名护士为对照组。在3个月培训过程中,观察组护士采PBL教学方法进行与高血压病延续护理相关的知识及操作技能进行培训,对照组护士采用以授课为基础的教学方法( LBL)和常规操作进行培训,比较两组社区护士对培训的满意度,高血压病延续护理理论及操作成绩,培训后两组高血压病患者治疗的依从性进行比较。结果 培训后观察组社区护士对培训的满意度,高血压病延续护理理论及操作成绩,培训后两组患者治疗依从性进行比较分析,差异有统计学意义(P<0．05或P<0．01) 结论 PBL教学法在社区护士高血压病延续护理培训中系统的应用,有效提高了社区护士对培训的满意度,提高社区护士学习相关知识的兴趣,提高患者依从性,值得推广和使用。     

兰州市婴幼儿饮食行为问题及影响因素

目的 了解甘肃省兰州市7月龄～3岁儿童饮食行为问题及影响因素.方法 分层随机整群抽取兰州市4 389名7月龄～3岁儿童,完成《儿童饮食行为调查问卷》,调查儿童的社会人口学资料、儿童饮食行为问题特点以及扶养人对饮食行为问题的应对.结果 兰州市7月龄～3岁儿童饮食行为问题检出率为67.62％,2～3岁儿童检出率达最高水平,各年龄组儿童饮食行为问题最显著的均为“不良进食习惯”,80.9％的抚养者选择用不当的行为去给予应对;多因素分析显示,早产儿、低体重儿更容易产生饮食行为问题,抚养者文化程度尤其是孩子母亲的文化程度及家庭经济收入水平是儿童饮食行为问题中的影响因素(P＜0.05),家长有不良喂养行为的儿童更易产生饮食行为问题(P＜0.05).结论 兰州市7月龄～3岁儿童饮食行为问题较为突出,临床医生应切实做好儿童的饮食行为早期干预.     

床头型急救车在病区急救准备中的效果观察

目的 观察自主创新研制的一款床头型急救车在急救准备中的使用效果。方法 选择泗洪县中医院呼吸内科、神经内科、胸外科、神经外科共30名护士为操作对象,采用模拟抢救考核的方式,要求她们分别采用传统的常规方法（常规法）和床头型急救车方法（改进法）完成急救准备,纪录两种方法的急救准备时间、物品准备齐全率及物品摆放合理性,并进行对比分析。结果 按常规方法,急救准备耗时（260.0±49.6）s,物品准备齐全率46.7%;使用床头型急救车方法,急救准备耗时（159.2±13.9）s,物品准备齐全率93.3%,两组比较差异均有统计学差异（P<0.01）。使用床头型急救车方法的物品摆放更为整齐、合理。结论 床头型急救车在抢救患者时可以减少抢救准备用时,提高物品准备齐全率,使物品摆放更规范安全,值得临床推广使用。