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The design and implementation of targeted policies and programmes are crucial to addressing climate challenges. In this study, we sought to understand the means by and extent to which the Central American Climate-Smart Agriculture Strategy (EASAC) has contributed to addressing the specific climate challenges facing the regional agricultural sector. We applied a theory-based evaluation and contribution analysis to examine this policy and identified 252 changes at the national and regional levels that align with the EASAC theory of change (ToC). Contribution analysis revealed that none of the 23 changes we assessed could be attributed solely to the EASAC; however, it contributed directly, in combination with other actors and institutions, to 10 such changes. Our results question the efficacy of non-binding regional policies, such as the EASAC, in influencing national policy changes and highlight the dependency of climate policy implementation on international cooperation in the Central American Integration System region. Intersectoral and actor coordination, the availability of technical and research expertise, and research input appear critical to the successful implementation of regional climate policy. Key policy insights Theory-based assessments and contribution analyses are relevant and rigorous tools that can be effectively applied to climate policy. They enable a deeper understanding and more effective management of climate governance. International organizations play crucial roles in translating international agreements into policies, particularly in Central America, where public climate finance remains limited. Such organizations are responsible for developing and funding policies that align with these agreements. Many changes aligned with the EASAC ToC framework have been observed, reflecting the dynamic process of up-scaling regional climate-smart agriculture. Most changes have been focused on policy formulation and adjustment, with fewer related to establishing alliances and platforms for dialogue. The EASAC strategy of targeting non-binding regional governance to influence national governments is inherently limited and may be ineffective on its own. [ABSTRACT FROM AUTHOR]

Overgrazing affects the grass‐livestock balance and endangers grassland ecological security. Despite extensive studies conducted on identifying and quantifying grazing intensity, there is still room for improvement in the research on gridding grazing intensity, particularly in areas with limited data on the Qinghai–Tibet Plateau. Therefore, we proposed a grazing intensity spatialization method using geographically weighted random forest (GWRF) to gain further insights into the spatial heterogeneity of alpine grassland grazing intensity. This method incorporates multiple remote sensing data related to human activities and natural factors, as well as annual livestock statistics at the township level over several years, while adequately considering the spatial autocorrelation of grazing intensity. Additionally, we employed Lindeman Merenda Gold (LMG), the geographical detector model, and the structural equation model (SEM) to assess the contribution and influence path of driving factors to grazing intensity. We also utilize partial correlation analysis and dual‐phase mapping to examine the impact of natural and human activities on the spatial distribution of grazing intensity. The results demonstrate that the GWRF‐based grazing intensity spatial model accurately predicts grazing intensity by demonstrating its consistency with township‐scale livestock data (R2 = 0.92 (p < 0.01), RMSE = 1.07). This provides valuable technical support for quantifying grazing intensity in alpine pastoral areas with limited data availability. We evaluate trends in grazing intensity and observe an increase in Gar and Purang counties. Furthermore, population density, normalized difference vegetation index (NDVI), and temperature are identified as three influential factors affecting grazing intensity in alpine pastoral areas. Additionally, other factors indirectly impact grazing intensity by influencing population density and NDVI levels, while their interactions amplify their overall influence. The dual‐phase mapping technique has demonstrated a significant impact of population density on 45.92% (p < 0.01) of the study area, emphasizing the substantial influence of human activities on grazing intensity. Our study provides a novel framework for spatially analyzing grazing intensity and unraveling the intricated driving mechanisms behind spatiotemporal changes, particularly in areas with limited data availability. [ABSTRACT FROM AUTHOR]

Partitioning evapotranspiration (ET) is challenging but essential for understanding the exchange of energy, water, and carbon between terrestrial ecosystems and the atmosphere. In this study, we applied the simple biosphere model (SiB2) to partition ET at a typical alpine grassland site on the Qinghai–Tibet Plateau (QTP). In addition, through process‐based model scenario experiments, we quantified the effects of four environmental factors on ET components and predicted their evolution under the two future carbon emission scenarios (ssp126 and ssp585). Our findings are summarized as follows: (1) The original version of SiB2, despite its simple structure, effectively simulates ET and its components. (2) The ratios of annual total transpiration (T), soil evaporation (Es), and canopy interception evaporation (Ei) to ET in the alpine grassland ecosystem were 51%, 43%, and 6%, respectively. (3) Each 100 mm increase in annual precipitation results in a significant increase in soil evaporation (2.77%). A 1°C increase in air temperature leads to a significant increase in vegetation transpiration (5.22%) and canopy interception evaporation (5.63%). Each 100 ppm increase in CO2 concentration causes a significant decrease in T (−5.43%) and ET (−2.97%). An increase in LAI (1 m2 m−2) has the largest effect on canopy interception evaporation (4.67%). (4) Under the high carbon emission scenario (ssp585), all ET components in this ecosystem show a significant growth trend, particularly vegetation transpiration and canopy interception evaporation. These findings will facilitate more precise predictions of the water cycle dynamics, reveal land‐atmosphere interaction mechanisms, and aid in the protection of the ecological environment of the QTP. [ABSTRACT FROM AUTHOR]

In scholarly publications, determining authorship- and the order of authors’ names- has become increasingly challenging. This is partly due to the evolving landscape of multidisciplinary teams, which can involve numerous contributors. Relying solely on descriptive and consensus-based qualitative approaches can lead to uncertainty. The dilemma also extends to cases where a valid contributor may simply receive an acknowledgement for his or her contribution, rather than being listed as a joint author. Trusting authors to adhere to moral standards when deciding authorship and acknowledgments can result in conflicts that are difficult for readers to navigate. This poses accountability challenges, especially when dealing with a substantial number of authors. This study proposes a simplified and transparent quantitative approach to address these concerns. The methodology is based on two key parameters: first, the evaluation of the time invested by each contributor in the publication; and, second, the normalized assessment of the value of their time. Through a contribution analysis utilizing these parameters, a predetermined threshold is established to define authorship. Contributors falling below this threshold can be recognized in some other way, for example, in acknowledgments for their valuable input. While the approach proposed may be more applicable in some disciplines and higher education systems than others, it does offer a quantitative foundation to support qualitative discussions among potential authors, helping them reach a consensus on authorship and authorship order without conflict. This method seeks to prevent injustices and ensure that all contributors have a voice, regardless of their power and influence. [ABSTRACT FROM AUTHOR]

The carbon footprint of a product represents the amount of greenhouse gas (GHG) emissions released during its production, transportation, and consumption and is calculated as carbon dioxide equivalent (CO2-eq). It should be integrated into different existing and future seafood awareness campaigns to create more holistic yardsticks by which consumers, retail businesses, and producers can assess the environmental impacts of seafood. This study used the life cycle assessment (LCA) method for the first time to quantify the carbon footprint of salmon fillet products processed in Vietnam for export. The carbon footprint of 1-kg salmon fillet at the factory gate ranges between 7.20 and 15.05 kg CO2-eq, depending on transportation modes of head-on-gutted (HOG) salmon from Norway to Vietnam. Transportatiton by airfreight doubled carbon footprint of salmon fillet products processed in Vietnam compared to sea freight. Feed and electricity were identified as the two most respective contributing factors during the stage of cultivation, processing fresh salmon in Norway, and the stage of salmon fillet processing in Vietnam. They accounted for about 95% and 50% of the total carbon footprint in these stages of the production chain, respectively. To reduce the carbon footprint of the salmon fillet products processed in Vietnam, the company should (i) make a careful production plan to use sea freight transportation instead of airfreight and (ii) use more electricity from renewable energy sources. Furthermore, the carbon footprint of these products can be reduced by improving the cultivation process via changing feed ingredients and enhancing the feed conversion ratio (FCR). [ABSTRACT FROM AUTHOR]

In industrial ecology, approaches have been developed to analyze the contribution of specific sectors to environmental impacts within supply chains. In economics, a range of methods addresses the forward linkage (use of output) and backward linkage (dependency on inputs) of sectors, and the analysis of key sectors. This article offers a formal investigation of the relationship between these. It shows that both the analysis of supply chain impacts and of intersectoral linkages can be seen as special cases of a more general hypothetical extraction method (HEM). In HEM, sectors' role is assessed as the effect of their removal on the input–output model's solution. HEM also allows for the (partial) extraction of individual transactions. HEM thus offers a flexible approach to assessing the contribution of one or several sectors, or transactions, or parts thereof, to value added or footprint of any final demand. It can be applied to study the environmental footprints of companies or intermediate products, the contribution of certain inputs to sectors, or the potential impact of disruptions of supply chains on producers and consumers. In this article, the price model for HEM is introduced to identify the contribution of the extracted (target) sectors to the price or unit footprint of a commodity. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET), as an indicator of atmospheric evaporative demand, is a critical hydrological and meteorological factor to reflect regional and global hydrological cycles and environmental change. Understanding these nuanced responses of PET to environmental changes is important for agricultural production and water demand estimation. This study rigorously evaluated fluctuations in PET using the Penman–Monteith model over a 50-year span from 1970 to 2020 in the Sichuan–Chongqing region, an area notably susceptible to climate change. The changing characteristics of PET and local meteorological factors were detected by integrating the Mann–Kendall method and Pettitt test. Furthermore, the contribution and sensitivity of key meteorological variables to the observed variation in PET were also thoroughly investigated. Breakpoint analysis revealed that abrupt changes appeared in 1996 for annual PET. The detrending method indicated that substantial decreases in net radiation and wind speed (p < 0.01) were responsible for the decrease in annual PET from 1970 to 1996. Marked increases in minimum temperature and wind speed were the driving forces behind the uptick in annual PET in 1997–2020. At seasonal scales, wind speed and net radiation predominantly influenced PET in 1970–1996 in general. However, from 1997 to 2020, the factors controlling PET fluctuations displayed considerable seasonal variation. Sensitivity analysis showed that Ws and Tmin were the second-most sensitive factors. By exploring the impacts of PET changes and shifts, attention must be paid when allocating water resources reasonably under the background of ongoing climate change and likelihood of future drought. [ABSTRACT FROM AUTHOR]

This article evaluates the effectiveness of the Shelter.Ukraine Initiative, a civic response to the humanitarian crisis resulting from Russia's full-scale invasion of Ukraine. The Initiative aims to address pressing needs through a local umbrella funding approach. Utilizing an evaluative framework that combines multiple approaches, including causal attribution techniques, contribution analysis, and regression analysis, we assess the effectiveness of the Shelter.Ukraine Initiative. We uncover influencing factors that contribute to the Initiative success. These include organizational ambidexterity, learning-by-doing, inter-organizational structure, and advanced technology for financial resource distribution. Our findings underscore the importance of reconfiguring the humanitarian aid architecture to enhance effectiveness. In addition, we suggest that the insights gained from our study can inform future evaluations of humanitarian aid initiatives. By incorporating similar evaluative frameworks and methodologies, researchers can assess the effectiveness of other initiatives and identify key factors contributing to success. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The S(IV)–Fe(II)/PM pretreatment has demonstrated preliminary potential as an effective ultrafiltration (UF) pretreatment technology. However, a comprehensive understanding of its impact on UF membrane fouling control and the dynamic evolution of membrane fouling during prolonged operation is still lacking. In this study, a relatively prolonged fouling experiment was conducted. Results revealed that the S(IV)–Fe(II)/PM pretreatment exhibited superior performance over Al(III) coagulation pretreatment in mitigating the transmembrane pressure difference and addressing both reversible and irreversible membrane fouling. The application of a cluster analysis method to classify membrane fouling evolution stages further confirmed that S(IV)–Fe(II)/PM pretreatment effectively decelerated the rate of membrane fouling evolution. The surface cake layer of UF membranes pretreated with S(IV)–Fe(II)/PM exhibited greater looseness and smoothness. It also showed better results than Al(III) coagulation pretreatment in reducing the accumulation of organic foulants, controlling the Si content and reducing the total microorganisms and live microorganisms in the UF feed water. Variance Partitioning Analysis indicated that the combined contribution of organic, inorganic, and biological foulants was the most significant for UF membranes after S(IV)–Fe(II)/PM pretreatment (50.4%) and UF membranes after Al(III) coagulation pretreatment (70.2%). These findings underscore the efficacy of S(IV)–Fe(II)/PM pretreatment in controlling UF membrane fouling under prolonged operation. HIGHLIGHTS S(IV)/Fe(II)/PM pretreatment efficiently mitigated UF membrane fouling during long-term operation.; S(IV)/Fe(II)/PM pretreatment yielded a long uncontaminated stage and accelerated contaminated stage.; S(IV)/Fe(II)/PM pretreatment suppressed the contribution of organic–inorganic–biological composite fouling.;

This paper introduces a method for predicting the flexural ultimate load of prestressed fiber-reinforced polymer/plastics sheet-reinforced concrete beams. The main work of this paper is as follows: three artificial neural network models of back propagation, Levenberg-Marquardt, and Bayesian Regularization predict the flexural ultimate loads of prestressed FRP-RC and were constructed using a database of 243 concrete beams from various previous studies. The optimal hidden layer node of the neural network is determined as 17 using MSE value, R-value and average error as evaluation metrics. The optimal value of each parameter in the neural network is determined using MSE value as metrics. A series of simulations showed that the Bayesian Regularization model with 17 nodes in the hidden layer best matches the experimental results with root mean square error and correlation coefficient (R) values of 0.002256 and 0.97972, respectively, and an average error of 8.79%. The contribution analysis of the input variables indicates that the flexural capacity of the RC beam was most significantly influenced by its width, elastic modulus, and lower reinforcement, with relative importance of 10.38%, 9.88%, and 9.56%, respectively. Noisy data in the input layer is eliminated using Singular Value Decomposition, optimizing the performance of neural networks.

The body of a high-speed train is a composite structure composed of different materials and structures. This makes the design of a noise-reduction scheme for a car body very complex. Therefore, it is important to clarify the key factors influencing sound insulation in the composite structure of a car body. This study uses machine learning to evaluate the key factors influencing the sound insulation performance of the composite floor of a high-speed train. First, a comprehensive feature database is constructed using sound insulation test results from a large number of samples obtained from laboratory acoustic measurements. Subsequently, a machine learning model for predicting the sound insulation of a composite floor is developed based on the random forest method. The model is used to analyze the sound insulation contributions of different materials and structures to the composite floor. Finally, the key factors influencing the sound insulation performance of composite floors are identified. The results indicate that, when all material characteristics are considered, the sound insulation and surface density of the aluminum profiles and the sound insulation of the interior panels are the three most important factors affecting the sound insulation of the composite floor. Their contributions are 8.5%, 7.3%, and 6.9%, respectively. If only the influence of the core material is considered, the sound insulation contribution of layer 1 exceeds 15% in most frequency bands, particularly at 250 and 500 Hz. The damping slurry contributed to 20% of the total sound insulation above 1000 Hz. The results of this study can provide a reference for the acoustic design of composite structures. [ABSTRACT FROM AUTHOR]

Gravity field and steady-state ocean circulation explorer (GOCE) satellite gravity gradiometry (SGG) data play an important role in modeling static gravity fields, particularly considering the decrease in orbital altitude from 259.5 to 229.0 km during the final 15 months. However, there is limited discussion regarding the contribution of decreases in the GOCE satellite's orbital altitude to the static gravity field solutions. Therefore, by utilizing reprocessed Level-1B SGG data and gravity recovery and climate experiment (GRACE) satellite data, we have solved a satellite-only static gravity field model entitled Tongji-GMMG2021S up to 300 d/o. Subsequently, a quantitative analysis was conducted to assess the contribution of SGG data during the period of low-orbital altitude (LOA) from August 2012 to October 2013 towards the Tongji-GMMG2021S solution. The validation results indicated that the accuracy of the Tongji-GMMG2021S model was comparable to that of the latest combined GOCE/GRACE models. During the LOA period, the analysis conducted at the normal matrix level demonstrated that the SGG data obtained during the LOA period significantly contributed to the solutions at medium and high degrees. Notably, the final 5-month SGG data contributed more than 25% to the Tongji-GMMG2021S solution within the range of 150 to 260 d/o. Furthermore, beyond 180 degrees, the contribution of the solution for the LOA period was even more significant than that for the high-orbital altitude (HOA) period from November 2009 to July 2012. The LOA solution exhibited a standard deviation of 7.24 cm in geoid height error with respect to Tongji-GMMG2021S, resulting in a reduction of 51.15% in standard deviation compared to the HOA solution (14.82 cm).

The body of a high-speed train is a composite structure composed of different materials and structures. This makes the design of a noise-reduction scheme for a car body very complex. Therefore, it is important to clarify the key factors influencing sound insulation in the composite structure of a car body. This study uses machine learning to evaluate the key factors influencing the sound insulation performance of the composite floor of a high-speed train. First, a comprehensive feature database is constructed using sound insulation test results from a large number of samples obtained from laboratory acoustic measurements. Subsequently, a machine learning model for predicting the sound insulation of a composite floor is developed based on the random forest method. The model is used to analyze the sound insulation contributions of different materials and structures to the composite floor. Finally, the key factors influencing the sound insulation performance of composite floors are identified. The results indicate that, when all material characteristics are considered, the sound insulation and surface density of the aluminum profiles and the sound insulation of the interior panels are the three most important factors affecting the sound insulation of the composite floor. Their contributions are 8.5%, 7.3%, and 6.9%, respectively. If only the influence of the core material is considered, the sound insulation contribution of layer 1 exceeds 15% in most frequency bands, particularly at 250 and 500 Hz. The damping slurry contributed to 20% of the total sound insulation above 1000 Hz. The results of this study can provide a reference for the acoustic design of composite structures.

Social care workers benefit from multiple types of evidence to enhance citizen well-being, support their own well-being and improve social care services. Building capacity within social care to find, collect and use different forms of evidence is an international concern. The Developing Evidence Enriched Practice (DEEP) programme in Wales is informed by the values and aims of social pedagogy. It aspires to enhance both the generation and use of evidence in social care. To learn about what works in the programme, we conducted an evaluation based on contribution analysis that explored programme impacts between 2020 and 2023. Based on a co-produced theory of change the evaluation drew on exemplar cases, questionnaire responses, documentary evidence, process data and unsolicited feedback. There was evidence that the DEEP programme contributed to people better valuing and gaining a better understanding of different forms of evidence. Citizen voice could become more central in decision-making, and there were examples of practice, policy and research being informed by diverse evidence. Many people who attended the DEEP learning course enhanced their confidence and skills by using the DEEP approach and said that they would put their learning into practice. It was harder to evidence longer-term impacts and the sustainability of the approach. These findings suggest that there can be merit in developing capacity-building programmes informed by social pedagogy. Such programmes can be characterised as relational, holistic, practice-focused, multifaceted, contextualised and co-produced with intended beneficiaries. [ABSTRACT FROM AUTHOR]

Introduction: Exiting sex work is a complex process which can be facilitated by integrated action on health and its social determinants such as housing and employment. Few programs offer such coordinated support, and even fewer have been evaluated. We assessed if and how Exit Doors Here, a program anchored in the Critical Time Intervention (CTI) model, facilitated women's progress towards their goals, and exit from sex work. Description: We performed a contribution analysis by combining pre-post questionnaire and administrative data from 55 women enrolled in the program (2018-2021), yearly interviews with program staff and peer mentors, and literature reviews to assess program outcomes and mechanisms as described in the theory of change. Discussion: We found evidence that the program contributed to participants progressing on their pre-employment, housing, income, and sex work exiting goals. We identified four "key ingredients" facilitating success: trust building, collaborative goal setting, connecting with community supports and weekly drop-in sessions. Conclusion: This rigorous theory-based evaluation provides much needed evidence on the process and effectiveness of an integrated sex work exiting program. Findings regarding key program ingredients can inform other interventions serving similarly marginalized populations. [ABSTRACT FROM AUTHOR]

Rockburst can cause significant damage to infrastructure and equipment, and pose a substantial risk to the safety of mine workers. Effective prediction of short-term rockburst damage can greatly assist in the implementation of preventive measures and risk mitigation strategies. Therefore, the development of accurate and reliable models for rockburst damage prediction is of great importance. This study has developed a novel hybrid model, based on eXtreme Gradient Boosting (XGBoost) and Sand Cat Swarm Optimization (SCSO), for predicting the scale of short-term rockburst damage. The model, which leveraged a data set of 254 rockburst damage cases collected from rockburst event reports across multiple mines in Australia and Canada, was comprehensively evaluated via non-parametric statistical tests and compared with other prevalent models. The results demonstrate that the SCSO–XGBoost model achieved a test accuracy of 88.46% for the rockburst damage scale, presenting the best comprehensive performance among the models in this study. Furthermore, the SCSO showed significant advantages in reducing overfitting, greatly enhancing the generalization capability of the XGBoost model. Lastly, various model explanation methods were employed to analyze the contribution of input variables. It was found that Peak Particle Velocity (PPV), Geological Structure (GS), Ground Support System Capacity (GSSC), and Stress Condition Factor (SCF) provide the most significant contributions to predicting rockburst damage. This study explains the decision-making rationale behind the SCSO–XGBoost model's identification of different damage scales from both a holistic and sample perspective. In addition, a graphical user interface has been developed and demonstrated for testing purposes. In conclusion, as a study integrating a new SCSO metaheuristic method with the XGBoost ensemble model for rockburst damage identification, the established SCSO–XGBoost model effectively predicts the types of short-term rockburst damage, offering superior predictive performance and explainability compared to previous works. Highlights: This study presents a novel hybrid ensemble model for short-term rockburst damage prediction, combining a new metaheuristic method with the XGBoost model. It provides a comprehensive and detailed discussion on the optimization process and performance evaluation of the hybrid model. The model demonstrates significant advantages in reducing overfitting and enhancing its generalization capability. It exhibits excellent predictive performance and explainability in forecasting the types of short-term rockburst damage. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) serves as a pivotal metric for studying ecohydrological processes. Its dynamics are intricately linked to the interplay among water balance, energy balance, and land use. However, the precise estimation of ET at a regional scale, coupled with a comprehensive understanding of its response to changes in water, heat, and vegetation factors, presents an ongoing challenge. In this study, we assessed the necessity to refine soil evaporation simulations in arid and semiarid regions. Subsequently, we introduced the random forest algorithm to optimize the soil evaporation module of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model to enable a more accurate prediction of ET. Then, we quantitatively explored the correlation between ET and pertinent factors, such as soil moisture, solar radiation, and the normalized difference vegetation index (NDVI) in the Inner Mongolia section of the Yellow River Basin (IMSYRB), which is a typical arid and semiarid region. The findings underscore that soil evaporation is the predominant constituent of ET in the IMSYRB. Moreover, soil moisture, as opposed to relative humidity, better represent the near-surface moisture conditions within the study area. The refined model (PT-JPL-RF) exhibited enhanced simulation performance at both individual stations and in the overall region for ET. Furthermore, the application of the PT-JPL-RF model for scrutinizing the spatiotemporal ET variations in the study area revealed an average ET of 250 mm over the past four decades. Since 1982, a fluctuating upward trend of 0.99 mm/year has been observed. Spatially, ET exhibits an eastward distribution, with elevated values in the eastern sectors compared to those in the western regions, particularly in the Big Black River Basin (296 mm). Vegetation was the primary factor influencing ET variations in the IMSYRB, contributing to an annual increase by 1.58 mm. These findings demonstrate the benefits of integrating machine learning algorithms with physical models and provide a practical example for accurately simulating and predicting ET across diverse regions.

Optimizing the structure of the carbon metabolic network is essential for enhancing network performance. However, a knowledge gap remains in systematically evaluating the network structure and comprehensively identifying the key nodes and paths, particularly in a carbon metabolism system that involves the frequent transition of carbon element across the air, water, and soil mediums. Hence, taking Guangzhou, China, as a case study, we constructed a cross-media metabolism network model based on material flow analysis. Subsequently, the general structural characteristics and key nodes/paths of the network were investigated through the integration of trophic structure analysis, contribution analysis, and stability analysis in ecological network analysis. The results primarily suggest that the connections among nodes in the carbon metabolism network have become looser, and the metabolic system structure was in a medium state with some defects from 2010 to 2020. Sectors of construction and transportation, as well as carbon flows from the agriculture sector to the construction sector and the energy conversion sector to the transportation sector, were identified as the key nodes and paths influencing the structural characteristics. Subsequently, twenty-one optimization scenarios were developed, with the results suggesting the advantage of collaborative regulation of key nodes/paths (e.g., increasing the carbon flows both from the agriculture sector to the construction sector and from the energy conversion sector to the transportation sector by 50%). This work may provide useful guidance for the structure optimization of the carbon metabolism system.

Potential evapotranspiration (PET), as an indicator of atmospheric evaporative demand, is a critical hydrological and meteorological factor to reflect regional and global hydrological cycles and environmental change. Understanding these nuanced responses of PET to environmental changes is important for agricultural production and water demand estimation. This study rigorously evaluated fluctuations in PET using the Penman–Monteith model over a 50-year span from 1970 to 2020 in the Sichuan–Chongqing region, an area notably susceptible to climate change. The changing characteristics of PET and local meteorological factors were detected by integrating the Mann–Kendall method and Pettitt test. Furthermore, the contribution and sensitivity of key meteorological variables to the observed variation in PET were also thoroughly investigated. Breakpoint analysis revealed that abrupt changes appeared in 1996 for annual PET. The detrending method indicated that substantial decreases in net radiation and wind speed (p < 0.01) were responsible for the decrease in annual PET from 1970 to 1996. Marked increases in minimum temperature and wind speed were the driving forces behind the uptick in annual PET in 1997–2020. At seasonal scales, wind speed and net radiation predominantly influenced PET in 1970–1996 in general. However, from 1997 to 2020, the factors controlling PET fluctuations displayed considerable seasonal variation. Sensitivity analysis showed that Ws and Tmin were the second-most sensitive factors. By exploring the impacts of PET changes and shifts, attention must be paid when allocating water resources reasonably under the background of ongoing climate change and likelihood of future drought.

Social care workers benefit from multiple types of evidence to enhance citizen well-being, support their own well-being and improve social care services. Building capacity within social care to find, collect and use different forms of evidence is an international concern. The Developing Evidence Enriched Practice (DEEP) programme in Wales is informed by the values and aims of social pedagogy. It aspires to enhance both the generation and use of evidence in social care. To learn about what works in the programme, we conducted an evaluation based on contribution analysis that explored programme impacts between 2020 and 2023. Based on a co-produced theory of change the evaluation drew on exemplar cases, questionnaire responses, documentary evidence, process data and unsolicited feedback. There was evidence that the DEEP programme contributed to people better valuing and gaining a better understanding of different forms of evidence. Citizen voice could become more central in decision-making, and there were examples of practice, policy and research being informed by diverse evidence. Many people who attended the DEEP learning course enhanced their confidence and skills by using the DEEP approach and said that they would put their learning into practice. It was harder to evidence longer-term impacts and the sustainability of the approach. These findings suggest that there can be merit in developing capacity-building programmes informed by social pedagogy. Such programmes can be characterised as relational, holistic, practice-focused, multifaceted, contextualised and co-produced with intended beneficiaries.

Background: The practice of giving water before 6 mo of age is the biggest barrier to exclusive breastfeeding in West and Central Africa. To address this challenge, a regional initiative, "Stronger with Breastmilk Only" (SWBO), was rolled out at country level in several countries of the region. Objective: We examined the implementation process of the SWBO initiative and the contribution of its advocacy component to a more supportive environment for breastfeeding policies and programs. Methods: This study was based on 2 assessments at the national level carried out in 5 countries (Burkina Faso, Chad, Democratic Republic of the Congo, Senegal, and Sierra Leone) using qualitative methods. We combined 2 evaluative approaches (contribution analysis and outcome harvesting) and applied 2 theoretical lenses (Breastfeeding Gear Model and Consolidated Framework for Implementation Research) to examine the implementation process and the enabling environment for breastfeeding. Data sources included ~300 documents related to the initiative and 43 key informant interviews collected between early 2021 and mid-2022. Results: First, we show how a broad initiative composed of a set of combined interventions targeting multiple levels of determinants of breastfeeding was set up and implemented. All countries went through a similar pattern of activities for the implementation process. Second, we illustrate that the initiative was able to foster an enabling environment for breastfeeding. Progress was achieved notably on legislation and policies, coordination, funding, training and program delivery, and research and evaluation. Third, through a detailed contribution story of the case of Burkina Faso, we illustrate more precisely how the initiative, specifically its advocacy component, contributed to this progress. Conclusion: This study shed light on how an initiative combining a set of interventions to address determinants of breastfeeding at multiple levels can be implemented regionally and contributes to fostering an enabling environment for breastfeeding at scale. [ABSTRACT FROM AUTHOR]

Copyright of Gruppe. Interaktion. Organisation: Zeitschrift für Angewandte Organisationspsychologie (GIO) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Exiting sex work is a complex process which can be facilitated by integrated action on health and its social determinants such as housing and employment. Few programs offer such coordinated support, and even fewer have been evaluated. We assessed if and how Exit Doors Here, a program anchored in the Critical Time Intervention (CTI) model, facilitated women’s progress towards their goals, and exit from sex work. Description: We performed a contribution analysis by combining pre-post questionnaire and administrative data from 55 women enrolled in the program (2018–2021), yearly interviews with program staff and peer mentors, and literature reviews to assess program outcomes and mechanisms as described in the theory of change. Discussion: We found evidence that the program contributed to participants progressing on their pre-employment, housing, income, and sex work exiting goals. We identified four “key ingredients” facilitating success: trust building, collaborative goal setting, connecting with community supports and weekly drop-in sessions. Conclusion: This rigorous theory-based evaluation provides much needed evidence on the process and effectiveness of an integrated sex work exiting program. Findings regarding key program ingredients can inform other interventions serving similarly marginalized populations.

Introduction/backgroundCourse evaluation in health education is a common practice yet few comprehensive evaluations of health education exist that measure the impact and outcomes these programs have on developing health graduate capabilities.Aim/objectivesTo explore how curricula contribute to health graduate capabilities and what factors contribute to the development of these capabilities.MethodsUsing contribution analysis evaluation, a six-step iterative process, key stakeholders in the six selected courses were engaged in an iterative theory-driven evaluation. The researchers collectively developed a postulated theory-of-change. Then evidence from existing relevant documents were extracted using documentary analysis. Collated findings were presented to academic staff, industry representatives and graduates, where additional data was sought through focus group discussions - one for each discipline. The focus group data were used to validate the theory-of-change. Data analysis was conducted iteratively, refining the theory of change from one course to the next.ResultsThe complexity in teaching and learning, contributed by human, organizational and curriculum factors was highlighted. Advances in knowledge, skills, attitudes and graduate capabilities are non-linear and integrated into curriculum. Work integrated learning significantly contributes to knowledge consolidation and forming professional identities for health professional courses. Workplace culture and educators’ passion impact on the quality of teaching and learning yet are rarely considered as evidence of impact.DiscussionCapturing the episodic and contextual learning moments is important to describe success and for reflection for improvement. Evidence of impact of elements of courses on future graduate capabilities was limited with the focus of evaluation data on satisfaction.ConclusionContribution analysis has been a useful evaluation method to explore the complexity of the factors in learning and teaching that influence graduate capabilities in health-related courses.

Expanding on Haeckel's classical definition, ecology can be defined as the study of strong and weak interactions between the organism and the environment, hence the need for identifying strong interactions as major drivers of population and community dynamics. The solution to this problem is facilitated by the fact that the frequency distribution of interaction strengths is highly skewed, resulting in few or, according to Liebig's law of the minimum, just one strong interaction. However, a single strong interaction often remains elusive. One of the reasons may be that, due to the ever‐present dynamics of ecological systems, a single strong interaction is likely to exist only on relatively short time intervals, so methods with sufficient temporal resolution are required. In this paper, we study the temporal resolution of contribution analysis of birth rate in zooplankton, a method to assess the relative strength of bottom‐up (food) versus top‐down (predation) effects. Birth rate is estimated by the Edmondson–Paloheimo model. Our test system is a population of the cladoceran Bosmina longirostris inhabiting a small northern lake with few planktivorous predators, and thus likely controlled by food. We find that the method's temporal resolution in detecting bottom‐up effects corresponds well to the species' generation time, and the latter seems comparable to the lifetime of a single strong interaction. This enables one to capture a single strong interaction "on the fly," right during its time of existence. We suggest that this feature, the temporal resolution of about the lifetime of a single strong interaction, may be a generally desirable property for any method, not only the one studied here, intended to identify and assess strong interactions. Success in disentangling strong interactions in ecological communities, and thus solving one of the key issues in ecology, may critically depend on the temporal resolution of the methods used. [ABSTRACT FROM AUTHOR]

Analyzing the characteristics and causes of runoff variation in a typical small basin is beneficial for ecological restoration in the Loess Plateau. This study employed a series of statistical methodologies to examine the characteristics of meteorological changes and underlying surface evolution in the Qishui River Basin (QRB). To differentiate the impacts of climate change and human activities on runoff variation, we applied the Choudhury–Yang formula and the Double Mass Curve (DMC) method. Subsequently, by incorporating future watershed protection strategies and various SSP scenarios, we utilized the Soil and Water Assessment Tool to simulate future runoff while employing the DMC to identify underlying causes of runoff variation. The results suggested that human activity has a slightly greater impact than climate change on reducing runoff during the historical period, with only a 1% difference. However, this will change in the future as human impact becomes increasingly significant. Human activities such as afforestation have dual effects, encompassing positive effects such as improving water quality and mitigating soil erosion, as well as negative consequences such as diminishing local water availability and exacerbating drought. Effective policies should be implemented, involving the use of appropriate tree species and planting methods, finding an appropriate value of forest area, monitoring and evaluation, etc., in order to ensure that the policies are aligned with the broader social, economic, and environmental goals of the QRB. These findings provide valuable guidance for policy-makers in developing management strategies for future environmental changes. [ABSTRACT FROM AUTHOR]

In previous works, the authors have shown the feasibility of using classical and robust principal component analysis for damage detection on structures when ultrasonic guided waves are used. It has also been demonstrated that robust principal component analysis presents a higher probability of detection accuracy when data are corrupted. In the present work, a robust principal component analysis orthogonal distance is proposed as a new feature for damage detection strategy based on ultrasonic guided waves on structures subjected to uniform temperature changes. The effect of this temperature fluctuation on the signal propagation and also in the new feature is analyzed. Temperature compensation is applied to mitigate the effect of temperature changes on the reliability of the damage detection methodology. The proposed feature and damage detection strategy that considers these effects are tested on two structures: a laboratory scale composite plate and a large-scale complex composite that is representative of a component from an aerospace application. The promising result proves the ability of the new feature as a damage detection tool.

Elevation plays a crucial role in modulating the spatiotemporal distributions of climatic variables in mountainous regions, which affects water and energy balances, among which reference evapotranspiration (ET0) is a key hydrological indicator. However, the response of ET0 to climate change with elevation continues to be poorly understood, especially in the Tibetan Plateau (TP) which has elevation variations of more than 4,000 m. The spatiotemporal variations of ET0 with elevation were investigated using long‐term (1960–2017) meteorological observations from 82 stations on the TP. The results suggest that the average annual ET0 showed an insignificant increasing trend. A significant negative correlation between ET0 and elevation was found (p <.01). The positive trends of ET0 decreased with elevation, whereas the negative trends of ET0 increased significantly with elevation (p <.05). The magnitude of trends of ET0 becomes smaller at higher‐elevation stations. Sensitivity analysis indicated that ET0 was most sensitive to shortwave radiation (Rs). Moreover, the sensitivities of temperature (T) and wind speed (U) significantly decreased with elevation, whereas those of Rs and vapour pressure deficit (VPD) increased slightly with elevation. The contribution and path analyse indicated that increasing VPD was the dominant contributor to the increase in ET0. The effect of elevation on ET0 variation mainly depended on the tradeoff between the contributions of U and VPD. U was the largest contributing factor for the change in ET0 below 2,500 m, whereas VPD was the primary contributor to the increase in ET0 above 2,500 m. This study provides insights into the response of ET0 to climate change with elevation on the TP, which is of great significance to hydrometeorological processes in high‐altitude regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Program Evaluation is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Expanding on Haeckel's classical definition, ecology can be defined as the study of strong and weak interactions between the organism and the environment, hence the need for identifying strong interactions as major drivers of population and community dynamics. The solution to this problem is facilitated by the fact that the frequency distribution of interaction strengths is highly skewed, resulting in few or, according to Liebig's law of the minimum, just one strong interaction. However, a single strong interaction often remains elusive. One of the reasons may be that, due to the ever‐present dynamics of ecological systems, a single strong interaction is likely to exist only on relatively short time intervals, so methods with sufficient temporal resolution are required. In this paper, we study the temporal resolution of contribution analysis of birth rate in zooplankton, a method to assess the relative strength of bottom‐up (food) versus top‐down (predation) effects. Birth rate is estimated by the Edmondson–Paloheimo model. Our test system is a population of the cladoceran Bosmina longirostris inhabiting a small northern lake with few planktivorous predators, and thus likely controlled by food. We find that the method's temporal resolution in detecting bottom‐up effects corresponds well to the species' generation time, and the latter seems comparable to the lifetime of a single strong interaction. This enables one to capture a single strong interaction “on the fly,” right during its time of existence. We suggest that this feature, the temporal resolution of about the lifetime of a single strong interaction, may be a generally desirable property for any method, not only the one studied here, intended to identify and assess strong interactions. Success in disentangling strong interactions in ecological communities, and thus solving one of the key issues in ecology, may critically depend on the temporal resolution of the methods used.

Study region: The Tibetan Plateau Study focus: Evapotranspiration (ET) plays a critical role in the water balance, energy budget, and carbon cycle. However, the variations, trends, and controls of ET on the Tibetan Plateau (TP) are poorly understood because of uncertainties in ET estimates and sparse observations. In this study, the variations in ET and its components and their drivers and controls in the TP were analyzed at seasonal and annual scales during 1982–2015. New hydrological insights for the region: Spatially, the multiyear mean annual ET decreased from the southeastern to northwestern TP. Canopy transpiration (Ec) was the main component of ET (52.7%), followed by soil evaporation (Es) (34.4%) and interception (Ei) (10.7%). Regionally, the averaged ET and its components increased significantly at the seasonal and annual scales. Spatially, the controlling factor for ET changed from water to energy as the climatic zones transferred from aridity to humidity. The annual ET was controlled by soil moisture (SM) in arid and semi-arid zones, whereas Ta was the dominant factor in the other regions. The increased annual Es and Ei were primarily caused by SM, while the annual Ec was determined by Ta. In addition, NDVI played a certain role in regulating the annual Ec and Ei variations. This study improves our understanding of hydrological processes and water resource management under global climate change.

Accurate prediction of ambient PM2.5 concentrations using air quality models can provide governments with information for public health alerts. However, due to large uncertainties of input parameters and over‐simplification of the chemical mechanism, the model simulations tend to have a certain deviation from the observations. To provide an insight into the discrepancy and to explain the contributors to the model bias, we propose here a machine learning based method to identify the contributors to PM2.5 simulation biases. A fully connected deep neural network (noted as FCNN) was designed to correct the PM2.5 biases between the simulations from a common air quality model (i.e., Community Multiscale Air Quality, CMAQ) and observations with meteorological and pollutants variables. The FCNN was applied in two polluted regions in China including Beijing‐Tianjin‐Hebei (BTH) and Yangtze River Delta (YRD) in 2015, exhibiting excellent performance in reducing the root mean square error of annual PM2.5 by 46.6% and 37.2%, respectively. The relative contribution of each input feature for the bias correction was also estimated from the FCNN. Results suggest that the temperature and humidity exhibit the greatest contribution to the PM2.5 simulation bias among all meteorological factors, probably due to their high association with the physical and chemical reaction conditions. NO2 and SO2 concentrations and associated biases were also found to be crucial to CMAQ model accuracy, implying the importance of NO2‐ and SO2‐related reaction for PM2.5 formation. The study also revealed a cumulative effect of pollution and an enhancement effect of atmospheric oxidation on the formation of heavy pollution. Plain Language Summary: The study aims to provide a better understanding of the causes of the Community Multiscale Air Quality model (CMAQ) bias during the bias correction, compared to exclusively eliminating the inherent bias using statistical models. We proposed fully connected deep neural networks in two polluted regions in China ‐ Beijing‐Tianjin‐Hebei and Yangtze River Delta ‐ in 2015 as bias correction, during which the relative contribution of the variables under different meteorological conditions across different seasons has been calculated. We found that the temperature and humidity exhibit the greatest contribution to the PM2.5 simulation bias among all meteorological factors, probably due to their high association with the physical and chemical reaction conditions. NO2 and SO2 concentrations and biases were also found to be crucial to the improvement of CMAQ model accuracy, implying the importance of NO2‐ and SO2‐related reaction for PM2.5 formation. By introducing cutting edge artificial intelligence (AI) technologies, we provided an insight into the discrepancy and explain the contributors to the model bias in severe polluted regions, which can help with the improvement in PM2.5 forecasts and timely control strategy design. Key Points: The under‐prediction of PM2.5 by the CMAQ model over two polluted regions in China prevails throughout the year and is particularly significant in heavy pollutionThe fully connected neural network effectively captured the pattern of the PM2.5 simulation biasNO2 & SO2 concentrations and biases are found to be the keys to improving the accuracy of the CMAQ model [ABSTRACT FROM AUTHOR]

The paper addresses the challenges of evaluating the impact of business coaching programmes with a varied portfolio of firms working across sectors and countries. Observable indicators of changes in business management practices are rarely relevant across sectors. Therefore, evaluators need to rely on the perceptions of the managers who have received coaching. We designed an online survey to compare the effectiveness of business coaching within a portfolio and across programmes. The survey was applied to the portfolio of two private sector development programmes. We derived so-called 'contribution scores' from individuals' perceptions of how business management practices had changed and their perceptions of the role of business coaching in bringing about these changes. The survey included some features to reflect on response reliability. We show that the tool seems fairly reliable for comparative analysis and helped to identify the types of firms and contexts where business coaching support appears more effective. [ABSTRACT FROM AUTHOR]

Social care workers benefit from multiple types of evidence to enhance citizen well-being, support their own well-being and improve social care services. Building capacity within social care to find, collect and use different forms of evidence is an international concern. The Developing Evidence Enriched Practice (DEEP) programme in Wales is informed by the values and aims of social pedagogy. It aspires to enhance both the generation and use of evidence in social care. To learn about what works in the programme, we conducted an evaluation based on contribution analysis that explored programme impacts between 2020 and 2023. Based on a co-produced theory of change the evaluation drew on exemplar cases, questionnaire responses, documentary evidence, process data and unsolicited feedback. There was evidence that the DEEP programme contributed to people better valuing and gaining a better understanding of different forms of evidence. Citizen voice could become more central in decision-making, and there were examples of practice, policy and research being informed by diverse evidence. Many people who attended the DEEP learning course enhanced their confidence and skills by using the DEEP approach and said that they would put their learning into practice. It was harder to evidence longer-term impacts and the sustainability of the approach. These findings suggest that there can be merit in developing capacity-building programmes informed by social pedagogy. Such programmes can be characterised as relational, holistic, practice-focused, multifaceted, contextualised and co-produced with intended beneficiaries. [ABSTRACT FROM AUTHOR]

Abstract Operational transfer path analysis (OTPA) is an advanced vibration and noise transfer path identification and contribution evaluation method. However, the application of OTPA to rail transit vehicles considers only the excitation amplitude and ignores the influence of the excitation phase. This study considers the influence of the excitation amplitude and phase, and analyzes the contribution of the secondary suspension path to the floor vibration when the metro vehicle runs at 60 km/h, using an analysis based on the OTPA method. The results show that the vertical direction of the anti-rolling torsion bar area provides the maximum contribution to the floor vibration, with a contribution of 22.1%, followed by the longitudinal vibration of the air spring area, with a contribution of 17.1%. Based on the contribution analysis, a transfer path optimization scheme is proposed, which may provide a reference for the optimization of the transfer path of metro vehicles in the future.

The use of complexity-based evaluation methods remains relatively underexplored in the field of evaluation. While increasingly employed to assess complex interventions, Contribution Analysis (CA) continues to suffer from a lack of operationalization. In this article, we propose enhancing the implementation of CA by leveraging Participatory Systems Mapping as a tool to delineate and scrutinize contribution narratives. The research has a dual objective. It aims to experiment with the recently introduced Participatory Systems Mapping method in the field of evaluation while providing a tool for examining contribution narratives. The combination of CA and Participatory Systems Mapping was tested by evaluating a youth employability project. This approach enabled a collaborative and in-depth examination of impact stories and alternative explanations. It proved valuable not only in managing the complexity of the intervention but also in strengthening the implementation of CA. [ABSTRACT FROM AUTHOR]