Planning Guidance Research Articles

Advisory groups in realist reviews: Systematically mapping current research and recommendations for practice

AbstractIntroductionRealist reviews may involve groups or panels external to the research team who provide external and independent perspectives informing the review based on their experience of the topic area. These panels or groups are termed in this study as an “advisory group.” This study aims to map current practice of advisory groups in realist reviews and provide guidance for planning and reporting.MethodsA “best‐fit” framework synthesis methodology was used by first searching for a best‐fit framework and then conducting a systematic search to identify a sample of realist reviews and rapid realist reviews (RRRs) from the most recent year, 2021. Nine databases were searched: CINAHL Complete, Cochrane, Embase, ERIC, MEDLINE, PsycInfo, Social Services Abstracts, Sociological Abstracts, and Web of Science Core Collection. Screening and data extraction was conducted by two researchers. The chosen best‐fit framework (ACTIVE framework) informed the data extraction tool.ResultsOne hundred and seven reviews (93 realist reviews, 14 RRRs) were identified for inclusion. Of these, 40% (n = 37) of realist reviews and 71.5% (n = 10) of RRRs mentioned use of an advisory group, though there was considerable variation in terminology used. Individuals in advisory groups were involved at varying stages of the review and tended to bring experience in the topic area from the perspective of (i) a lived experience, i.e., patients, carers, family members (n = 15 realist reviews; n = 4 RRRs); (ii) professional experience, such as healthcare professionals (n = 20 realist reviews; n = 6 RRRs); or (iii) policy or research experience in the topic area (n = 19 realist reviews; n = 7 RRRs).ConclusionsThis study proposes a definition of advisory groups, considerations for advisory group use, and suggested items for reporting. The purpose of the advisory group should be carefully considered when deciding on their use in a realist review.

Non-interference slow tool servo turning method for complex surfaces with large undulation changes

Complex curved surface parts are integral to the mechanical industry, demanding precise machining techniques. Multi-axis slow tool servo turning has emerged as an efficient method due to its high precision and quality. However, the challenge of severe tool flank interference arises, especially on surfaces with large undulation changes. Conventional methods, such as increasing tool clearance angles or adding extra tool axes, detrimentally affect machining precision and stability. To address this, a non-interference toolpath planning method is proposed for complex surfaces with large undulation changes. The approach begins with establishing geometric models of the single-point diamond turning tool and typical complex surfaces, followed by analyzing the mechanism and evolution of tool flank interference. By considering geometric parameters and vector relationships, the practical tool clearance angles are calculated. And then a criterion for subregion division is introduced and the non-cutting stroke is designed, accounting for the maximal acceleration of machine tool axis. Multi-pass toolpaths are generated to navigate different subregions while avoiding interference by transforming spindle rotation direction. Furthermore, a method is proposed to optimize toolpath stitching in practical machining regions, ensuring continuous and smooth transitions while maintaining machining precision and surface quality. Experimental validation, conducted on a hyperbolic paraboloid surface with large undulation changes, demonstrates a significant reduction in surface roughness (Sa) from 380.503 nm to 75.664 nm—a reduction of 80.115 %. The proposed method not only enhances machining precision and surface quality but also extends tool life and improves working conditions. This research offers vital guidance for non-interference toolpath planning in engineering practice.

The correlation between left atrium voltage mapping and 3D late gadolinium enhancement cardiac magnetic resonance imaging findings in patients with known atrial fibrillation

Abstract Introduction The non-invasive cardiac magnetic resonance imaging-based late gadolinium enhancement (LGE-CMR) of the left atrium (LA) fibrosis distribution and degree can be utilized for preprocedural planning and real-time guidance of AF ablation. There is an ongoing discussion with somewhat conflicting results regarding the accuracy of LGE-CMR of the LA as compared with the invasive electroanatomical mapping (EAM). Earlier data mainly encompassed post-ablation patients with an EAM involving a relatively small number of acquired points. Aim To evaluate the accuracy of LGE-CMR-based LA fibrosis using the ADAS 3D LA software among ablation-naive AF patients as compared with EAM using the CARTO-3 navigation system. Methods Patients with non-permanent AF, naive to previous ablation/cardiac surgery, who underwent CARTO-based AF ablation with preceding LGE-CMR scan were selected for the study. The 3D substrate atrial maps were generated based on the LGE-CMR (CMR-fib) using the ADAS 3D LA software and the CARTO EAM (EAM-fib). A fibrosis threshold of 1.2-1.32 was established for the CMR-fib using the Image Intensity Ratio whereas a threshold of 0.2-0.5mV was applied for the EAM-fib. The two 3D-maps were assessed for fibrosis using anatomically synchronized quantitative point by point comparison. The EAM was set as the gold standard. Results The study included 26,777 points acquired from 17 patients (aged 65.8±7.92 yrs with 6 females and 10 paroxysmal AF cases). CMR-fib showed 22.0% of LGE, while the EAM-fib demonstrated 48.8% of low voltage areas. The CMR-fib vs. EAM-fib point by point comparison concluded with an agreement of 73.1% and interrater reliability of 0.45 (p<0.001). The sensitivity and specificity were 45.0% and 99.9%, respectively, while the PPV and NPV were 99.8% and 65.6%, respectively. The Area Under the Curve (AUC) was calculated as 72.4. Increase of the EAM-fib threshold for scar (to 0.5-1.5mV) led to a further decrease in the assessment diagnostic performance and the interrater reliability. The lowest agreement was tested in the LA roof segments and the higher in the inferoseptal and posterior walls. Conclusion The LGE-CMR underestimates the presence of scar as presented by the EAM, however, those detected are mostly accurate. The low sensitivity that was demonstrated between the two methods may be attributed to the lower spatial resolution of the CMR as well as the superficial subendocardial layer mapped by EAM as compared with the averaged transmural mapping of the CMR-fib. A follow-up larger-scale study is required to assess the accuracy of LGE-CMR-based assessment of fibrosis, considering various wall thicknesses and establishing the scar threshold on the ADAS software, particularly for ablation-naive patients.

Dynamic evolution and trend prediction in coupling coordination between urban and rural space utilization efficiency based local and tele-coupling model

Optimizing the pattern of territorial space utilization is one of the key tasks to achieve the sustainable development goals. With the accelerating rate of global urbanization, the understanding of territorial space utilization efficiency, role and potential is a prerequisite for alleviating contradictions in urban and rural space distribution. The city cluster is the main form of organization for urban development in future, so the study attempted to explore the urban and rural space utilization efficiency (URSUE) in Chengdu-Chongqing urban agglomeration (CCEC) from coupling coordination degree (CCD) perspective. Considering the gradual increase in the trend of remote interactions between URSUE, we further introduced the Local and Tele-coupling coordination (LTCCD) model that takes into account interactive development relationship between different systems. The results of the study show that: In CCEC, the more economically developed cities indicated that urban spatial utilization efficiency lags behind rural spatial utilization efficiency; The LTCCD in the geographic center region will indicate a higher level but the LTCCD in the economic core cities is higher compared with their CCD level, especially in Chengdu City. This suggests that the LTCCD model is better able to take into account regional development correlations and spatial spillovers effect. This study attempts to explore several key issues of urban-rural spatial allocation in the process of urbanization development and to provide guidance for the territorial space utilization planning in urban agglomerations.

Semi-supervised segmentation of abdominal organs and liver tumor: uncertainty rectified curriculum labeling meets X-fuse

Precise liver tumors and associated organ segmentation hold immense value for surgical and radiological intervention, enabling anatomical localization for pre-operative planning and intra-operative guidance. Modern deep learning models for medical image segmentation have evolved from convolution neural networks to transformer architectures, significantly boosting global context understanding. However, accurate delineation especially of hepatic lesions remains an enduring challenge due to models’ predominant focus solely on spatial feature extraction failing to adequately characterize complex medical anatomies. Moreover, the relative paucity of expertly annotated medical imaging data restricts model exposure to diverse pathological presentations. In this paper, we present a three-phrased cascaded segmentation framework featuring an X-Fuse model that synergistically integrates spatial and frequency domain’s complementary information in dual encoders to enrich latent feature representation. To enhance model generalizability, building upon X-Fuse topology and taking advantage of additional unlabeled pathological data, our proposed integration of curriculum pseudo-labeling with Jensen–Shannon variance-based uncertainty rectification promotes optimized pseudo supervision in the context of semi-supervised learning. We further introduce a tumor-focus augmentation technique including training-free copy-paste and knowledge-based synthesis that show efficacy in simplicity, contributing to the substantial elevation of model adaptability on diverse lesional morphologies. Extensive experiments and modular evaluations on a holdout test set demonstrate that our methods significantly outperform existing state-of-the-art segmentation models in both supervised and semi-supervised settings, as measured by the Dice similarity coefficient, achieving superior delineation of bones (95.42%), liver (96.26%), and liver tumors (89.53%) with 16.41% increase comparing to V-Net on supervised-only and augmented-absent scenario. Our method marks a significant step toward the realization of more reliable and robust AI-assisted diagnostic tools for liver tumor intervention. We have made the codes publicly available [https://github.com/lyupengju/X-Fuse].

Clinical and Surgical Applications of Large Language Models: A Systematic Review.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

Normscan: open-source Python software to create average models from CT scans.

Age-matched average 3D models facilitate both surgical planning and intraoperative guidance of cranial birth defects such as craniosynostosis. We aimed to develop an algorithm that accepts any number of CT scans as input and generates highly accurate, average models with minimal user input that are ready for 3D printing and clinical use. Using a compiled database of 'normal' pediatric computed tomography (CT) scans, we report Normscan, an open-source platform built in Python that allows users to generate normative models of CT scans through user-defined landmarks. We use the basion, nasion, and left and right porions as anatomical landmarks for initial correspondence and then register the models using the iterative closest points algorithm before downstream averaging. Normscan is fast and easy to use via our user interface and also creates highly accurate average models of any number of input models. Additionally, it is highly repeatable, with coefficients of variance for the surface area and volume of the average model being less than 3% across ten independent trials. Average models can then be 3D printed and/or visualized in augmented reality. Normscan provides an end-to-end pipeline for the creation of average models of skulls. These models can be used for the generation of databases of specific demographic anatomical models as well as for intraoperative guidance and surgical planning. While Normscan was designed for craniosynostosis repair, due to the modular nature of the algorithm, Normscan has many applications in other areas of surgical planning and research.

Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.

Investigating the use of protective technologies after data breach: The roles of psychological distance, technological service type and organizational justice

Protective information technologies (PITs) are crucial for preventing security and privacy issues, but their application by customers following a data breach has not been thoroughly studied in the past. In this work, we define post-data breach protective technologies (PDPT), an initiative-taking protective technology, as the use of PIT in the aftermath of a data breach. Based on the kinds of services violated, we hypothesize that users' psychological understanding of protective technology and the organization's justice procedures affect victims' intent to use PDPT. We validate two theoretical hypotheses for PDPT adoption by utilizing survey responses from consumers across utility-benefit and social-benefit services after data breaches. For practice, our research provides guidance for planning and encouraging the implementation of protective technology in the wake of a privacy crisis or data breach. In general, we draw the conclusion that a data breach tends to heighten the victims’ awareness of the breach, impact justice perceptions, and alter the distant psychological knowledge about the service benefits of protective technologies post data breach.

Food supply chain resilience in the digital era: a bibliometric analysis and development of conceptual framework

PurposeFood supply chain resilience is a critical aspect in ensuring the continuous and reliable flow of food, particularly in the face of disruptions. This study aims to address specific gaps in the existing literature by conducting a bibliometric analysis. The primary objective is to identify key areas of concern and lacunae related to disruptions and resilience within the food supply chain. The study also strives to contribute to the field by developing a comprehensive framework that evaluates the factors influencing resilience. Furthermore, the research intends to propose effective strategies for mitigating and recovering from disruptions, emphasizing the urgency of these measures in light of identified gaps in the current body of literature.Design/methodology/approachTo achieve these objectives, the authors extracted the most relevant papers from Scopus and Web of Science (WoS) databases. The analysis parameters included a comprehensive review of current food supply chain practices and an exploration of trending research topics, such as sustainability, adaptability, circular economy and agility. Notably, the study recognized the pervasive impact of COVID-19 on food supply chain disruptions, with a high occurrence in the literature. Using advanced analytics tools like VOSviewer and Biblioshiny, the research delved into the role of modern technologies, including Industry 4.0, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and blockchain in addressing disruptions and enhancing resilience.FindingsThe research reveals a significant impact of the COVID-19 pandemic on food supply chain disruptions, underscoring the critical need for strategies to bolster resilience. Notably, the study identifies the pivotal role of modern technologies (Industry 4.0, IoT, AI, ML and blockchain) in mitigating disruptions and enhancing resilience in the food supply chain. The bibliometric analysis conducted through VOSviewer and Biblioshiny provides valuable insights into research trends and focal areas within the literature.Practical implicationsThe observed importance of Industry 4.0, IoT, AI, ML and blockchain implies a practical need for integrating these technologies into food supply chain operations. Moreover, the paper discusses strategies for reducing the impact caused by disruptions, providing practical guidance for resilience planning in food supply chains. Researchers can leverage the findings to direct future efforts toward areas with identified gaps and opportunities, fostering advancements in the field and offering practical insights for real-world applications.Originality/valueBy amalgamating insights from bibliometric analysis and the developed framework, this study contributes to a holistic understanding of the challenges and opportunities in fortifying the resilience of the food supply chain. The identified factors and strategies offer valuable insights for researchers and practitioners seeking to address disruptions in food supply chains. The study’s unique contribution lies in bridging theoretical perspectives with practical applications, enhancing the relevance of business-to-business/industrial supply chain theories.

Exposure of wetlands important for nonbreeding waterbirds to sea-level rise in the Mediterranean.

Sea-level rise (SLR) is expected to cause major changes to coastal wetlands, which are among the world's most vulnerable ecosystems and are critical for nonbreeding waterbirds. Because strategies for adaptation to SLR, such as nature-based solutions and designation of protected areas, can locally reduce the negative effects of coastal flooding under SLR on coastal wetlands, it is crucial to prioritize adaptation efforts, especially for wetlands of international importance for biodiversity. We assessed the exposure of coastal wetlands important for nonbreeding waterbirds to projected SLR along the Mediterranean coasts of 8 countries by modeling future coastal flooding under 7 scenarios of SLR by 2100 (from 44- to 161-cm rise) with a static inundation approach. Exposure to coastal flooding under future SLR was assessed for 938 Mediterranean coastal sites (≤30km from the coastline) where 145 species of nonbreeding birds were monitored as part of the International Waterbird Census and for which the monitoring area was delineated by a polygon (64.3% of the coastal sites monitored in the Mediterranean region). Thirty-four percent of sites were threatened by future SLR, even under the most optimistic scenarios. Protected study sites and study sites of international importance for waterbirds were, respectively, 1.5 and 2 times more exposed to SLR than the other sites under the most optimistic scenario. Accordingly, we advocate for the development of a prioritization scheme to be applied to these wetlands for the implementation of strategies for adaptation to SLR to anticipate the effects of coastal flooding. Our study provides major guidance for conservation planning under global change in several countries of the Mediterranean region.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Large-scale vegetation restoration has markedly enhanced ecosystem services in the Three-North (TN) region, concurrently exacerbating the pre-existing water resource crisis stemming from climate change in this arid domain. However, the specific contributions of climate and vegetation changes to the evolution of water resources in the TN region have yet to be elucidated. This study leverages data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on mission (GRACE-FO), specifically the terrestrial water storage anomaly (TWSA) from 2003 to 2021. We identify two TWSA rising (TWSA_rⅠ and TWSA_rⅡ) and two declining (TWSA_dⅠ and TWSA_dⅡ) hotspots in the TN region. By exploring the relative importance of climate drivers and vegetation dynamics in influencing the temporal dynamics of TWSA, we found that vegetation restoration assumes a more pivotal role than climate factors in 45.2% of the total area within the identified hotspots, predominantly evident in TWSA declining hotspots. The heightened TWSA in TWSA_rⅠ and TWSA_rⅡ primarily stems from precipitation recharge, with precipitation-dominated areas constituting 19.3% of the area within the identified hotspots. Potential evapotranspiration dominates TWSA changes in only 4.6% of the region within the identified hotspots, primarily scattered in TWSA_dⅠ and TWSA_dⅡ. Furthermore, a comparative assessment of soil moisture and groundwater responses to environmental factors indicates that water storage at shallower layers is more responsive to climate factors, while the impacts of vegetation changes on TWSA are more discernibly reflected in water storage at deeper layers. These findings furnish scientific guidance for future ecological restoration planning and sustainable water resources management.

Overheating in the Tree Shade of Urban Parks: A Field Study of Thermal Adaption in China

With increased atmospheric temperature, temperatures in the shade of trees in parks also increase, and people are faced with high temperature challenges. In this study, thermal comfort in the shade of the trees of an urban park during summer in China was assessed. The subjective responses of the respondents were recorded via questionnaires, and environment parameters were measured. The results show that the air temperature in the shade was 31.1 ± 3.0 °C during the day, and that it peaked at 36.9 °C; the globe temperature was 31.3 ± 3.1 °C, and it peaked at 40.1 ℃. Respondents’ clothing insulation was 0.31 ± 0.08 clo, and the effect of clothing adjustment on thermal adaptation was limited. Thermal sensation is linearly related to standard effective temperature (SET), and the upper limit of 80% acceptable SET was 32.1 °C. At different temperature values, the proportion of expected airflow enhancement exceeded 50%. The respondents preferred a neutral-warm sensation. Moreover, there was an obvious thermal adaptation, with thermal history and psychological adaptation being the main factors affecting thermal comfort. This study confirmed the value of shade and provided us with guidance for park planning and design.

Enhancing NICU Care and Communication: Perspectives of Moderately Preterm Infant Parents.

Moderately preterm infants (MPTI) comprise a large proportion of NICU admissions and are an understudied population. The unique experience of families with MPTIs has yet to be examined in the literature. Describing MPTI parent needs and preferences may inform interventions to improve care and outcomes for this population. Semistructured qualitative interviews were performed with English-speaking birth parents of infants born between 32 and 34 weeks gestation to describe their NICU experience and identify areas for improvement specifically surrounding care team inclusion, education, discharge, and communication. Interviews were recorded, transcribed, and analyzed using directed content analysis. Enrollment ceased when the data reached thematic saturation. Sixteen birth parents participated. Four themes emerged around parent-medical team connectedness, parental confusion, discharge readiness, and the desire for a use of a mix of in-person and electronic communication methods (e-mail, texting, apps, etc) for communication. MPTI parents valued a strong connection with the medical team; however, they described a lack of knowledge regarding the reasons for admission and ongoing management. Near discharge, parents desired more information regarding feeding, reflux, and breathing patterns. Parents preferred in-person discussions but described a role for electronic methods to improve their understanding of their infant and discharge readiness. From the MPTI parent perspective, clinicians can focus improvement efforts on communication, specifically around reasons for admission, discharge planning, and anticipatory guidance. These results may serve as a foundation for initiatives to improve the MPTI parent experience and potentially parent and MPTI outcomes.

Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system.

Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans. We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37%vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.

Exploring the complex association between urban form and crime: Evidence from 1,486 U.S. counties

Urban form contributes to crime prevention. However, most studies not only simplify the relationship between urban form and crime, they also fail to reflect the geographic structure of urban areas. This study first measured a comprehensive multidimensional urban form indicator for 1486 counties in the United States, which was used to characterize the size and geospatial structure of urban areas. Specifically, it described urban form as consisting of three components: 1) size and population distribution; 2) built environment; and 3) landscape environment. Then, generalized additive mixed models were then used to explore linear or nonlinear associations between urban form indicators and different crime types (violent and property crime). This study's main findings were that 1) the relationship between urban form and crime is not only linear but may also be nonlinear; 2) urban size, fragmentation, connectivity, forest mixing, and shrub mixing are associated with all crime types; 3) population distribution and agricultural mixing are associated with violent crime; and 4) urban patch complexity and water mixing are associated with property crime. Overall, the results of this study can provide guidance for long-term planning of urban geographic structures, contributing to the safe and sustainable development of urban areas.

Establish an agricultural drought index that is independent of historical element probabilities

AbstractCurrently, there are three main shortcomings in meteorological drought indices: first, they rely on historical climate probability functions; second, the timescale used in calculations has a certain degree of subjectivity; third, the same index value may correspond to vastly different levels of actual drought in different climate types of regions. The purpose of this article is to establish a meteorological drought index that does not rely on historical meteorological element probability functions. Through theoretical derivation, four drought‐level maintenance lines are established on the cumulative precipitation‐cumulative water surface evaporation coordinate plane, and the coordinate quadrant is divided into five drought‐level areas. Through forward daily rolling accumulation, the maximum distance point is selected from the dynamically changing coordinate points to determine the corresponding cumulative precipitation and cumulative evaporation. The meteorological drought index is established by the distance from the selected coordinate point to each drought‐level maintenance line. Using daily precipitation and evaporation data from meteorological observation stations, the index is calculated based on the established meteorological drought index model, and compared with actual drought evolution and drought disaster records. The results show that the index can capture the development of drought well, and its changes are very consistent with drought disaster records. The index is of great significance for drought monitoring or assessment, and can provide guidance for water resource allocation, crop layout, and urban planning. Furthermore, it can also provide a way of thinking that does not rely on historical element probabilities for future drought research.

Promoting Labor Education in College Students’ Career Planning and Employment Guidance

Promoting Labor Education in College Students’ Career Planning and Employment Guidance

China's future wind energy considering air density during climate change

To assess the impact of air density on future wind resources under representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), the Coordinated Regional Downscaling Experiment-East Asia project was utilized to simulate the future air density characteristics of China (2021–2100). Empirical air density distributions were calculated, and fourteen probability density functions were fitted to the empirical air density distributions, determining the optimal fitted distribution functions. In this study, the effect of air density on the annual energy production and wind power density was quantified. The results indicated that only approximately 4 % of regional air density values reaches the standard air density under the three climate scenarios. The seven parameter Generalized Extreme Value-Generalized Extreme Value distribution, six parameter Gamma-Generalized Extreme Value distribution, and six parameter Exponentiated Weibull- Generalized Extreme Value distribution matched the empirical distributions well under RCP2.6, RCP4.5, and RCP8.5, respectively. Annual energy production and wind power density decreased by more than 15 % after considering air density. Under the different RCP scenarios (RCP2.6, RCP4.5, and RCP8.5), the errors of annual energy production and wind power density gradually escalate. In the Tibetan Plateau region, accounting for air density results in an overestimation of annual energy production by over 40 % and wind power density by more than 35 %. The findings of this study offer guidance for future wind energy resource planning, enabling optimal utilization of wind energy resources.

Matching supply and demand of cooling service provided by urban green and blue space

Urban blue-green space (UBGS) has been extensively investigated for its climate regulation capability in the context of climate change. However, current efforts are limited toward the cooling effects mismatch in supply and demand of UBGS. This study adapts the framework of ecosystem supply and demand to quantify the supply-demand matching of the cooling effects of UBGS. Two indices were proposed to evaluate cooling provision and demand of UBGS: Cooling Supply Index (CSI), accounting for cooling contribution and spatial effect, and Cooling Demand Index (CDI), considering physical and social dimensions of cooling demand. Four-quadrant analysis and bivariate local indicators of spatial autocorrelation (BiLISA) analysis were employed to distinguish the supply-demand matching status: high supply with high demand, low supply with high demand, low supply with low demand, and high supply with low demand. The results show that 1) CSI and CDI yield great performance in quantifying cooling supply and demand, respectively; 2) the four-quadrant analysis and bivariate LISA analysis are complementary to each other, and can target those areas in different matching statuses for climate-sensitive planning and design; and 3) particular attention should be given to areas characterized by low supply with high demand. This study marks a crucial step toward integrating social aspects into the analysis of cooling effects provided by UBGS. It enriches the analysis framework for urban ecosystem services and provides valuable guidance for landscape planning to mitigate heat stress.