bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention
Authors: Cui Ruochu , Ma Liwen , Hu Yaning , Wu Jiaji , Li Haiying
Published: February 2025

The analysis of marine environmental parameters plays an important role in areas such as sea surface simulation modeling, analysis of sea clutter characteristics, and environmental monitoring. However, ocean observation remote sensing satellites typically deliver large volumes of data with limited spatial resolution, which often does not meet the precision requirements of practical applications. To overcome challenges in constructing high-resolution marine environmental parameters, this study conducts a systematic comparison of various interpolation techniques and deep learning models, aiming to develop a highly effective and efficient model optimized for enhancing the resolution of marine applications. Specifically, we incorporated adaptive global attention (AGA) mechanisms and a spatial gating unit (SGU) into the model. The AGA mechanism dynamically adjusts the weights of different regions in feature maps, enabling the model to focus more on critical spatial features and channel features. The SGU optimizes the utilization of spatial information by controlling the information transmission pathways. The experimental results indicate that for four types of marine environmental parameters from ERA5, our model achieves an overall PSNR of 44.0705, an SSIM of 0.9947, and an MAE of 0.2606 when the resolution is increased by a upscale factor of 2, as well as an overall PSNR of 35.5215, an SSIM of 0.9732, and an MAE of 0.8330 when the resolution is increased by an upscale factor of 4. These experiments demonstrate the model’s effectiveness in enhancing the spatial resolution of satellite-derived marine environmental parameters and its ability to be applied to any marine region, providing data support for many subsequent oceanic studies.
Improved Landslide Deformation Prediction Using Convolutional Neural Network–Gated Recurrent Unit and Spatial–Temporal Data
Authors: Yang Honglei , Liu Youfeng , Han Qing , Xu Linlin , Zhang Tengjun , Wang Zeping , Yan Ao , Zhao Songxue , Han Jianfeng , Wang Yuedong
Published: February 2025

As one of the major forms of geological disaster, landslides cause huge casualties and economic losses in China every year. Given the importance of landslide prediction, it is a challenging task due to difficulties in efficiently leveraging the spatial–temporal information for enhanced prediction. This paper presents a novel spatial–temporal enhanced CNN-GRU model to improve landslide predictions with the following contributions. First, this paper explicitly models the spatial correlation in the dataset and constructs a spatial–temporal time-sequence deformation prediction model that greatly improves landslide predictions. This model integrates the spatial correlation of monitoring points into time-series deformation prediction to improve the prediction of landslide deformation trends. Second, we develop a complete data processing pipeline involving SBAS-InSAR, time-series data preprocessing, spatial–temporal homogeneous point selection and weighting, as well as CNN-GRU model training. The pipeline is tailor-designed to leverage the spatial–temporal correlation in the data to enhance the prediction performance. Third, we apply the proposed model to monitor landslide deformation around Woda Village, Chamdo City, Tibet. The results show that the root mean square error (RMSE) of the monitoring points in the landslide area is reduced by about 20.9% and the number of points with an RMSE of less than 3 mm is increased by 12.9%, leading to a significant improvement in prediction accuracy.
Physical and AI-Based Algorithms for Retrieving Cloud Liquid Water and Total Precipitable Water from Microwave Observation
Authors: Chen Wenxiang , Han Yang , Weng Fuzhong , Hu Hao , Yang Jun
Published: February 2025

Cloud liquid water (CLW) and total precipitable water (TPW) are two important parameters for weather and climate applications. These parameters are typically retrieved at 23.8 GHz and 31.4 GHz. Historically, the CLW and TPW physical retrievals always required the sea surface temperature (SST) and sea surface wind speed (SSW), which are difficult to obtain from conventional measurements. This study employs the multilayer perceptron (MLP) model to retrieve SST and SSW from FY-3F Microwave Radiometer Imager (MWRI) observations. Collocated with ERA5 reanalysis data, the MLP model predicts SST well, with a correlation coefficient of 0.98, the root mean squared error (RMSE) of 1.10, and mean absolute error (MAE) of 0.70 K. For SSW, the correlation coefficient is 0.82, RMSE is 1.80, and MAE is 1.30 m/s, respectively. The SST and SSW parameters derived from MWRI are then used to retrieve CLW and TPW based on the observations from the Microwave Temperature Sounder (MWTS) onboard the FY-3F satellite. The spatial distributions of CLW and TPW derived from this new algorithm agree well with those from ERA5 data. Cloud liquid water (CLW) and total precipitable water (TPW) are crucial parameters for weather and climate applications. The integration of physical and AI-based algorithms enables the retrieval of CLW and TPW directly from FY-3F satellite observations. This approach overcomes the limitations imposed by the need for other data sources, such as ERA5 reanalysis data, and offers distinct advantages in terms of data processing timeliness.
Spatial Estimation of Soil Organic Matter and Total Nitrogen by Fusing Field Vis–NIR Spectroscopy and Multispectral Remote Sensing Data
Authors: Xu Dongyun , Chen Songchao , Zhou Yin , Ji Wenjun , Shi Zhou
Published: February 2025

Accurate and timely acquisition of soil information is crucial for precision agriculture, food security, and environmental protection. Proximal visible near-infrared reflectance (vis–NIR) spectroscopy has been widely employed for rapid and accurate soil measurement, but its point measurement nature limits its direct applicability for large-scale soil surveys. On the other hand, remote sensing techniques can provide soil information at a larger scale, but their resolution is relatively coarse. While both techniques have been used independently for soil analyses, integrating vis–NIR spectroscopy with remote sensing remains a challenge and is underexplored, especially at the field scale. This study addresses this gap by combining field vis–NIR spectra with Gaofen-1 remote sensing data to spatially analyze soil organic matter and total nitrogen at the field scale. Unlike previous work, we first applied Gaofen-1 data and 10 derived spectral indices to estimate soil organic matter and total nitrogen using partial least squares regression and random forest, identifying the optimal combination of spectral indices. Then, we integrated the proximal vis–NIR spectra with this optimal spectral index combination for improved soil property estimation. This integration advanced existing methodologies by leveraging the high spatial resolution of Gaofen-1 data and the detailed spectral information from vis–NIR spectroscopy. The results showed the following: (1) the coefficient of variation across different crop growth stages of Gaofen-1 data was more crucial for modeling these two properties compared to bare soil Gaofen-1 data; (2) integrating proximal vis–NIR spectra with Gaofen-1 data improved model performance, yielding Lin’s concordance correlation coefficient (ρc) values of 0.63 and 0.72 and ratios of performance to interquartile distance (RPIQ) of 1.99 and 1.59 for soil organic matter and total nitrogen, respectively; and (3) the combined use of vis–NIR spectra and Gaofen-1 data provided higher spatial estimation accuracy (R2 of 0.68 and 0.57 for soil organic matter and total nitrogen) compared to ordinary kriging (R2 of 0.63 and 0.31 for soil organic matter and total nitrogen). These results demonstrate that the synergistic use of remote sensing and proximal soil sensing is a practical approach for spatially estimating soil organic matter and total nitrogen at the field scale.
Feasibility of Smartphone-Based Exercise Training Integrated with Functional Electrical Stimulation After Stroke (SETS): A Preliminary Study
Authors: Purohit Rudri , Appelgren-Gonzalez Juan Pablo , Varas-Diaz Gonzalo , Wang Shuaijie , Hosiasson Matias , Covarrubias-Escudero Felipe , Bhatt Tanvi
Published: February 2025

One emerging method in home stroke rehabilitation is digital technology. However, existing approaches typically target one domain (e.g., upper limb). Moreover, existing interventions do not cater to older adults with stroke (OAwS), especially those with high motor impairment, who require adjunct therapeutic agents to independently perform challenging exercises. We examined the feasibility of Smartphone-based Exercise Training after Stroke (SETS) with Functional Electrical Stimulation (FES). A total of 12 participants (67 ± 5 years) with stroke (onset > 6 months) exhibiting moderate-to-high motor impairment (Chedoke McMaster Leg ≤ 4/7) underwent 6 weeks of multicomponent (gait, functional strength, dynamic balance) training integrated with FES to paretic lower limb muscles. Primary measures included safety and adherence. Secondary measures included motivation, acceptability and attitude, usability, and clinical measures of gait and balance function like the 10-Meter Walk Test and Mini-BESTest. Participants reported no adverse events and moderate-to-high adherence (84.17 ± 11.24%) and improvement (up to 40%) in motivation, acceptability, and attitude and system usability. Participants also showed pre-post improvements in all measures of gait and balance function (p < 0.05). Integrating SETS and FES is feasible and yields short-term gains in gait and balance function among OAwS. Future studies could validate our findings by examining its efficacy with control groups to identify the differential effects of SETS and FES.
Technological Progress Toward Peanut Disease Management: A Review
Authors: Asif Muhammad , Rayamajhi Aleena , Mahmud Md Sultan
Published: February 2025

Peanut (Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer technologies have enabled the development of precision or variable-rate sprayers, both ground-based and drone-based, that apply agrochemicals more accurately. Historically, crop disease scouting has been labor-intensive and costly. Recent innovations in computer vision, artificial intelligence (AI), and remote sensing have transformed disease identification and scouting, making the process more efficient and economical. Over the past decade, numerous studies have focused on developing technologies for peanut disease scouting and sprayer technology. The current research trend shows significant advancements in precision spraying technologies, facilitating smart spraying capabilities. These advancements include the use of various platforms, such as ground-based and unmanned aerial vehicle (UAV)-based systems, equipped with sensors like RGB (red–blue–green), multispectral, thermal, hyperspectral, light detection and ranging (LiDAR), and other innovative detection technologies, as highlighted in this review. However, despite the availability of some commercial precision sprayers, their effectiveness is limited in managing certain peanut diseases, such as white mold, because the disease affects the roots, and the chemicals often remain in the canopy, failing to reach the soil where treatment is needed. The review concludes that further advances are necessary to develop more precise sprayers that can meet the needs of large-scale farmers and significantly enhance production outcomes. Overall, this review paper aims to provide a review of smart spraying techniques, estimating the required agrochemicals and applying them precisely in peanut fields.
Method for Detecting Disorder of a Nonlinear Dynamic Plant
Authors: Wang Xuechun , Eliseev Vladimir
Published: February 2025

This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder. The CCF-AE method does not use the reference model of the dynamic object, but only considers real-time behavior changes, given by input and output time series. The proposed method was used to detect disorder in the process of a nonlinear pH neutralization reaction, and was compared with the cumulative sum control chart (CUSUM) and the exponentially weighted moving variance control chart (EWMV). The CCF-AE method demonstrates a better true detection rate and lower false alarm rate than CUSUM and EWMV. Also, CCF-AE has more advantages in detecting disorder of complex nonlinear processes.
Circularly Polarized Reconfigurable MIMO Antenna for WLAN Applications
Authors: Le-Tuan Tu , Nguyen Thai Dinh , Tran Nguyen Viet-Duc , Tran Hung , Nguyen-Tien Dat
Published: February 2025

This paper presents a simple design of a two-element antenna with circularly polarized (CP) reconfigurability for multiple-input multiple-output wireless local-area network (WLAN) applications. A MIMO element consists of a reconfigurable feeding network, a CP source, and a 2 × 2 unit-cell metasurface (MS). By controlling the ON/OFF state of PIN diodes, the proposed MIMO system can operate in either right-hand CP (RHCP) or left-hand CP (LHCP) for all ports, or either RHCP or LHCP for each port. For all operating modes, the proposed antenna exhibits good performance with a matching performance of less than –10 dB, an axial ratio of lower than 3 dB, as well as an inter-port isolation of better than 24 dB at 2.45 GHz. Additionally, the MIMO diversity performance is also satisfied by the proposed antenna. Compared to related works, the proposed antenna has advantages of high gain and compact size, as well as a simple switching mechanism with a small number of PIN diodes.
Evaluation of Long-Term Performance of Six PM2.5 Sensor Types
Authors: Barkjohn Karoline K. , Yaga Robert , Thomas Brittany , Schoppman William , Docherty Kenneth S. , Clements Andrea L.
Published: February 2025

From July 2019 to January 2021, six models of PM2.5 air sensors were operated at seven air quality monitoring sites across the U.S. in Arizona, Colorado, Delaware, Georgia, North Carolina, Oklahoma, and Wisconsin. Common PM sensor data issues were identified, including repeat zero measurements, false high outliers, baseline shift, varied relationships between the sensor and monitor, and relative humidity (RH) influences. While these issues are often easy to identify during colocation, they are more challenging to identify or correct during deployment since it is hard to differentiate between real pollution events and sensor malfunctions. Air sensors may exhibit wildly different performances even if they have the same or similar internal components. Commonly used RH corrections may still have variable bias by hour of the day and seasonally. Most sensors show promise in achieving the U.S. Environmental Protection Agency (EPA) performance targets, and the findings here can be used to improve their performance and reliability further. This evaluation generated a robust dataset of colocated air sensor and monitor data, and by making it publicly available along with the results presented in this paper, we hope the dataset will be an asset to the air sensor community in understanding sensor performance and validating new methods.
Contrastive Learning with Global and Local Representation for Mixed-Type Wafer Defect Recognition
Authors: Yin Shantong , Zhang Yangkun , Wang Rui
Published: February 2025

Recognizing defect patterns in semiconductor wafer bin maps (WBMs) poses a critical challenge in the integrated circuit (IC) manufacturing industry. The accurate classification and segmentation of these defect patterns are of utmost significance as they are key to tracing the root causes of defects, thereby reducing costs and enhancing both product efficiency and quality. As the manufacturing process grows in complexity, the WBM becomes intricate when multiple defect patterns coexist on a single wafer, making the recognition task increasingly complicated. In addition, traditional supervised learning methods require a large number of labeled samples, which is labor-intensive. In this paper, we present a self-supervised contrastive learning framework for the classification and segmentation of mixed-type WBM defect patterns. Our model incorporates a global module for contrastive learning that captures image-level representations, alongside a local module that targets the comprehension of regional details, which is helpful for the segmentation of defective patterns. Experimental results demonstrate that our model performs effectively in scenarios where there is a limited number of labeled examples and a wealth of unlabeled ones.
Involuntary Celibacy (Incel) Identity: A Thematic Analysis of an Online Community’s Beliefs and Emotional Experiences
Authors: De Vettor Marco , Lo Buglio Gabriele , Barsanti Alice , Ciocca Giacomo , Gennaro Alessandro , Goksal Renan , Lingiardi Vittorio , Giovanardi Guido , Boldrini Tommaso
Published: February 2025

The term incel is a neologism combining “involuntary” and “celibate”, describing men who experience involuntary absence from sexual and romantic relationships. Incels frequently espouse conspiratorial and misogynistic ideologies, often engaging in verbal aggression. The present study aimed at qualitatively exploring the emotional experiences and beliefs articulated within an online incel community, contributing to the formation of its collective identity. A thread (453 comments) sampled from an Italian “incel” forum was analyzed by two independent raters, using thematic analysis. Four themes emerged: (a) “non-persons (i.e., women) are not like us” revealed perceived gender inequality and misogynistic beliefs, (b) “without experiences in adolescence you are ruined” reflected the belief that early romantic and sexual experiences are crucial for later relationship satisfaction, (c) “I have no life” captured expressions of profound distress and life dissatisfaction, and (d) “only ours is true suffering” highlighted a sense of unique victimhood. Incel group identity was shaped by gender role stress, primarily stemming from the perceived failure to meet socially constructed expectations of hegemonic masculinity. Suffering emerged as the dominant emotional experience, seemingly exacerbated by feelings of hopelessness regarding romantic and sexual prospects. The perceived subordinate status of men and male suffering were exploited to rationalize and perpetuate misogyny. The observed incels’ openness in discussing experiences of suffering and trauma may present an opportunity for the development of preventive interventions aimed at increasing help-seeking behavior in this population.
Does Mindfulness Mediated by Virtual Reality Help People with Functional Diversity?
Authors: Puente-Torre Paula , Delgado-Benito Vanesa , Rodríguez-Cano Sonia , Lozano-Álvarez María
Published: February 2025

The integration of virtual reality with mindfulness practice is an effective tool for reducing anxiety levels because it promotes a comprehensive improvement of the educational process and the emotional well-being of students. The aim of this study is to analyse the impact of a mindfulness programme carried out using Virtual Reality with people with functional diversity who are studying Adapted Vocational Training in a secondary school. The intervention was carried out for three months, with the aim of improving their attentional and self-regulation skills. This innovative approach allowed students to experience immersive environments that facilitate mindfulness practice, significantly improving their concentration and reducing anxiety levels. Students who participated in this case study and used this technology experienced an increase in their ability to concentrate on tasks, as well as a marked improvement in their emotional well-being. Descriptive analyses and guided interviews were conducted with both students and teachers involved in the teaching–learning process. The results showed the potential of Virtual Reality-mediated mindfulness programmes as an inclusive and powerful strategy to support learning and personal development in adapted vocational training environments. The study highlights its innovation in personalising learning, reducing anxiety, and improving attentional skills through Virtual Reality. It contributes by providing empirical evidence that supports the development of educational programmes and promotes mental health in the educational context.
Aiming to Improve Social Inclusion in Physician Assistant Students’ Classroom Experience Through Partnership
Authors: Dunbar Haili , Loder Rayne , Coleman-Plourde Hope , Dwyer Heather
Published: February 2025

Pedagogical partnership programs often facilitate collaborations between students and faculty to bolster inclusive instructional methodologies and equitable opportunities for classroom engagement. Improving equitable learning experiences for students is of particular importance within physician assistant (PA) education given the efforts toward increasing diversity within the profession. We began to explore ways to increase social equity and inclusion in a PA program by incorporating a student–faculty partnership, which was established between an undergraduate student and a graduate PA program faculty member. The partnership included classroom observations, syllabus/lecture review, and mid-semester feedback from the student partner, as well as weekly meetings between the student partner and faculty member to iteratively revise approaches to course instruction and student engagement. At the end of the course, we gathered survey data on the partnership’s impact on PA students’ perceptions of classroom climate and the PA program in general. Student responses indicate that students experienced a sense of improved classroom climate, open communication, trust, and support because of the partnership.
Point-of-Care No-Specimen Diagnostic Platform Using Machine Learning and Raman Spectroscopy: Proof-of-Concept Studies for Both COVID-19 and Blood Glucose
Authors: Chefitz Allen B. , Singh Rohit , Birch Thomas , Yang Yongwu , Hussain Arib , Chefitz Gabriella
Published: February 2025

Significance: We describe a novel, specimen-free diagnostic platform that can immediately detect both a metabolite (glucose) or an infection (COVID-19) by non-invasively using Raman spectroscopy and machine learning. Aim: Current diagnostic testing for infections and glucose monitoring requires specimens, disease-specific reagents and processing, and it increases environmental waste. We propose a new hardware–software paradigm by designing and constructing a finger-scanning hardware device to acquire Raman spectroscopy readouts which, by varying the machine learning algorithm to interpret the data, allows for diverse diagnoses. Approach: A total of 455 patients were enrolled prospectively in the COVID-19 study; 148 tested positive and 307 tested negative through nasal PCR testing conducted concurrently with testing using our viral detector. The tests were performed on both outpatients (N = 382) and inpatients (N = 73) at Holy Name Medical Center in Teaneck, NJ, between June 2021 and August 2022. Patients’ fingers were scanned using an 830 nm Raman System and then, using machine learning, processed to provide an immediate result. In a separate study between April 2023 and August 2023, measurements using the same device and scanning a finger were used to detect blood glucose levels. Using a Dexcom sensor and an Accu-Chek device as references, a cross-validation-based regression of 205 observations of blood glucose was performed with a machine learning algorithm. Results: In a five-fold cross-validation analysis (including asymptomatic patients), a machine learning classifier using the Raman spectra as input achieved a specificity for COVID-19 of 0.837 at a sensitivity of 0.80 and an area under receiver operating curve (AUROC) of 0.896. However, when the data were split by time, with training data consisting of observations before 1 July 2022 and test data consisting of observations after it, the model achieved an AUROC of 0.67, with 0.863 sensitivity at a specificity of 0.517. This decrease in AUROC may be due to substantial domain shift as the virus evolves. A similar five-fold cross-validation analysis of Raman glucose detection produces an area under precision–recall curve (AUPR) of 0.58. Conclusions: The combination of Raman spectroscopy, AI/ML, and our patient interface admitting only a patient’s finger and using no specimen offers unprecedented flexibility in introducing new diagnostic tests or adapting existing ones. As the ML algorithm can be iteratively re-trained with new data and the software deployed to field devices remotely, it promises to be a valuable tool for detecting rapidly emerging infectious outbreaks and disease-specific biomarkers, such as glucose.
Study on the Distortionary Effects of Water Resources Allocation in the Yangtze River Economic Belt, China
Authors: Tong Jinping , Cao Jiawen , Qin Teng , Qin Guodong , Ma Jianfeng
Published: February 2025

As one of the most dynamic natural elements, water resources play a vital role in both ecological and economic activities. The rational allocation of water resources is essential for the sustainable development of human society. Using data from prefecture-level cities in the Yangtze River Economic Belt from 2013 to 2022, this study employs the Cobb–Douglas (C-D) production function to measure the degree of water resource allocation distortion across provinces and cities. Additionally, a panel data model is applied to analyze the influencing factors. The key findings are as follows: The issue of excessive water resource allocation is widespread in the Yangtze River Economic Belt, with the highest degree of distortion observed in the middle reaches, where the average distortion index reaches 1.43—significantly higher than that in the upstream (1.15) and downstream (1.20) regions. Water resource endowment, the South-to-North Water Diversion Project, and rising water prices contribute significantly to improving water resource allocation, whereas increased industrial water use efficiency and excessive government intervention exacerbate allocation distortions. Regionally, the influencing factors exhibit broadly similar effects across provinces. In sectoral analysis, only the estimated coefficients of water resource policies show directional differences, whereas other factors have no significant impact on allocation distortion. By identifying the extent and causes of water resource misallocation, this study provides empirical evidence to support region-specific water management strategies, aiming to enhance resource efficiency and promote sustainable development.
Economic Feasibility of Hydrogen Generation Using HTR-PM Technology in Saudi Arabia
Authors: Al-Shikh Saud A. , Al-Ammar Essam A. , Alomari Abdullah S.
Published: February 2025

The global push for clean hydrogen production has identified nuclear energy, particularly high-temperature gas-cooled reactors (HTGRs), as a promising solution due to their ability to provide high-temperature heat. This study conducted a techno-economic analysis of hydrogen production in Saudi Arabia using the pebble bed modular reactor (HTR-PM), focusing on two methods: high-temperature steam electrolysis (HTSE) and the sulfur–iodine (SI) thermochemical cycle. The Hydrogen Economic Evaluation Program (HEEP) was used to assess the economic viability of both methods, considering key production factors such as the discount rate, nuclear power plant (NPP) capital cost, and hydrogen plant efficiency. The results show that the SI cycle achieves a lower levelized cost of hydrogen (LCOH) at USD 1.22/kg H2 compared to HTSE at USD 1.47/kg H2, primarily due to higher thermal efficiency. Nonetheless, HTSE offers simpler system integration. Sensitivity analysis reveals that variations in the discount rate and NPP capital costs significantly impact both production methods, while hydrogen plant efficiency is crucial in determining overall economics. The findings contribute to the broader discourse on sustainable hydrogen production technologies by highlighting the potential of nuclear-driven methods to meet global decarbonization goals. The paper concludes that the HTR-PM offers a viable pathway for large-scale hydrogen production in Saudi Arabia, aligning with the Vision 2030 objectives.
Towards Indoor Well-Being: Synthetic Indices for Monitoring Built Environment Quality Based on UN’s 2030 Agenda
Authors: De Vita Mariangela , Rotilio Marianna , Berardinis Pierluigi De , Pignatelli Federica
Published: February 2025

The goals of the United Nations’ 2030 Agenda declare that sustainable development is a complex objective, which can only be achieved if we work simultaneously on different areas of action, at different levels of detail. Indoor well-being, in addition to being one of the aspects that the 2030 Agenda considers as a pillar (goal 3), is in turn determined by various factors that pertain to similar but different research fields, which are found in the disciplines that deal with the built environment as a common denominator, a meeting place. Despite to the complex phenomenon of indoor well-being, in the built environment we can identify constructive aspects and environmental parameters that can be strictly correlated to indoor well-being from a quantitative point of view. In this paper, a methodological study to check physiological well-being’s relationship to Built Environment Quality aligned with the United Nations’ 2030 Agenda has been developed. The result is an assessment and monitoring system suitable to be improved by further qualitative aspects, IoT, and new enabling technologies for the management of the indoor spaces.
Low-Carbon Economic Scheduling of Integrated Energy System Considering Flexible Supply–Demand Response and Diversified Utilization of Hydrogen
Authors: Ma Chengcheng , Hu Zhijian
Published: February 2025

With the large-scale deployment of renewable energy, the issue of wind power consumption has become increasingly prominent, leading to serious wind energy abandonment. In order to promote energy sustainability, this paper proposes a low-carbon economic scheduling model of an integrated energy system (IES) that combines the flexible supply–demand response with the diversified utilization of hydrogen energy. A mixed-integer linear programming model is developed and solved using the commercial solver GUROBI to obtain the scheduling scheme that minimizes total costs. First, decoupling analysis is performed for combined heat and power (CHP) units, and the organic Rankine cycle (ORC) is introduced to enable dynamic output adjustments. On the demand side, a flexible demand response mechanism is introduced, which allows various types of loads to transfer within the scheduling cycle or substitute for each other within the same period. Additionally, combining the clean characteristics of hydrogen, this paper introduces hydrogen-doped CHP and other utilization strategies and develops a diversified utilization structure of hydrogen. A small IES is used for case analysis to verify the effectiveness of the above strategies. The results show that the proposed strategy can entirely consume wind power, reduce total cost by 21.32%, and decrease carbon emissions by 44.83%, thereby promoting low-carbon economic operation and sustainable energy development of the system.
The Impact of Vegetation Layouts on Thermal Comfort in Urban Main Streets: A Case Study of Youth Street in Shenyang
Authors: Fan Lei , Zhao Meiyue , Huo Jiayi , Sha Yixuan , Zhou Yan
Published: February 2025

Urban streets are critical public spaces that significantly influence the thermal comfort of city dwellers. However, the issue of summer thermal discomfort in severely cold regions has been largely overlooked. This study focuses on Youth Street in Shenyang, a city in a severely cold region, to explore the impact of various street spaces and vegetation layouts on the thermal environment and comfort using ENVI-met modeling and correlation analysis. The study varied the aspect ratio (AR) of the street, street tree species, and plant spacing across 60 scenarios and simulated thermal comfort over a 10-h period on a typical summer day. Results show that air temperature (Ta), mean radiant temperature (Tmrt) and sky view factor (SVF) are positively correlated with physiologically equivalent temperature (PET). Street trees effectively reduce Ta, increase RH and lower wind speed (WS), but plant spacing has minimal impact on WS. Higher AR values lead to greater improvements in pedestrian thermal comfort. Specifically, the highest heat mitigation rate (HMR) is observed at low AR (9.87% at AR = 0.5 and 9.94% at AR = 1.0), while it is lower at high AR (8.16% at AR = 2.0). Conversely, larger plant spacing of street trees diminishes the effectiveness of thermal comfort improvements. The improvement effect of plant spacing is more pronounced in street spaces with smaller AR. In these spaces, closer plant spacing significantly enhances thermal comfort by providing more shade and reducing Ta and Tmrt. However, in street spaces with higher AR, overly dense plant configurations can reduce WS and limit the cooling effect of ventilation, thereby diminishing overall heat mitigation ability. Conclusions suggest that urban planners should consider both street space and vegetation layouts to optimize thermal comfort. For urban main streets in severely cold regions, an AR of 1:1 with deciduous broadleaf trees and hedges planted at 6 m spacing is recommended. In high-AR streets, dense plant configurations should be avoided. This study provides valuable insights for improving the thermal comfort and sustainable design of urban street spaces, supporting new construction and development in similar climate environments.
Measurement of New Quality Productivity Development Level and Factor Identification of Obstacle Factors Based on the Analysis of Provincial Panel Data in China
Authors: Miao Shunfang , Hu Yanyong
Published: February 2025

New quality productivity (NQP) is an important engine that promotes China’s economy in order to achieve high-quality development in the new era. The study of the measurement of the development level of NQP is conducive to accelerating its formation and development. Based on the panel data of 30 provinces in China from 2010 to 2022 and the connotation of NQP, this paper constructs an evaluation index system for the development level of NQP in Chinese provinces in four dimensions: new industry, new kinetic energy, new model, and new factor. This paper uses the entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) for quantitative measurement. It also uses the Dagum Gini coefficient decomposition and kernel density estimation methods to analyze the regional differences and dynamic evolution trend of the development of NQP in China and makes a scientific diagnosis of the obstacles affecting the development of NQP. The results show that there are significant regional differences in the development of NQP in China, which are mainly driven by regional differences, resulting in a huge gap between the eastern and non-eastern regions. This study shows that the overall gap in the development level of NQP in China is gradually increasing, and there is a “Matthew effect” in which the quality of laborers is the key factor restricting the rapid development of NQP in China. This study expands the research perspective of NQP evaluation, provides valuable suggestions and guidance for government decision-making and policy-making, and is conducive to promoting the rapid development of NQP in China.
The Impact of the National Civilized City Evaluation on Urban Resilience from the Perspective of Administrative Competition
Authors: Jiao Yafei , Song Dian , Meng Qiuming
Published: February 2025

Resilience refers to a city’s ability to withstand risks. Research into the impacts of Chinese urban governance models on urban resilience is limited; thus, in this study, we used a difference-in-differences approach and a spatial spillover model to examine the effects of the national civilized city evaluation on urban resilience based on data from 263 prefecture-level cities in China from 2009 to 2019. Data analysis results indicate that the national civilized city evaluation not only enhances urban resilience but also promotes the urban resilience levels of neighboring cities. Furthermore, a city’s digital economy can strengthen the effects of the national civilized city evaluation on its urban resilience. Our conclusions suggest that the government should refine the national civilized city evaluation system, coordinate digital economic development, optimize the spatial layouts of cities, and better utilize the urban governance efficacy of the evaluation.
Exploring Links Between Green HRM, Greenwashing, and Sustainability: The Role of Individual and Professional Traits
Authors: Cesário Francisco , Sabino Ana , Palma-Moreira Ana , Pinto-Coelho Mafalda
Published: February 2025

This study aims to investigate the influence of Green Human Resources Management practices and greenwashing on the three pillars of sustainability—social, environmental, and economic. The moderating role of age and gender as individual characteristics and managerial position and sector as professional characteristics is also analyzed. A convenience sample of 232 respondents was used. Path analysis was employed to assess hypothesized relationships in the proposed model. Results suggest that different Green Human Resources Management practices influence social, environmental, and economic dimensions and individual and professional characteristics play a pivotal role in this relationship. Specifically, it is important to highlight the importance of green performance management and rewards and the non-significant influence of green training. Greenwashing seems to be a determinant of social and economic sustainability. The results emphasize that, for organizations to boost their employees’ perception of the three pillars of organizational sustainability, they must be aware of the specificities of their workforce and act accordingly. This work is relevant because it emphasizes the unique role of each GHRM practice as an antecedent of sustainability. It also contributes to the understanding that specific individual and professional characteristics affect the employees’ perception of sustainability.
An Optimal Investment Decision Problem Under the HARA Utility Framework
Authors: Wang Aiyin , Ji Xiao , Zhang Lu , Li Guodong , Li Wenjie
Published: February 2025

This paper is dedicated to studying the optimal investment proportions of three types of assets with symmetry, namely, risky assets, risk-free assets, and wealth management products, when the stochastic expenditure process follows a jump-diffusion model. The stochastic expenditure process is treated as an exogenous cash flow and is assumed to follow a stochastic differential process with jumps. Under the Cox–Ingersoll–Ross interest rate term structure, it is presumed that the prices of multiple risky assets evolve according to a multi-dimensional geometric Brownian motion. By employing stochastic control theory, the Hamilton–Jacobi–Bellman (HJB) equation for the household portfolio problem is formulated. Considering various risk-preference functions, particularly the Hyperbolic Absolute Risk Aversion (HARA) function, and given the algebraic form of the objective function through the terminal-value maximization condition, an explicit solution for the optimal investment strategy is derived. The findings indicate that when household investment behavior is characterized by random expenditures and symmetry, as the risk-free interest rate rises, the optimal proportion of investment in wealth-management products also increases, whereas the proportion of investment in risky assets continually declines. As the expected future expenditure increases, households will decrease their acquisition of risky assets, and the proportion of risky-asset purchases is sensitive to changes in the expectation of unexpected expenditures.
Bridging the Gender Gap in Climate-Resilient Sweet Potato Farming: A Case Study from Goromonzi District, Zimbabwe
Authors: Munyaka Jean-Claude Baraka , Gallay Olivier , Chenal Jérôme , Mutandwa Edward , Salgado Ximena , Pindayi Tariro , Gondo Davison , Pfuma Herbert , Mhembere Rumbidzai , Tamanikwa Tinotenda , Chipise Shawn
Published: February 2025

This study delves into the gender-specific challenges and opportunities in sweet potato farming in Goromonzi District, Zimbabwe, against the backdrop of escalating droughts. Through a blend of surveys, expert analysis, and high-resolution satellite imagery, the research uncovers critical factors shaping sweet potato production—ranging from land access and cultivation techniques to harvesting and market dynamics. By leveraging the multi-Criteria Decision-Making (MCDM) framework, the study evaluates these factors’ importance and presents innovative, gender-inclusive strategies to foster climate resilience. Remote sensing tools map the severity of droughts, while data analysis reveals the interconnected challenges faced by farmers. The findings spotlight the urgent need for equitable resource access and support systems to empower both female farmers, paving the way for sustainable agriculture in an era of climate uncertainty.
Spatial–Temporal Coupling and Interactive Effects of Reclaimed Water Usage Efficiency and High-Quality Development of the Financial Sector
Authors: Zhou Ying , Wu Fengping , Li Gen , Feng Chen
Published: February 2025

In China, the current policy of the financial sector on water conservation and management is being vigorously pursued; therefore, efficient synergy between the two systems is of great significance. In this study, the coupling and coordination degree (CCD) between reclaimed water usage efficiency (RWUE) and high-quality development of the financial sector (HQDFS) was assessed using a coupling coordination model with panel data from 27 provinces in China during 2010–2021, and a more in-depth coupling and coordination relationship (CCR) was carried out using a spatiotemporal evolution methodology and PVAR model. The results of this study show the following: (1) CCD exhibits a continuous upward trajectory. At the end of the study period, the eastern, central, western, and northeastern regions moved to the primary coordination level. (2) The eastern and northeastern regions show an increasing trend in absolute differences and polarization. Meanwhile, the central region experiences a gradual rise in polarization. (3) The elliptical plot of the CCD’s standard deviation tends toward a circular shape with a positive aspect ratio. An expanding trend of absolute differences and polarization is observed in the eastern and northeastern regions. (4) The PVAR results show that the two systems can promote each other in the early stages and have a negative impact in the later stages. This study provides policy recommendations for a balanced development of the two systems and the formulation of regional development strategies based on the state of coupling and coordination between the two.
A “C3-TOPSIS-Pareto” Based Model for Identifying Critical Nodes in Complex Networks
Authors: Zeng Ziqiang , Zhang Weiye , Jin Hongling
Published: February 2025

This study addresses the critical challenge of identifying key nodes in complex networks, an essential task for optimizing network stability, efficiency, and resilience. Traditional approaches often rely on single-dimensional metrics, which may fail to fully capture the multifaceted roles that nodes play in maintaining network functionality. To overcome this limitation, a novel framework was proposed, integrating the C3 dimensions (Cohesion, Connectivity, Conciseness) with the TOPSIS method and Pareto dominated set (PDS) to enable a comprehensive, multi-dimensional evaluation of node importance. The method introduces an optimized parameter α for relative conciseness, validated through area-under-curve (AUC) minimization, ensuring adaptability across diverse networks. Scalability analysis demonstrates its feasibility for large-scale systems, with computational complexity managed through approximation algorithms. The method was applied to eight real-world networks including transportation systems, power grids, and social networks. Results demonstrated that the PDS-based STS method outperformed traditional centrality measures, such as degree centrality and betweenness centrality, particularly during the early stages of network degradation. The framework effectively identified critical nodes in highly connected systems, with the conciseness metric proving instrumental in highlighting irreplaceable nodes whose removal would severely disrupt network functionality. This study concludes that the “C3-TOPSIS-Pareto” based model provides a more accurate and robust approach for critical node identification, offering a reliable tool for enhancing network design and resilience, particularly in systems with complex interdependencies. Limitations in fragmented networks are discussed, with future directions proposed for dynamic weight adaptation and functional dynamics integration.
Shades of Green: A Visual Approach to Assessing Environmental Inequality in Cleveland, Ohio
Authors: Meshkani Taraneh
Published: February 2025

This study explores environmental inequality through a novel quantitative approach, analyzing urban vegetation in two contrasting neighborhoods in Cleveland, Ohio—East Cleveland and Shaker Heights. Utilizing computer vision and Google Street View image analysis techniques, this research quantifies the distribution, quality, and visual characteristics of tree canopy and grass. This study addresses the following question: how can visual data illuminate environmental inequalities in urban landscapes? The findings reveal significant disparities between the two neighborhoods, not only in tree canopy coverage but also in the visual characteristics and density of vegetation. Shaker Heights displays more abundant, healthier, and better-maintained greenery compared to East Cleveland. These results highlight the intersection of socio-economic factors and urban vegetation quality, suggesting that both the quantity and visual attributes of greenery serve as indicators of neighborhood wealth and environmental investment. This study demonstrates the effectiveness of computer vision techniques in accessing and analyzing visual data to represent environmental inequalities. It advocates for targeted urban planning interventions to promote equitable green space policies, considering both the quantity and quality of urban vegetation.