bluuebunny/crossref_metadata_2025 huggingface dataset:
Multidimensional Particle Separation by Tilted-Angle Standing Surface Acoustic Waves—Physics, Control, and Design
Authors: Sachs Sebastian , König Jörg , Cierpka Christian
Published: January 2025

Lab-on-a-Chip devices based on tilted-angle standing surface acoustic waves (tasSAWs) emerged as a promising technology for multidimensional particle separation, highly selective in particle size and acoustic contrast factor. For this active separation method, a tailored acoustic field is used to focus and separate particles on stationary pressure nodes by means of the acoustic radiation force. However, additional non-linear acoustofluidic phenomena, such as the acoustically induced fluid flow or dielectrophoretic effects, are superimposed on the separation process. To obtain a particle separation of high quality, control parameters that can be adjusted during the separation process as well as design parameters are available. The latter are specified prior to the separation and span a high-dimensional parameter space, ranging from the acoustic wavelength to the dimensions and materials used for the microchannel. In this paper, the physical mechanisms to control and design tasSAW-based separation devices are reviewed. By combining experimental, semi-analytical, and numerical findings, a critical channel height and width are derived to suppress the influence of the acoustically induced fluid flow. Dealing with the three-dimensional nature of the separation process, particles are focused at different height levels of equal force balance by implementing a channel cover of high acoustic impedance while achieving an approx. three-times higher acoustic pressure. Using this improved channel design, the particle shape is identified as an additional separation criterion, rendering the continuous acoustofluidic particle separation as a multidimensional technology capable of selectively separating microparticles below 10 μm with regard to size, acoustic contrast, and shape.
Exploring the Thermodynamics and Dynamics of CO2 Using Rigid Models
Authors: Pinheiro Lucas Avila , Silva-Oliveira Walas , de Moraes Elizane E. , Bordin José Rafael
Published: January 2025

Understanding the behavior of carbon dioxide (CO2) under varying thermodynamic conditions is essential for optimizing processes such as Carbon Capture and Storage (CCS) and supercritical fluid extraction. This study employs molecular dynamics (MD) simulations with the EPM2 and TraPPE-small force fields to examine CO2 phase behavior, structural characteristics, and transport properties across a temperature range of 228–500 K and pressures from 1 to 150 atm. Our findings indicate a good agreement between simulated and experimental liquid–vapor coexistence curves, validating the capability of both force fields to model CO2 accurately in a wide range of thermodynamical conditions. Radial distribution functions (RDFs) reveal distinct interaction patterns in liquid and supercritical phases, while mean squared displacement (MSD) analyses show diffusivity increasing from 5.2×10−9 m2/s at 300 K to 1.8×10−8 m2/s at 500 K. Additionally, response functions such as the heat capacity effectively capture phase transitions. These findings provide quantitative insights into CO2 phase behavior and transport properties, enhancing the predictive reliability of simulations for CCS and related industrial technologies. This work bridges gaps in the CO2 modeling literature and highlights the potential of MD simulations in advancing sustainable applications.
Volatile Organic Compound–Drug Receptor Interactions: A Potential Tool for Drug Design in the Search for Remedies for Increasing Toxic Occupational Exposure
Authors: Ogbodo John Onyebuchi , Egba Simeon Ikechukwu , Ikechukwu Gavin Chibundu , Paul Promise Chibuike , Mba Joseph Obinna , Ugwu Okechukwu Paul-Chima , Ezike Tobechukwu Christian
Published: January 2025

Volatile organic compounds (VOCs) can impact the actions of drugs due to their effects on drug receptors and the activities of enzymes involved in various metabolic processes, especially those relating to gene regulation. They can disrupt cellular functions and potentially affect human drug metabolism and utilization receptors. They mimic or inhibit the actions of endogenous ligands, leading to carcinogenesis, neurotoxicity, endocrine disruption, and respiratory disorders. Chronic exposure to VOCs due to human occupation can lead to an increased generation of reactive oxygen species (ROS), which could lead to oxidative stress and damage to lipids, affecting the formation and proper functioning of gene regulation, enzyme activity, and cell membranes. The presence of oxidative stress could interfere with drug activity and potentially impact the body’s ability to process and utilize drugs effectively. This is because drugs such as antioxidant drugs play an essential role in cell protection against oxidative damage. Therefore, disruptions in their metabolism could distort the overall health condition through the breakdown of antioxidant defense mechanisms. In this study, the aim is to assess the effect of VOC exposure on drug receptors and the way forward in designing and maintaining optimal drug activity for workers’ overall well-being.
Competitive Adsorption of Pb2+ from Aqueous Solutions by Multi-Source Lignocellulose-Derived Hydrothermal Humic Acid
Authors: Song Changzhi , Liu Junhao , Zhang Libo , Wang Jianing , Shu Xinqian
Published: January 2025

This study explores the conversion of lignocellulosic biomass from softwood, hardwood, and grasses into humic acid via a mild hydrothermal process and its application in Pb2+ adsorption. The investigation focused on adsorption isotherms, kinetics, thermodynamics, and the intraparticle diffusion model to evaluate the adsorption performance of humic acids from different sources. The results indicate that the humic acid of broad-leaved wood (Eucalyptus-HA) possesses the optimal adsorption capacity and removal efficiency of Pb2+. When the initial concentration of Pb2+ is 100 mg/L, the adsorption capacity and removal efficiency of Eucalyptus-HA reach 49.75 mg/g and 25.57%, respectively, which are far higher than the adsorption capacity (26.82 mg/g) and removal efficiency (13.71%) of commercial humic acid (Commercial-HA). The pore structure of humic acid plays a critical role in its Pb2+ adsorption capacity. High Pb2+ concentrations and a low pH negatively impact adsorption efficiency, and instability in the humic acid pore structure affects reproducibility. Adsorption isotherm fitting showed that Pb2+ adsorption conforms most closely to the Langmuir model. While commercial humic acid exhibited faster adsorption rates, its capacity was constrained by thermodynamic limitations and lower specific surface areas. The intraparticle diffusion model revealed that Pb2+ diffusion proceeded more efficiently in hydrothermal humic acids than in commercial ones due to lower diffusion resistance. This study highlights the potential of feedstock source regulation to enhance humic acid’s heavy metal adsorption capabilities, expanding its application across various fields.
Prediction of the Effect of Fly Ash on the Unconfined Compressive Strength of Basalt Fiber Reinforced Clay Using Artificial Neural Networks
Authors: Aslan Topçuoğlu Yasemin
Published: January 2025

In this study, the effects of fly ash (FA) and basalt fiber (BF) additives on the unconfined compressive strength (qu) of kaolin clay were experimentally investigated, and a dataset was created based on the results. This dataset was used in an artificial neural network (ANN) model to predict the qu based on the additive ratio, water content, and curing time. For this purpose, samples were prepared by adding 1% BF with a length of 24 mm and FA at ratios of 3%, 6%, 9%, 12%, and 15% to the clay, followed by the addition of 25% and 30% water. Unconfined compressive tests were performed before curing and after 28, 42, and 56 days of curing to determine the qu values. The evaluation of the obtained experimental results was carried out by creating an ANN model. To validate the prediction capabilities of the ANN, a comparative analysis was performed using various artificial intelligence models, and the model’s overall performance was assessed with a 5-fold cross-validation technique. The evaluations revealed that the ANN model, using data from experimental studies, demonstrated the highest prediction accuracy and was in close agreement with the experimental results. According to the results obtained, the R value of the ANN model was calculated as 0.97, while the RMSE values were found as 0.09, 0.10, 0.06 and 0.04 for pre-curing, 28th day, 42nd day and 56th day, respectively.
Impulsive Buying Tendencies and Personality: Cognitive and Affective Aspects
Authors: Qureshi Fayyaz Hussain , Sokić Katarina , Khawaja Sarwar
Published: January 2025

Impulsive buying is a growing problem in modern society with significant financial and psychological consequences, and it is necessary to examine the mechanisms underlying it. Understanding the mechanisms driving this behavior is crucial. Impulsive buying consists of two key aspects: a cognitive aspect, characterized by a lack of planning and reflection, and an affective aspect, involving emotional responses that can occur before, during, or after a purchase. This study aims to explore the relationship between personality traits, as measured by the HEXACO model, and tendencies toward impulsive buying. Specifically, it examines how the HEXACO traits differentially predict the cognitive and affective components of impulsive buying tendencies. Data were collected from a sample of 420 students, with 32 excluded due to incomplete responses, resulting in a final sample of 388 students (155 men, 233 women) with an average age of 22.8. Hierarchical regression analyses revealed that HEXACO personality domains significantly predicted overall impulsive buying tendencies. As expected, Honesty–Humility and Conscientiousness were negatively associated with impulsive buying, while Emotionality and Extraversion positively predicted impulsive buying tendencies. Moreover, Honesty–Humility, Conscientiousness, and Extraversion significantly predicted the cognitive component of impulsive buying, while Honesty–Humility and Emotionality were significant predictors of the affective component. These findings suggest the importance of educating individuals, from an early age, on the negative consequences of impulsive buying, effective money-saving strategies, and the principles of mental accounting.
Efficient Hyperspectral Video Reconstruction via Dual-Channel DMD Encoding
Authors: Ma Mingming , Niu Yi , Gao Dahua , Li Fu , Shi Guangming
Published: January 2025

Hyperspectral video acquisition requires a precise balance between spectral and temporal resolution, often achieved through compressive sampling using two-dimensional detectors and spectral reconstruction algorithms. However, the reliance on spatial light modulators for coding reduces optical efficiency, while complex recovery algorithms hinder real-time reconstruction. To address these challenges, we propose a digital-micromirror-device-based complementary dual-channel hyperspectral (DMD-CDH) video imaging system. This system employs a DMD for simultaneous light splitting and spatial encoding, enabling one channel to perform non-aliasing spectral sampling at lower frame rates while the other provides complementary high-rate sampling for panchromatic video. Featuring high optical throughput and efficient complementary sampling, the system ensures reliable hyperspectral video reconstruction and serves as a robust ground-based validation platform for remote sensing applications. Additionally, we introduce tailored optical error calibration and fixation techniques alongside a lightweight hyperspectral fusion network for reconstruction, achieving hyperspectral frame rates exceeding 30 fps. Compared to the existing models, this system simplifies the calibration process and provides a practical high-performance solution for real-time hyperspectral video imaging.
Cross-Line Fusion of Ground Penetrating Radar for Full-Space Localization of External Defects in Drainage Pipelines
Authors: Fang Yuanjin , Yang Feng , Qiao Xu , Xu Maoxuan , Fang Liang , Liu Jialin , Li Fanruo
Published: January 2025

Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide the precise spatial coordinates of the defects. To address this limitation, this study introduces a novel GPR-based drainage pipeline inspection robot system integrated with multiple sensors. The system incorporates MEMS-IMU, encoder modules, and ultrasonic ranging modules to control the GPR antenna for axial and circumferential scanning. A novel Cross-Line Fusion of GPR (CLF-GPR) method is introduced to integrate axial and circumferential scan data for the precise localization of external pipeline defects. Laboratory simulations were performed to assess the effectiveness of the proposed technology and method, while its practical applicability was further validated through real-world drainage pipeline inspections. The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. These findings indicate that the proposed approach is reliable and has the potential to support the transparency and digitalization of urban underground drainage networks.
Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions
Authors: Feng Lingyun , Ma Danyang , Xie Min , Xi Mengzhu
Published: January 2025

Anthropogenic heat is the heat generated by human activities such as industry, construction, transport, and metabolism. Accurate estimates of anthropogenic heat are essential for studying the impacts of human activities on the climate and atmospheric environment. Commonly applied methods for estimating anthropogenic heat include the inventory method, the energy balance equation method, and the building model simulation method. In recent years, the rapid development of computer technology and the availability of massive data have made machine learning a powerful tool for estimating anthropogenic heat fluxes and assessing its effects. Multi-source remote sensing data have also been widely used to obtain more details of the spatial and temporal distribution characteristics of anthropogenic heat. This paper reviews the main approaches for estimating anthropogenic heat emissions. The typical algorithms of the abovementioned three methods are introduced, and their advantages and limitations are also evaluated. Moreover, the recent progress in the application of remote sensing data and machine learning are discussed as well. Based on big data and machine learning techniques, the research on feature engineering and model fusion will bring about major changes in data analysis and modeling of anthropogenic heat. More in-depth research of this issue is recommended to provide important support for curbing global warming, mitigating air pollution, and achieving the national goals of carbon peak and a carbon neutrality strategy.
Characterizing Groundwater Level Response to Precipitation at Multiple Timescales in the Lubei Plain Region Using Transfer Function Analysis
Authors: Xu Lewei , Gong Huili , Chen Beibei , Zhou Chaofan , Zhong Xueting , Ma Ziyao , Meng Dexin
Published: January 2025

Groundwater is essential for ecosystem stability and climate adaptation, with precipitation variations directly affecting groundwater levels (GWLs). Human activities, particularly groundwater exploitation, disrupt the recharge mechanism and the regional water cycle. In this study, we propose a new research framework: On the basis of analyzing the spatiotemporal variability characteristics of precipitation and shallow GWL, we used transfer function analysis (TFA) to quantify the multi-timescale characteristics of precipitation–GWL response under the effects of climate change and human activities. In addition, we evaluated the GWL seasonality and seasonal response while also considering apportionment entropy. We applied this framework to the Lubei Plain (LBP), and the findings indicated the following: (1) Annual precipitation in the LBP decreased from southeast to northwest, with July and August contributing 51.5% of total rainfall; spatial autocorrelation of GWL was high and was influenced by geological conditions and cropland irrigation. (2) The coherence between GWL and precipitation was 0.96 in the high-precipitation areas but was only 0.6 in overexploited areas, and sandy soils enhanced the effective groundwater recharge, with a gain of 1.65 and a lag time of 2.1 months. (3) Over interannual scales, GWL response was driven by precipitation distribution and aquifer characteristics, while shorter timescales (4 months) were significantly affected by human activities, with a longer lag time in overexploited areas, which was nearly 60% longer than areas that were not overexploited. (4) Groundwater exploitation reduced the seasonality of GWL, and irrigation reduced the coherence between GWL and precipitation (0.5), with a gain of approximately 0.5, while a coherence of 0.8 and a gain of 3.5 were observed in the non-irrigation period. This study clarified the multi-timescale characteristics of the precipitation–GWL response, provided a new perspective for regional research on groundwater response issues, and proposed an important basis for the short-term regulation and sustainable development of water resources.
Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network
Authors: Zhai Zihao , Luo Liyan , Chen Yuhan , Zhang Xiaoguo
Published: January 2025

This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well as the issue of low fault diagnosis accuracy resulting from small sample quantities. This approach transforms the one-dimensional vibration signal into time–frequency diagrams using an SWT based on complex Morlet wavelet basis functions, which redistributes (squeezes) the values of the wavelet coefficients at different localized points in a time–frequency plane to the estimated instantaneous frequencies. This allows the energy to be more fully concentrated in actual corresponding frequency components. This strategy improves both the time–frequency aggregation and the resolution, which better reflects the eigenvalues of non-stationary signals. In this process, transfer learning and a residual structure are used in the training of a convolutional neural network. The resulting time–frequency diagrams, acquired using the steps discussed above, are then input to the TRCNN for diagnosis. A series of validation experiments confirmed that applying the TRCNN structure made it possible to achieve high diagnostic accuracy, even when training the network with only a small number of fault samples, as all 12 fault types from the test dataset were diagnosed correctly. Further simulation experiments demonstrated that our proposed method improved fault diagnosis accuracy compared to that of conventional techniques (with increases of 1.74% over RCNN, 1.28% over TCNN, 1.62% over STFT, 1.73% over WT, 2.83% over PWVD, and 1.39% over STFA-PD). In addition, diagnostic accuracy reached 100% during the application of three-time transfer learning, validating the effectiveness of the proposed method for rolling bearing fault diagnosis.
Performance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management Architecture
Authors: Thalagala Ravindu G. , De Silva Oscar , Oldford Dan , Molyneux David
Published: January 2025

The retreat of Arctic sea ice has opened new maritime routes, offering faster shipping opportunities; however, these routes present significant navigational challenges due to the harsh ice conditions. To address these challenges, this paper proposes a deep learning-based Arctic ice risk management architecture with multiple modules, including ice classification, risk assessment, ice floe tracking, and ice load calculations. A comprehensive dataset of 15,000 ice images was created using public sources and contributions from the Canadian Coast Guard, and it was used to support the development and evaluation of the system. The performance of the YOLOv8n-cls model was assessed for the ice classification modules due to its fast inference speed, making it suitable for resource-constrained onboard systems. The training and evaluation were conducted across multiple platforms, including Roboflow, Google Colab, and Compute Canada, allowing for a detailed comparison of their capabilities in image preprocessing, model training, and real-time inference generation. The results demonstrate that Image Classification Module I achieved a validation accuracy of 99.4%, while Module II attained 98.6%. Inference times were found to be less than 1 s in Colab and under 3 s on a stand-alone system, confirming the architecture’s efficiency in real-time ice condition monitoring.
A New Approach to Non-Invasive Microcirculation Monitoring: Quantifying Capillary Refill Time Using Oximetric Pulse Waves
Authors: Xia Yuxiang , Wang Xinrui , Guo Zhe , Wang Xuesong , Wang Zhong
Published: January 2025

(1) Background: To develop a novel capillary refill time measurement system and evaluate its reliability and reproducibility. (2) Methods: Firstly, the utilization of electromagnetic pressure technology facilitates the automatic compression and instantaneous release of the finger. Secondly, the employment of pressure sensing technology and photoelectric volumetric pulse wave analysis technology enables the dynamic monitoring of blood flow in distal tissues. Thirdly, the subjects were recruited to compare the average measurement time and the number of measurements required for successful measurements. The satisfaction of doctors and patients with the instrument was investigated through the administration of questionnaires. Finally, 71 subjects were recruited and divided into two groups, A and B. Three doctors repeated the measurement of the right index fingers of the subjects. In Group A, the same measuring instrument was used, and the consistency of the measurements was evaluated using the intragroup correlation coefficient. In Group B, one doctor repeated the measurement of each subject three times using the same measuring instrument, and the reproducibility of the CRT was evaluated using the analysis of variance of the repeated measurement data. (3) Results: The development of the capillary refill time meter was successful, with an average measurement time of 18 s and a single measurement. This study found that doctor–patient satisfaction levels were 98.3% and 100%, respectively. The intraclass correlation coefficient was 0.995 in Group A, and the p-value was greater than 0.05 in Group B. (4) Conclusions: The non-invasive monitoring of microcirculation has been rendered both rapid and effective, thus paving the way for the further mechanization and standardization of this process. The CRT, when measured using the capillary refill time meter test machine, demonstrated consistent and reproducible results, both when assessed by different researchers and when evaluated across varying measurement sets.
Bridging the Gaps: Examining the Impact of Technology-Based Active Learning in Workplace Safety Training Through a Systematic Literature Review
Authors: Margheritti Simona , Marcucci Sofia , Miglioretti Massimo
Published: January 2025

Theoretical background: Occupational safety training is crucial for reducing workplace risks, but traditional approaches often struggle to fully engage participants or guarantee effective knowledge retention. Over the past decade, interest in using technology-based active learning strategies has grown, with active learning focusing on practical approaches that actively engage workers in the learning process. Objective: The research aims to identify the active learning strategies currently employed in OS training and assess the benefits and challenges of these approaches. Method: A systematic literature review was conducted in line with the PRISMA guidelines. After a rigorous selection process based on inclusion and exclusion criteria, 24 eligible articles were identified from the Scopus, Web of Science, and PubMed databases. Results: Technology-based active learning strategies, especially serious games, enhance the comprehension of safety procedures. Simulating risk scenarios and providing immediate feedback facilitates knowledge transfer to real-world environments. However, limitations are evident, such as technical complexity, high implementation costs, and difficulties in acceptance by less experienced users. Conclusions: The study concludes by emphasizing the need for targeted research to overcome the identified challenges, such as improving simulation realism, reducing costs through collaborative partnerships, and addressing usability issues for different worker populations.
An Adult with Fever and Progressive Ulcerative Lesions: A Case of Malignant Syphilis
Authors: Pipitò Luca , D’Avenia Simona , Orlando Elisabetta , Cascio Antonio
Published: January 2025

Background: Syphilis has recently reemerged as a significant public health concern, with rising incidence rates globally. Malignant syphilis is a rare and severe variant of secondary syphilis, often associated with immunocompromised states, particularly HIV infection. Methods: Here, we reported a rare case of malignant syphilis in a young man with well-controlled HIV. Case: A 35-year-old man with well-controlled HIV presented with disseminated ulcerated nodules and plaques, accompanied by fever, asthenia, and mild itching. Histopathology of the scapular ulcer revealed a granulomatous infiltrate. Cutaneous leishmaniasis, atypical mycobacteriosis, and T-cell lymphomas were excluded. Serological testing and polymerase chain reaction confirmed a diagnosis of malignant syphilis.
Digital Inclusive Finance and Carbon Emission Efficiency: Evidence from China’s Economic Zones
Authors: Jia Wei , Wang Zhihao , Liu Lei
Published: January 2025

In the current tide of technological revolution and industrial transformation, digitalization and greening have surfaced as two prominent trends, serving as the inherent requisites and impelling forces of new productive forces. Augmenting carbon emission efficiency is a crucial pathway for attaining green development. Digital inclusive finance, as an innovative financial paradigm, is a significant determinant influencing carbon emission efficiency and the progression of green development. By harnessing panel data from 108 cities within the Yangtze River Economic Belt spanning from 2011 to 2021, this study deploys the super-efficiency SBM model to gauge carbon emission efficiency. Additionally, it employs fixed effects and mediating mechanism models to empirically scrutinize the impact of digital inclusive finance on carbon emission efficiency within urban agglomerations in the Yangtze River Economic Belt. The study further probes its spatial effects through a spatial Durbin model. The research findings disclose that digital inclusive finance can substantially augment carbon emission efficiency in regional cities along the Yangtze River Economic Belt, manifesting notable regional correlations. This enhancement is accomplished by propelling industrial structure upgrades and augmenting scientific and technological capabilities. To enhance carbon emission efficiency in the Yangtze River Economic Belt, efforts should be centered around advancing digital inclusive finance, expediting industrial structural transformation, and fortifying scientific and technological development.
The Progress and Framework of Ecological Welfare Performance Within the Context of the “Dual Carbon” Goal: A Comprehensive Literature Review
Authors: Hu Meijuan , Chen Gong , Li Zaijun
Published: January 2025

The “dual carbon” goal has become a major national strategy, an inherent requirement for high-quality economic development. As a crucial indicator of measuring sustainable development capabilities, ecological welfare performance has emerged as a core tool for enhancing human welfare, achieving sustainable development, advancing ecological civilization construction, and promoting green development. This study focuses on the “dual carbon” goal appeal, clarifies the concept connotation of ecological welfare performance, assesses the research progress of ecological welfare performance, deeply analyzes the internal relationship between the “dual carbon” goal and ecological welfare performance, builds a research framework for ecological welfare performance, and points out the marginal contribution, research prospects, and shortcomings of ecological welfare performance research under the “dual carbon” goal. The findings are as follows: (1) Research on ecological welfare performance has shifted from a single dimension to a multi-dimensional and multi-level comprehensive consideration, involving multiple disciplines. Literature research focuses on four aspects, namely, the connotation and representation of welfare, the interaction between ecosystems and welfare, ecological welfare performance research, and “dual carbon” target and ecological welfare performance research. (2) From the perspective of research hotspots and historical evolution, most scholars pay more and more attention to empirical research and application-oriented research, and it is still necessary to constantly explore new theoretical frameworks and methodologies in the future to better understand the changing rules and driving mechanisms of ecological welfare performance. (3) From the perspective of the ecological welfare performance research framework, an in-depth analysis of the relationship between natural ecological consumption, economic growth, and welfare is carried out. Based on the change in research paradigm, a two-stage ecological welfare performance evaluation framework is constructed to promote the realization of the “dual carbon” goal and the continuous optimization of ecological welfare performance, so as to provide a reference basis for the scientific assessment of sustainable development capacity.
The Power of Collaboration: How Does Green Innovation Network Affect Urban Green Total Factor Productivity?
Authors: Jiao Hongrui , Deng Hongbing , Hu Shengmei
Published: January 2025

Global climate change has necessitated a transition to sustainable development, prompting nations to prioritize green total factor productivity (GTFP) as a key indicator of economic and environmental efficiency. This study examines the role of the green innovation network (GIN) in enhancing urban GTFP within China’s Yangtze River Delta (YRD)—a region pivotal to national economic growth and ecological sustainability. Using data from 41 cities spanning 2011 to 2020, we constructed the GIN based on inter-city green cooperative patents and analyzed the network positions of cities using a social network analysis (SNA). Urban GTFP was assessed through the Super-SBM model, and two-way fixed-effects panel models, along with a threshold effect model, were applied to evaluate the impacts of GIN on GTFP. The findings reveal that stronger network positions within the GIN significantly enhance urban GTFP, with green finance further amplifying this effect. These results provide actionable insights for policymakers in developing countries, highlighting the importance of integrated innovation strategies and enhanced green financial systems to promote sustainable urban development.
Early Driver Fatigue Detection System: A Cost-Effective and Wearable Approach Utilizing Embedded Machine Learning
Authors: Lin Chengyou , Zhu Xinying , Wang Renpeng , Zhou Wei , Li Na , Xie Yu
Published: January 2025

Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features and embedded machine learning to estimate the driver’s fatigue level. The driver’s HRV is derived from electrocardiogram (ECG) signals captured by a wearable device for analysis. Time- and frequency-domain HRV features are then extracted and used as the input for a machine learning classifier. A dataset of HRV features is collected from a driving simulation experiment involving 18 participants. Four machine learning classifiers are evaluated, and a backpropagation neural network (BPNN) is selected for its superior performance, achieving up to 94.35% accuracy. The optimized classifier is successfully deployed on an embedded system, providing a cost-effective and portable solution for the early detection of driver fatigue. The results demonstrate the feasibility of using HRV-based machine learning models for the early detection of driver fatigue, contributing to enhanced road safety and a reduced accident risk.
Technostress, technosuffering, and relational strain: a multi-method qualitative study of how remote and digital work affects staff in UK general practice
Authors: Dakin Francesca H , Hemmings Nina , Kalin Asli , Moore Lucy , Ladds Emma , Payne Rebecca , Rosen Rebecca , Byng Richard , Wherton Joseph , Wieringa Sietse , Greenhalgh Trisha
Published: January 2025

Background

The introduction of remote and digital forms of working in UK general practice has driven the development of new routines and working styles.

Aim

To explore and theorise how new forms of work have affected general practice staff.

Design and setting

Multi-sited, qualitative case study in UK general practice.

Method

Using longitudinal ethnography by researchers in residence, we followed 12 practices for 28 months (September 2021 to December 2023). This core dataset was supplemented by workshops and stakeholder interviews. Data analysis applied theories from the sociology of work, organisation studies, and internet studies.

Results

Staff made significant efforts to adapt to and embed digital services into their work. When technologies work well they can offer improved convenience, efficiency, more comprehensive patient care, and workplace fulfilment for staff. However, for many clinical and administrative staff, compromises and frictions embedded in digitalised workplace routines and processes could also lead to job dissatisfaction, worsened wellbeing, and misalignments with professional values and identities. We found that this workplace suffering caused relational strain between team members and had an impact on team cohesiveness and coordination.

Conclusion

The digitalisation of working routines in UK general practice poses a unique challenge to the workforce, risking technostress, workplace suffering, and increased relational strain within and between teams. To embed the benefits of digitalisation, we must first improve practice teams’ readiness for change, which includes strengthening practices’ relational structures that provide support during periods of adaptation. Practices must be empowered to determine a locally appropriate configuration of digital tools and given the resources and time to adapt working routines.
Inequalities in cancer two-week-wait referrals: a cross-sectional study in English general practice
Authors: Wynne Stephanie C , Ashworth Mark
Published: January 2025

Background

Practices with higher two-week-wait (2WW) referral-rates demonstrate higher survival for several cancers. Yet, there is little up-to-date evidence exploring factors influencing 2WW-referral-rates and whether health inequalities exist, particularly after COVID-19.

Aim

To establish which patient-factors (eg, age, sex, ethnicity, deprivation) and practice-factors (eg, remote consultations, frequency of seeing a preferred-GP) independently predict 2WW-referral-rates.

Design & setting

A cross-sectional, observational study was performed using data from English general practices for 2021-2022.

Method

Multivariable linear regression was used to identify the strongest, independent predictors of 2WW-referral-rates for all-cancers (primary outcome) and for breast, lower-gastrointestinal, lung and skin cancers separately (secondary outcome).

Results

The analysis included 6307 practices. Practices with more females, patients aged 75+and with a greater burden of long-term conditions were associated with higher 2WW-referrals for all-cancers, as were practices in Northwest England and those with higher scores for patients feeling involved in care decisions. Conversely, practices with a higher frequency of seeing a preferred-GP were predictive of fewer all-cancer 2WW-referrals. Whilst practices with a higher proportion of current smokers and Asian and Black ethnicity patients predicted fewer all-cancer 2WW-referrals, these associations were strongest for skin cancer, and for breast cancer (except for Black ethnicity). Higher socioeconomic deprivation predicted lower 2WW-referrals for lung cancer only.

Conclusion

This study analyses factors influencing 2WW-referral-rates and highlights potential inequalities. This work identifies priority populations, including smokers and Asian and Black ethnicity patients, who may benefit from interventions to increase primary care access. Shared-decision-making may be an underexplored resource for increasing all-cancer 2WW-referral-rates.
Trehalose and Dimethyl Sulfoxide As Cryoprotective Solution Inclusions in a Static Liquid Nitrogen Vapour Vitrification Method for African Catfish (Clarias gariepinus Burchell, 1822) Sperm
Authors:   Aquaculture Innovation and Technology Development, Department of Forestry, Fisheries and the Environment, Cape Town, South Africa, FOUCHE CHRIS HENRI , GOODMAN MARK
Published: January 2025

Captive breeding of Clarias gariepinus (Burchell, 1822) entails stripping of mature females, while males must be killed or testes partially excised to obtain spermatozoa. Cryopreservation could significantly improve the reproductive potential of male catfish. In this study, Ginzburg fish Ringer (GFR) was the common extender in all protocols with or without 0.3 M trehalose. Dimethyl sulfoxide (DMSO) and methanol were tested to determine the preferred equilibration period for the complex before cryofreezing. The cryopreservation method and sperm fertilisation capacity were assessed in terms of hatchability rate. A feasible semen rationing protocol for maximised fertilisation was also evaluated. Semen equilibration with cryoprotectants was allowed for 30 minutes at 4 °C. Liquid nitrogen (LN2) vapour-induced freezing rates ranged from -2.7 to -5 °C.min⁻¹, and cryovials/straws were immersed in LN2 when -41 °C was reached after 12 min. Straws showed significantly improved cryopreservation efficacy over cryovials in terms of fertilisation. Optimal fertilisation occurred with trehalose and DMSO-treated cryostored sperm, comparable to fresh semen. Only 0.05 mL diluted semen was required to fertilise 35g eggs, compared with a similar volume efficacy for undiluted sperm. Trehalose enhanced egg fertilisation for both diluted fresh sperm and DMSO-treated thawed sperm. All methods produced healthy larvae to 21 days post-hatch. The validated method can yield 60 straws (0.5 mL) from 3 mL semen, fertilising approximately 2.1 kg eggs (~162 × 10⁴ eggs). The LN2 vapor vitrification method was shown to be both as effective as and more cost efficient than controlled rate freezing equipment.
The Effect of Occupational Stress on the Psychological Well-being of Healthcare Workers: Basis for Stress Management
Authors: Sanido Kenneth L. , Echalar Lovely V.
Published: January 2025

Stress in the workplace is something that almost every worker experiences. It is not simply the notion of physical labor but also the wear and tear of work on a person's mental health. Long-term and extreme occupational stress can be a severe threat to the psychological well-being of an employee. This research aimed to investigate the effect of occupational stress on the psychological well-being of healthcare workers in General Santos City, Philippines. A simple random sampling method was applied to select respondents from the city's Tertiary Hospitals, and data were gathered using a survey questionnaire. The results revealed that healthcare workers experienced average levels of occupational stress and moderate levels of psychological well-being. Occupational stress significantly influenced psychological well-being, with a moderate positive correlation of 35.8%. This correlation suggests that as occupational stress increases, psychological well-being also tends to increase. However, additional factors contribute to psychological well-being, accounting for 64.2% of the variance. In light of the findings, a "primary-and-secondary-organizational" level of stress management intervention has been developed for the General Santos City, Philippines healthcare staff.
Responsible management in administrative management: Innovative approaches and forecasts
Authors: Tkach Oleg , Saienko Volodymyr , Vader Tetyana , Morhulets Oksana , Bielikova Nadiia
Published: January 2025

In modern business, a rational combination of administrative and responsible management is an integrated management instrument promoting adaptive development and implementation of innovative technologies. The study aims to generalise approaches to defining the essence and role of responsible management in the administrative management system and clarify the factors that ensure the efficiency of management processes. To achieve the research objective, factor analysis was used to systematise the main factors that influence the effectiveness of the responsible business management system. As a result of the factor analysis on the example of PJSC CB “Privat Bank”, it was found that for today's business, it is essential to analyse a significant set of indicators and study them in dynamics. Two factors were explicitly identified for the selected company: the factor of strategic development and business reliability, which includes the banking business reliability ratio, the equity protection ratio, the maximum risk ratio, the rating of the reliability of banking products, the rating of public confidence in the bank; as well as the factor of investment attractiveness of the business, which includes the capital multiplier coefficient, the standard of significant risks. In general, the combination of administrative and responsible management can open up significant additional opportunities for effective development.
The power of partnership in higher education
Authors: Chovriy Sofiya , Yatsenko Olena , Kozhevnikova Alla , Anisimova Olena , Horbatiuk Oksana , Kolbina Lyudmila
Published: January 2025

The article considers the content and essential features of partnership, identifies the main principles of partnership, and shows their role in the internal system of ensuring the quality of education in a higher education institution; indicates significant approaches and barriers in the process of implementing partnership pedagogy, the most common methods of cooperation between enterprises and organizations and higher education institutions, clusters of partnership forms and partnership models of business structures and universities. The advantages and directions of partnership in the organization of the educational process of higher education institutions and cooperation with business corporations are substantiated. The purpose of the experimental work was to prove the need to implement the developed author's methodology for forming the communicative competence of future specialists when implementing partnership relations, which will contribute to the growth of the creative level of partnership interaction and, as a result, the communicative competence of the future specialist. The results of the comparison of the ascertaining and formative stages of the experiment indicate positive changes in the levels of formation of communicative competence of future specialists of the experimental group when implementing partnership relations. The results of the experiment make it possible to speak about the effectiveness of the methodology for the formation of communicative competence of future specialists when implementing partnership relations at the formative stage of the experiment.
Adapting accounting to the digital age: The urgent need for new regulatory standards
Authors: Ievsieieva Olga , Tsikalo Yevhen , Mulyk Tetiana , Udodova Yana , Nazarenko Oleksandr
Published: January 2025

This paper discusses the regulatory challenges that account practices face in dealing with the rapid changing digital economy in the context of the need for new standards to reflect the complexity of modern financial transactions. This research reviews the recent academic and professional literature (2019–2023) to identify key challenges: the integration of emerging technologies such as AI and blockchain, data security and privacy, transparent AI generated financial data, adapting reporting for continuous accounting, and regulating big data analytics. The thesis of the study calls for policymakers to focus on the creation of new accounting standards and regulatory approaches to increase transparency, audit and efficiency in the digital economy. In addition, it implies that there needs to be transparency about data handling, auditing digital financial procedures and incorporating standardized reporting systems in the global accounting effectiveness in the digital age. Future research may investigate strategies to assist developing countries and reduce vulnerabilities in data manipulation of digital accounting.
Pregnancy and Childbirth Outcomes of Gestational Diabetes Mellitus: A Retrospective Cohort Study
Authors: Darsareh Fatemeh , Shirzadfard Jahromi Malihe , Ranjbar Amene , Shekari Mitra , Mehrnoush Vahid , Roozbeh Nasibeh
Published: January 2025

Background: Understanding the various outcomes of gestational diabetes mellitus (GDM) is essential for initiating a cascade of preparatory steps to address them. Therefore, this study aimed to evaluate the pregnancy and childbirth outcomes of GDM. More precisely, the study retrospectively assessed singleton pregnant mothers diagnosed with GDM who gave birth at a tertiary hospital in Bandar Abbas, Iran, between January 1st, 2020, and January 1st, 2022. Materials and Methods: To this end, pregnancy and childbirth outcomes were investigated, including the incidence of the need for induction of labor, placenta abruption, preterm birth, intrauterine growth restriction (IUGR), intrauterine fetal death (IUFD), meconium fluid, fetal distress, methods of delivery, perineal lacerations, and postpartum hemorrhage. The other outcomes were childbirth injury, shoulder dystocia, macrosomia, low birth weight, congenital malformation, neonatal asphyxia, need for cardiac and respiratory resuscitation, transfer to a neonatal intensive care unit (NICU), and newborn death. Results: The records of 7748 eligible mothers were included in the analysis. GDM was diagnosed in 1,087 (14%) mothers. Age was the only sociodemographic factor that significantly differed between GDM and non-GDM mothers. GDM mothers had a significantly higher risk of macrosomia (adjusted odds ratio [aOR]: 2.94, 95% confidence interval [CI]: 1.71–4.61, P<0.001), labor induction (aOR: 3.14, 95% CI: 1.72–6.95, P<0.001), instrumental delivery (aOR: 3.16, 95% CI: 1.51–3.87, P<0.001), IUFD (aOR: 1.95, 95% CI: 1.17–3.06, P<0.01), and NICU admission (aOR: 2.19, 95% CI: 0.99–3.82, P<0.01). Other common complications, such as cesarean section (CS), postpartum hemorrhage, shoulder dystocia, childbirth trauma, and extensive perineal lacerations, were not significantly different between GDM and non-GDM mothers. Conclusion: Despite the higher incidence of labor induction and instrumental delivery, no significant association was found between GDM and adverse maternal outcomes. Regarding neonatal outcomes, GDM was linked to macrosomia, IUFD, and NICU admission.