bluuebunny/crossref_metadata_2025 huggingface dataset:
Phenolic Fractions from Walnut Milk Residue: Antioxidant Activity and Cytotoxic Potential
Authors: Toledo-Merma Pamela Ruth , Arias-Santé María Fernanda , Rincón-Cervera Miguel Ángel , Porras Omar , Bridi Raquel , Rhein Samantha , Sánchez-Contreras Martina , Hernandez-Pino Paulina , Tobar Nicolás , Puente-Díaz Luis , de Camargo Adriano Costa
Published: December 2024

Walnut milk residues (WMR) were investigated for the first time through their phenolic characterization including soluble (free, esterified, and etherified) phenolics and those released from their insoluble-bound form (insoluble-bound phenolic hydrolysates, IBPHs) and their antioxidant properties. Free phenolics were recovered and alkaline or acid hydrolysis were used to recover the remaining phenolic fractions. Total phenolic compounds (TPCs) and their antioxidant activity were analyzed by Folin–Ciocalteu, FRAP, and ORAC methods, respectively. Soluble phenolics (free + esterified + etherified fractions) showed a higher TPC (275.3 mg GAE 100 g−1 dw) and antioxidant activity (FRAP: 138.13 µmol TE g−1 dw; ORAC: 45.41 µmol TE g−1 dw) with respect to the IBPH. There was a significant correlation between TPC and FRAP and ORAC values regardless of the fraction and tested sample. Phenolic acids and flavonoids were identified and quantified by ultra-performance liquid chromatography–electrospray tandem mass spectrometry (UPLC-ESI-MS/MS). Gallic acid, mainly in the free form (3061.0 µg 100 g−1), was the most representative, followed by biochanin A, identified for the first time in a walnut product and mostly present in the fraction released from the esterified form (593.75 µg 100 g−1). No detrimental cytotoxic impact on Caco-2 cells was observed. Hence, WMR could be considered a potential source for the development of nutraceutical and/or antioxidant food additives.
Effects of Straw Management and Nitrogen Application Rate on Soil’s Physicochemical Properties and Rice Yield in Saline–Sodic Paddy Fields
Authors: Ran Cheng , Li Jiaquan , Gao Ya , Xie Yaoru , Li Yangyang , Yang Jiguo , Geng Yanqiu , Guo Liying , Gao Dapeng , Shao Xiwen
Published: December 2024

Straw return plays a vital role in crop yield and sustainable agriculture. Extensive research has focused on the potential to enhance soil fertility and crop yield through straw return. However, the potential impacts of straw return on saline–sodic soils have been relatively neglected due to the unfavorable characteristics of saline–sodic soils, such as high salinity, poor structure, and low nutrient contents, which are not conducive to crop growth. Therefore, a two-year field experiment was conducted to assess the effects of straw management (retention or removal) with nitrogen fertilizers (0, 90, 180, 270, and 360 kg N ha−1) on soil aggregates, soil chemical properties, and rice yields in saline–sodic soil. The results showed that straw return with nitrogen fertilization significantly decreased the soil exchange sodium percentage (ESP) and the percentage and organic carbon contribution of silty clay particles and also significantly increased the soil aggregate stability, organic matter (SOM), and percentage and organic carbon contribution of macroaggregates. However, there was no significant difference between 270 kg N ha−1 and 360 kg N ha−1 for all soil indicators under straw return. Straw return significantly increased rice grain yield by 5.77% (two-year average) compared to straw removal. The highest grain yield of 8.01 t ha−1 (two-year average) was obtained from straw return combined with 270 kg N ha−1. However, since this experiment was conducted for only two years, the positive effects of long-term straw return on soil and rice yield could have been greater. Therefore, the application of 270 kg N ha−1 in the early stages of straw return is a promising management practice for improving saline–sodic soils and increasing rice yields.
Design and Testing of a Bionic Seed Planter Furrow Opener for Gryllulus Jaws Based on the Discrete Element Method (DEM)
Authors: Jiang Xinming , Wang Xiaoxuan , Yang Senbo , Yu Yajun , Xu Tianyue , Li Chunrong
Published: December 2024

In addition to improving the efficacy of the furrow opener by ensuring consistent seeding depth, the gryllulus jaw geometry curve was integrated into the furrow opener. Soil particles were modeled using the DEM combined with the Hertz–Mindlin with JKR model, and simulation tests were conducted using the DEM corn stover model. Three geometric curves of gryllulus jaws were extracted. The effect of each curve and magnification on the manipulation results was clarified by the simulation test. Subsequently, field trials were conducted to evaluate the stability of the seeding depth of the bionic structure. The experiment showed that the No. 1 structure with a magnification of 1000 was the best, and the stability was 42.10% higher than that of the original structure. The results of this research can provide key structural and simulation parameters for the development of planter furrow openers with both efficient straw crushing and stable sowing depth functions, which is of great significance for the improvement of agricultural machinery.
An Improved Multi-Threshold Clutter Filtering Algorithm for W-Band Cloud Radar Based on K-Means Clustering
Authors: Shi Zhao , Huang Lingjiang , Wu Fengyuan , Lei Yong , Wang Huiying , Tang Zhiya
Published: December 2024

This study investigates the application of an improved multi-threshold method based on the K-means algorithm for clutter filtering in W-band cloud and fog radar observations. Utilizing W-band millimeter-wave cloud and fog radar data collected from March to July 2023 in the Qingdao area, a dataset of cloud and fog echo of different types was constructed and statistically analyzed. Subsequently, a multi-threshold clutter filtering method was proposed to identify and eliminate abnormal interferences such as noise spikes, radial interference, and suspended matter clutter. This method employs the basic data and spatiotemporal information from the cloud radar as feature variables for K-means clustering and dynamically adjusts thresholds based on the clustering results. The clutter-filtered data were further used for the verification analysis of cloud and fog identification. The results demonstrate that the proposed multi-threshold method effectively removes clutter and significantly reduces its impact on cloud and fog echo under weather conditions of clouds, fog, and coexisting cloud–fog, while controlling the loss of cloud and fog echo within the required accuracy range.
Affinity Peptide-Based Circularly Permuted Fluorescent Protein Biosensors for Non-Small Cell Lung Cancer Diagnosis
Authors: Xu Dengyue , Jiang Qingyun , Li Zhi , Shang Angyang , Liu Jiaqi , Xue Chengyu , Shao Shuai , Zhang Hangyu , Yuan Hong , Wu Bin , Liu Bo
Published: December 2024

Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and poses a significant public health challenge. Early detection is crucial for improving patient outcomes, with serum biomarkers such as carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCAg), and cytokeratin fragment 19 (CYFRA 21-1) playing a critical role in early screening and pathological classification of NSCLC. However, due to being mainly based on corresponding antibody binding reactions, existing detection technologies for these serum biomarkers have shortcomings such as complex operations, high false positive rates, and high costs. This study aimed to develop new methods for detecting CEA, SCCAg, and CYFRA 21-1 to assist in the diagnosis of NSCLC. Affinity peptides of CEA, SCCAg, and CYFRA 21-1, respectively, were screened by phage display technology, and the peptides’ binding affinities were determined by enzyme-linked immunosorbent assay and biolayer interferometry. Peptides with high affinity were then integrated as binding domains into biosensors by fusing them with circularly permuted fluorescent proteins (cpFPs) through genetic coding. The resulting biosensors, C4 biosensor for CEA, S1 biosensor for SCCAg, and Y3 biosensor for CYFRA 21-1, demonstrated robust sensitivity and specificity even at concentrations as low as 1 ng/mL for their respective tumor markers. When applied to clinical samples and recalibrated for the upper limit of normal concentrations, the biosensors exhibited enhanced sensitivity and specificity for NSCLC diagnosis. This study introduced innovative biosensors for the detection of CEA, SCCAg, and CYFRA 21-1, providing a highly sensitive, specific, rapid, and cost-effective diagnostic alternative that could significantly improve NSCLC screening rates.
DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
Authors: Imdad Maria , Fazil Adnan , Ramli Sofia Najwa Binti , Ryu Jihyoung , Mahdin Hairulnizam Bin , Manzoor Zahid
Published: December 2024

The vast interconnection of resource-constrained devices and the immense amount of data exchange in the Internet of Things (IoT) environment resulted in the resurgence of various security threats. This resource-constrained environment of IoT makes data security a very challenging task. Recent trends in integrating lightweight cryptographic algorithms have significantly improved data security in the IoT without affecting performance. The PRESENT block cipher, a standard and lightweight benchmark algorithm, is a widely accepted and implemented algorithm with a simple design, low-cost implementation, and optimum performance. However, this simple design utilizing lightweight linear and non-linear functions led to slow confusion and diffusion properties. The static bits in the permutation layer are the leading cause of slow diffusion, showcasing dependencies between plaintext and ciphertext bits. This research addresses and seeks to overcome this shortcoming of slow confusion and diffusion using the Deoxyribonucleic Acid (DNA) replication process and shift-aided operations, leading to the DNA-PRESENT block cipher. Security, cost, and performance analyses were performed to verify the improvements. The results demonstrated that with only 33.5% additional cost, DNA-PRESENT increased key sensitivity to 73.57%, plaintext sensitivity to 33%, and consistently ensured an average bit error rate (BER) of 50.2%. An evident increase of 176.47 kb/s in throughput and reduced latency to 17 cycles/block kept the good hardware efficiency of 43.41 kbps/KGE, and the reduction in execution time by 0.2333 s led to better performance. Considering the security advances achieved, this cost increase is a trade-off between security and performance.
Personalized Shared Control for Automated Vehicles Considering Driving Capability and Styles
Authors: Sun Bohua , Shan Yingjie , Wu Guanpu , Zhao Shuai , Xie Fei
Published: December 2024

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers’ main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers’ driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers’ driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems.
Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with Sensor Faults
Authors: Wang Zhenyou , Cai Xiaoquan , Luo Ao , Ma Hui , Xu Shengbing
Published: December 2024

This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The consensus error converges to the prescribed performance bounds in fixed-time by an improved performance function and coordinate transformation. Due to the unknown faults in sensors, the system states cannot be gained correctly; therefore, an adaptive compensation strategy is constructed based on the approximation capabilities of neural networks to solve the negative impact of sensor failures. The reinforcement-learning-based backstepping method is proposed to realize the optimal control of the system. Utilizing Lyapunov stability theory, it is shown that the designed controller enables the consensus error to converge to the prescribed performance bounds in fixed time and that all signals in the closed-loop system are bounded in probability. Finally, the simulation results prove the effectiveness of the proposed method.
Robust Automatic Modulation Classification via a Lightweight Temporal Hybrid Neural Network
Authors: Wang Zhao , Zhang Weixiong , Zhao Zhitao , Tang Ping , Zhang Zheng
Published: December 2024

In the rapidly developing field of wireless communications, the precise classification of modulated signals is essential for optimizing spectrum utilization and improving communication quality. However, existing networks face challenges in robustness against signals containing phase shift keying and computational efficiency. This paper introduces TCN-GRU, a lightweight model that combines the advantages of multiscale feature extraction of the temporal convolutional network (TCN) and global sequence modeling of gated recurrent unit (GRU). Compared to the state-of-the-art MCLDNN, TCN-GRU reduces parameters by 37.6%, achieving an accuracy of 0.6156 and 0.6466 on the RadioML2016.10a and RadioML2016.10b, respectively (versus MCLDNN’s 0.6101 and 0.6462). Furthermore, TCN-GRU demonstrates superior ability in distinguishing challenging modulations such as QAM16 and QAM64, and it improves classification accuracy by about 10.5% compared to MCLDNN. These results suggest that TCN-GRU is a robust and efficient solution for enhancing AMC in complex and noisy environments.
A Novel Intelligent Fault Diagnosis Method of Rolling Bearings Based on the ConvNeXt Network with Improved DenseBlock
Authors: Song Jiahao , Nie Xiaobo , Wu Chuang , Zheng Naiwei
Published: December 2024

Rolling bearings are critical rotating components in machinery and equipment; they are essential for the normal operation of such systems. Consequently, there is a pressing need for a highly efficient, applicable, and reliable method for bearing fault diagnosis. Currently, one-dimensional data-driven fault diagnosis methods, which rely on one-dimensional data, represent a mainstream approach in this field. However, these methods exhibit weak diagnostic capabilities in noisy environments and when confronted with insufficient sample sizes. In order to solve these limitations, a new fault diagnosis method for rolling bearings is proposed, which combines the ConvNeXt network and improved DenseBlock into a parallel network with a feature fusion function. The network can fully extract the global feature and the detail feature of the signal and integrate them, which shows a good diagnostic ability in the face of a strong noise environment. Additionally, the Dy-ReLU function is introduced into the network, which enhances the generalization ability of the network and improves the convergence speed. Comparative experiments show that this method still has strong fault diagnosis capability under the condition of noise pollution and insufficient training samples.
Comparison of Mean Values and Entropy in Accelerometry Time Series from Two Microtechnology Sensors Recorded at 100 vs. 1000 Hz During Cumulative Tackles in Young Elite Rugby League Players
Authors: Fernández-Valdés Bruno , Jones Ben , Hendricks Sharief , Weaving Dan , Ramirez-Lopez Carlos , Whitehead Sarah , Toro-Román Víctor , Trabucchi Michela , Moras Gerard
Published: December 2024

Several microtechnology devices quantify the external load of team sports using Global Positioning Systems sampling at 5, 10, or 15 Hz. However, for short, explosive actions, such as collisions, these sample rates may be limiting. It is known that very high-frequency sampling is capable of capturing changes in actions over a short period of time. Therefore, the aim of this study was to compare the mean acceleration and entropy values obtained from 100 Hz and 1000 Hz tri-axial accelerometers in tackling actions performed by rugby players. A total of 11 elite adolescent male rugby league players (mean ± SD; age: 18.5 ± 0.5 years; height: 179.5 ± 5.0 cm; body mass: 88.3 ± 13.0 kg) participate in this study. Participants performed tackles (n = 200), which were recorded using two triaxial accelerometers sampling at 100 Hz and 1000 Hz, respectively. The devices were placed together inside the Lycra vests on the players’ backs. The mean acceleration, sample entropy (SampEn), and approximate entropy (ApEn) were analyzed. In mean acceleration, the 1000 Hz accelerometer obtained greater values (p < 0.05). However, SampEn and ApEn were greater with the 100 Hz accelerometer (p < 0.05). A large relationship was observed between the two devices in all the parameters analyzed (R2 > 0.5; p < 0.0001). Sampling frequency can affect the quality of the data collected, and a higher sampling frequency potentially allows for the collection of more accurate motion data. A frequency of 1000 Hz may be suitable for recording short and explosive actions.
ppb-Level SO2 Photoacoustic Sensor for SF6 Decomposition Analysis Utilizing a High-Power UV Laser with a Power Normalization Method
Authors: Yang Xiu , Chen Baisong , He Yuyang , Zhu Chenchen , Zhou Xing , Liang Yize , Li Biao , Yin Xukun
Published: December 2024

A highly sensitive sulfur dioxide (SO2) photoacoustic gas sensor was developed for the sulfur hexafluoride (SF6) decomposition detection in electric power systems by using a novel 266 nm low-cost high-power solid-state pulse laser and a high Q-factor differential photoacoustic cell. The ultraviolet (UV) pulse laser is based on a passive Q-switching technology with a high output power of 28 mW. The photoacoustic signal was normalized to the laser power to solve the fluctuation of the photoacoustic signal due to the power instability of the UV laser. A differential photoacoustic cell can obtain a high Q-factor and reduce the gas flow noise in SF6 buffer gas. The parameters of the SO2 sensor system were optimized in terms of laser power and operating pressure. A 1σ detection limit (SNR = 1) of 2.34 ppb was achieved with a 1 s integration time, corresponding to a normalized noise equivalent absorption (NNEA) coefficient of 7.62 × 10−10 cm−1WHz−1/2.
In-Motion Forward–Forward Backtracking Fine Alignment Based on Displacement Observation for SINS/GNSS
Authors: Zhu Yongyun , Zhu Yaohui , Wei Xinhua , Cui Bingbo , Liu Shede
Published: December 2024

To solve the problem of slow convergence seen in the traditional fine alignment algorithm based on linear Kalman filtering, a forward–forward backtracking fine alignment algorithm for SINS is proposed after reanalyzing the fine alignment model in this paper. First, the forward–forward backtracking fine alignment model in initial navigation frame was derived. The displacement vector of the carrier in the initial navigation frame solved by GNSS positioning was utilized as the observation of the fine alignment model. Second, under the premise of storing only part of the navigation data, the initial alignment convergence speed was improved by backtracking and reusing the navigation data. The experimental results of the simulation and vehicle tests showed that each backtracking alignment can improve the accuracy of the fine alignment to the performance requirements of the initial alignment, which proved the effectiveness and feasibility of the backtracking fine alignment algorithm proposed in this paper.
Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems
Authors: Khalil Adnan Elahi Khan , Perez-Diaz Jesus Arturo , Cantoral-Ceballos Jose Antonio , Antelis Javier M.
Published: December 2024

With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user’s intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.
Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption
Authors: Agbehadji Israel Edem , Millham Richard C. , Freeman Emmanuel , Wu Wanqing , Zhang Xianbin
Published: December 2024

Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features. An example of a human-related attribute includes the electrical signal generated from the heart, mostly referred to as an Electrocardiogram (ECG) signal. The methods to extract features from ECG signals are time domain-based; however, the challenge with relying only on the time-domain or frequency-domain method is the inability to capture the intra-leading relationship of Variational Mode Decomposition signals. In this research, fusing multiple domains ECG feature and adaptive Variational Mode Decomposition approaches are utilized to mitigate the challenge of losing the intra-leading correlations of mode decompositions, which might reduce the robustness of encryption algorithms. The features extracted using the reconstructed signal have a mean (0.0004), standard deviation (0.0391), skewness (0.1562), and kurtosis (1.2205). Among the lightweight encryption methods considered, Chacha20 has a total execution time of 27µs. The study proposes a lightweight encryption technique based on the fused vector representation of extracted features to provide an encryption scheme in addition to a bio-inspired key generation technique for data encryption.
A Review of Asynchronous Byzantine Consensus Protocols
Authors: Ji Zhenyan , Zhang Xiao , Hu Jianghao , Lu Yuan , Liu Jiqiang
Published: December 2024

Blockchain technology can be used in the IoT to ensure the data privacy collected by sensors. In blockchain systems, consensus mechanisms are a key technology for maintaining data consistency and correctness. Among the various consensus protocols, asynchronous Byzantine consensus protocols offer strong robustness as they do not rely on any network timing assumptions during design. As a result, these protocols have become a research hotspot in the field of blockchain. Based on different structural design approaches, asynchronous Byzantine consensus protocols can be divided into two categories: protocols based on the DAG structure and protocols based on the ACS structure. The paper describes their principles and summarizes the related research works. The advantages and disadvantages of the protocols are also compared and analyzed. At the end of the paper, future research directions are identified.
Gastronomy as a Predictor of Tourism Development—Defining Food-Related Factors from the Perspective of Hospitality and Tourism Employees in Srem (A.P. Vojvodina, R. Serbia)
Authors: Kalenjuk Pivarski Bojana , Tešanović Dragan , Šmugović Stefan , Ivanović Velibor , Paunić Maja , Vuković Vojnović Dragana , Vujasinović Vesna , Gagić Jaraković Snježana
Published: December 2024

Gastronomy is a key driver of regional tourism development. Despite growing recognition of its role in enhancing tourist experiences, the specific contributions of food-related factors remain insufficiently explored, especially from the perspective of hospitality and tourism employees. To address this gap, an investigation was carried out in the tourist region of Srem (A.P. Vojvodina, Republic of Serbia) through the perspective of employees in the hospitality and tourism sector. The custom-made GastroRegDevelop scale, developed for the purpose of this research, was used to comprise the food-related factors. The scale was carefully crafted through expert consultations, pilot testing, and its adaptation to the specific features of the gastronomy of the Srem Region, to ensure its clarity and validity. From a sample of 254 respondents, the survey identified four key food-related factors that contribute to tourism development: authenticity, events, quality, and food offer. Exploratory factor analysis (EFA) was used to analyze the collected data to reveal the initial factors, followed by the ordinal regression to evaluate their impact on employees’ satisfaction with the role of food in tourism and its economic effects in the region. The results indicated the importance of these factors for economic growth and tourist attractiveness, where the respondents singled out the authenticity and quality of local products as key elements in attracting tourists and increasing tourism-related income. This study not only highlights the urgent need for addressing specific food-related factors to maximize visitor satisfaction and regional economic impact, but also underscores the critical role of gastronomy in fostering sustainable regional development and establishing a unique regional identity within the global tourism market.
Pathway to Zero-Emission Buildings: Energy and Economic Comparison of Different Demand Coverage by RES for a New Office Building
Authors: Sannino Raniero , Ronchetti Laura , Di Turi Silvia
Published: December 2024

In recent years, energy efficiency and the decarbonization of the building sector have become key goals in Europe. However, substantial efforts are still needed to support the increase in on-site energy generation from renewable energy sources (RES) and to phase-out the use of fossil fuels. Moreover, this challenge must be backed by financial mechanisms provided by Member States. In this context, the aim of this work is to investigate different RES generation layouts for a reference office building in Italy and to determine whether it can achieve the goal of a net zero energy building (net ZEB) or a positive energy building (PEB). Different layouts are analyzed from both energy and economic perspectives through dynamic energy simulation, considering different financial schemes, such as “net metering” (SSP) and “premium tariff” (RID). The results show that the energy performance improves due to the size and the characteristics of the PV system. However, the avoided operating costs due to RID increase linearly, while the economic benefits provided by the SSP scheme encourage an appropriate design of the renewable energy system, avoiding oversizing for economic profitability.
Low-Carbon Transformational Leadership: Conceptualization, Measurement, and Its Impact on Innovation Outcomes
Authors: Zhang Hongsi , Huang Haixia
Published: December 2024

This study introduces and develops the concept of low-carbon transformational leadership (LCTL), focusing on leadership behaviors essential for achieving carbon reduction objectives within organizations. Addressing gaps in current green leadership research, this study distinguishes LCTL from broader green transformational leadership by emphasizing carbon reduction goals rather than general environmental aims. Using a grounded-theory approach, this study conceptualizes LCTL across three core dimensions: fostering a collective vision and alignment in low-carbon transition opportunities, strategic steering and feedback for an effective low-carbon transformation, and adaptive integration and iteration for a resilient low-carbon transformation. To ensure empirical rigor, a comprehensive LCTL scale was developed and validated through exploratory and confirmatory factor analyses, demonstrating strong internal consistency and construct validity. The predictive validity of the LCTL scale was further tested, showing a significant positive impact on green innovation and green product development outcomes, particularly in carbon-intensive industries. This research provides a nuanced and practical framework for leadership in low-carbon transitions, offering new insights into green leadership by highlighting the role of leadership in advancing climate-centered corporate innovation. The findings underscore the critical importance of adaptive, visionary, and strategic leadership in aligning organizations with carbon-neutral goals and enhancing corporate sustainability practices. These results suggest that companies can embed LCTL principles into their daily operations by setting clear sustainability visions, investing in leadership training, and prioritizing collaborative strategies. Future research could explore sector-specific applications of LCTL, particularly in emerging economies, to further expand its theoretical and practical implications.
The Relationship Between Artificial Intelligence (AI) and Building Information Modeling (BIM) Technologies for Sustainable Building in the Context of Smart Cities
Authors: Li Jinyi , Liu Zhen , Han Guizhong , Demian Peter , Osmani Mohamed
Published: December 2024

The development of information technologies has been exponentially applied to the architecture, engineering, and construction (AEC) industries. The extent of the literature reveals that the two most pertinent technologies are building information modeling (BIM) and artificial intelligence (AI) technologies. The radical digitization of the AEC industry, enabled by BIM and AI, has contributed to the emergence of “smart cities”, which uses information technology to improve urban operational and sustainable efficiency. Few studies have investigated the roles of AI and BIM in AEC from the perspective of sustainable buildings in assisting designers to make sustainable decisions at building and city levels. Therefore, the purpose of this paper is to explore the research status and future development trends in the relationship between AI and BIM-aided sustainable building in the context of the smart city to provide researchers, designers, and technology developers with potential research directions. This paper adopted a macro and micro bibliographic method, which is used to map out the general research landscape. This is followed by a more in-depth analysis of the fields of sustainable design, sustainable construction, sustainable development, and life cycle assessment (LCA). The results show that the combination of AI and BIM helps to make optimal decisions on materials, cost, energy, construction scheduling, and monitoring and promotes the development of sustainable buildings in both technical and human aspects so to achieve Sustainable Development Goals 7 (ensuring access to affordable, reliable, and sustainable modern energy for all), 9 (building resilient infrastructure, promote inclusive and sustainable industries, and foster innovation), 11 (building inclusive, safe, risk-resilient, and sustainable cities and human settlements), and 12 (ensuring sustainable consumption and production patterns). In addition, the combination of AI, BIM, and LCA technologies offers great potential to improve building performance, and the future development of AI and BIM integration should not only consider the sustainability of buildings but also consider the human-centered design concept and the health, safety, and comfort of stakeholders as one of the goals to realize the multidimensional development of smart city based on city information model.
Framing Concepts of Agriculture 5.0 via Bipartite Analysis
Authors: Bergier Ivan , Barbedo Jayme G. A. , Bolfe Édson L. , Romani Luciana A. S. , Inamasu Ricardo Y. , Massruhá Silvia M. F. S.
Published: December 2024

Cultural diversity often complicates the understanding of sustainability, sometimes making its concepts seem vague. This issue is particularly evident in food systems, which rely on both renewable and nonrenewable resources and drive significant environmental changes. The widespread impacts of climate change, aggravated by the overuse of natural resources, have highlighted the urgency of balancing food production with environmental preservation. Society faces a pivotal challenge: ensuring that food systems produce ample, accessible, and nutritious food while also reducing their carbon footprint and protecting ecosystems. Agriculture 5.0, an innovative approach, combines digital advancements with sustainability principles. This study reviews current knowledge on digital agriculture, analyzing scientific data through an undirected bipartite network that links journals and author keywords from articles retrieved from Clarivate Web of Science. The main goal is to outline a framework that integrates various sustainability concepts, emphasizing both well-studied (economic) and underexplored (socioenvironmental) aspects of Agriculture 5.0. This framework categorizes sustainability concepts into material (tangible) and immaterial (intangible) values based on their supporting or influencing roles within the agriculture domain, as documented in the scientific literature.
Spatiotemporal Evolution and Influencing Factors of New-Quality Productivity
Authors: Feng Nan , Yan Mingyue , Yan Mingtao
Published: December 2024

New-quality productivity (NQP) serves as a critical indicator for measuring the level of high-quality economic development. Gaining insights into the spatial and temporal patterns along with the key drivers of NQP is essential for promoting the global industrial transformation and revitalizing old cities. This study utilized spatial analysis, Dagum Gini coefficient, Markov chains, and optimal parameter geographical detectors to analyze spatial patterns and influencing factors of NQP across 271 Chinese prefecture-level cities from 2011 to 2021. Findings reveal that the average index of NQP increased from 0.045 in 2011 to 0.072 in 2021, with spatial patterns showing higher levels in coastal regions compared to inland areas. The overall disparity of NQP has diminished, although significant internal imbalances persist, particularly in the eastern region, where the pronounced gap between eastern and central areas remains the primary source of variation. Local NQP development is strongly influenced by proximity to adjacent areas, characterized by path dependence and club convergence effects. Additionally, cultural foundation, urbanization, and economic development play pivotal roles in fostering NQP, with their interactions exhibiting notable nonlinear and dual-factor enhancement effects. These findings provide valuable theoretical and practical insights for advancing NQP levels in China and globally.
Development of Delay and Disruption Cause Monitoring Framework for Megaprojects: A Claim Management Approach from the Contractor’s Perspective to Enhance Sustainability in the Built Environment
Authors: Okudan Ozan , Çevikbaş Murat , Işık Zeynep
Published: December 2024

Delays and disruptions (D&D) are considered chronic peculiarities of the construction phase of the built environment, especially in megaprojects. Systematic monitoring of claimable D&D causes becomes crucial for the contractors to compensate for their losses caused by delays and disruptions, enabling sustainable use of resources. Thus, this study proposed a delay and disruption (D&D) cause monitoring framework that enables contractors to timely and accurately detect claimable delays and disruption causes/events in megaprojects. At the outset, a systematic literature review was conducted to design an initial version of the framework and extract claimable D&D. Then, focus group discussion (FGD) sessions were conducted to revise and refine the initial version of the framework and a list of claimable D&D causes. Next, a fuzzy Analytical Hierarchy Process (AHP) analysis was conducted to determine the relative importance of each claimable D&D cause in terms of its impact on the megaprojects. Finally, a consistency analysis was conducted to demonstrate the reliability of the dataset. Findings revealed that claimable D&D causes are indispensable parts of the claim management process. In this manner, the proposed framework recommends monitoring the claimable D&D causes regularly during the whole construction phase of the megaprojects. The fuzzy AHP analysis also revealed that causes such as “Suspension of project activities by the owner”, “Errors and clashes in the design”, “Shortage of construction materials in the market”, “Discovery of fossils and historical artifacts”, “Unavailability of the commissioning team on the due date”, and “Late delivery of testing materials and utilities by the owner” were particularly rated as highly critical causes, needing urgent and sophisticated monitoring plan for timely detection and data collection. By introducing a proactive approach to avoid lengthy and costly dispute resolution processes, this study enables decision-makers to enhance sustainability in the built environment.
Coupling Coordination of Urban Resilience and Urban Land Use Efficiency in Hunan Province, China
Authors: Zhao Shuangfei , Zeng Wei , Feng Da
Published: December 2024

Urban resilience and urban land use efficiency are inevitable topics in urban planning and development, and the coupling coordination between the two will contribute substantially to urban sustainability. With panel data from 14 cities in Hunan from 2010 to 2021 and by combining the entropy method, the Super-SBM model, and the coupling coordination degree model, this study analyzed the dynamic spatial–temporal evolution pattern of urban resilience and land utilization efficiency and their coupling coordination through a multi-dimensional evaluation index system in 14 cities in Hunan from 2010 to 2021. The main findings were as follows: overall, the urban resilience in Hunan stayed low over the years of the study. Temporally, the mean resilience increased gradually from 0.1962 to 0.3331, and spatially, the urban resilience was higher in the eastern region than in the western area of the province, with Changsha having the highest level of resilience. Second, the urban land use efficiency in Hunan rose with volatility from 0.7162 to 0.9299, and spatially, urban land use efficiency was higher in the northern region than in the southern region, with Zhangjiajie having the highest level of urban land use efficiency. Third, the province had a high coupling degree between urban resilience and urban land use efficiency, and the average coupling value was 0.8531, with higher coupling degrees observed in the southern area and the Chang–Zhu–Tan urban agglomeration in the province. Fourth, the coordination degree between urban resilience and urban land use efficiency stayed moderate across the province, rising from 0.5788 to 0.6453, from marginally coordinated to primarily coordinated, where the northern area had a higher coordination degree. All 14 cities were in a coordinated state by the mean coordination level. Changsha was in a highly coordinated state. The research here is expected to provide some references for urban administrators in Hunan and beyond to release policies that will achieve stronger urban resilience, higher urban land use efficiency, and better coupling coordination.
Thermal Hazard Evaluation and Prediction in Deep Excavations for Sustainable Underground Mining
Authors: Huang Linqi , Wei Yunfeng , Chen Zhiying , Wang Zhaowei , Liu Yinan , Sun Lu , Li Chao
Published: December 2024

With the advent of the deep mining era, thermal damage in mines has become increasingly significant. The high-temperature environment in underground mines adversely impacts the physiological and psychological health of operators, reduces work efficiency, elevates the risk of accidents, and disrupts sustainable mining operations. Consequently, the development of accurate and reliable methods for classifying thermal hazards is essential for enabling mining enterprises to implement effective prevention strategies. Furthermore, such methods provide a theoretical basis for the sustainable management and utilization of geothermal energy. This study systematically considered factors influencing underground thermal damage and selected 10 quantitative indicators, encompassing both natural and human factors, as evaluation criteria. The CRITIC method was employed to determine the weight of each indicator, which was then integrated with uncertainty measurement theory to develop a novel thermal hazard assessment framework (CRITICUM). This framework enables the classification of thermal hazards in deep mine roadways. The evaluation results generated by the CRITICUM system were subsequently used to train machine learning predictive models. During the training process, the particle swarm optimization algorithm (PSO) was utilized to identify the most suitable prediction model parameters for the complex thermal environment of deep mines by leveraging its capability for continuous iterative evolution. The optimized parameters replaced the original random forest (RF) model parameters, resulting in an enhanced thermal damage prediction model (PSO-RF) with an accuracy of 96.55%, outperforming the standard RF model by 3%. Finally, the prediction model’s accuracy was validated using engineering case data, demonstrating that the results met practical engineering requirements. In summary, the proposed CRITICUM-PSO-RF evaluation and prediction model can accurately classify thermal damage in deep mines and provide a valuable reference for ensuring site safety and supporting the sustainable utilization of geothermal energy.
Axial-Vector and Tensor Spin Polarization and Chiral Restoration in Quark Matter
Authors: Maruyama Tomoyuki , Tatsumi Toshitaka
Published: December 2024

We study spontaneous the spin polarization of quark matter with flavor SU(2) symmetry at zero temperature in the NJL model. In a relativistic framework, there are two types of spin–spin interactions: axial vector (AV) and tensor (T), which accordingly give rise to different types of spin-polarized materials. When the spin–spin interaction is sufficiently strong, the spin-polarized phase emerges within a specific density region. As the spin–spin interaction becomes stronger, this phase extends over a higher-density region beyond the critical density of chiral restoration in normal quark matter. We show that the spin-polarized phase leads to another kind of spontaneous chiral symmetry breaking phase.
Clonal Integration Promotes the Photosynthesis of Clonal Plant Under Heterogeneous Pb and/or Pyrene Stress
Authors: Liu Yichen , Xu Sunan , Li Xuemei , Zhang Lihong
Published: December 2024

Clonal plants can support the growth of their ramets in heterogeneous environments through clonal integration between the ramets. However, the role of clonal integration in modulating ramet photosynthesis under toxic stress, especially combined stress, is unclear. This study examines the impact of clonal integration on Zoysia japonica under three heterogeneous stresses (Pb, pyrene, and Pb+Pyrene) with two stolon connection conditions (connected and disconnected). Our results show that clonal integration significantly enhances PN, gs, Ci, E, and CE while reducing WUE. It also improves ΦPSII, Fv′/Fm′, Fv/Fm, Fv/F0, and qP while reducing NPQ. Clonal integration lowers MDA levels, increases SOD activity, and mitigates the decline in CAT and POD activity, resulting in increased biomass under stress. Furthermore, we observed that the synergistic effects of the Pb+Pyrene mixture negatively impacted the adaptability of clonal integration. Our study underscores the role of clonal integration in maintaining photosynthesis and supporting the success of clonal plants in toxic environments.
Determinants of Indoor NO2 and PM2.5 Concentration in Senior Housing with Gas Stoves
Authors: Kadiri Khafayat , Turcotte David , Gore Rebecca , Bello Anila , Woskie Susan R.
Published: December 2024

Nitrogen dioxide (NO2) and particulate matter of 2.5 microns (PM2.5) are air pollutants that impact health, especially among vulnerable populations with respiratory disease. This study identifies factors influencing indoor NO2 and PM2.5 in low-income households of older adults with asthma who use gas stoves in Lowell, Massachusetts. Environmental sampling was conducted in 73 homes, measuring NO2, PM2.5, fractional stove-use, temperature, and humidity for 5–7 days. Participants were recruited between December 2020 and July 2022. Questionnaires were used to collect data on factors influencing indoor NO2 and PM2.5 concentrations. Daily outdoor NO2 and PM2.5 concentrations were obtained from a United States Environmental Protection Agency (EPA) monitoring station. Paired t-tests were conducted between indoor and outdoor NO2 and PM2.5 concentrations, and linear regression was used to evaluate factors influencing indoor NO2 and PM2.5 concentrations. The average indoor concentration for NO2 and PM2.5 were 21.8 (GSD = 2.1) ppb and 16.2 (GSD = 2.7) µg/m3, respectively. Indoor NO2 and PM2.5 concentrations exceeded outdoor concentrations significantly. In multiple regression models, season and pilot light stove use significantly predicted indoor NO2. Season and air freshener use for 6–7 days/week significantly predicted indoor PM2.5. Season-influenced higher indoor concentrations are likely due to reduced ventilation in colder months in the Northeast U.S.