bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Powered Pressure–Temperature Bimodal Sensing Based on the Piezo-Pyroelectric Effect for Robotic Perception
Authors: Yu Xiang , Ji Yun , Shen Xinyi , Le Xiaoyun
Published: April 2024

Multifunctional sensors have played a crucial role in constructing high-integration electronic networks. Most of the current multifunctional sensors rely on multiple materials to simultaneously detect different physical stimuli. Here, we demonstrate the large piezo-pyroelectric effect in ferroelectric Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) single crystals for simultaneous pressure and temperature sensing. The outstanding piezoelectric and pyroelectric properties of PMN-PT result in rapid response speed and high sensitivity, with values of 46 ms and 28.4 nA kPa−1 for pressure sensing, and 1.98 s and 94.66 nC °C−1 for temperature detection, respectively. By leveraging the distinct differences in the response speed of piezoelectric and pyroelectric responses, the piezo-pyroelectric effect of PMN-PT can effectively detect pressure and temperature from mixed-force thermal stimuli, which enables a robotic hand for stimuli classification. With appealing multifunctionality, fast speed, high sensitivity, and compact structure, the proposed self-powered bimodal sensor therefore holds significant potential for high-performance artificial perception.
A Versatile Approach for Adaptive Grid Mapping and Grid Flex-Graph Exploration with a Field-Programmable Gate Array-Based Robot Using Hardware Schemes
Authors: Basha Mudasar , Siva Kumar Munuswamy , Chinnaiah Mangali Chinna , Lam Siew-Kei , Srikanthan Thambipillai , Divya Vani Gaddam , Janardhan Narambhatla , Hari Krishna Dodde , Dubey Sanjay
Published: April 2024

Robotic exploration in dynamic and complex environments requires advanced adaptive mapping strategies to ensure accurate representation of the environments. This paper introduces an innovative grid flex-graph exploration (GFGE) algorithm designed for single-robot mapping. This hardware-scheme-based algorithm leverages a combination of quad-grid and graph structures to enhance the efficiency of both local and global mapping implemented on a field-programmable gate array (FPGA). This novel research work involved using sensor fusion to analyze a robot’s behavior and flexibility in the presence of static and dynamic objects. A behavior-based grid construction algorithm was proposed for the construction of a quad-grid that represents the occupancy of frontier cells. The selection of the next exploration target in a graph-like structure was proposed using partial reconfiguration-based frontier-graph exploration approaches. The complete exploration method handles the data when updating the local map to optimize the redundant exploration of previously explored nodes. Together, the exploration handles the quadtree-like structure efficiently under dynamic and uncertain conditions with a parallel processing architecture. Integrating several algorithms into indoor robotics was a complex process, and a Xilinx-based partial reconfiguration approach was used to prevent computing difficulties when running many algorithms simultaneously. These algorithms were developed, simulated, and synthesized using the Verilog hardware description language on Zynq SoC. Experiments were carried out utilizing a robot based on a field-programmable gate array (FPGA), and the resource utilization and power consumption of the device were analyzed.
Design of a Negative Temperature Coefficient Temperature Measurement System Based on a Resistance Ratio Model
Authors: Liu Ziang , Huo Peng , Yan Yuquan , Shi Chenyu , Kong Fanlin , Cao Shiyu , Chang Aimin , Wang Junhua , Yao Jincheng
Published: April 2024

In this paper, a temperature measurement system with NTC (Negative Temperature Coefficient) thermistors was designed. An MCU (Micro Control Unit) primarily operates by converting the voltage value collected by an ADC (Analog-to-Digital Converter) into the resistance value. The temperature value is then calculated, and a DAC (Digital-to-Analog Converter) outputs a current of 4 to 20 mA that is linearly related to the temperature value. The nonlinear characteristics of NTC thermistors pose a challenging problem. The nonlinear characteristics of NTC thermistors were to a great extent solved by using a resistance ratio model. The high precision of the NTC thermistor is obtained by fitting it with the Hoge equation. The results of actual measurements suggest that each module works properly, and the temperature measurement accuracy of 0.067 °C in the range from −40 °C to 120 °C has been achieved. The uncertainty of the output current is analyzed and calculated with the uncertainty of 0.0014 mA. This type of system has broad potential applications in industry fields such as the petrochemical industry.
Wide Use of Hyaluronic Acid in the Process of Wound Healing—A Rapid Review
Authors: Antoszewska Magdalena , Sokolewicz Ewa Maria , Barańska-Rybak Wioletta
Published: April 2024

Hyaluronic acid (HA), as one of the main components of the extracellular matrix (ECM), plays an important role in the process of wound-healing and tissue-repair processes due to its unique properties and different physiological functions. HA has an ability to maintain a moist environment that promotes healing, the stimulation of growth factors and cellular constituents, and the migration of various cells essential for healing. This paper offers a review of HA use in the process of wound healing, with emphasis on hard-to-heal wounds, and examines its various applications in ophthalmology and otorhinolaryngology. It proves HA to be a versatile agent which finds its use in various fields of medicine for its antioxidant, anti-inflammatory, antibacterial properties and accelerated wound healing.
Biodiversity-Centric Habitat Networks for Green Infrastructure Planning: A Case Study in Northern Italy
Authors: Lami Francesco , Boscutti Francesco , Peccol Elisabetta , Piani Lucia , De Luca Matteo , Zandigiacomo Pietro , Sigura Maurizia
Published: April 2024

Green infrastructure (GI) networks comprising multiple natural and artificial habitats are important tools for the management of ecosystem services. However, even though ecosystem services are deeply linked with the state of biodiversity, many approaches to GI network planning do not explicitly consider the ecological needs of biotic communities, which are often threatened by anthropic activities even in presence of protected areas. Here, to contribute in fill this gap, we describe an easy-to-apply, biodiversity-centric approach to model an ecological network as a backbone for a GI network, based on the ecological needs of a range of representative species. For each species, ideal habitats (nodes) were identified, and crossing costs were assigned to other habitat types depending on their compatibility with the species ecology. Corridors linking the nodes were then mapped, minimizing overall habitat crossing costs. We applied the method to the Isonzo–Vipacco river area in Northern Italy, highlighting a potential ecological network where nodes and corridors occupied 27% and 11.8% of the study area, respectively. The prospective of its conflicts with anthropic activities and possible solutions for its implementation was also discussed. Our method could be applied to a variety of situations and geographic contexts, being equally useful for supporting the protection of entire biocenoses or of specific sensitive species, as well as enhancing the ecosystem services they provide.
The Impact of the Digital Economy on Urban Ecological Resilience: Empirical Evidence from China’s Comprehensive Big Data Pilot Zone Policy
Authors: Zhang Youzhi , Wang Jingyi , Liu Yinke , Zhao Jing
Published: April 2024

The present study examines the effects of China’s comprehensive big data pilot zone policy on urban ecological resilience. This is achieved through the utilization of a quasi-natural experiment, employing panel data from 217 prefecture-level cities in China spanning the years 2010 to 2021. The research revealed that China’s extensive policy on big data pilot zones has a notable and favorable influence on the ecological resilience of urban areas. This impact is both constant and subject to variation across different regions. The aforementioned impact is attained by means of progressions in industrial structure and the introduction of innovative green technologies. Furthermore, the strategy exerts a beneficial impact on the ecological resilience of urban areas in adjacent regions by means of spatial spillover effects.
Impact of Urban Air Quality on Total Factor Productivity: Empirical Insights from Chinese Listed Companies
Authors: Ding Xiaowei , Wang Panfeng , Jiang Xuyan , Zhang Wenyi , Sokolov Boris I. , Liu Yali
Published: April 2024

Urban air quality is inextricably linked to the operations of micro-firms. This paper employs the “Qinling-Huaihe” River demarcation as an instrumental variable to construct a regression discontinuity design (RDD) coupled with the two-stage least squares (2SLS) approach. This methodological framework is utilized to investigate the influence of urban air quality on the corporate total factor productivity (CTFP) of publicly listed manufacturing firms from 2015 to 2020. Drawing on the broken windows theory of urban decay and the general equilibrium theory, this research elucidates a significant adverse effect of urban air pollution on CTFP. We rigorously confirm the validity of the RDD by conducting covariate continuity tests and manipulating distributional variables. Furthermore, the robustness of the baseline regression outcomes is substantiated through a series of sensitivity, robustness, and endogeneity checks, employing alternative instrumental variables. The analysis extends to examining the heterogeneity across environmental attributes, regional features, and green branding. The mechanistic investigation reveals that public environmental concerns, financing constraints, and investments in technological innovation serve as mediators in the nexus between urban air pollution and CTFP. Additionally, it is observed that environmental regulation exerts a positive moderating influence, whereas female leadership has a negative impact in this context. The imperative for timely environmental governance is underscored by these findings, which offer crucial insights for policymakers seeking to refine business environment strategies and for corporations aiming to pursue sustainable growth.
The Effects of Local Food on Carbon Emissions: The Case of the Republic of Korea
Authors: Jung Da-Eun , Yang Sung-Bum , Yang Seung-Ryong
Published: April 2024

This study addresses the urgent environmental issue of climate change, focusing specifically on the role of local food, which is produced and consumed locally, in helping to reduce carbon emissions by eliminating the need for long-distance transportation. Through quantitative analysis, this study elucidates the potential benefits of environmental impact achieved through the consumption of local food, i.e., the significant reduction in carbon emissions. Specifically, the consumption of local food has been found to yield an annual decrease of 2,421,296 tCO2 emissions, representing 2.5% of the Republic of Korea’s total greenhouse gas emissions from the transportation sector. This reduction further translates into an estimated economic value of USD 54.23 million (KRW 70.5 billion). These findings underscore the potential of local food consumption as a tangible strategy to overcome environmental problems. Moreover, the academic contribution of this study lies in its comprehensive analysis of the economic and empirical impacts of local food consumption on the environment. Moving forward, we propose policies such as supporting local food distribution networks, providing public education on local food, fostering local food industries, and implementing incentives to revitalize local food consumption.
Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions
Authors: Duanmu Chenrui , Shi Linjun , Jian Deping , Ding Renshan , Li Yang , Wu Feng
Published: April 2024

In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering the simultaneous execution of the dual conditions of energy storage in fluctuation smoothing and primary frequency regulation is proposed. Firstly, a two-layer model is established to optimize the capacity allocation under dual operating conditions, i.e., the planning layer takes into account the lifetime, cost, and benefit, and the operation layer considers the wind turbine reserve backup and storage control to participate in the primary frequency regulation in a cooperative manner. Then, the dual battery pack operation strategy is embedded with the variational modal decomposition method to determine the charging and discharging operation strategy of energy storage after considering the grid-optimized reference power. An improved particle swarm algorithm with inverse learning pre-optimization combined with variational crossover post-optimization is embedded in the GUROBI computation to obtain the optimal battery storage capacity allocation scheme. Finally, the superiority of the model proposed in this paper in terms of improving energy storage utilization, service life, and economic efficiency as well as reducing wind power load shedding is verified by comparing it with a single execution working condition scenario and traditional battery control strategy.
The Effect of Humic Substances on the Colony Growth and Conidial Germination of Entomopathogenic Fungi from the Genus Metarhizium
Authors: Majchrowska-Safaryan Anna , Tkaczuk Cezary , Wrzosek Marta
Published: April 2024

The development of sustainable agricultural production involves the use of new plant protection products, with low toxicity to non-target organisms and high biodegradability. The aim of this study was to investigate the effect of commercially available preparations containing humic substances, in comparison with pure humic acids, on the growth and germination of spores of entomopathogenic fungi (EPFs) from the genus Metarhizium in vitro. AmiAGRA, HumiAGRA, AlgoHUM (recommended field dose) and pure humic acids extracted from peat, brown coal and spent mushroom substrate were added to Sabouraud’s culture substrate. Observation of the growth of the colonies of the tested species of EPFs was carried out every 5 days until day 20, measuring their diameter (mm). In the second stage of the experiment, the culture medium with the addition of preparations and pure humic acids was applied in a thin layer to the surface of glass slides and an aqueous solution with spores was introduced. Observation was carried out after 24 and 48 h, and the results obtained were expressed as percentages in relation to the control. The conducted research showed that on the 20th day of the culture (on average), preparations containing humic substances had a stimulating effect, while pure acids limited the growth of the colonies of the tested isolates slightly. The growth of M. anisopliae fungal colonies was most strongly stimulated by AlgoHUM, and M. flavoviride by HumiAGRA. The share of germinated spores after 48 h of contact with the substrate was higher than after 24 h, and more spores germinated on substrates with the addition of preparations containing humic substances than on pure humic acids.
Correction Control Model of L-Index Based on VSC-OPF and BLS Method
Authors: Yang Yude , Long Jingru , Yang Lizhen , Mo Shuqin , Wu Xuesong
Published: April 2024

With the advancement of artificial intelligence (AI) technology, the real-time measurement and control technology of power systems has also progressed. This paper proposes a correction control model for L-indexes based on voltage stability constrained optimal power flow (VSC-OPF) and a broad learning system (BLS) (BLS-VSC-OPF). This model aims to quickly assess the system’s voltage stability and accurately correct the operation mode when the voltage stability indexes are out of the security range. Firstly, the BLS is used to predict the L-index and to analyze the voltage stability of the power system. Secondly, the approximate first-order sensitivity of the L-index is calculated by the combination of the BLS and the perturbation method. This method solves the problem of the complex sensitivity derivation process in the modeling process of the VSC-OPF model. Meanwhile, when the L-index exceeds the threshold, the BLS and VSC-OPF models are combined to correct this operation mode. The feasibility of the proposed method is verified by the simulation of IEEE-30, IEEE-118, and 1047 bus systems. Finally, the BLS-VSC-OPF model is compared with the linear programming correction model based on BLS (BLS-LPC). The results show that the BLS-VSC-OPF model provides a better correction and control performance.
Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China
Authors: Chen Weiya , Chen Ying , Wang Yufen , Fang Xiaoping
Published: April 2024

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.
A Study of the System Performance and the Microbial Community Composition of Chemical Wastewater in an AO-MBBR Treatment Process
Authors: Wang Jiancheng , Yang Dongdong , Qing Qing , Zhang Yue , Zhu Jie , Wang Liqun
Published: April 2024

To improve the nitrogen removal and reduce the chemical oxygen demand (COD) of a full-scale wastewater treatment plant, two sequential batch reactor devices were used to treat chemical wastewater with biocarriers in low carbon-to-nitrogen (C/N) ratio conditions. The results showed that the addition of biocarriers to the anoxic tank reduced the average concentration of COD in the effluent from 98.1 mg/L to 80.7 mg/L and increased total nitrogen (TN) removal by 9.4%. Metagenomic sequencing was performed to study the composition and function of microbial community samples taken from anoxic sludge and anoxic-carrier biofilms in this wastewater treatment plant. The results showed that Proteobacteria and Actinobacteria were the dominant phyla in the two samples, ensuring their capability for organic matter removal. The anoxic-carrier biofilms were mainly enriched with denitrifying bacteria such as Thauera (10.7%) and Comammonas (2.2%) and the anammox bacteria Candidatus Kuenenia (0.03%). Meanwhile, the nitrogen metabolism pathway was elaborated and the abundance of the functional genes involved in the nitrogen metabolism pathway was quantified. In addition, results from qPCR showed increased copy numbers of denitrification and anammox genes in the anoxic-carrier biofilms compared to those in the anoxic sludge, further confirming the enrichment of functional bacteria.
Evaluating Partners for Renewable Energy Trading: A Multidimensional Framework and Tool
Authors: Schulze Kai , Mišić Mile , Radojičić Nikola , Serin Berkin
Published: April 2024

The worsening climate crisis has increased the urgency of transitioning energy systems from fossil fuels to renewable sources. However, many industrialized countries are struggling to meet their growing demand for renewable energy (RE) through domestic production alone and, therefore, seek to import additional RE using carriers such as hydrogen, ammonia, or metals. The pressing question for RE importers is therefore how to select trading partners, i.e., RE exporting countries. Recent research has identified a plethora of different selection criteria, reflecting the complexity of energy systems and international cooperation. However, there is little guidance on how to reduce this complexity to more manageable levels as well as a lack of tools for effective partner evaluation. This article aims to fill these gaps. It proposes a new multidimensional framework for evaluating and comparing potential RE trading partners based on four dimensions: economy and technology, environment and development, regulation and governance, and innovation and cooperation. Focusing on Germany as an RE importer, an exploratory factor analysis is used to identify a consolidated set of composite selection criteria across these dimensions. The results suggest that Germany’s neighboring developed countries and current net energy exporters, such as Canada and Australia, are among the most attractive RE trading partners for Germany. A dashboard tool has been developed to provide the framework and composite criteria, including adjustable weights to reflect the varying preferences of decision-makers and stakeholders. The framework and the dashboard can provide helpful guidance and transparency for partner selection processes, facilitating the creation of RE trade networks that are essential for a successful energy transition.
Factors Influencing Purchase Intention toward Recycled Apparel: Evidence from China
Authors: Jin Xiaofang , Omar Azizah , Fu Kai
Published: April 2024

This study aimed to develop a conceptual framework that integrates both consumer-specific and product-specific factors, with a particular focus on consumer value perceptions regarding recycled apparel and their impact on consumer attitudes and purchase intention. The framework combines the Perceived Consumer Value (PCV) with the Knowledge–Attitude–Behavior (KAB) model. To validate the proposed conceptual framework, an online survey was conducted with a purposive sample of 716 participants in China. Using structural equation modeling, the hypotheses of the conceptual model were tested. This study identified four dimensions of perceived value and environmental knowledge as significant factors that positively influenced customers’ attitudes toward purchasing recycled apparel. Notably, it was found that perceived quality value had a more pronounced effect on purchase intention than other factors. Interestingly, product knowledge did not demonstrate a significant impact on attitudes. By exploring this under-researched topic, this study provides new insights into consumer behavior in the context of sustainable fashion in a non-Western context.
Secondary Stage Science Teachers’ Perceptions toward STEM Education in Saudi Arabia
Authors: Alsalamat Mohammad Khair M.
Published: April 2024

Many Saudi students lack the motivation to pursue STEM careers due to their teachers’ limited experience and low efficacy in teaching science, technology, engineering, and mathematics (STEM). Teachers’ perceptions are central to efficient STEM education; however, little is known about the perceptions of teachers toward STEM education in Saudi Arabia. Therefore, this study attempted to identify the perceptions of secondary stage science teachers toward STEM education and their knowledge about the requirements for its implementation. The study also investigated whether there were statistically significant differences that could be attributed to teachers’ qualifications, years of experience, and specialization variables. A questionnaire was applied to 175 in-service science teachers. The results showed that the secondary stage science teachers had a medium level of positive perceptions toward STEM education and a high level of knowledge about the requirements for its implementation. It was also revealed that there were statistically significant differences in teachers’ perceptions due to their qualifications and years of experience, in favor of teachers with graduate degrees and with more years of experience, while there were no statistically significant differences due to teachers’ specialization. Based on the findings of this study, a number of recommendations on improving science teachers’ knowledge of STEM education and the requirements for its implementation are provided.
Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm
Authors: Zheng Qiuli , Yue Chunfang , Zhang Shengjiang , Yao Chengbao , Zhang Qin
Published: April 2024

Xinjiang is located in the arid region of northwestern China, and agriculture accounts for an absolute share of total water use. Resource-based, engineering, structural, and managed water shortages coexist. Therefore, it is of great significance to vigorously develop water conservation technology and improve the efficiency of water transmission and distribution in canal systems. This research aims at addressing the problems of difficult manual regulation and the overall optimization of the final canal system, low-water-resource utilization efficiency, and management efficiency. Taking the branch-double two-stage canal system of Dongfeng branch canal in Mangxiang, Jinghe irrigation district, as a case study, and the rotation irrigation group and irrigation duration as decision variables, canal distribution is modeled with the goal of minimizing seepage losses. The improved grey wolf algorithm combined with particle swarm optimization is used for the first time and compared with the traditional grey wolf algorithm, genetic particle swarm optimization fusion algorithm, and northern goshawk algorithm. The results show that (1) on the basis of meeting the water discharge capacity and water demand requirements of the canal system, the diversion time of the water distribution scheme obtained by using the improved grey wolf algorithm is shortened from 11 d to 8.91 d compared with the traditional empirical water distribution scheme. (2) The improved grey wolf algorithm converges to the optimal value within 10 generations compared to the remaining methods, and the total water leakage is reduced from 16.15 × 104 m3 to 11.75 × 104 m3. (3) The number of gate adjustments is reduced, and the canal gates are opened and closed at the same time within each rotational irrigation group. The grey wolf algorithm improved by its combination with particle swarm has stronger optimization ability and convergence, which can better meet the requirements of efficient water resource allocation in irrigation canal systems, as well as a high application value.
Diversified Filtering Mechanism for Evaluation Indicators of Urban Road Renewal Schemes
Authors: Du Juan , Liu Bing , Wu Yimeng , Li Xiufang , Sugumaran Vijayan
Published: April 2024

As global urban infrastructure renewal progresses, the urgent need for sustainability-driven urban renewal intensifies amidst resource scarcity and environmental concerns. Effective evaluation and decision making regarding urban road renewal schemes are prerequisites for their successful implementation. However, variation in the prioritization of indicators in project evaluations and the poor adaptability of existing frameworks hinder the quick assessment of diverse projects. To address this issue, this paper proposes a scheme evaluation framework with embedded renewal project features comprising four modules. Following the initial construction of a sustainability-driven evaluation system, an indicator-filtering mechanism combining the Latent Dirichlet Allocation (LDA) model with a text similarity algorithm is developed. The Entropy Weight-–TOPSIS method is then employed to derive the final optimal decision based on selected indicators. Applying the decision framework to the G15 Jialiu Widening and Reconstruction Project in Shanghai, China, indicators are reduced by 48.3%, with the optimal scheme decision consistent with the traditional Entropy Weight–TOPSIS method. The framework is robust and enhances decision efficiency, filling theoretical gaps in existing indicator-filtering mechanisms.
Spatial-Temporal Evolution and Influencing Factors of Animal Husbandry Carbon Emissions: A Case Study of Shandong Province, China
Authors: Wei Chunbo , Sha Yanyu , Hou Yongwei , Li Jiaqi , Qu Yongli
Published: April 2024

To further study the spatial distribution and dynamic evolution of carbon emissions from animal husbandry in Shandong Province, the panel data of 16 prefecture-level cities in Shandong Province from 2001 to 2022 were used to measure the carbon emissions of animal husbandry and the carbon emission intensity of animal husbandry. Based on the combination of space, kernel density estimation, and LMDI decomposition model, the spatial and temporal evolution of carbon emissions from animal husbandry in Shandong Province and its driving factors were investigated. The results show that: (1) The total amount of animal husbandry carbon emissions in Shandong Province showed a fluctuating downward trend, with a decrease of 10.10% during the investigation period, showing a peripheral-agglomeration distribution pattern. The carbon emission intensity showed a gradual downward trend, with an average annual decline of 7.47%, showing stepped distribution characteristics of high in the west and low in the east. (2) The difference in carbon emissions of animal husbandry among cities in Shandong Province increased first and then decreased, and the growth distribution was basically in the form of “bimodal”, showing a polarization pattern. (3) The intensity effect has the most obvious inhibitory effect on the carbon emission of animal husbandry; the effect of agricultural structure changes from a promoting effect to an inhibiting effect. The inhibitory effect of the industrial structure effect is second only to the intensity effect; the economic effect has the greatest promoting effect; and the promotion effect of the population size effect is small.
Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market
Authors: Ezennaya Samuel O. , Kowal Julia
Published: April 2024

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark for energy-storage systems in electricity markets, this research focuses on the integration of LFP batteries, considering their unique characteristics and market responsiveness. Regression and regularization techniques, coupled with temporal cross-validation, were employed to ensure model robustness and accuracy in predicting energy trading outcomes. This methodological approach allows for a nuanced analysis of battery degradation, power capacity, energy content, and real-time market prices. The model, validated using Belgium’s system imbalance market data from the 2020–2023 period, incorporates both capital and operational expenditures to assess the economic and operational viability of LFP battery energy-storage systems (BESSs). The findings reveal that considering a broader range of operational parameters in energy arbitrage, beyond just the usual energy prices and round-trip efficiency, significantly influences the cost-effectiveness and performance benchmarking of energy storage solutions. This paper advocates for the strategic use of LFP batteries in energy markets, highlighting their potential to enhance grid stability and energy trading profitability. The proposed benchmark model serves as a critical tool for energy traders, providing a detailed framework for informed decision making in the evolving landscape of energy storage technologies.
Dynamic Response of Steel–Timber Composite Beams with Varying Screw Spacing
Authors: Abramowicz Małgorzata , Chybiński Marcin , Polus Łukasz , Szewczyk Piotr , Wróblewski Tomasz
Published: April 2024

Steel–timber composite beams are a relatively new type of composite structure. They have many important advantages, owing to which they may be considered a sustainable solution. Their connectors may be demountable, which makes it possible to separate steel girders from LVL panels at the end of their service life. After disassembly, the structural elements can be recycled. One of their advantages is that they are lighter than steel–concrete composite beams. However, this may result in the poor performance of floors with steel–timber composite elements subjected to dynamic loadings. For this reason, the dynamic characteristics of floors should be investigated to verify the serviceability limit state of human-induced vibrations. In this study, the dynamic response of the three steel–timber composite beams with varying screw spacing was captured and used to validate their numerical models. The frequencies obtained from the numerical analyses correspond to the experimental results. A very high agreement between the vibration mode shapes was obtained because the MAC index values were close to 1. The validated numerical model of a single steel–timber beam may be used in future studies to create a complex numerical model of a steel–timber composite floor.
Coupling and Coordination between Tourism, the Environment and Carbon Emissions in the Tibetan Plateau
Authors: Wang Jiayuan , Yi Lin , Chen Lingling , Hou Yanbing , Zhang Qi , Yang Xuming
Published: April 2024

Studying the relationships among tourism, the environment and carbon emissions is key to understanding how tourism activity affects the sustainable development of tourism in the Tibetan Plateau. Using Lhasa, Tibet, as a case study, the coupling and coordination relationships among the three systems were analysed to explore the impact of tourism behaviour on sustainable tourism development. Utilising panel data from 2010 to 2020, the carbon emissions of tourism activities were calculated using a bottom-up approach. The coupling coordination model was employed to examine the interrelationship of the economy, the ecological environment and carbon emissions. Additionally, the Tapio model was utilised to further analyse the dependency relationship between economic development and carbon emissions. This assessment of the sustainability of Lhasa’s tourism industry revealed that (1) the economy, the environment and carbon emissions are indeed closely intertwined with sustainable development and that (2) there has been a significant increase in the coupling coordination among the economy, the environment and carbon emissions from 2010 to 2020, coupled with a gradual decrease in economic dependency on carbon emissions. Despite providing favourable conditions for sustainable development, there remains considerable disparity among the three subsystems, with relatively low overall coordination. Accordingly, some practical low-carbon tourism policies are recommended to guide tourism practices and promote long-term sustainability.
Can Arbuscular Mycorrhizal Fungi Enhance Crop Productivity and Quality in Hydroponics? A Meta-Analysis
Authors: Othman Yahia A. , Alananbeh Kholoud M. , Tahat Monther M.
Published: April 2024

Finding environmentally friendly solutions for crop growth and productivity has been gaining more attention recently. This meta-analysis aims to understand the combined application of arbuscular mycorrhizal fungi (AMF) and hydroponic systems compared to AMF in conventional (soil) systems. The analysis of up-to-date studies revealed that the root colonization, calculated as the proportion of colonized root segments relative to the total root length, by AMF in conventional (soil-based) culture exceeded hydroponic (or soilless) culture systems by 16.8%. The mean root colonization by AMF was determined to be 52.3% in hydroponic systems and 61.1% in conventional systems. Within hydroponic systems, the root colonization ranged from 2% to 20% after 10 days of inoculation, and notably, it exceeded 50% after 30 to 65 days, depending on the growing substrate and species. Under hydroponics, AMF application had a higher (compared to none-inoculated) positive effect on crop biomass and yield than fruit and leaf quality (antioxidants, phenols, and sugars) as well as leaf nutrients. However, AMF do not always have the potential to improve crop growth, quality and productivity in hydroponics. Among the studies analyzed in this review, approximately 34% (no effect: 29%; negative: 5%) reported no discernible positive effect on biomass or yield, 37% (no effect: 16%; negative: 21%) on fruit or leaf quality, and 60% (no effect: 47%; negative: 13%) on nutrient levels within plant tissues. To improve the performance of AMF in hydroponic systems, the meta-analysis recommended maintaining phosphorus levels in the nutrient solution within the range of 0.15 to 15.5 mg L−1 as elevated levels (40–75 mg L−1) were found to significantly reduce AMF colonization. Additionally, it was observed that certain hydroponic techniques, such as the presence of air bubbles generated by air pumps in floating hydroponic systems (Deep Flow technique) and continuous circulation of the nutrient solution (Ebb and Flow systems), may create dynamic conditions that could potentially hinder the introduction of AMF spores into hydroponic systems and potentially compromise the integrity of the spores and hyphae.
Tourists’ Willingness to Adopt AI in Hospitality—Assumption of Sustainability in Developing Countries
Authors: Gajić Tamara , Ranjbaran Alireza , Vukolić Dragan , Bugarčić Jovan , Spasojević Ana , Đorđević Boljanović Jelena , Vujačić Duško , Mandarić Marija , Kostić Marija , Sekulić Dejan , Bugarčić Marina , Drašković Bojana D. , Rakić Sandra R.
Published: April 2024

This study explores the impact of artificial intelligence (AI) on customer perceptions and behavior in restaurants, airline companies, and hotel sectors within the hospitality industry of Iran. The primary objective is to analyze how AI affects customer trust, brand engagement, electronic word-of-mouth (eWOM), and tourists’ readiness to use AI technologies. Using a comparative analysis approach and surveys, this research tests hypotheses about the effects of artificial intelligence on various dimensions of customer interaction. The findings highlight significant relationships between the quality of artificial intelligence and customer engagement metrics, such as trust and brand loyalty, which are crucial for understanding and predicting customer behavior in response to technological advancements. This study lays the groundwork for theoretical assumptions about sustainability in these sectors in developing countries, providing a basis for future empirical research that could validate these assumptions and explore broader implications of AI integration in enhancing sustainable practices within the hospitality industry.
Research on a Capsule Network Text Classification Method with a Self-Attention Mechanism
Authors: Yu Xiaodong , Luo Shun-Nain , Wu Yujia , Cai Zhufei , Kuan Ta-Wen , Tseng Shih-Pang
Published: April 2024

Convolutional neural networks (CNNs) need to replicate feature detectors when modeling spatial information, which reduces their efficiency. The number of replicated feature detectors or labeled training data required for such methods grows exponentially with the dimensionality of the data being used. On the other hand, space-insensitive methods are difficult to encode and express effectively due to the limitation of their rich text structures. In response to the above problems, this paper proposes a capsule network (self-attention capsule network, or SA-CapsNet) with a self-attention mechanism for text classification tasks, wherein the capsule network itself, given the feature with the symmetry hint on two ends, acts as both encoder and decoder. In order to learn long-distance dependent features in sentences and encode text information more efficiently, SA-CapsNet maps the self-attention module to the feature extraction layer of the capsule network, thereby increasing its feature extraction ability and overcoming the limitations of convolutional neural networks. In addition, in this study, in order to improve the accuracy of the model, the capsule was improved by reducing its dimension and an intermediate layer was added, enabling the model to obtain more expressive instantiation features in a given sentence. Finally, experiments were carried out on three general datasets of different sizes, namely the IMDB, MPQA, and MR datasets. The accuracy of the model on these three datasets was 84.72%, 80.31%, and 75.38%, respectively. Furthermore, compared with the benchmark algorithm, the model’s performance on these datasets was promising, with an increase in accuracy of 1.08%, 0.39%, and 1.43%, respectively. This study focused on reducing the parameters of the model for various applications, such as edge and mobile applications. The experimental results show that the accuracy is still not apparently decreased by the reduced parameters. The experimental results therefore verify the effective performance of the proposed SA-CapsNet model.
Distilling Knowledge from a Transformer-Based Crack Segmentation Model to a Light-Weighted Symmetry Model with Mixed Loss Function for Portable Crack Detection Equipment
Authors: Zhang Xiaohu , Huang Haifeng
Published: April 2024

The detection of cracks is extremely important for maintenance of concrete structures. Deep learning-based segmentation models have achieved high accuracy in crack segmentation. However, mainstream crack segmentation models have very high computational complexity, and therefore cannot be used in portable crack detection equipment. To address this problem, a knowledge distilling structure is designed by us. In this structure, a large teacher model named TBUNet is proposed to transfer crack knowledge to a student model with symmetry structure named ULNet. In the TBUNet, stacked transformer modules are used to capture dependency relationships between different crack positions in feature maps and achieve contextual awareness. In the ULNet, only a tiny U-Net with light-weighted parameters is used to maintain very low computational complexity. In addition, a mixed loss function is designed to ensure detail and global features extracted by the teacher model are consistent with those of the student model. Our designed experiments demonstrate that the ULNet can achieve accuracies of 96.2%, 87.6%, and 75.3%, and recall of 97.1%, 88.5%, and 76.2% on the Cracktree200, CRACK500, and MICrack datasets, respectively, which is 4–6% higher than most crack segmentation models. However, the ULNet only has a model size of 1 M, which is suitable for use in portable crack detection equipment.
A Deterministic Calibration Method for the Thermodynamic Model of Gas Turbines
Authors: Jiang Zhen , Wang Xi , Yang Shubo , Zhu Meiyin
Published: April 2024

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model’s accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts.
The Impact of a Skill-Driven Model on Scrum Teams in Software Projects: A Catalyst for Digital Transformation
Authors: Haputhanthrige Vayodya , Asghar Ikram , Saleem Sidra , Shamim Saqib
Published: April 2024

Human skills are a critical factor in the success or failure of a digital project. Limited studies have been conducted to identify the industry demand for skills of scrum roles (product owner, scrum master, web developer) and levels (entry, associate, mid-senior). The evaluation of skills over time benefits both decision-makers and associated team members, which leads to successful project completions. The aim of this research is to improve decision making concerning the level-specific skills of selected scrum roles for digital projects. The study identifies major and minor skills, patterns, and relationships between levels, and formulates the mathematical equations as the most important inputs to the skill-driven model’s implementation and evaluation. Both qualitative and quantitative research methods were used to analyse 900 surveyed job advertisements published on LinkedIn in Europe. Descriptive analysis was used to analyse quantitative data while the deductive approach was followed with thematic analysis. There are required skill sets for each level of roles, level-specific skills, industry-demanded skills, and formulas related to the initial and individual skill ratings that are investigated. A new mechanism for evaluation is introduced based on “the time spent with skills”. As a result, the proposed model is implemented by feeding research findings into the Mendix programming platform. The skill-driven model is a decision-support solution in software project management to evaluate skills which assist in assigning the right person to the right digital project. Further investigation on different job portals can help to improve the accuracy of industry standards and reduce the lack of progression skills by overcoming limitations identified in this paper.