bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding “Love” in the English Lyrics of the Original Songs by the Multilingual New Creation Church Singapore
Authors: Toh H. Leng , Thornton Daniel
Published: May 2024

This article explores the way in which love is understood and expressed through the original English lyrics of songs by New Creation Church Singapore (NCC) in comparison to the original songs from Hillsong Church Australia (Hillsong) through the period of 2014–2020. While NCC has a multilingual congregation, reflective of the larger Singaporean society, it composes and releases original contemporary congregational songs (CCS) with English lyrics. English is the primary language in Singapore; however, it is shaped by the languages spoken in homes (e.g., Mandarin, Malay, Tamil). Combined with the theological emphases of NCC, its CCS provide a unique lens into English as a common language of worship. This article demonstrates that while the use of English lyrics is a unifying force for multilingual congregational worship, it is also not benign, but actively shaping Christian confession and associated theology and being shaped by wider multilingual contexts.
The Preacher as Artist: An Exploration of Sermon Creation as Art-Making
Authors: Hooke Ruthanna B.
Published: May 2024

Preaching is one of the most creative things a pastor does. This essay explores how a theology of creativity, the imagination, and the arts can encourage preachers to embrace proclamation as creative work. The invitation to preachers to engage their creativity and imagination in preaching rests on the theological claim that creativity is intrinsic to human beings as made in the image of God the Creator. To create is to realize a core human vocation and to deepen knowledge of God. The imagination is a primary avenue to such knowledge, since the imagination is a faculty that allows for a holistic grasp of realities both seen and unseen. An artistic approach to preaching is appropriate in that art functions in similar ways to preaching: like preaching, art explores the depths of human existence, creates wholes out of fragments, and makes connections between seemingly disparate phenomena. The dispositions of the artist are vital for preachers, especially the courage and risk-taking required in art-making as a venture into the unknown. These functions of art and qualities of the artist lead to reflections concerning the particular challenges involved in being a Christian artist, and to the role of beauty in the knowledge of God and hence in preaching.
Journeys without End: Narrative Endings and Implied Readers in Acts of the Apostles and Philostratus’ Life of Apollonius of Tyana
Authors: Hartog Pieter B.
Published: May 2024

This contribution compares the final sections of Acts of the Apostles and Philostratus’ Life of Apollonius of Tyana. Through this comparison, I aim to show that these two writings resemble one another in their attention to travel as a literary theme. Both Acts and Life employ this theme to communicate their message and, in their narrative endings, set up their implied readers as travelers who are meant to continue the journeys of the protagonists in these writings. At the same time, Acts and Life differ in how exactly they envision their readers to continue the journeys of their protagonists. I will argue that these similarities and differences can be explained by the shared social and intellectual climate that Acts and Life inhabit: both writings result from discourses on travel and self that were rife among intellectuals in the Roman Empire in the first three centuries of our era, irrespective of their ethnic, legal, or cultural affiliations.
Multi-Robot Task Planning for Efficient Battery Disassembly in Electric Vehicles
Authors: Erdogan Cansu , Contreras Cesar , Stolkin Rustam , Rastegarpanah Alireza
Published: May 2024

With the surging interest in electric vehicles (EVs), there is a need for advancements in the development and dismantling of lithium-ion batteries (LIBs), which are highly important for the circular economy. This paper introduces an intelligent hybrid task planner designed for multi-robot disassembly and demonstrates its application to an EV lithium-ion battery pack. The objective is to enable multiple robots to operate collaboratively in a single workspace to execute battery disassembly tasks efficiently and without collisions. This approach can be generalized to almost any disassembly task. The planner uses logical and hierarchical strategies to identify object locations from data captured by cameras mounted on each robot’s end-effector, orchestrating coordinated pick-and-place operations. The efficacy of this task planner was assessed through simulations with three trajectory-planning algorithms: RRT, RRTConnect, and RRTStar. Performance evaluations focused on completion times for battery disassembly tasks. The results showed that completion times were similar across the planners, with 543.06 s for RRT, 541.89 s for RRTConnect, and 547.27 s for RRTStar, illustrating that the effectiveness of the task planner is independent of the specific joint-trajectory-planning algorithm used. This demonstrates the planner’s capability to effectively manage multi-robot disassembly operations.
Modelling, Analysis and Comparison of Robot Energy Consumption for Three-Dimensional Concrete Printing Technology
Authors: Kajzr Daniel , Myslivec Tomáš , Černohorský Josef
Published: May 2024

The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area.
A Direct Approach for Local Quasi-Geoid Modeling Based on Spherical Radial Basis Functions Using a Noisy Satellite-Only Global Gravity Field Model
Authors: Yu Haipeng , Chang Guobin , Yu Yajie , Zhang Shubi
Published: May 2024

The remove–compute–restore (RCR) approach is widely used in local quasi-geoid modeling. However, the classical RCR approach usually does not take into account the noise of the satellite-only global gravity field model (GGM), which may lead to a suboptimal result. This paper presents an approach for local quasi-geoid modeling based on spherical radial basis functions that combines local noisy datasets and a noisy satellite-only GGM. This approach includes an RCR procedure using a satellite-only GGM. This is a direct approach that takes the spherical harmonic coefficients of satellite-only GGM as a noisy dataset and includes the corresponding full-noise covariance matrix in the least-squares estimation, aiming to obtain a statistically optimal local quasi-geoid model. The direct approach goes beyond the indirect approach, which treats the height anomalies generated from the satellite-only GGM as a noisy dataset. However, the generated GGM height anomaly dataset is not an equivalent representation of the satellite-only GGM, which may result in the loss of information from the satellite-only GGM. Through mathematical deduction, we demonstrate the theoretical consistency between the direct approach and the indirect approach. The direct approach also has an advantage over the indirect approach in terms of computational complexity due to the simpler algorithm. We conducted a synthetic closed-loop test with a real data distribution in Colorado, and numerical results demonstrated the advantage of the direct approach in local quasi-geoid modeling. In terms of the root mean square of the differences between the predicted values and the true reference values, the direct approach provided an improvement of approximately 14% compared to the indirect approach.
Light Gradient Boosting Machine-Based Low–Slow–Small Target Detection Algorithm for Airborne Radar
Authors: Liu Jing , Huang Pengcheng , Zeng Cao , Liao Guisheng , Xu Jingwei , Tao Haihong , Juwono Filbert H.
Published: May 2024

For airborne radar, detecting a low–slow–small (LSS) target is a hot and challenging topic, which results from the rapidly increasing number of non-cooperative flying LSS targets becoming of widespread concern, and the low signal-to-clutter ratio (SCR) of LSS targets results in the targets being particularly easily overwhelmed by the clutter. In this paper, a novel light gradient boosting machine (LightGBM)-based LSS target detection algorithm for airborne radar is proposed. The proposed method, based on the current real-time clutter environment of the range cell to be detected, firstly designs a specific real-time space-time LSS target signal repository with special dimensions and structures. Then, the proposed method creatively designs a new fast-built real-time training feature dataset specifically for the LSS target and the current clutter, together with a series of unique data transformations, sample selection, data restructuring, feature extraction, and feature processing. Finally, the proposed method develops a unique machine learning-based LSS target detection classifier model for the designed training dataset, by fully excavating and utilizing the advantages of the ensemble decision trees-based LightGBM. Consequently, the pre-processed data in the range cell of interest are classified using the proposed algorithm, which achieves LSS target detection by evaluating the output results of the designed classifier. Compared with the traditional classical target detection methods, the proposed algorithm is capable of providing markedly superior performance for LSS target detection. With an appropriate computational time, the proposed algorithm attains the highest probability of detecting LSS targets under the low SCR. The simulation outcomes and detection results with the experimental data are employed to validate the effectiveness and merits of the proposed algorithm.
Membrane-Based Pulsed Sampling Method for Extended Dynamic Range of Ion Mobility Spectrometry
Authors: Chen Xinzhi , Lu Wencheng , Lan Di , Zhang Bo , Gu Hao , Shen Mutong , Li Lingfeng , Li Peng
Published: May 2024

Ion mobility spectrometry (IMS) has been widely studied and applied as an effective analytical technology for the on-site detection of volatile organic compounds (VOCs). Despite its superior selectivity compared with most gas sensors, its limited dynamic range is regarded as a major drawback, limiting its further application in quantitative measurements. In this work, we proposed a novel sample introduction method based on pulsed membrane adsorption, which effectively enhanced IMS’s ability to measure analytes at higher concentrations. Taking N-methyl-2-pyrrolidone (NMP) as an example, this new sampling method expanded the dynamic range from 1 ppm to 200 ppm. The working principle and measurement strategy of this sampling method were also discussed, providing new insights for the design and application of IMS-based instruments.
Optimization of Surface Functionalizations for Ring Resonator-Based Biosensors
Authors: Ardoino Niccolò , Lunelli Lorenzo , Pucker Georg , Vanzetti Lia , Favaretto Rachele , Pasquardini Laura , Pederzolli Cecilia , Guardiani Carlo , Potrich Cristina
Published: May 2024

Liquid biopsy is expected to become widespread in the coming years thanks to point of care devices, which can include label-free biosensors. The surface functionalization of biosensors is a crucial aspect that influences their overall performance, resulting in the accurate, sensitive, and specific detection of target molecules. Here, the surface of a microring resonator (MRR)-based biosensor was functionalized for the detection of protein biomarkers. Among the several existing functionalization methods, a strategy based on aptamers and mercaptosilanes was selected as the most highly performing approach. All steps of the functionalization protocol were carefully characterized and optimized to obtain a suitable protocol to be transferred to the final biosensor. The functionalization protocol comprised a preliminary plasma treatment aimed at cleaning and activating the surface for the subsequent silanization step. Different plasma treatments as well as different silanes were tested in order to covalently bind aptamers specific to different biomarker targets, i.e., C-reactive protein, SARS-CoV-2 spike protein, and thrombin. Argon plasma and 1% v/v mercaptosilane were found as the most suitable for obtaining a homogeneous layer apt to aptamer conjugation. The aptamer concentration and time for immobilization were optimized, resulting in 1 µM and 3 h, respectively. A final passivation step based on mercaptohexanol was also implemented. The functionalization protocol was then evaluated for the detection of thrombin with a photonic biosensor based on microring resonators. The preliminary results identified the successful recognition of the correct target as well as some limitations of the developed protocol in real measurement conditions.
Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment
Authors: Tricomi Giuseppe , Giacobbe Maurizio , Ficili Ilenia , Peditto Nicola , Puliafito Antonio
Published: May 2024

The arising of the Cyber–Physical Systems’ vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context.
Tides of Blockchain in IoT Cybersecurity
Authors: Ahakonye Love Allen Chijioke , Nwakanma Cosmas Ifeanyi , Kim Dong-Seong
Published: May 2024

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper’s structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.
Implementation of a Universal Framework Using Design Patterns for Application Development on Microcontrollers
Authors: Babiuch Marek , Foltynek Petr
Published: May 2024

This article focuses on the area of software development for microcontrollers and details the implementation of modern programming practices and principles in embedded systems and IoT applications. This article explains how we implemented previously unimplemented principles and applied design patterns for quality software design on microcontrollers, which are currently only used for developing applications on the higher layers of the IoT reference model. A custom modular framework for microcontrollers is presented, based on applying SOLID principles and adapting design patterns specific to the microcontrollers’ application development needs. The implemented framework enables independent communication between modules and flexible integration of hardware components. It is designed with platform independence in mind, contributing to its wide adaptability and ease of use in diverse development environments. By applying these technological approaches, we can create applications that are not only testable and extensible in terms of application logic but also allow for easy adaptation to changes in these hardware resources. Utilizing these capabilities represents an innovative approach to development for microcontrollers that fundamentally improves the long-term sustainability and scalability of applications.
Digital Twins in Agriculture and Forestry: A Review
Authors: Tagarakis Aristotelis C. , Benos Lefteris , Kyriakarakos George , Pearson Simon , Sørensen Claus Grøn , Bochtis Dionysis
Published: May 2024

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic.
UAV Path Optimization for Angle-Only Self-Localization and Target Tracking Based on the Bayesian Fisher Information Matrix
Authors: Dogancay Kutluyil , Hmam Hatem
Published: May 2024

In this paper, new path optimization algorithms are developed for uncrewed aerial vehicle (UAV) self-localization and target tracking, exploiting beacon (landmark) bearings and angle-of-arrival (AOA) measurements from a manoeuvring target. To account for time-varying rotations in the local UAV coordinates with respect to the global Cartesian coordinate system, the unknown orientation angle of the UAV is also estimated jointly with its location from the beacon bearings. This is critically important, as orientation errors can significantly degrade the self-localization performance. The joint self-localization and target tracking problem is formulated as a Kalman filtering problem with an augmented state vector that includes all the unknown parameters and a measurement vector of beacon bearings and target AOA measurements. This formulation encompasses applications where Global Navigation Satellite System (GNSS)-based self-localization is not available or reliable, and only beacons or landmarks can be utilized for UAV self-localization. An optimal UAV path is determined from the optimization of the Bayesian Fisher information matrix by means of A- and D-optimality criteria. The performance of this approach at different measurement noise levels is investigated. A modified closed-form projection algorithm based on a previous work is also proposed to achieve optimal UAV paths. The performance of the developed UAV path optimization algorithms is demonstrated with extensive simulation examples.
Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System
Authors: Shan Xiaoyu , Cabani Adnane , Chafouk Houcine
Published: May 2024

The data incest problem causes inter-estimate correlation during data fusion processes, which yields inconsistent data fusion results. Especially in the multi-sensor multi-vehicle (MSMV) system, the data incest problem is serious due to multiple relative position estimations, which not only lead to pessimistic estimation but also cause additional computational overhead. In order to address the data incest problem, we propose a new data fusion method termed the interval split covariance intersection filter (ISCIF). The general consistency of the ISCIF is proven, serving as supplementary proof for the split covariance intersection filter (SCIF). Moreover, a decentralized MSMV localization system including absolute and relative positioning stages is designed. In the absolute positioning stage, each vehicle uses the ISCIF algorithm to update its own position based on absolute measurements. In the relative position stage, the interval constraint propagation (ICP) method is implemented to preprocess multiple relative position estimates and initially prepare input data for ISCIF. Then, the proposed ISCIF algorithm is employed to realize relative positioning. In addition, comparative simulations demonstrate that the proposed method can achieve both accurate and consistent results compared with the state-of-the-art methods.
Enhancing Road Safety Decision-Making through Analysis of Youth Survey Data: A Descriptive Statistical Approach
Authors: Bohdidi Zohra , Cherif El Khalil , El Azhari Hamza , Bnoussaad Ayman , Babounia Aziz
Published: May 2024

Unsafe roads have posed a significant threat to public health, economic stability, societal well-being, and national interests for numerous decades. Road traffic accidents constitute the primary cause of death among Morocco’s youth population. This study facilitates a comprehensive exploration of the opinions held by youth in Morocco with regard to decision-making in road safety. Through a rigorous analysis of their perspectives, the research endeavors to scientifically categorize these opinions. 97.8% of the surveyed youth strongly agree that road safety holds national importance and 58% recognize road safety as a personal responsibility. The overarching aim is to provide decision-makers with valuable insights to enhance the implementation of effective strategies and actively involve youth in shaping future road safety initiatives.
Evolution of Occupational Safety and Health Disclosure Practices: Insights from 8 Years in Taiwan’s Construction Industry
Authors: Chang Chieh-Wen , Nagata Tomohisa , Anthony Louise E. , Lin Ro-Ting
Published: May 2024

The construction industry has been identified as a major contributor to occupational accidents that can lead to fatalities. As a result, this study aims to evaluate the effectiveness of new safety and health regulations and revised guidelines in improving safety and health disclosures and performance within the construction industry. We retrieved safety and health disclosure reports from 25 Taiwanese construction companies between 2013 and 2020 using the Market Observation Post System website. We analyzed the data using the Kaplan–Meier method to assess the timing of disclosures and differences between larger (≥300 employees) and smaller (<300 employees) companies. We found that construction companies reported safety indicators more promptly than health indicators and that larger companies disclosed earlier compared to smaller ones. Only 45% of companies provide detailed reviews and preventative measures in their sustainability reports despite 64% disclosing occupational accidents. We found that from 2013 to 2020, more companies improved their occupational safety and health (OSH) reporting. This improvement coincided significantly with the adoption of international standards and Taiwan’s government regulations. In summary, the study found that larger companies were more likely to disclose OSH data compared to smaller ones. This suggests that company size and available resources could have an impact on reporting practices. While some progress was made, companies still struggle to provide detailed reports on major accidents, balancing transparency with competitiveness.
Online Mothering: The Empowering Nature of a Hashtag Movement Founded on Social Sharing and Stereotype Deconstruction
Authors: Scardigno Rosa , Sportelli Carmela , Cicirelli Paolo Giovanni , Lops Angelica , D’Errico Francesca
Published: May 2024

Online mothering is an arising and multifaceted phenomenon as the online world offers unique opportunities and risks in a particularly challenging period. The overall aim of this work is to investigate the features of the hashtag movement #itcouldbeme, which was born after a terrible news event concerning a newborn’s death, specifically concerning (a) the e-activation levels; (b) the emotional arousal; (c) the potential presence of deconstruction of stereotypes concerning intensive mothering. To investigate positioning, reactions, and emotions, a database of 394 online posts gathered from popular Italian social networks was created, manually codified, and analyzed through the chi-square test. The results enabled us to deepen the associations among these variables, thus revealing the opportunities for empowerment offered by the socio-cultural positioning, different emotional pathways, and adhesion to this online movement. Finally, implications for professionals and public health issues are discussed.
Greening the Workplace: Exploring the Influence of Corporate Sustainability Governance on Corporate Labour Rights in the Case of Indian Listed Companies for the Period of 2010 to 2021
Authors: Helfaya Akrum , Muthuthantrige Nadeesha , Xu Shirley
Published: May 2024

Amidst today’s globalized economy, embedding sustainable practices into corporate governance frameworks has become important. This era underscores a heightened focus on CSR and sustainability, drawing considerable scholarly and practical interest to the nexus of corporate governance, sustainability initiatives, and corporate labour rights (CLRs). Hence, this study investigates the relationship between corporate sustainability governance initiatives (CSGIs) and CLR practices in India. Despite regulatory efforts, issues such as modern slavery and low wages persist in the country. Drawing on legitimacy, agency, stakeholder, and resource dependency theories, this study examines how CSGIs influence CLR practices. Data from 1212 observations of top Indian listed companies between 2010 and 2021 indicate positive correlations between CSGI dimensions and CLR practices across industries and board CSR orientations. This research underscores the importance of CSGIs in promoting sustainable corporate practices and improving CLRs in emerging economies. It also aligns with the United Nations sustainable development goals (SDGs), particularly SDG 3, SDG 5, SDG 8, and SDG 10. By combining theoretical rigour with practical relevance, this study provides insights for businesses, policymakers, workers, investors, and CSR scholars, contributing to efforts to enhance CLRs in India and beyond. For instance, the study offers actionable guidance for businesses and policymakers aiming to improve CLR practices. It highlights the positive correlation between specific attributes of CSGIs and CLR practices, providing insights for corporate decision-making and emphasizing the importance of aligning operations with the UN’s sustainable development goals. The findings serve as critical decision-making tools for investors concerned with corporate sustainability governance and CLRs to identify ethically responsible companies and mitigate investment risks.
Unveiling Transportation Socio-Economic Adaptability Using a Node–Place Model: A Case from Zhejiang Province, China
Authors: Shi Jianwei , Wang Shuwei , Liu Siyang , Zhou Chenjing
Published: May 2024

Effective investment in transportation infrastructure ensures sustainable urban development and high-quality economic development. Understanding transportation and socioeconomic relationships is crucial for developing investment strategies. Existing research focuses on analyzing the contribution of transportation to the economy or the contribution of economic development to transportation, neglecting the identification of transportation infrastructure weaknesses to initiate targeted investment and construction. In this study, we propose an assessment framework based on the node–place model to identify the shortcomings in transportation infrastructure. The framework encompasses the adaptability of the current transportation infrastructure and the stage of economic development, evaluating whether the current transportation infrastructure is ahead of or lagging behind the level of economic development. We conduct a case study on three kinds of transportation infrastructure, including railways, highways, and civil aviation, in Zhejiang Province, China. The results show that Zhejiang Province’s transportation infrastructure generally meets economic development requirements, and most cities have relatively advanced transportation systems. However, significant gaps in transportation facilities in certain areas still need to be targeted and supplemented. These findings help to explore the theoretical relationship between transportation and socioeconomics, providing a basis for economic investment decisions on transportation infrastructure.
Academic Intrapreneurs: Exploring the Antecedents of Academic Intrapreneurial Intention
Authors: Kanısoy Kemal , Şeşen Harun , Ekemen Mehmet Ali
Published: May 2024

It is widely accepted that intrapreneurial behavior is imperative for any type of organization to maintain sustainability in terms of growth and performance. In this context, it is also important to investigate the factors that play a role in promoting intrapreneurship within dynamic industries, such as the higher education industry. For this purpose, this study aimed to explore the antecedents of intrapreneurial intention in academic staff to provide theoretical as well as practical contributions for universities. We used a survey method to collect data with a sample consisting of 236 academic staff from universities in Northern Cyprus, and tested hypotheses through three different structural models by using structural equation modeling (SEM). Following a deductive approach to establish constructs in the models, we treated academic intrapreneurial intention as the dependent variable, and self-leadership, self-efficacy, and psychological capital as the independent variables. In addition, attitudes toward intrapreneurship, subjective norms, and perceived behavioral control were individually tested as mediating variables. The results of this study indicate that self-leadership, self-efficacy, and psychological capital have direct and indirect effects on academic intrapreneurial intention, and attitudes toward intrapreneurship, subjective norms, and perceived behavioral control play a mediating role between these antecedents and academic intrapreneurial intention.
Fiber-Reinforced Coal Gangue-Based Alumina Aerogel Composites with Highly Thermal Stability by Ambient Pressure Drying
Authors: Bo Kai , Liu Hongwei , Zhang Yanlan , Wang Yongzhen
Published: May 2024

An aluminum silicate fiber/alumina aerogel (ASF/AA) composite was prepared via the sol-gel method and atmospheric drying (APD) method using coal gangue (CG) solid waste from Xingxian county, Shanxi Province, as the aluminum source. Utilizing N2 adsorption, scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and thermal conductivity meters, researchers examined the microstructure, composition, pore structure, and thermal insulation performance of ASF/AA composites. The thermal conductivity mechanism of the composite was analyzed. The experimental results show that most of the aluminum in CG is used. More importantly, in the process of aerogel synthesis, the atmospheric pressure drying method is used to obtain similar properties to supercritical drying. The composite material exhibits a low thermal conductivity of 0.047 W/(m·K), a high specific surface area of 416 m2/g, and a low density of 0.26 g/cm3 at room temperature. After heating at 1200 °C for 2 h, the thermal conductivity was as low as 0.071 W/(m·K). This strategy can not only effectively achieve a reduction in the harmfulness of solid waste coal gangue, but also alleviate the shortage of related energy and resources in our country.
Can Mandatory Disclosure of CSR Information Drive the Transformation of Firms towards High-Quality Development?
Authors: Xu Rong , Cui Yongze , Ban Qi , Xie Yang , Fan Xiaoyun
Published: May 2024

This paper establishes a quasi-natural experiment grounded in the exogenous shock occasioned by the implementation of a compulsory corporate social responsibility (CSR) information disclosure policy. It investigates the ramifications of this mandated CSR information disclosure policy on firms’ total factor productivity (TFP) through the integration of the difference-in-differences (DID) methodology. The investigation reveals that obligatory disclosure of CSR information significantly augments firms’ total factor productivity (TFP) by mitigating agency conflicts and financial constraints. Further analysis elucidates investment efficiency and innovation enhancement as pivotal conduits through which the mandatory CSR information disclosure policy fosters firms’ TFP. The study explores the impact of mandatory CSR information disclosure on firms’ TFP mechanism, which has significant policy value and can provide useful reference for the high-standard development of China’s corporate economic transformation.
The Effects of Digitalization on the Sustainability of Small Farms
Authors: Šermukšnytė-Alešiūnienė Kristina , Melnikienė Rasa
Published: May 2024

Digitalization of agriculture is one of the priorities of the EU’s rural development strategy “From Field to Table”, which promotes the creation of more added value and climate change mitigation in agriculture. A growing body of the literature argues that digitalization enables better information management, reduces production costs, and increases the potential for farm income growth, but only a few papers provide empirical studies on how digitalization improves the performance of small farms. To fill this gap in the literature, this paper presents a case study as empirical evidence of the impact of digital innovation on smallholder performance through a sustainable development lens. This paper reports research based on a pilot digitalization project implemented on a small organic farm. It examines the identification of logical links between the digitalization processes introduced and the impact of digitalization on the economic, social, and environmental performance of the small farm. The case study data were collected through semi-structured interviews and based on the results of a pilot project. The findings of this study provide evidence that the introduction of digital technologies has improved the economic performance of the farm, including a reduction in labor costs, improved customer relations, improvements in farmers’ investment planning, and process redesign. Based on this study, recommendations are made to policymakers on how to promote the uptake of digital technologies in smallholder farming.
Future Foods in the Face of Hunger and Surplus: From Sustainable Production to Responsible Consumption
Authors: Aadil Rana Muhammad , Radicetti Emanuele , Haider Ghulam , Tedeschi Paola
Published: May 2024

This Editorial refers to the Special Issue “Future Foods in the Face of Hunger and Surplus: From Sustainable Production to Responsible Consumption” [...]
Machine Learning-Based Research for Predicting Shale Gas Well Production
Authors: Qi Nijun , Li Xizhe , Wu Zhenkan , Wan Yujin , Wang Nan , Duan Guifu , Wang Longyi , Xiang Jing , Zhao Yaqi , Zhan Hongming
Published: May 2024

The estimated ultimate recovery (EUR) of a single well must be predicted to achieve scale-effective shale gas extraction. Accurately forecasting EUR is difficult due to the impact of various geological, engineering, and production factors. Based on data from 200 wells in the Weiyuan block, this paper used Pearson correlation and mutual information to eliminate the factors with a high correlation among the 31 EUR influencing factors. The RF-RFE algorithm was then used to identify the six most important factors controlling the EUR of shale gas wells. XGBoost, RF, SVM, and MLR models were built and trained with the six dominating factors screened as features and EUR as labels. In this process, the model parameters were optimized, and finally the prediction accuracies of the models were compared. The results showed that the thickness of a high-quality reservoir was the dominating factor in geology; the high-quality reservoir length drilled, the fracturing fluid volume, the proppant volume, and the fluid volume per length were the dominating factors in engineering; and the 360−day flowback rate was the dominating factor in production. Compared to the SVM and MLR models, the XG Boost and the RF models based on integration better predicted EUR. The XGBoost model had a correlation coefficient of 0.9 between predicted and observed values, and its standard deviation was closest to the observed values’ standard deviation, making it the best model for EUR prediction among the four types of models. Identifying the dominating factors of shale gas single-well EUR can provide significant guidance for development practice, and using the optimized XGBoost model to forecast the shale gas single-well EUR provides a novel idea for predicting shale gas well production.
A Deterministic and Stochastic Fractional-Order ILSR Rumor Propagation Model Incorporating Media Reports and a Nonlinear Inhibition Mechanism
Authors: Yue Xuefeng , Zhu Weiwei
Published: May 2024

Nowadays, rumors spread more rapidly than before, leading to more panic and instability in society. Therefore, it is essential to seek out propagation law in order to prevent rumors from spreading further and avoid unnecessary harm. There is a connection between rumor models and symmetry. The consistency of a system or model is referred to as the level of symmetry under certain transformations. For this purpose, we propose a fractional-order Ignorant–Latent–Spreader–Remover (ILSR) rumor propagation model that incorporates media reports and a nonlinear inhibition mechanism. Firstly, the boundedness and non-negativeness of the solutions are derived under fractional differential equations. Secondly, the threshold is used to evaluate and illustrate the stability both locally and globally. Finally, by utilizing Pontryagin’s maximum principle, we obtain the necessary conditions for the optimal control in the fractional-order rumor propagation model, and we also obtain the associated optimal solutions. Furthermore, the numerical results indicate that media reports can decrease the spread of rumors in different dynamic regions, but they cannot completely prevent rumor dissemination. The results are also exhibited and corroborated by replicating the model with specific hypothetical parameter values. It can be inferred that fractional order yields more favorable outcomes when rumor permanence in the population is higher. The presented method facilitates the acquisition of profound insights into the dissemination dynamics and subsequent consequences of rumors within a societal network.
Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics
Authors: Luo Rongzheng , Zhang Lu , Li You , Shen Jiwei
Published: May 2024

This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the “explosion of complexity” issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller.
Delay Effects on Plant Stability and Symmetry-Breaking Pattern Formation in a Klausmeier-Gray-Scott Model of Semiarid Vegetation
Authors: Medjahdi Ikram , Lachachi Fatima Zohra , Castro María Ángeles , Rodríguez Francisco
Published: May 2024

The Klausmeier–Gray–Scott model of vegetation dynamics consists of a system of two partial differential equations relating plant growth and soil water. It is capable of reproducing the characteristic spatial patterns of vegetation found in plant ecosystems under water limitations. Recently, a discrete delay was incorporated into this model to account for the lag between water infiltration into the soil and the following water uptake by plants. In this work, we consider a more ecologically realistic distributed delay to relate plant growth and soil water availability and analyse the effects of different delay types on the dynamics of both mean-field and spatial Klausmeier–Gray–Scott models. We consider distributed delays based on Gamma kernels and use the so-called linear chain trick to analyse the stability of the uniformly vegetated equilibrium. It is shown that the presence of delays can lead to the loss of stability in the constant equilibrium and to a reduction of the parameter region where steady-state vegetation patterns can arise through symmetry-breaking by diffusion-driven instability. However, these effects depend on the type of delay, and they are absent for distributed delays with weak kernels when vegetation mortality is low.
Study of an LLC Converter for Thermoelectric Waste Heat Recovery Integration in Shipboard Microgrids
Authors: Rigogiannis Nick , Roussos Ioannis , Pechlivanis Christos , Bogatsis Ioannis , Kyritsis Anastasios , Papanikolaou Nick , Loupis Michael
Published: May 2024

Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions.
Evaluating the Environmental Impact of Anthropogenic Activities on Human Health: A Systematic Review
Authors: Cofone Luigi , Sabato Marise , Di Rosa Enrico , Colombo Chiara , Paglione Lorenzo
Published: May 2024

Due to major global urbanisation, a careful evaluation of plans (town planning and mobility) and projects (industrial and development) is required in order to measure their impact on health and environmental matrices. In Italy, Legislative Decree No 152/06 introduced two procedures: the EIA (Environmental Impact Assessment) and SIA (Strategic Impact Assessment). Their focus, however, does not consider human health. Recently, the Integrated Environmental and Health Impact Assessment (IEHIA) was introduced; this defines the parameters necessary to provide an EIA that includes human health as a factor. This systematic review was conducted, including both the population impacted by new facilities and the method used to define their impact. Our database search produced 724 articles, of which 33 were eligible. Studies included landfill plans, manufacturing industries, mobility policies, energy production, and the environmental health of an area. All studies show how an approach encompassing multiple parameters can analyse the impact of a new facility in a comprehensive manner. This review shows that the use of health-related environmental impact parameters is essential for the integration of a project into a community, and can allow a wider understanding of the possible impacts on human health, both direct and indirect.