IntroductionEnhancing the total factor productivity (TFP) of forestry ecosystems is central to shifting the forestry industry toward high-quality development. This study investigates the impact of digital government initiatives on forestry ecosystem TFP to understand how digital governance can drive ecological and economic efficiency.MethodsUtilizing panel data from 30 Chinese provinces between 2015 and 2022, this study employs a Dual Machine Learning (DML) model to mitigate endogeneity and estimation bias. This rigorous methodological framework allows for a precise quantitative assessment of the effects and transmission mechanisms of digital government development on forestry ecosystem TFP.ResultsThe empirical results demonstrate three key findings: (1) The expansion of digital government significantly boosts the TFP of forestry ecosystems. (2) Mechanism analysis identifies three primary channels for this improvement: the cultivation of new quality productive forces, the upgrading of forestry industrial structures, and the simplification of operational processes. Furthermore, the broader digital economy acts as a significant positive moderator in this relationship. (3) Heterogeneity analysis reveals that the magnitude of these effects varies across regions, contingent upon local economic development levels and forest resource endowments.DiscussionBased on these findings, the paper proposes policy recommendations to foster institutional innovation, accelerate digital government construction, and implement region-specific strategies. Globally, this study provides empirical evidence for the synergy between digital governance and ecological sustainability. It offers a replicable model for other nations seeking to leverage digital tools to balance economic growth with environmental conservation, thereby contributing to the advancement of global ecological civilization and sustainable development goals.
Industry 4.0 is a recent trend representing the vision for the integration of information, objects and people in cyber-physical scenarios in order to transform factories into intelligent environments. Although this transition is still ongoing, the corresponding vision of Industry 5.0 has already emerged. Industry 5.0 aims to bring the human factor back into the production system, with the collaborative work paradigm of human–robot collaboration (HRC) at its core. This paper first discusses how Industry 4.0 has conceptually evolved and is being implemented in the context of construction, through the lens of a literature review and bibliometric analysis. Additionally, it clarifies the scope of Industry 5.0 and assesses its momentum as a literature trend, drawing on bibliometric comparisons with the Industry 4.0/Construction 4.0 vision. Furthermore, it makes a realistic assessment of the potential of the Industry 5.0 paradigm to evolve into Construction 5.0. In this context, it reviews the prospects of HRC use in construction, highlights its distinct challenges and proposes new directions. This paper is, to the author’s best knowledge, the first consideration of ‘Construction 5.0’ and the first bibliometric analysis comparing data from Industry 4.0, Construction 4.0 and Industry 5.0 literature.
This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system. The scarcity of real interview data, absence of structured variable modeling, and lack of interpretability in inference mechanisms have limited the analytical accuracy and policy responsiveness of EII research. To resolve these challenges, we propose a structural modeling paradigm based on the National Institute of Standards and Technology (NIST) synthetic data quality framework, focusing on consistency, authenticity, and traceability. We design a five-layer architecture that includes prompt-driven synthetic dialogue generation, a structured variable system covering skills, institutional, and emotional dimensions, dependency and causal path modeling, graph-based structure design, and an interactive inference engine. Empirical results demonstrate the effectiveness of the approach using a 15-segment synthetic corpus, with 41,597 tokens, 127 annotated variables, and 820 semantic relationship triples. The model exhibits strong structural consistency (Krippendorff alpha = 0.83), construct validity (RMSEA = 0.048, CFI = 0.93), and semantic alignment (mean cosine similarity > 0.78 via BERT). A key causal loop is identified: system mismatch leads to emotional frustration, reduced participation, skill gaps, and recurrence of mismatch, revealing a structural degradation cycle. This research introduces the first NIST-compliant AI modeling framework for stakeholder systems and provides a foundation for policy simulation, curriculum design, and collaborative strategy modeling.
Based on the Wronski determinant, we propose the construction of linearly independent orthogonal functions in any Hilbert function space. The method requires only an initial function from the space of the functions under consideration, that satisfies minimal properties. Two applications are considered, including solutions to ordinary differential equations and the construction of basis functions. We also present a conjecture that connects the latter two concepts, which leads to what we call the Wronski basis.
Increasingly in recent years, probabilistic computation has been investigated through the lenses of categorical algebra, especially via string diagrammatic calculi. Whereas categories of discrete and Gaussian probabilistic processes have been thoroughly studied, with various axiomatisation results, more expressive classes of continuous probability are less understood, because of the intrinsic difficulty of describing infinite behaviour by algebraic means. In this work, we establish a universal construction that adjoins infinite tensor products, allowing continuous probability to be investigated from discrete settings. Our main result applies this construction to $\mathsf{FinStoch}$, the category of finite sets and stochastic matrices, obtaining a category of locally constant Markov kernels, where the objects are finite sets plus the Cantor space $2^{\mathbb{N}}$. Any probability measure on the reals can be reasoned about in this category. Furthermore, we show how to lift axiomatisation results through the infinite tensor product construction. This way we obtain an axiomatic presentation of continuous probability over countable powers of $2=\lbrace 0,1\rbrace$.
The usage-based constructionist (UCx) approach to language posits that language comprises a network of learned form-meaning pairings (constructions) whose use is largely determined by their meanings or functions, requiring them to be graded and probabilistic. This study investigates whether the internal representations in Large Language Models (LLMs) reflect the proposed function-infused gradience. We analyze representations of the English Double Object (DO) and Prepositional Object (PO) constructions in Pythia-$1.4$B, using a dataset of $5000$ sentence pairs systematically varied by human-rated preference strength for DO or PO. Geometric analyses show that the separability between the two constructions' representations, as measured by energy distance or Jensen-Shannon divergence, is systematically modulated by gradient preference strength, which depends on lexical and functional properties of sentences. That is, more prototypical exemplars of each construction occupy more distinct regions in activation space, compared to sentences that could have equally well have occured in either construction. These results provide evidence that LLMs learn rich, meaning-infused, graded representations of constructions and offer support for geometric measures for representations in LLMs.
Waqas Arshad Tanoli, Abid Ullah, Abubakar Sharafat
et al.
The construction of large underground caverns fundamentally differs from building and above ground civil infrastructure projects due to their complex geometries and variable geological conditions. These projects are complex and challenging because a large amount of data is generated from dispersed, independent, and heterogeneous sources. The underground construction industry often uses traditional project management techniques to manage complex interactions between these data sources that are hardly linked, and independent decisions are often made without considering all the relevant aspects. In this context, cavern construction exhibits uncertainties and risks due to unforeseen circumstances, an intricate design, and ineffective information management. Existing research has considered general BIM semantic models at the design stage; however, the digital transformation of cavern construction remains underdeveloped and fails to integrate digital construction throughout the project lifecycle. To address that gap, a novel BIM-based multi-model cavern information modeling framework is presented here to improve project management, construction, and delivery by integrating multiple interlinked data models and project performance data for large underground cavern construction. Data models of cavern construction processes are linked to propose an extension of the Industry Foundation Classes (IFC) schema based on the cavern-specific elements, relationships, and property set definitions. To illustrate the potential of the proposed framework, a theoretical application to the powerhouse cavern construction is presented. The results indicate that the framework has significant potential to improve construction efficiency and safety and establish a robust foundation for the digital transformation of underground cavern projects. The theoretical implementation on the Neelum–Jhelum powerhouse cavern showed that the framework enabled a 92 m cavern realignment to avoid fault zones, achieved a 12.4% reduction in rock bolt usage, and a 9.8% reduction in shotcrete volume. These quantitative improvements illustrate its potential to enhance safety, reduce material costs, and optimize construction efficiency compared to conventional workflows.
夏岚1， 刘清雷1， 邓肖丽2, 张鑫鹏1， 张健1，2 XIA Lan1, LIU Qinglei1, DENG Xiaoli2, ZHANG Xinpeng1, ZHANG Jian1,2
旨在提高火麻仁油的稳定性，扩大其在食品工业中的应用，采用火麻仁油为原料，单硬脂酸甘油酯、米糠蜡、蜂蜡、甘蔗蜡和巴西棕榈蜡为凝胶剂，制备火麻仁油凝胶，以持油率和硬度为指标，对凝胶剂种类、凝胶剂复配比例和复合凝胶剂添加量进行优化，并对所得油凝胶的微观结构、流变特性、傅里叶红外光谱（FT-IR）、热学性质（DSC分析）以及运载β-胡萝卜素的储藏稳定性进行表征。结果表明：在甘蔗蜡与米糠蜡质量比1∶ 1、复合凝胶剂添加量8%条件下，所形成的火麻仁油凝胶具有较高的持油率（95.04%）和适中的硬度（147.6 g），油凝胶的晶体分布更加均匀、网络结构更加致密，有利于束缚油脂；相比25 ℃，4 ℃下形成的油凝胶体系的表观黏度、储能模量更高，体系更稳定；FT-IR分析表明，油凝胶体系内部通过氢键、范德华力等分子间作用力实现了自组装或结构单元的构建；DSC结果显示，火麻仁油凝胶体系在45 ℃开始吸热熔化，在冷却过程中，该体系在相同温度下开始结晶，说明该体系具有良好的热稳定性；相较于火麻仁油，火麻仁油凝胶体系中 β-胡萝卜素的保留率更高，降解速度更慢，表明凝胶体系可更好地保护易氧化物质。综上，采用甘蔗蜡与米糠蜡作为复配凝胶剂，以富含不饱和脂肪酸的火麻仁油为基油，可以制备结构稳定、可塑性好、持油率高、具有保护易氧化物质的油凝胶。
Aim to improve the stability of hemp seed oil and expand its application in food industry, hemp seed oleogel was prepared using hemp seed oil as raw material, glycerol monostearate (GMS), rice bran wax (RBW), beeswax (BW), sugarcane wax (SW) and carnauba wax (CBW) as gelators. The types of gelator, the mass ratio and dosage of composite gelator were optimized by taking oil holding rate and hardness as indicators, and the microstructure, rheological properties, Fourier infrared spectroscopy (FT-IR), thermal properties (DSC analysis) and storage stability for carrying β-carotene of the obtained oleogel were characterized. The results showed that when the mass ratio of SW to RBW was 1∶ 1 and their total dosage was 8%, the hemp seed oleogel had high oil holding rate (95.04%) and moderate hardness (147.6 g), the crystal distribution of the oleogel was more uniform and the network structure was more compact, which was conducive to binding oil. Compared with 25 ℃, the oleogel system formed at 4 ℃ had higher apparent viscosity and storage modulus, and the system was more stable. FT-IR analysis showed that the oleogel system realized self-assembly or structural unit construction by intermolecular forces such as hydrogen bond and van der Waals forces. DSC analysis showed that the hemp seed oleogel system began to absorb heat and melt at 45 ℃, and began to crystallize at the same temperature in the crystallization process, indicating good thermal stability of the system. Compared with hemp seed oil, the retention rate of β -carotene in hemp seed oleogel system was higher and the degradation rate was slower, which indicated that the oleogel system could better protect oxidizable substances. To sum up, an oleogel with a stable structure, good plasticity, high oil holding rate and good protection for oxidizable substances can be prepared by using SW and RBW as composite gelator and hemp seed oil rich in unsaturated fatty acids as base oil.
Márquez Álvaro, Ramallo Laura, Cerqueira Yaiza
et al.
3D printing (3DP) of concrete is an innovative technology towards digitalisation and sustainability of construction industry. The design of printable materials is in the spotlight of research and some issues related to durability of 3DP members, such as cold joints between successive layers and cracking due to shrinkage, need to be overcome for widespread applications. Self-healing, also referred to as self-repairing capacity, combined with short-fiber reinforcement to control crack width, are some possible solutions to increase 3DP members durability. This study addresses this gap evaluating the self-healing capacity of cement-lime 3DP mortars reinforced with vegetal fibers and self-healing capacity. Printability was assessed through a rheological characterization using a Dynamic Shear Rheometer (DSR) and field-oriented tests, such as penetration and flow table tests. Extrudability and buildability were also studied by extrusion and uniaxial unconfined compression tests (UUCT). The evolution of mortar properties was monitored at Early age (EA) to better understand the hydration and shrinkage underlying mechanisms. Finally, small-scale prototypes were constructed by a robotic 3D printing arm and the self-healing capacity was evaluated. This work could have an impact on the design of new materials with self-healing capacity applied on digital fabrication techniques for more durable construction.
Continuum robots with outstanding compliance, dexterity, and lean bodies are successfully applied in medicine, aerospace engineering, the nuclear industry, rescue operations, construction, service, and manipulation. However, the inherent low stiffness characteristics of continuum bodies make it challenging to develop ultra-long and small-diameter continuum robots. To address this size–scale challenge of continuum robots, we developed an 8 m long continuum robot with a diameter of 23 mm by a tip actuation and growth mechanism. Meanwhile, we also realized the untethered design of the continuum robot, which greatly increased its usable space range, portability, and mobility. Demonstration experiments prove that the developed growing continuum robot has good flexibility and manipulability, as well as the ability to cross obstacles and search for targets. Its continuum body can transport liquids over long distances, providing water, medicine, and other rescue items for trapped individuals. The functionality of an untethered growing continuum robot (UGCR) can be expanded by installing multiple tools, such as a grasping tool at its tip to pick up objects in deep wells, pits, and other scenarios. In addition, we established a static model to predict the deformation of UGCR, and the prediction error of its tip position was within 2.6% of its length. We verified the motion performance of the continuum robot through a series of tests involving workspace, disturbance resistance, collision with obstacles, and load performance, thus proving its good anti-interference ability and collision stability. The main contribution of this work is to provide a technical reference for the development of ultra-long continuum robots based on the tip actuation and steering principle.
INTRODUCTION The construction industry has hit a plateau in terms of safety performance. Safety climate is regarded as a leading indicator of safety performance; however, relatively little safety climate research has been done in the Canadian construction industry. Safety climate may be geographically sensitive, thus it is necessary to examine how the construct of safety climate is defined and used to improve safety performance in different regions. On the other hand, more and more attention has been paid to job related stress in the construction industry. Previous research proposed that individual resilience may be associated with a better safety performance and may help employees manage stress. Unfortunately, few empirical research studies have examined this hypothesis. This paper aims to examine the role of safety climate and individual resilience in safety performance and job stress in the Canadian construction industry. METHOD The research was based on 837 surveys collected in Ontario between June 2015 and June 2016. Structural equation modeling (SEM) techniques were used to explore the impact of individual resilience and safety climate on physical safety outcomes and on psychological stress among construction workers. RESULTS The results show that safety climate not only affected construction workers' safety performance but also indirectly affected their psychological stress. In addition, it was found that individual resilience had a direct negative impact on psychological stress but had no impact on physical safety outcomes. CONCLUSIONS These findings highlight the roles of both organizational and individual factors in individual safety performance and in psychological well-being. PRACTICAL APPLICATIONS Construction organizations need to not only monitor employees' safety performance, but also to assess their employees' psychological well-being. Promoting a positive safety climate together with developing training programs focusing on improving employees' psychological health - especially post-trauma psychological health - can improve the safety performance of an organization.
Corporate social responsibility (CSR) is a widely embraced social phenomenon and has attracted increasing research interests in the construction industry recent years. However, their coverage of the issues pertaining to CSR in the construction industry are isolated and less comprehensive, failing to encompass the multifaceted nature of the construction industry. This study aimed to reveal and conceptualise the CSR's state of art in the construction industry. Following a systematic selection of 68 papers published in different journals between 2000 and 2017, the inductive and deductive content analysis of these papers reveal four research themes of current CSR research in the construction industry, comprising CSR perception, CSR dimensions, CSR implementation and CSR performance. A conceptual framework was developed accordingly to reflect the CSR research sate of art in the construction industry. Furthermore, given the nexus between CSR and sustainable development, insights for enhancing CSR contribution to sustainable development, and sustainable development goals (SDGs) in the construction industry were proposed, including changing the traditional procurement practices, improving legislation for environmental responsibility, integrating CSR dimensions and increasing CSR implementation in small to medium enterprises (SME). The findings of this study will deepen the understanding of CSR in the construction industry, and provide practical implications for different stakeholders in the construction industry to contribute more effectively to sustainable development.
Abstract By positioning in the discourse that economic output is always coupled with natural resource depletion, pollution, and carbon emission, decoupling analysis is widely adopted to evaluate how “quality” economic growth can lead to fewer such downsides so as to encourage sustainable development. This paper aims at examining the decoupling relationship between economic output and carbon emission by focusing on China's construction industry, which is a pillar industry for national economic growth, meanwhile contributes a huge amount of carbon emission. The method of Tapio decoupling model is used to examine the decoupling relationships at both national and provincial levels from 2005 to 2015. It continues to identify the driving force leading to a certain decoupling state by adopting the logarithmic mean Divisia index (LMDI). Results show that: (1) there existed an expansive decoupling relationship between economic growth and construction carbon emission in most provinces of China during 2005–2015; (2) Shanghai presented the best decoupling performance, while in contrast, other provinces such as Guizhou and Fujian displayed expansive negative decoupling state; and (3) “Economic output” played the most significant role in inhibiting the decoupling at both national and provincial levels, while “Indirect carbon intensity” was the main driver for promoting the national decoupling. Although the paper refers to the specific construction of China, the decoupling analysis approach can be extended to other countries as well as to other pollutants such as land pollution, waste water and haze. The understanding of driving forces for the decoupling state in China's construction industry provides international policy-makers with valuable reference for formulating effective measures to balance the dilemma between economic output and carbon emission.
In Part I we constructed the Quantum Mechanics of a charged unitary entity and prescribed the form in which such a particle interacts with other charged particles and matter in general. In this second part we extend the description to the hydrogen atom testing the correctness and accuracy of the general description. The relation between electron and proton in the atom is described systematically in a construction that is free from analogies or ad-hoc derivations and it supersedes conventional Quantum Mechanics (whose equations linked to measurements can be recovered). We briefly discuss why the concept of isolation built in Schrödinger's time evolution is not acceptable and how it immediately results in the well known measurement paradoxes of quantum mechanics. We also discuss the epistemic grounds of the development as well as those of conventional Quantum Mechanics.
Industry is at the forefront of adopting new technologies, and the process followed by the adoption has a significant impact on the economy and society. In this work, we focus on analysing the current paradigm in which industry evolves, making it more sustainable and Trustworthy. In Industry 5.0, Artificial Intelligence (AI), among other technology enablers, is used to build services from a sustainable, human-centric and resilient perspective. It is crucial to understand those aspects that can bring AI to industry, respecting Trustworthy principles by collecting information to define how it is incorporated in the early stages, its impact, and the trends observed in the field. In addition, to understand the challenges and gaps in the transition from Industry 4.0 to Industry 5.0, a general perspective on the industry's readiness for new technologies is described. This provides practitioners with novel opportunities to be explored in pursuit of the adoption of Trustworthy AI in the sector.
Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk for highly diversified multi-sector conglomerates like Amazon. Previously, we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of GICS that utilizes topic modeling, a natural language processing approach. Although our initial version, MIS-1, was able to improve upon GICS by providing multi-industry representations, it relied on an overly simple architecture that required prior knowledge about the number of industries and relied on the unrealistic assumption that industries are uncorrelated and independent over time. We improve upon this model with MIS-2, which addresses three key limitations of MIS-1 : we utilize Bayesian Non-Parametrics to automatically infer the number of industries from data, we employ Markov Updating to account for industries that change over time, and we adjust for correlated and hierarchical industries allowing for both broad and niche industries (similar to GICS). Further, we provide an out-of-sample test directly comparing MIS-2 and GICS on the basis of future correlation prediction, where we find evidence that MIS-2 provides a measurable improvement over GICS. MIS-2 provides portfolio managers with a more robust tool for industry classification, empowering them to more effectively identify and manage risk, particularly around multi-sector conglomerates in a rapidly evolving market in which new industries periodically emerge.
Mikhail Oliveira Leastro, Elliot Watanabe Kitajima, Vicente Pallás
et al.
Reverse genetics systems represent an important tool for studying the molecular and functional processes of viral infection. Citrus leprosis virus C (CiLV-C) (genus Cilevirus, family Kitaviridae) is the main pathogen responsible for the citrus leprosis (CL) disease in Latin America, one of the most economically important diseases of the citrus industry. Molecular studies of this pathosystem are limited due to the lack of infectious clones. Here, we report the construction and validation of a CiLV-C infectious cDNA clone based on an agroinfection system. The two viral RNA segments (RNA1 and RNA2) were assembled into two binary vectors (pJL89 and pLXAS). Agroinfiltrated Nicotiana benthamiana plants showed a response similar to that observed in the natural infection process with the formation of localized lesions restricted to the inoculated leaves. The virus recovered from the plant tissue infected with the infectious clones can be mechanically transmitted between N. benthamiana plants. Detection of CiLV-C subgenomic RNAs (sgRNAs) from agroinfiltrated and mechanically inoculated leaves further confirmed the infectivity of the clones. Finally, partial particle-purification preparations or sections of CiLV-C-infected tissue followed by transmission electron microscopy (TEM) analysis showed the formation of CiLV-C virions rescued by the infectious clone. The CiLV-C reverse genetic system now provides a powerful molecular tool to unravel the peculiarities of the CL pathosystem.