Vacuum glazing is highly regarded for its ability to transmit light while providing heat preservation, sound insulation, lightweight characteristics, and resistance to condensation. Scholars have made significant strides in the study of vacuum glazing through their notable efforts. This study systematically reviewed vacuum glazing and its composite structures, including material selection, fabrication techniques, research methods, and performance evaluation. This review initially presented fundamental techniques for preparing vacuum glazing, with a focus on edge seal and support pillar arrangements, and introduced common composite structures such as hybrid and tinted vacuum glazing. Furthermore, this review summarized the analytical, numerical, and experimental methodologies used to assess the thermal performance of vacuum glazing. This study also outlined heat transfer coefficients associated with various vacuum glazing structures, investigated the influence of different parameters on their heat transfer coefficients, and evaluated their potential for energy conservation across diverse climatic regions. Finally, the research delineated future trends in the advancement of vacuum glazing to provide guidance for both theoretical studies and practical applications in industry. This research serves as a valuable resource for both theoretical exploration and practical integration of vacuum glazing, facilitating its improvement and optimization to advance sustainable low-carbon building practices.
Buildings are crucial for achieving carbon reduction and mitigating climate change. China’s dual-carbon strategy includes ambitious goals for carbon peaking and carbon neutrality in the building sector. However, clear technical pathways and roadmaps for achieving these objectives remain lacking. In this study, we examined the trajectory and characteristics of energy consumption and emissions in China’s building sector and conducted future scenario analyses informed by global comparative studies. Ecological development pathways were proposed as target scenarios to achieve carbon neutrality in the building sector. Detailed pathways to achieve carbon neutrality were delineated, covering various tasks and timelines. These included managing building stocks, improving energy efficiency and sufficiency, promoting electrification, implementing photovoltaic–energy storage–direct current–flexibility (PEDF) buildings, decarbonizing heating systems, and developing new energy systems for rural areas. In addition, we discussed and recommended policy measures to enhance building service provisions within the framework of the ecological development concept and promote key technologies within the context of a low-carbon energy system transition. The findings of this study provide high-level recommendations for policymakers in the building and energy sectors and offer insights into technological perspectives and development targets for future research and engineering practice.
Intermittent heating is an energy-saving heating mode, which can save energy in terms of time, and thus is worth promoting, particularly in residential heating scenarios. Conventional radiant heating terminals, that is floor heating, and convective heating terminals, that is fan coils, cannot achieve both intermittent and thermal comfort during intermittent heating. Therefore, this study proposes a switchable convective–radiant heating regulation method for floor heating and fan coils to achieve a comfortable indoor environment with high thermal response speed. Furthermore, a novel combined radiant–convective heating terminal was proposed for a reliable and effective solution. Results showed that the proposed switchable method could increase both intermittence and thermal comfort. In addition, the heating terminal showed better heating performance than the combination of two conventional terminals at the key points of heating capacity, flexibility, and thermal response. It could initially heat up a typical residential space within 20–40 min and then stabilize the room temperature in a comfortable range of 18–22 °C, showing great potential for intermittent heating in room-scale heating conditions. This study provides a reference technique for intermittent heating with reduced system complexity and precise environmental control.
Pressure has been introduced into power systems owing to the intermittent and uncertain nature of renewable energy. As a result, energy resource aggregators are emerging in the electricity market to realize sustainable and economic advantages through distributed generation, energy storage, and demand response resources. However, resource aggregators face the challenge of dealing with the uncertainty of renewable energy generation and setting appropriate incentives to exploit substantial energy flexibility in the building sector. In this study, a risk-aware optimal dispatch strategy that integrates probabilistic renewable energy prediction and bi-level building flexibility engagements is proposed. A natural gradient boosting algorithm (NGBoost), which requires no prior knowledge of uncertain variables, was adopted to develop a probabilistic photovoltaic (PV) forecasting model. The lack of suitable flexibility incentives is addressed by a novel interactive flexibility engagement scheme that can take into account building users’ willingness and optimize the building flexibility provision. The chance-constrained programming method was applied to manage the supply–demand balance of the resource aggregator and ensure risk-aware decision-making in power dispatch. The case study results show the strong economic and environmental performance of the proposed strategy. The proposed strategy leads to a win–win situation in which profit increases through a load reduction of 13% and a carbon emission reduction of 3% is achieved for different stakeholders, which also shows a trade-off between the economic benefits and the risk of supply shortage.
This work proposes an adaptive quantum approximate optimization-based model predictive control (MPC) strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems. The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters. When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems, this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses. The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus. The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources. A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbon-neutral building operations.
The integrated energy systems (IESs) offer a practical solution for achieving low-carbon targets in residential buildings. However, IES encounters several challenges related to increased energy consumption and costs due to fluctuations in renewable energy generation. Leveraging building flexibility to address these power fluctuations within IES is a promising strategy, which requires coordinated control between air-conditioning systems and other IES components. This study proposes a cross-time-scale control framework that contains optimal scheduling and on-the-fly flexible control to reduce the cost impacts of a residential IES system equipped with photovoltaic (PV) panels, batteries, a heat pump, and a domestic hot water tank. The method involves three key steps: solar irradiance prediction, day-ahead optimal scheduling of energy storage, and intra-day flexible control of the heat pump. The method is validated through a high-fidelity residential building model with actual weather and energy usage data in Frankfurt, Germany. Results reveal that the proposed method limits the cost increase to just 2.67% compared to the day-ahead schedule, whereas the cost could increase by 7.39% without the flexible control. Additionally, computational efficiency is enhanced by transforming the mixed-integer programming (MIP) into nonlinear programming (NLP) problem via introducing action-exclusive constraints. This approach offers valuable support for residential IES operations.
The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers, however, they are not extensively used in clinical studies owing to their spatiotemporal limitations. In this study, we developed a wearable stethoscope for wireless, skin-attachable, low-power, continuous, real-time auscultation using a lung-sound-monitoring-patch (LSMP). LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics. The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise. The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease (COPD) patients where wheezing sounds were classified at specific frequencies. In addition, we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set. We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle. The artificial intelligence-based adventitious breathing event counter distinguished > 80% of the events (especially wheezing) in long-term clinical applications in patients with COPD.
Actively controllable microswarms have been a rapidly developing research field with appealing characteristics. Autonomous collision-free navigation of microswarms in confined environments is suitable for various applications, including targeted therapy and delivery. However, several challenges remain unaddressed. First, microswarms possess varying dimensions, and a path planning method suitable to swarms with different dimensions is essential to avoid obstacles. Second, studies on the environment-adaptive navigation of reconfigurable microswarms are limited. Therefore, the planning of the pattern distribution of microswarms based on the local working environment should be examined. This study proposes a deep learning (DL)-based environment-adaptive navigation scheme for swarms. The controller provides reference moving directions for swarms of different sizes in static and dynamic scenarios. Moreover, a pattern-distribution planner was designed to navigate transformable swarms in unstructured environments. To validate the proposed scheme, we applied Fe3O4 nanoparticles swarms as a case study. The proposed scheme enables motion and pattern planning for microrobots of multiple sizes and reconfigurability in various working environments, which could foster a general navigation system for reconfigurable microswarms of different sizes.
Machine learning (ML) can optimize the research paradigm and shorten the time from discovery to application of novel functional materials, pharmaceuticals, and fine chemicals. Besides supporting material and drug design, ML is a potentially valuable tool for predictive modeling and process optimization. Herein, we first review the recent progress in data-driven ML for molecular crystal design, including property and structure predictions. ML can accelerate the development of the solvates, co-crystals, and colloidal nanocrystals, and improve the efficiency of crystal design. Next, this review summarizes ML algorithms for crystallization behavior prediction and process regulation. ML models support drug solubility prediction, particle agglomeration prediction, and spherical crystal design. ML-based in situ image processing can extract particle information and recognize crystal products. The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented. Finally, emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.
Inspired by the remarkable surface wetting behavior of natural organisms, artificially designed superwettable systems have attracted significant attention from multidisciplinary scientists over the past two decades. Starch is an eco-friendly, nontoxic, and low-cost natural polymer that serves as an alternative to nonbiodegradable and/or bioincompatible synthetic polymers in these systems. This review explores the unique contributions of starch to superwettable systems from design principles to emerging applications. First, the fundamental theories and design principles underlying starch-involved superwettable systems are introduced. The specific design principles of these systems are comprehensively discussed from the aspects of intrinsic properties (e.g., hydrophilicity, film-forming properties, adhesiveness, and thermal decomposition), dimensionality (e.g., colloidal systems, zero-dimensional granules/particles, one-dimensional fibers, two-dimensional films/fibrous membranes/coatings, and three-dimensional fillers/porous materials/food textures), and biotransformation. It also provides an overview of their applications in functional biomaterials, oral delivery systems, emulsion polymerization, packaging technology, food taste modulation, and water treatment, with particular emphasis on intelligent systems. Each section summarizes recent advancements, highlighting the chemical and structural features. Finally, the review considers prospects for these superwettable systems, focusing on underutilized starch attributes and technical challenges.
Depressurization and heat injection are viewed as the main methods to be used in natural gas hydrate (NGH) exploitation. However, these methods have limitations, such as low energy-utilization efficiency or a limited extraction range, and are still far from commercial exploitation. In this work, we propose a potential commercial method to exploit NGHs by effectively using geothermal energy inside deep reservoirs. Specifically, a loop well structure is designed to economically extract geothermal energy. Based on an analysis of our developed model, when the looping well is coupled with depressurization, the profits of high NGH production can surpass the drilling costs of extracting geothermal energy. Moreover, as the temperature of fluids from the geothermal layer exceeds 62 °C, the fluid heat is mainly consumed by the rock matrix of the hydrate formation, instead of promoting NGH dissociation. Based on this threshold temperature, a loop well drilled to a depth of about 4000 m for hydrate sediment in the Shenhu area of the South China Sea would be able to efficiently extract geothermal energy, leading to an approximate 73% increase in gas production in comparison with conventional depressurization. An economic analysis suggests that our proposed method can reduce the exploitation cost of methane to 0.46 USD·m−3. Furthermore, as the hydrate saturation increases to 0.5, the exploitation cost can be further reduced to 0.14 USD·m−3. Overall, a looping well coupled with geothermal energy and depressurization is expected to pave the way for commercial NGH exploitation.
A glacier hazard chain can form a long-runout mass flow and generate a large flood, affecting downstream areas hundreds of kilometers away from the initiating hazard site. This study focuses on the Yarlung Zangbo Daxiagu. The objective is to address two key unresolved issues: the evolution of detached glacier materials into debris flows or debris floods and the amplification of the impact range and threats. A comprehensive framework is developed that considers the impacts of near-field and far-field hazards. Numerical modeling, remote sensing, and field investigations were integrated to understand the interactions, transformations, and amplifications of hazards in the glacier hazard chain. The results indicate that extensive, nearly saturated sediments on the glacier valley floor, when entrained, amplify the magnitude of the mass flow. The topography plays a crucial role. When the valley outlet is perpendicular to the river course, topographic obstacles cause immediate halting, resulting in the formation of high barrier dams. Conversely, when the glacier valley aligns nearly parallel to the river course, the mass flow can travel a much longer distance upon entering the river, causing an enlarged affected area. The barrier dams can breach rapidly, causing breaching floods that amplify the downstream impact from several kilometers to hundreds of kilometers. Our analysis reveals that the overall impacts remain spatially limited. Specifically, downstream areas along the Yarlung Zangbo–Brahmaputra River are unlikely to face greater threats from the upstream floods than local monsoon floods. Our findings provide the foundation for the management of glacier hazard chains.
A gravity-driven membrane (GDM) system is a cleaning-free ultrafiltration (UF) process for decentralized water purification. However, GDM has a poor permeate quality and low stable flux when the feed water contains high levels of particulates, organic matter, and micropollutants. To address these challenges, this study used riverbank filtration (BF) as a pretreatment for GDM. The experimental results showed that BF could effectively reduce turbidity and particulate organic matter, and preferentially remove biopolymers and protein-like fluorescent components from natural organic matter. The removal efficiencies of micropollutants (diclofenac, carbamazepine, acetamidophenol, and bisphenol A) increased by 15.2%–65.3% in the presence of BF. Moreover, BF-GDM improved the removal of assimilable organic carbon (AOC) by 42%, thereby enhancing the biological stability of the permeate. Despite a modest increase of approximately 20% in the removal of dissolved organic matter, the BF significantly improved the stable flux from 2.8 to 7.3 L·m−2·h−1. This remarkable improvement is attributed to the effective removal of key foulants, including particulate substances, biopolymers, and protein-like fluorescent substances, which leads to a thinner bio-cake layer with a higher density of microorganisms. Additionally, because of the high microbial diversity of the soil, BF pretreatment enriched the microbial diversity of the bio-cake layer, thereby enriching functional microorganisms capable of degrading pollutants in BF-GDM, such as Nitrospirota and Ascomycota. Overall, BF is a highly effective pretreatment for GDM, which potentially broadens its application to polluted source water.
PbS quantum dot (QD) image sensors have emerged as promising chips for a wide range of infrared (IR) imaging applications due to their monolithic integration with silicon-based readout integrated circuits. However, avoiding primary toxic Pb usage and reducing the cost of PbS QDs remains crucial for widespread application. We present a novel cost-effective and environmentally friendly hydrometallurgical process for recovering PbCl2 from spent lead-acid battery paste to synthesize high-quality PbS QDs. The method recovers PbCl2 with a production ratio of 97% and a purity of 99.99%. PbS QDs and photodetectors synthesized from recycled PbCl2 (R-PbCl2) have comparable performance and quality to those made using commercial PbCl2. R-PbCl2-derived photodetectors exhibit a high external quantum efficiency of 49.6% and a high specific detectivity of 6.95 × 1012 Jones compared to published studies. Additionally, based on R-PbCl2, a PbS QD image sensor with 640 × 512 resolution successfully discriminated common solvents. Moreover, through life-cycle assessment and economic cost analysis, this novel synthesis route offers great advantages in the environmentally friendly use of chemical reagents and reduces the production cost of PbS QDs by 23.2% compared to commercial PbCl2. Thus, this work not only contributes to the green recycling of spent lead paste but also provides a low-cost strategy for synthesizing PbS QDs and their optoelectronic application.
Early risk detection and management are essential for cognitive preservation. While particulate matter with a diameter smaller than 2.5 μm (PM2.5) is considered harmful to cognition, the effect of smaller, more penetrative particulate matter with a diameter smaller than 1 μm (PM1) requires further evidence and explicit safety thresholds. In this study, we explored the effects of long-term PM1 exposure on early cognitive impairment and longitudinal cognitive changes in middle-aged and older populations. This study assessed data from two large-scale longitudinal surveys: the China Health and Retirement Longitudinal Study (CHARLS) and UK Biobank (UKB). Cross-sectional, longitudinal, and trajectory analyses were conducted to investigate the association between long-term PM1 exposure and cognition. Additionally, the exposure–response curves were fitted to determine the customized thresholds. The findings indicated that sustained PM1 exposure may lead to mild cognitive impairment, particularly at concentrations exceeding 30 and 5.6 μg·m−3 in CHARLS and UKB participants, respectively. Furthermore, we found that long-term PM1 exposure can contribute to rapid cognitive decline at concentrations exceeding 23 and 5.5 μg·m−3 in CHARLS and UKB participants, respectively. In conclusion, reducing PM1 exposure can improve the cognitive health of middle-aged and older adults.
Tetracycline (TC) residues from anthropogenic activities undesirably present in nature as an emerging sustainability challenge and thereby require innovations in remediation technologies. Herein, as inspired by the microcompartment structure in living organisms, we adopt a synthetic biology approach to engineer the FerTiG, a modular enzyme assembly, to robustly scavenge TC residues with improved performance. The FerTiG consists of three functional modules, namely, a TC degradation module (Tet(X4)), a cofactor recycling module glucose dehydrogenase (GDH) , and a protection module (ferritin), to organize diverse catalytic processes simultaneously as a biological circuit. The incorporation of GDH suitably fuels the FerTiG-dependent TC degradation by regenerating expensive nicotinamide adenine dinucleotide phosphate (NADPH) cofactor with glucose. The ferritin shields the catalytic core of FerTiG to resiliently decompose TC under unfavorable conditions. Due to collaboration among functional modules, FerTiG strongly catalyzes the residual TC removal from multiple environmental matrices. The degradation pathways and environmental/biological safety of FerTiG are then elaborated, indicating the promising readiness for the application of FerTiG. In summary, this work presents a synthetic biology-based strategy to spontaneously impose residual antibiotic biodegradation for better sustainability management. The FerTiG is engineered as a proof-of-principle for TC removal; however, this ‘microcompartment-mimicking’ concept is of great interest in mitigating other sustainability challenges where modular catalytic machinery is applied.
Tigecycline is one of the most critical drugs for treating Gram-negative bacterial infections; however, the emergence of the tigecycline resistance efflux pump TMexCD1-TOprJ1 poses a global health threat. The evolutionary relationships and epidemiological trends of tmexCD1-toprJ1-positive strains across various ecological niches remain largely unexplored. In this study, we employed whole-genome sequencing (WGS) of tmexCD1-toprJ1-positive bacteria from humans, food, animals, and the environment in China to assess the epidemiological and genomic features of these strains, analyzing both newly collected strains and data from the GenBank database. From 3 434 samples collected during 2019–2022, 145 tmexCD1-toprJ1-carrying strains (4.5%) were isolated. The majority of the tmexCD1-toprJ1-positive Enterobacterales exhibited resistance to nearly all antimicrobials, including colistin (42.13%). tmexCD1-toprJ1 was predominantly identified in Klebsiella pneumoniae (K. pneumoniae) from chicken feces in China but was also detected in multiple ecological niches and other countries. Phylogenetic analysis revealed the clonal transmission of tmexCD1-toprJ1-positive ST37 K. pneumoniae across diverse ecological niches as well as the international spread of the ST15 K. pneumoniae clone-producing TMexCD1-TOprJ1. tmexCD1-toprJ1 is mainly carried by Klebsiella spp. specific narrow host range plasmids, which may limit the spread of tmexCD1-toprJ1 across different bacterial species. Notably, due to the fitness cost posed by tmexCD1-toprJ1, the occurrence of tmexCD1-toprJ1-positive Enterobacterales in both food animals and humans in China has declined significantly following the withdrawal of antibiotics as growth promoters in food animals in China since 2020. However, tmexCD1-toprJ1 has been captured by broad-host-range plasmids and hypervirulent carbapenem-resistant K. pneumoniae ST11-KL64 strains in healthcare settings. The frequent use of tetracyclines in chicken farming likely contributes to the high detection rate of tmexCD1-toprJ1; therefore, to reduce the threat of tmexCD1-toprJ1-positive K. pneumoniae, continuous monitoring of tmexCD1-toprJ1 across different ecological niches and strict enforcement of antimicrobial policies in animal husbandry, particularly in the poultry industry, are urgently required.
Intervertebral disc degeneration (IVDD) is a leading cause of chronic lower back pain, affecting a significant portion of the global population. Traditional treatments, including drug administration and surgery, focus primarily on symptom relief but fail to address the underlying pathological mechanisms of IVDD. Extracellular matrix (ECM) degradation is closely related to the senescence of nucleus pulposus cells (NPCs) caused by highly levels of inflammation, overproduction of reactive oxygen species (ROS), DNA damage, low levels of autophagy, and the acidic microenvironment in the disc. This review explores the pathogenesis of IVDD mediated by NPC senescence, summarizes recent advances in biological therapy, and highlights the latest developments in antisenescent biomaterials. These biomaterials have the potential to delay disc degeneration by clearing senescent cells, inhibiting oxidative stress and inflammation, activating autophagy, and modulating the acidic microenvironment of the disc. A deeper understanding of the molecular mechanisms underlying IVDD, coupled with the design of more effective antisenescent biomaterials, offers promising avenues for optimizing therapeutic outcomes and improving patients’ quality of life.
Current organoid-generation strategies rely predominantly on intricate in vitro manipulations of dissociated stem cells, including isolation, expansion, and genetic modification. However, these approaches present significant challenges in terms of safety and scalability for clinical applications. An alternative strategy involves the direct generation of organoids from readily available tissues. Herein, we report the generation of functional organoids representing all three germ layers from human adult adipose tissue without single-cell processing steps. Specifically, by employing a specialized suspension culture system, we have developed reaggregated microfat (RMF) tissues, which differentiated into mesodermal bone marrow organoids capable of reconstituting human normal hematopoiesis in immunodeficient mice, endodermal insulin-producing organoids that reversed hyperglycemia in streptozotocin (STZ)-induced diabetic mice, and ectodermal nervous-like tissues resembling neurons and neuroglial cells. These findings therefore highlight the potential of human adipose tissue as a safe, scalable, and clinically viable source for organoid-based regenerative therapies.
Microneedle technology is valuable in wound treatment. Current studies focus on optimizing the function of microneedles and screening for effective encapsulated actives. Herein, we develop innovative MXene hydrogel microneedles with nitric oxide (NO) and hypoxia-inducible factor-1α (HIF-1α) plasmid controllable release for diabetic wound treatment. These microneedles consist of gelatin coupled with tert-butyl nitrite (Gel-SNO) polymers obtained by conjugating the –SNO group on the gelatin side chain, therefore, NO can be generated and released under near-infrared (NIR) light irradiation owing to the thermal effect. Simultaneously, by harnessing the enhanced photothermal conversion efficiency of the MXene additive, the microneedle patch can quickly dissolve and liberate the enclosed HIF-1α plasmid nanoparticles into the dermis when exposed to NIR radiation. The released NO effectively reduced the inflammatory response and released HIF-1α plasmid induced neovascularization. Thus, in vivo experiments showed that these microneedles could accelerate wound closure by alleviating inflammation, and promoting re-epithelialization and angiogenesis. These results indicated the potential value of MXene hydrogel microneedles in wound healing and other related biomedical fields.
As large language models (LLMs) continue to demonstrate their potential in handling complex tasks, their value in knowledge-intensive industrial scenarios is becoming increasingly evident. Fault diagnosis, a critical domain in the industrial sector, has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human–machine collaboration efficiency. Traditional fault diagnosis systems, which are primarily based on expert systems, suffer from three major limitations: ① ineffective organization of fault diagnosis knowledge, ② lack of adaptability between static knowledge frameworks and dynamic engineering environments, and ③ difficulties in integrating expert knowledge with real-time data streams. These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty. In this study, we proposed an intelligent computer numerical control (CNC) fault diagnosis system, integrating LLMs with knowledge graph (KG). First, we constructed a comprehensive KG that consolidated multi-source data for structured representation. Second, we designed a retrieval-augmented generation (RAG) framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process. Finally, we introduced a learning mechanism to facilitate dynamic knowledge updates. The experimental results demonstrated that our system significantly improved fault diagnosis accuracy, outperforming engineers with two years of professional experience on our constructed benchmark datasets. By integrating LLMs and KG, our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning, offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.