Although self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive, in real manufacturing scenarios it is often feasible to collect a limited number of anomalous samples. Therefore, we study open-set supervised 3D anomaly detection, where the model is trained with only normal samples and a small number of known anomalous samples, aiming to identify unknown anomalies at test time. We present Open-Industry, a high-quality industrial dataset containing 15 categories, each with five real anomaly types collected from production lines. We first adapt general open-set anomaly detection methods to accommodate 3D point cloud inputs better. Building upon this, we propose Open3D-AD, a point-cloud-oriented approach that leverages normal samples, simulated anomalies, and partially observed real anomalies to model the probability density distributions of normal and anomalous data. Then, we introduce a simple Correspondence Distributions Subsampling to reduce the overlap between normal and non-normal distributions, enabling stronger dual distributions modeling. Based on these contributions, we establish a comprehensive benchmark and evaluate the proposed method extensively on Open-Industry as well as established datasets including Real3D-AD and Anomaly-ShapeNet. Benchmark results and ablation studies demonstrate the effectiveness of Open3D-AD and further reveal the potential of open-set supervised 3D anomaly detection.
Vision Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200,000 PDFs, 25,326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25,326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3-8 times efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.
Ferromagnetic resonance (FMR) serves as a powerful probe of magnetization dynamics and anisotropy in percolating ferromagnets, where short-range interactions govern long-range magnetic order. We apply this approach to Ga$_{1-x}$Mn$_x$N ($x \simeq 8$\%), a dilute ferromagnetic semiconductor, combining FMR and superconducting quantum interference device magnetometry. Our results confirm the percolative nature of ferromagnetism in (Ga,Mn)N, with a Curie temperature $T_{\mathrm{C}} = 12$ K, and reveal that despite magnetic dilution, key features of conventional ferromagnets are retained. FMR measurements establish a robust uniaxial anisotropy, dictated by Mn$^{3+}$ single-ion anisotropy, with an easy-plane character at low Mn content. While excessive line broadening suppresses FMR signals below 9 K, they persist up to 70~K, indicating the presence of non-percolating ferromagnetic clusters well above $T_{\mathrm{C}}$. The temperature dependence of the FMR intensity follows that of the magnetization, underscoring the stability of these clusters. We quantitatively describe both FMR and SQUID observables using atomistic spin model operating on a common set of parameters. The level of agreement, achieved without tuning parameters between datasets, demonstrates the robustness and practical applicability of the approach in capturing the essential physics of spin-diluted, percolating ferromagnets. This study advances the understanding of percolating ferromagnetic systems, demonstrating that FMR is a key technique for probing their unique dynamic and anisotropic properties. Our findings contribute to the broader exploration of dilute ferromagnets and provide new insights into percolating ferromagnetic systems, which will be relevant for spintronic opportunities.
Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf
et al.
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering failures often missed by manual testing. In a pilot phase, generated test suites revealed critical weaknesses in one experimental algorithm (40.3% success rate) and served as an effective benchmark to prove the superior robustness of another (71.2% success rate). The framework was then integrated into the ANYbotics workflow for a six-month industrial evaluation, where it was used to test five proprietary algorithms. A formal survey confirmed its value, showing it enhances the development process, uncovers critical failures, provides objective benchmarks, and strengthens the overall verification pipeline.
Particle adsorbents have gained significant traction in flue gas desulfurization applications, primarily attributed to their high structural homogeneity and large specific surface area. To address the multifaceted requirements of industrial sectors regarding the structural configurations and physicochemical properties of particle adsorbents while promoting sustainable manufacturing practices, this study systematically evaluates and critically appraises contemporary advancements in particle desulfurizing agent technologies. The synthesis of these findings establishes a theoretical framework to facilitate technological innovation and industrial progress within the particle desulfurizer domain. The research systems of particle adsorbents, encompassing active components, inert carriers, preparation methodologies, and gas–solid reaction models, were comprehensively reviewed. The advantages and current limitations of these systems were then systematically summarized. Finally, the fundamental principles and research trajectories in the application fields of distinct particle adsorbent research systems were elucidated. An analysis of the developmental trends indicated that enhancing the utilization efficiency of active components and improving the cyclic stability of adsorbents remained critical engineering challenges. It is posited that the pursuit of high reaction activity, thermal stability, mechanical strength, and superior anti-aggregation/sintering performance constitutes key directions for the advancement of particle adsorbents in China’s flue gas desulfurization industry.
The Ordos Basin is the largest natural gas producing region in China. Recent discoveries of two helium-rich natural gas fields (Dongsheng and Qingyang) shows promising helium resource potential. Sulige Gas Field, the largest natural gas field in China, was analyzed to evaluate its helium resource potential. Comprehensive geochemical analyses were conducted, examining natural gas components, alkane gases, carbon isotopic signatures of carbon dioxide, helium concentrations, and helium isotopic ratios within the gas field. Preliminarily studies identified the geochemical characteristics of natural gas and helium in the Paleozoic strata of Sulige Gas Field, and explored the main controlling factors of helium reservoir formation. The results show that the composition of natural gas in the Upper Paleozoic is obviously different. Specifically, Upper Paleozoic natural gas exhibited typical wet gas at the mature stage and dry gas at the over-mature stage, while Lower Paleozoic natural gas is mainly dry gas with partial contribution of wet gas. The Upper Paleozoic is dominated by thermogenic natural gas, predominantly middle-late humic gas (coal-derived) originating from Carboniferous and Permian coal measure source rocks. In contrast, the Lower Paleozoic is dominated by late sapropelic dry gas and oil cracking gas. The helium concentrations in Paleozoic natural gas is higher than in conventional natural gas (0.03%), which belongs to middle helium gas, and the Upper Paleozoic is exceeding those of the Lower Paleozoic. The helium accumulation in the Sulige Gas Field is influenced by the ancient and modern structural location, the high helium generation intensity and relatively low hydrocarbon generation potential of helium source rocks (such as U–Th-rich basement granite and granite gneiss), the development of basement faults, and the complex gas–water relationship, which is favorable for the helium to dissolve out of the water and enter into the natural gas reservoirs.
Abstract The fashion industry contributes 2–8% of global greenhouse gas emissions, driven by rising clothing consumption and the proliferation of fast fashion. Fast fashion accelerates environmental harm through rapid production cycles, low costs, and short garment lifespans. Secondhand clothing markets are often promoted as a sustainable alternative, promising extended use and reduced waste. These markets have grown rapidly, with global sales reaching $177 billion in 2022 and projected to double by 2027. Despite this growth, few studies have empirically examined whether secondhand purchasing displaces or merely supplements primary market consumption. This study addresses that gap using a nationally representative survey of 1,009 U.S. consumers. We find that secondhand consumption is positively correlated with new clothing purchases (r = 0.58, p < 0.01), particularly among younger consumers and frequent shoppers. Cluster and principal component analyses reveal that highly engaged secondhand consumers also exhibit high overall consumption and short garment retention. Although many report high sustainability knowledge, such knowledge does not reliably predict sustainable behavior. Drawing on rebound and moral licensing theories, we suggest that secondhand purchases may psychologically or economically justify continued overconsumption. These findings challenge assumptions about resale’s environmental benefits and support policy interventions to realign resale practices with sustainability goals.
Abstract This paper reviews the utilization of Big Data analytics, as an emerging trend, in the upstream and downstream oil and gas industry. Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume, variety, velocity, veracity, value, and complexity. With the recent advent of data recording sensors in exploration, drilling, and production operations, oil and gas industry has become a massive data intensive industry. Analyzing seismic and micro-seismic data, improving reservoir characterization and simulation, reducing drilling time and increasing drilling safety, optimization of the performance of production pumps, improved petrochemical asset management, improved shipping and transportation, and improved occupational safety are among some of the applications of Big Data in oil and gas industry. Although the oil and gas industry has become more interested in utilizing Big Data analytics recently, but, there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry. Furthermore, quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.
Jimmy Xuekai Li, Tiancheng Zhang, Yiran Zhu
et al.
Artificial General Intelligence (AGI) is set to profoundly impact the oil and gas industry by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the oil and gas landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the oil and gas industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream oil and gas industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.
The Metaverse is a concept that proposes to immerse users into real-time rendered 3D content virtual worlds delivered through Extended Reality (XR) devices like Augmented and Mixed Reality (AR/MR) smart glasses and Virtual Reality (VR) headsets. When the Metaverse concept is applied to industrial environments, it is called Industrial Metaverse, a hybrid world where industrial operators work by using some of the latest technologies. Currently, such technologies are related to the ones fostered by Industry 4.0, which is evolving towards Industry 5.0, a paradigm that enhances Industry 4.0 by creating a sustainable and resilient world of industrial human-centric applications. The Industrial Metaverse can benefit from Industry 5.0, since it implies making use of dynamic and up-to-date content, as well as fast human-to-machine interactions. To enable such enhancements, this article proposes the concept of Meta-Operator: an Industry 5.0 worker that interacts with Industrial Metaverse applications and with his/her surroundings through advanced XR devices. This article provides a description of the technologies that support Meta-Operators: the main components of the Industrial Metaverse, the latest XR technologies and the use of Opportunistic Edge Computing communications (to interact with surrounding IoT/IioT devices). Moreover, this paper analyzes how to create the next generation of Industrial Metaverse applications based on Industry 5.0, including the integration of AR/MR devices with IoT/IIoT solutions, the development of advanced communications or the creation of shared experiences. Finally, this article provides a list of potential Industry 5.0 applications for the Industrial Metaverse and analyzes the main challenges and research lines. Thus, this article provides useful guidelines for the researchers that will create the next generation of applications for the Industrial Metaverse.
An accurate and comprehensive understanding of shale pore structure is fundamental and critical for accurate reserves evaluation and efficient hydrocarbon development. Thus, by taking the shale of Paleogene Eocene Shahejie Formation in the Jiyang Depression, Bohai Bay Basin, as an example, the 2D and 3D multi-resolution images of the shale microstructure are obtained by multiple imaging technologies, including X-ray computed tomography, large-field scanning electron microscopy, scanning electron microscopy and focused ion beam scanning electron microscopy. By integrating image processing and machine learning algorithms, the shale pore structure is characterized at a single scale and multi scales. The results are obtained as follows. First, the shale pore space in the study area is mainly composed of microfractures, inorganic pores, organic matters and organic pores, and exclusively shows multi-scale characteristics. Second, there are various types of inorganic pores, and abundant dissolution pores; organic matters are distributed as strips and patches, and no organic pores are found in some organic matters. Third, pores with radius less than 20 nm account for 25%, those with radius between 20 and 50 nm account for 19%, those with radius between 50 and 100 nm account for 29%, those with radius between 100 and 500 nm account for 14%, those with radius between 500 nm and 20 μm account for 11%, and those with radius between 20 and 50 μm account for 2%. Fourth, the organic pores are less connected than the inorganic pores. The connectivity between organic pores and inorganic pores plays a crucial role in hydrocarbon migration, and microfractures control fluid flow channels. Fifth, pores with radius less than 50 nm are dominantly organic pores, those with radius between 50 and 500 nm are mainly organic and inorganic pores, and microfractures mainly contribute to the pores with radius more than 500 nm. It is concluded that a single imaging experiment cannot accurately and comprehensively reveal the multi-scale micro pore structure of a shale reservoir. Through integration of multiple imaging technologies and machine learning algorithms, the shale pore structure can be recognized and characterized at both single scale and multi scales. The proposed new method provides accurate and comprehensive information of multi-scale pore structures.
SANG Shuxun,HAN Sijie,ZHOU Xiaozhi,LIU Shiqi,WANG Yuejiang
Deep coalbed methane（CBM） development in East China is of great significance to ensure regional energy demand, optimize regional energy structure and realize the dual carbon goal. Based on the systematic investigation and previous works, the current situations of CBM extraction in East China were summarized, and the gas-bearing attributes and resources potential of deep CBM were analyzed. Then, the applicability of existing deep CBM exploration and development technologies in East China was discussed, and the potential favorable areas of deep CBM exploration and development in East China were discussed and predicted. Finally, the advantages and challenges of deep CBM exploration and development in East China are put forward. Previous results show that: East China has a good CBM development accumulation on the tectonically deformed coal and in the coal mine area, such as “Huainan CBM extraction model” and horizontal well staged fracturing in the roof of the tectonically deformed coal. Deep coal in East China has a high gas content（greater than10 cm3/g） and gas-bearing saturation（greater than 80 %）. The predicted geological resources of deep CBM are 8 984.69×108 m3 in the Huannan-Huanbei mining area, suggesting that Huainan and Huaibei coal field has an attractive deep CBM resources potential. Horizontal well development and hydraulic fracturing techniques for deep CBM have great application prospects in East China. Panxie mine area in Huainan coal field is expected to be a pilot area for deep CBM exploration and development in these areas. However, the overall exploration and development degree of deep CBM is low, so it is necessary to carry out the more detailed resource evaluation and analysis of deep CBM geological accumulation in the type area, like deep Panxie coal mine in Huainan coal field.
Petroleum refining. Petroleum products, Gas industry
Abstract The application of nanotechnology in the oil and gas industry is on the rise as evidenced by the number of researches undertaken in the past few years. The quest to develop more game-changing technologies that can address the challenges currently facing the industry has spurred this growth. Several nanoparticles, of different sizes and at different concentrations, have been used in many investigations. In this work, the scope of the study covered the application of nanotechnology in drilling and hydraulic fracturing fluids, oilwell cementing, enhanced oil recovery (which includes transport study, and foam and emulsion stability), corrosion inhibition, logging operations, formation fines control during production, heavy oil viscosity reduction, hydrocarbon detection, methane release from gas hydrates, and drag reduction in porous media. The observed challenges associated with the use of nanoparticles are their stability in a liquid medium and transportability in reservoir rocks. The addition of viscosifier was implemented by researchers to ensure stability, and also, surface-treated nanoparticles have been used to facilitate stability and transportability. For the purpose of achieving better performance or new application, studies on synergistic effects are suggested for investigation in future nanotechnology research. The resulting technology from the synergistic studies may reinforce the current and future nanotechnology applications in the oil and gas industry, especially for high pressure and high temperature (HPHT) applications. To date, majority of the oil and gas industry nanotechnology publications are reports of laboratory experimental work; therefore, more field trials are recommended for further advancement of nanotechnology in this industry. Usually, nanoparticles are expensive; so, it will be cost beneficial to use the lowest nanoparticles concentration possible while still achieving an acceptable level of a desired performance. Hence, optimization studies are also recommended for examination in future nanotechnology research.
Abstract The industrial world of oil and gas involves critical processes and machinery for the exploration, extraction, refining, transporting and marketing petroleum products. Oil and gas companies need to control, monitor, maintain and secure the processes and industrial assets in an efficient manner. To resolve the critical challenges of pipeline condition, corrosion and integrity monitoring, gas leak detection, and other related problems, Wireless Sensor Networks (WSN) provide promising solutions. WSN is the most prevalent technology used in oil and gas industry that has provided remote facilities to detect and report the anomalous events like the positions of leakage, corrosion or any other damage. A few existing studies in the literature do not cover the recent WSN based systems and techniques and only review the pipeline monitoring systems. These surveys lack the recent WSN based systems developed for monitoring and detecting damages to pipelines as well as other assets of oil and gas industry. In this paper we present a comprehensive review and detailed comparison of the most recent systems or techniques developed for monitoring various anomalous events that are involved in the three sectors (upstream, midstream, downstream) of oil and gas industry. We also describe the important requirements of WSNs to be deployed in the oil and gas industry. Finally, we highlight critical challenges of oil and gas industry.
Using exact Bethe ansatz solution, we rigorously study excitation spectra of the spin-1/2 Fermi gas (called Yang-Gaudin model) with an attractive interaction. Elementary excitations of this model involve particle-hole excitations, hole excitations and adding particles in the Fermi seas of pairs and unpaired fermions. The gapped magnon excitations in spin sector show a ferromagnetic coupling to the Fermi sea of the single fermions. By numerically and analytically solving the Bethe ansatz equations and the thermodynamic Bethe ansatz equations of this model, we obtain excitation energies for various polarizations in the phase of the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO)-like state. For a small momentum (long-wavelength limit) and in the strong interaction regime, we analytically obtained their linear dispersions with curvature corrections, effective masses as well as velocities in particle-hole excitations of pairs and unpaired fermions. Such a type of particle-hole excitations display a novel separation of collective motions of bosonic modes within paired and unpaired fermions. Finally, we also discuss magnon excitations in spin sector and the application of the Bragg spectroscopy for testing such separated charge excitation modes of pairs and single fermions.