Search Results (45)

Well integrity is paramount for the safe, environmentally responsible, and economically viable operation of wells throughout their lifecycle, encompassing conventional oil and gas production, unconventional resource extraction (e.g., shale gas and tight oil), and geological storage applications (CO₂, H₂, and natural gas). This review presents a comprehensive synthesis of well integrity challenges, failure mechanisms, monitoring technologies, and management strategies across these operational domains. Key integrity threats—including cement sheath degradation (chemical attack, debonding, cracking, microannuli), casing failures (corrosion, collapse, burst, buckling, fatigue, wear, and connection damage), sustained casing pressure (SCP), and wellhead leaks—are examined in detail. Unique challenges posed by hydraulic fracturing in unconventional wells and emerging risks in CO₂ and hydrogen storage, such as corrosion, carbonation, embrittlement, hydrogen-induced cracking (HIC), and microbial degradation, are also highlighted. The review further explores the evolution of integrity standards (NORSOK, API, ISO), the implementation of Well Integrity Management Systems (WIMS), and the integration of advanced monitoring technologies such as fiber optics, logging tools, and real-time pressure sensing. Particular emphasis is placed on the role of digital technologies—including artificial intelligence, machine learning, and digital twin systems—in enabling predictive maintenance, early failure detection, and lifecycle risk management. The novelty of this review lies in its integrated, cross-domain perspective and its emphasis on digital twin applications for continuous, adaptive well integrity surveillance. It identifies critical knowledge gaps in modeling, materials qualification, and data integration—especially in the context of long-term CO₂ and H₂ storage—and advocates for a proactive, digitally enabled approach to lifecycle well integrity. Full article

Rahul Sankrityayan, a twentieth-century Indian polymath, is known for his contributions to Buddhism, Marxism, and Hindi literature. While his writing has been analyzed for its engagement with Buddhism and Tibet, he is also credited with inaugurating Hindi travel-writing. Though his contributions to this genre are well-recognized, one crucial work—ghummakaṛa śāstra (1945; lit. The Treatise of a Wanderer)—has received insufficient scholarly attention. This article investigates the intersection of religion, travel-writing, and satire in two chapters of Sankrityayan’s treatise: athāto ghummakaṛa jijñāsā (lit. Thus, the Curiosity of a Wanderer) and dharma aur ghummakaṛī (lit. Religion and Wandering). It argues that Sankrityayan employs the figure of the Wanderer to critique religions, religious ideals, and religious figures in two key ways. First, by framing his work as a śāstra (treatise) in the classical sense, he appropriates authoritative discourse to contest religious ideas. Second, the Wanderer functions as a transcendental subject who pervades history. Blending satire with polemic, the text subverts traditional religious hermeneutics. Through close analysis, this paper demonstrates how Sankrityayan’s unconventional form—a dialogic interplay between treatise and satire—invites readers to interrogate religious authority, offering a model for engaging with religion beyond doctrinal frameworks. Full article

Conventional immunotherapy, including immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, has revolutionized cancer therapy over the past decade. Yet, the efficacy of these therapies is limited by tumor resistance, antigen escape mechanisms, poor persistence, and T-cell exhaustion, particularly in the treatment of solid tumors. The emergence of unconventional immunotherapies offers novel opportunities by leveraging diverse immune cell subsets and synthetic biologics. This review explores various immunotherapy platforms, including gamma delta T cells, invariant natural killer T cells, mucosal-associated invariant T cells, engineered regulatory T cells, and universal CAR platforms. Additionally, it expands on biologics, including bispecific and multispecific antibodies, cytokine fusions, agonists, and oncolytic viruses, showcasing their potential for modular engineering and off-the-shelf applicability. Distinct features of unconventional platforms include independence from the major histocompatibility complex (MHC), tissue-homing capabilities, stress ligand sensing, and the ability to bridge adaptive and innate immunity. Their compatibility with engineering approaches highlights their potential as scalable, efficient, and cost-effective therapies. To overcome translational challenges such as functional heterogeneity, immune exhaustion, tumor microenvironment-mediated suppression, and limited persistence, novel strategies will be discussed, including metabolic and epigenetic reprogramming, immune cloaking, gene editing, and the utilization of artificial intelligence for patient stratification. Ultimately, unconventional immunotherapies extend the therapeutic horizon of cancer immunotherapy by breaking barriers in solid tumor treatment and increasing accessibility. Continued investments in research for mechanistic insights and scalable manufacturing are key to unlocking their full clinical potential. Full article

To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersive instructional system integrating digital twin and federated learning technologies. The four-tier architecture (sensing layer, digital twin layer, federated layer, and interaction layer) synthesizes multimodal data (motion trajectories and physiological signals) with Multi-Agent Reinforcement Learning (MARL) to enable virtual–physical integrated tactical simulation and real-time error correction. Experimental results demonstrate that the experimental group achieved 35.2% higher tactical execution accuracy (TEA) (p < 0.01), 1.8 s faster decision making (p < 0.05), and 47% improved team coordination efficiency compared to the controls. The hierarchical federated learning framework (trajectory ε = 0.8; physiology ε = 0.3) maintained model precision loss at 2.4% while optimizing communication efficiency by 23%, ensuring privacy preservation. A novel three-dimensional “Skill–Creativity–Load” evaluation system revealed a 22% increase in unconventional tactical applications (p = 0.013) through the Tactical Creativity Index (TCI). By implementing lightweight federated architecture with dynamic cognitive offloading mechanisms, the system enables resource-constrained institutions to achieve 87% of the pedagogical effectiveness observed in elite programs, offering an innovative solution to reconcile educational equity with technological ethics. Future research should focus on long-term skill transfer, multimodal adaptive learning, and ethical framework development to advance intelligent sports education from efficiency-oriented paradigms to competency-based transformation. Full article

The last decade has seen significant progress in the development of flexible electronics and sensors, particularly for display technologies and healthcare applications. Advancements in scalable manufacturing, miniaturization, and integration have further extended the use of this new class of devices to smart agriculture, where multimodal sensors can be seamlessly attached to plants for continuous and remote monitoring. Among the various types of sensing devices for agriculture, flexible mechanical sensors have emerged as promising candidates for monitoring vital parameters, including growth rates and water flow, providing a new avenue for understanding plant health and growth under varied environmental conditions. This perspective provides a snapshot of recent progress in this exciting and unconventional area of research and highlights potential opportunities for the future. Full article

In this work, a novel optical–chemical sensor for the detection of per- and polyfluorinated substances (PFASs) in a real scenario is presented. The proposed sensing approach exploits the multimode characteristics of plastic optical fibers (POFs) to achieve unconventional sensors via surface plasmon resonance (SPR) phenomena. The sensor is realized by the coupling of an SPR-POF platform with a novel chemical chip based on different polymeric nanolayers over the core of a D-shaped POF, one made up of an optical adhesive and one of a molecularly imprinted polymer (MIP) for PFAS. The chemical chip is used to launch the light into the SPR D-shaped POF platform, so the interaction between the analyte and the MIP’s sites can be used to modulate the propagated light in the POFs and the SPR phenomena. Selectivity tests and dose–response curves by standard PFOA water solutions were carried out to characterize the detection range sensor response, obtaining a wide PFAS response range, from 1 ppt to 1000 ppt. Then, tests performed on river water samples collected from the Bormida river paved the way for the applicability of the proposed approach to a real scenario. Full article

Unconventional food plants, popularized in Brazil as PANC, remain underutilized globally. In that sense, this study aims to explore the nutritional and functional properties of taioba (Xanthosoma sagittifolium), a plant with edible leaves and tubers, and to investigate its potential for industrial-scale application as a source of starch. A systematic review was carried out and meta-analysis following the PRISMA guidelines was conducted based on a random effects synthesis of multivariable-adjusted relative risks (RRs). The searches were carried out in seven search sources, among which were Web of Science, Elsevier’s Science Direct, Wiley Online Library, Springer Nature, Taylor & Francis, Hindawi, Scielo, ACS—American Chemical Society, and Google Scholar. The systematic review was guided by a systematic review protocol based on the POT strategy (Population, Outcome, and Types of studies), adapted for use in this research. Mendeley was a resource used for organization, to manage references, and to exclude duplicates of studies selected for review. The findings revealed that taioba leaves are abundant in essential nutrients, proteins, vitamins, and minerals. Additionally, the tubers offer rich starch content along with vitamins and minerals like iron, potassium, and calcium, making them an ideal substitute for conventional sources on an industrial scale. This research highlights the significance of studying the functionalities, applicability, and integration of this PANC in our diets, while also emphasizing its capability as a substitute for traditional starch varieties. Moreover, exploiting this plant’s potential adds value to Amazonian resources, reduces import costs, and diversifies resource utilization across multiple industrial sectors. Full article

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones. Full article

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility. Full article

This study investigates the application of fiber optic technology to optimize completion design in a hydraulic fracture stimulation for Marcellus Shale Reservoir. With a focus on improving cluster efficiencies and overcoming interstage communication challenges, the research utilizes real-time data from distributed acoustic (DAS), temperature (DTS), and strain (DSS) measurements. The methodology comprises a comprehensive analysis of completion and stimulation reports, fiber optics, microseismic data, and well logs. Conducted at the MSEEL well pads, MIP, and Boggess, and equipped with permanent and deployable fiber optic cables, this study emphasizes that engineered/geomechanical completion design leads to sustained cluster efficiency and stage production performance. Inefficient cluster efficiencies are primarily linked to fracture communication. Recommendations include employing a geomechanical completion design, avoiding non-uniform high natural fracture zones during hydraulic fracture stimulations, implementing short stage length, and using more 100 mesh sand. These insights, derived from correlations between fracture counts, distributed strain sensing (DSS), cluster efficiency, production logging, and production data, offer significant implications for optimizing completion design in unconventional reservoirs. The effective application of fiber optic technology, providing real-time DAS, DTS, and slow strain data, proves instrumental in addressing interstage communication challenges, contributing to improved reservoir performances and cost-effective operations in hydraulic fracture stimulations. Full article

The need for robust rainfall estimation has increased with more frequent and intense floods due to human-induced land use and climate change, especially in urban areas. Besides the existing rainfall measurement systems, citizen science can offer unconventional methods to provide complementary rainfall data for enhancing spatial and temporal data coverage. This demand for accurate rainfall data is particularly crucial in the context of smart city innovations, where real-time weather information is essential for effective urban planning, flood management, and environmental sustainability. Therefore, this study provides proof-of-concept for a novel method of estimating rainfall intensity using its recorded audio in an urban area, which can be incorporated into a smart city as part of its real-time weather forecasting system. This study proposes a convolutional neural network (CNN) inversion model for acoustic rainfall intensity estimation. The developed CNN rainfall sensing model showed a significant improvement in performance over the traditional approach, which relies on the loudness feature as an input, especially for simulating rainfall intensities above 60 mm/h. Also, a CNN-based denoising framework was developed to attenuate unwanted noises in rainfall recordings, which achieved up to 98% accuracy on the validation and testing datasets. This study and its promising results are a step towards developing an acoustic rainfall sensing tool for citizen-science applications in smart cities. However, further investigation is necessary to upgrade this proof-of-concept for practical applications. Full article

Virtual reality and augmented reality are increasingly used for immersive engagement by utilizing information from real environments. In particular, three-dimensional model data, which is the basis for creating virtual places, can be manually developed using commercial modeling toolkits, but with the advancement of sensing technology, computer vision technology can also be used to create virtual environments. Specifically, a 3D reconstruction approach can generate a single 3D model from image information obtained from various scenes in real environments using several cameras (multi-cameras). The goal is to generate a 3D model with excellent precision. However, the rules for choosing the optimal number of cameras and settings to capture information from in real environments (e.g., actual people) employing several cameras in unconventional positions are lacking. In this study, we propose an optimal camera placement strategy for acquiring high-quality 3D data using an irregular camera placement, essential for organizing image information while acquiring human data in a three-dimensional real space, using multiple irregular cameras in real environments. Our results show that installation costs can be lowered by arranging a minimum number of multi-camera cameras in an arbitrary space, and automated virtual human manufacturing with high accuracy can be conducted using optimal irregular camera location. Full article

Electrophysiological mapping (EM) using acute electrode probes is a common procedure performed during functional neurosurgery. Due to their constructive specificities, the EM probes are lagging in innovative enhancements. This work addressed complementing a clinically employed EM probe with carbonic and circumferentially segmented macrocontacts that are operable both for neurophysiological sensing (“recording”) of local field potentials (LFP) and for test stimulation. This paper illustrates in-depth the development that is based on the direct writing of functional materials. The unconventional fabrication processes were optimized on planar geometry and then transferred to the cylindrically thin probe body. We report and discuss the constructive concept and architecture of the probe, characteristics of the electrochemical interface deduced from voltammetry and chronopotentiometry, and the results of in vitro and in vivo recording and pulse stimulation tests. Two- and three-directional macrocontacts were added on probes having shanks of 550 and 770 μm diameters and 10–23 cm lengths. The graphitic material presents a ~2.7 V wide, almost symmetric water electrolysis window, and an ultra-capacitive charge transfer. When tested with clinically relevant 150 μs biphasic current pulses, the interfacial polarization stayed safely away from the water window for pulse amplitudes up to 9 mA (135 μC/cm²). The in vivo experiments on adult rat models confirmed the high-quality sensing of LFPs. Additionally, the in vivo-prevailing increase in the electrode impedance and overpotential are discussed and modeled by an ionic mobility-reducing spongiform structure; this restricted diffusion model gives new applicative insight into the in vivo-uprisen stimulation overpotential. Full article

Designing modern antenna structures is a challenging endeavor. It is laborious and heavily reliant on engineering insight and experience, especially at the initial stages oriented towards the development of a suitable antenna architecture. Due to its interactive nature and hands-on procedures (mainly parametric studies) for validating the suitability of particular geometric setups, typical antenna development requires many weeks and significant involvement of a human expert. The same reasons only allow the designer to try out a very limited number of options in terms of antenna geometry arrangements. Automated topology development and dimension sizing is therefore of high interest, especially from an industry perspective where time-to-market and expert-related expenses are of paramount importance. This paper discusses a novel approach to unsupervised specification-driven design of planar antennas. The presented methodology capitalizes on a flexible and scalable antenna parameterization, which enables the realization of complex geometries while maintaining reasonably small parameter space dimensionality. A customized nature-inspired algorithm is employed to carry out space exploration and identification of a quasi-optimum antenna topology in a global sense. A fast gradient-based procedure is then incorporated to fine-tune antenna dimensions. The design framework works entirely in a black-box fashion with the only input being design specifications, and optional constraints, e.g., concerning the structure size. Numerous illustration case studies demonstrate the capability of the presented technique to generate unconventional antenna topologies of satisfactory performance using reasonable computational budgets, and with no human expert interaction necessary whatsoever. Full article

In recent years, the applications of lidars for remote sensing of the environment have been expanding and deepening. Among them, continuous-wave (CW) range-resolved (RR) S-lidars (S comes from Scheimpflug) have proven to be a new and promising class of non-contact and non-perturbing laser sensors. They use low-power CW diode lasers, an unconventional depth-of-field extension technique and the latest advances in nanophotonic technologies to realize compact and cost-effective remote sensors. The purpose of this paper is to propose a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path. To set the desired far and near borders of operating range by adjusting the optical transceiver, it was shown how to properly select the lens plane and image plane tilt angles, as well as the focal length, the lidar base, etc. For a generalized analysis of characteristic relations between S-lidar parameters, we introduced several dimensionless factors and criteria applicable to different range scales, including an S-lidar-specific magnification factor, angular function, dynamic range, “one and a half” condition, range-domain quality factor, etc. It made possible to show how to reasonably select named and dependent non-energetic parameters, adapting them to specific applications. Finally, we turned to the synthesis task by demonstrating ways to achieve a compromise between a wide dynamic range and high range resolution requirements. The results of the conducted analysis and synthesis allow increasing the validity of design solutions for further promotion of S-lidars for environmental remote sensing and their better adaptation to a broad spectrum of specific applications and range scales. Full article