Search Results (49)

Hydraulic fracture diagnostics are essential for characterizing fracture geometry, connectivity, and effectiveness in unconventional reservoirs. However, the diversity of available techniques and fragmented understanding of their physical mechanisms hinder multidisciplinary communication and lead to inconsistent field decisions. This review provides a systematic assessment of diagnostic methods, focusing on their physical foundations, applicability, and limitations, and proposes a unified reference framework. Direct diagnostics, including microseismic monitoring, fiber-optic sensing (DTS and DAS), and tiltmeter measurements, are evaluated in terms of data characteristics, interpretation challenges, and field applicability. Indirect methods based on pressure, production, and tracer data—such as DFITs, pressure interference tests, and tracer analysis—are examined for their roles in fracture closure evaluation and interwell connectivity. The review further distinguishes between single-well and multi-well applications, providing a structured classification framework. It highlights that individual methods are constrained by non-uniqueness, modeling assumptions, and non-ideal field conditions, especially in complex fracture networks. Therefore, reliable characterization requires integrating multiple diagnostics with physics-based modeling and uncertainty-aware interpretation. Recent advances in AI and machine learning are also briefly discussed as tools to enhance automated analysis and support real-time, predictive diagnostics. Full article

Fracture diagnosis with water hammer pressure data is an important technology for petroleum exploration and development. Efficient filtering of acquired pressure data is a critical process to enhance diagnostic accuracy. The water hammer pressure monitored at the wellhead is observed data, whose generation process is influenced by multiple known or unknown factors such as fractures and the pipeline friction, and contains uncertain noise. Existing filtering algorithms mainly focus on the water hammer signal itself, ignore the uncertainty of its generation factors, and have poor filtering ability for the complex wellbore and formation environments. From the perspective of data generation, this study formulates the factors affecting water hammer generation as latent variables, and proposes a latent space convolutional filtering (LSCF) model. The model estimates the probability distribution of latent variables, samples to obtain latent variables, then uses a convolutional neural network to filter out noise factors, and finally infers the probability distribution of the clean water hammer signal. Filtering experiments were conducted on both water hammer simulated datasets and field datasets using the model, with correlation coefficient (CC), mean squared error (MSE) and signal-to-noise ratio (SNR) adopted as quantitative evaluation metrics, combined with qualitative analysis of spectrum and cepstrum. Compared with existing advanced filtering algorithms, the LSCF model achieves optimal performance across all filtering metrics, verifying the advancement of the filtering strategy, providing a new technical reference for noise reduction and filtering of fracturing pump shutdown water hammer data. Full article

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

Background/Objectives: Neurogenic heterotopic ossification (HO) is an abnormal formation of lamellar bone in soft tissues, frequently developing near major joints in patients with spinal cord injury. While imaging provides valuable diagnostic insights, large and anatomically complex HO often requires advanced preoperative planning to minimize surgical risks. This study presents the development and clinical application of a structured six-stage workflow integrating three-dimensional (3D) technologies for the preoperative planning and surgical resection of giant iliofemoral HO. Materials and Methods: A workflow was developed comprising: (1) 3D imaging acquisition, (2) creation of a virtual model, (3) production of a life-size physical model, (4) preoperative simulation, (5) surgical resection, and (6) postoperative imaging validation. The workflow was applied to a 50-year-old male with paraplegia after a T12 fracture who developed a 26 cm iliofemoral bony bridge, confirmed by computed tomography and 3D reconstruction. Results: The physical model provided a precise anatomical reference, enabling detailed surgical rehearsal and safe planning of neurovascular dissection. Resection was performed using combined orthopedic and vascular techniques. The hip joint was preserved, and postoperative rehabilitation achieved improved range of motion and patient handling without major complications. Conclusions: This structured 3D-assisted workflow enhanced anatomical understanding and surgical precision in this complex case. The framework is applicable to other extensive ossifications with intricate anatomical relationships and warrants further evaluation in larger series. Full article

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article

The drilling and blasting method remains fundamental to mining and tunneling projects, prized for its simplicity and economy. However, conventional techniques are increasingly challenged by modern safety and environmental standards, particularly in complex geological settings. Slotted charge blasting technology addresses these limitations by offering exceptional control over fracture propagation and damage. This paper provides a comprehensive review of the field, synthesizing global research on its theoretical foundations, advanced diagnostic methodologies, key performance parameters, and engineering applications. We critically analyze the current challenges facing the technology, particularly in weak rock conditions, where extensive plastic deformation and rapid energy dissipation often compromise directional control, and identify promising trends for its future development. Specifically, the integration of intelligent adaptive control and additive manufacturing is highlighted as a key direction. By mapping out a clear trajectory for future research, this work provides a scientific basis to advance the efficacy and safety of slotted charge blasting in demanding engineering environments. Full article

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value. Full article

Background and Objectives: Osteoporosis is a prevalent skeletal disorder characterized by decreased bone mass and compromised bone microarchitecture, leading to an elevated risk of fractures and significant morbidity, particularly among aging populations. Early diagnosis and personalized management are critical to reducing fracture incidence and associated healthcare burdens. Recent advances in artificial intelligence (AI) and machine learning (ML) have led to potential improvements in enhancing osteoporosis care by enabling accurate diagnostic imaging analysis, robust fracture risk prediction, and personalized therapeutic strategies. Materials and Methods: We performed a narrative review to summarize and critically evaluate the current literature on AI and ML applications in osteoporosis diagnosis and management. We searched relevant literature from inception to January 2025 to provide a comprehensive perspective, focusing on key themes, methodological approaches, and clinical implications. Results: Deep learning models, especially convolutional neural networks, facilitate rapid and accurate bone mineral density assessment from routine radiographs, expanding screening capabilities beyond conventional dual-energy X-ray absorptiometry (DXA). Machine learning algorithms harness clinical and demographic data to generate fracture risk models that often outperform traditional tools, enabling timely identification of high-risk individuals. Furthermore, AI-driven analyses of historical treatment responses coupled with real-time monitoring through wearable technologies and mobile applications allow for personalized therapeutic optimization and enhance patient engagement. Despite these promising advances, challenges remain regarding ethical considerations, data privacy, legal liability, incomplete model validation, lack of standardization, and the need for critical appraisal of real-world clinical efficacy for widespread clinical adoption. Conclusions: This narrative review indicates that AI and ML hold significant promise to revolutionize osteoporosis management by enabling early detection, precise risk stratification, and tailored interventions. However, the current evidence is heterogeneous, often lacking robust external validation and quantitative synthesis. Critical gaps include insufficient evaluation of model robustness across diverse populations, discussion of negative or conflicting results, and a comprehensive assessment of the limitations inherent in current AI evidence. Strategic efforts to validate, regulate, and critically integrate these technologies into routine clinical workflows are essential to realize their full potential and address the growing burden of osteoporosis worldwide. Full article

Cone-beam computed tomography (CBCT) has transformed endodontic practice by enabling more precise diagnosis and treatment of pulpal and apical pathologies. The aim of this review was to summarize the current clinical applications, benefits and limitations of CBCT in endodontic practice. A search of electronic databases identified relevant literature on CBCT applications, innovations, and limitations. Emphasis was placed on identifying contemporary studies published in the last 5 years. In general, CBCT demonstrates better diagnostic efficacy across multiple applications, including identifying complex anatomy, detection of apical periodontitis, pre-surgical planning and the diagnosis and management of longitudinal root fractures, traumatic dental injuries and root resorptions. However, clinicians should balance the benefits of CBCT against its shortcomings, such as increased radiation exposure, presence of artifacts and higher costs. Proper use requires adherence to guidelines, optimized machine settings, and interpretation by trained individuals. Recent research explores the integration of CBCT with emerging technologies like artificial intelligence and guided systems. In summary, CBCT remains an essential tool for clinical decision-making in endodontics when used judiciously, with ongoing research continuing to expand its potential applications. Full article

Distributed acoustic sensing (DAS) technology is gaining popularity for real-time monitoring during the hydraulic fracturing of unconventional reservoirs. By transforming a standard optical fiber into a dense array of acoustic sensors, DAS provides continuous spatiotemporal measurements along the entire wellbore. Although accurate DAS-based real-time diagnosis of multistage hydraulic fracturing is critical for optimizing the efficiency of stimulation operations and mitigating operational risks in horizontal tight oil wells, existing methods often fail to provide integrated qualitative and quantitative insights. To address this gap, we present an original diagnostic workflow that synergistically combines frequency band energy (FBE), low-frequency DAS (LF-DAS), and surface injection data for simultaneous fluid/proppant allocation and key downhole anomaly identification. Field application of the proposed framework in a 47-stage well demonstrates that FBE (50–200 Hz) enables robust cluster-level volume estimation, while LF-DAS (<0.5 Hz) reveals fiber strain signatures indicative of mechanical integrity threats. The workflow can successfully diagnose sand screenout, diversion, out-of-zone flow, and early fiber failure—events often missed by conventional monitoring. By linking distinct acoustic fingerprints to specific physical processes, our approach transforms raw DAS data into actionable operational intelligence. This study provides a reproducible, field-validated framework that enhances understanding in the context of fracture treatment, supports real-time decision making, and paves the way for automated DAS interpretation in complex completions. Full article

Distributed Acoustic Sensing (DAS) technology has emerged as a valuable tool for monitoring fluid and proppant injection during hydraulic fracturing. One of its applications involves estimating cluster-level fluid and proppant allocations in real time. However, significant uncertainties remain in the quantitative calculation of injected volumes due to limitations in frequency band energy (FBE) data extraction, cluster depth determination, and volume estimation algorithms. This study presents an enhanced workflow for quantitatively estimating fluid and proppant allocations from DAS-derived FBE data while minimizing uncertainties. The workflow integrates multi-band and summed-energy analyses with the optimized selection of calculation algorithms to reduce interpretation uncertainties. The results show that FBE [50–200 Hz] exhibits the highest sensitivity to injection activities, local minima on summed FBE can accurately pinpoint top and bottom depths of each cluster, and a power-law model linking acoustic energy to flow rate allows for quantitative calculation. Field applications demonstrate consistent improvements in fluid and proppant volume estimation accuracy. Validation against post-frac numerical simulations shows that estimated fluid and proppant allocations agree within a 6% error, confirming the method’s quantitative reliability. By addressing key sources of uncertainty, this approach enhances DAS-based fracture diagnostics and provides actionable guidance for real-time decision making in unconventional completions. Full article

This study introduces nanoparticle-enhanced pulsed power plasma stimulation (NP-3PS) as a waterless fracturing technology for enhanced geothermal systems (EGS), employing ultrafast high-pressure plasma discharges from a 20 kJ capacitor charged to 40 kV to initiate and propagate complex fractures in 8-inch (20.32 cm) granite cubes via single pulses of 10, 12, and 16 kJ and a staged 4 + 6 kJ sequence. A 2-inch (5.03 cm) borehole was filled with nanofluid containing 0.3 wt % aluminum NP (60–80 nm) suspended in 7 wt % potassium chloride (KCl) + 0.18 wt % guar gum to sustain thermite reactions and multi-cycle shockwaves, generating peak pressures exceeding 100,000 psi (690 MPa) within microseconds. Post-stimulation diagnostics using 13 µm micro-CT, thin-section microscopy, and acoustic velocity analysis revealed dense branched fractures, porosity increase from 1.3% to 4.6% (~250%), and thermal conductivity reduction of 9–16%, indicating enhanced permeability and convective heat-transfer potential. The NP-driven multi-pulse mechanism reactivated existing fractures at lower energy without wire replacement, establishing a quantitative framework linking plasma dynamics, rock damage evolution, and thermal response, thus confirming NP-3PS as a scalable and sustainable alternative to hydraulic fracturing for geothermal reservoir stimulation. Full article

Background/Objectives: Thoracic trauma remains a leading cause of trauma-related illness and death. Despite advances in imaging, ventilation strategies, and surgical fixation, its management remains a topic of debate, with varying practices across hospitals. Current Gaps: Although surgical stabilization of rib fractures (SSRF) has shown a mortality benefit in cases of flail chest and in elderly patients, its indications for non-flail cases remain uncertain. Analgesia strategies are evolving, and epidural remains the gold standard; however, it is limited by contraindications. In contrast, regional blocks, such as the erector spinae plane block (ESPB) and serratus anterior plane block (SAPB), are emerging as safer alternatives to opioid and thoracic epidural analgesia (TEA). Artificial intelligence (AI) is transforming imaging interpretation and risk stratification; however, its integration into daily trauma care is still in its early stages of development. Perspective: This article examines the integration of surgical innovation, regional anesthesia, and AI-powered diagnostics as integral components of future thoracic trauma care. We emphasize the importance of standardized surgical criteria, multimodal pain management approaches, and AI-assisted decision-making tools. Conclusions: Thoracic trauma care is shifting toward a personalized, multidisciplinary, and technology-enhanced approach. Incorporating evidence-based SSRF, advanced pain management techniques, and AI-supported imaging can help reduce mortality, enhance recovery, and optimize resource utilization. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article