Search Results (497)

Reliable change detection in indoor environments remains a challenge for autonomous robotic systems using 3D LiDAR. Existing methods often require manual annotation, computationally intensive architectures, or focus on outdoor scenes. This paper presents SPICD-Net, a lightweight Siamese PointNet framework for indoor 3D change detection trained exclusively on synthetically generated anomalies, eliminating manual labeling. The framework offers three deployment-oriented contributions: a three-class Siamese formulation separating no-change, changed, and geometrically inconsistent tile pairs; a pre-FPS anomaly injection strategy that aligns synthetic training with inference-time preprocessing; and a stochastic-gated Chamfer-statistics branch that complements learned embeddings with explicit geometric cues under consumer-grade hardware constraints. Evaluated on 14 controlled simulation experiments in an indoor corridor dataset, SPICD-Net achieved aggregated Precision = 0.86, Recall = 0.82, F1-score = 0.84, and Accuracy = 0.96, with zero false positives in the no-change baseline and mean inference time of 22.4 s for a 172-tile map on a single consumer GPU. Additional robustness experiments identified registration accuracy as the main operational prerequisite. A limited real-world validation in one unseen room (four scans, 67 tiles) achieved Precision = 0.583, Recall = 1.000, and F1 = 0.737. Full article

Plastic recycling technologies are developing rapidly as countries seek to reduce carbon emissions, use resources more efficiently, and move toward circular economy models. Although mechanical recycling remains the most widely applied option worldwide, its environmental performance depends strongly on process design, feedstock quality, and operational stability, especially in emerging economies where automation and process control may be limited. This study provides a process-level environmental assessment of an industrial mechanical recycling facility in Gaziantep, Türkiye, using twelve months of real, meter-based operational data. Unlike many previous assessments based on simplified or short-term assumptions, the present study combines long-term industrial monitoring, scenario-based process modeling, and probabilistic uncertainty analysis within a single facility-scale evaluation. An ISO 14040/14044-compliant life cycle assessment was performed for four major polymers (PET, HDPE, LDPE, and PP), combining digital energy monitoring with Monte Carlo-based uncertainty analysis. The results show that extrusion is the dominant energy hotspot, accounting for 72–79% of cumulative energy demand (CED), and that the baseline configuration leaves substantial room for improvement in terms of energy and emissions performance. Scenario analysis indicates that combining high-efficiency extrusion with sensor-based sorting can reduce CED and GWP by up to 17.6% and 18.1%, respectively. Monte Carlo simulations demonstrate reduced operational variability under improved configurations and confirm the statistical robustness of these improvements. Overall, the findings provide process-level evidence for improving the environmental performance of mechanical recycling systems in developing industrial contexts. Full article

Background/Objectives: Adult spinal deformity (ASD) correction is a complex surgery to restore spinal alignment and relieve patients’ symptoms. Modern techniques and technologies allow for aggressive surgical correction in tissue-friendly ways that preserve anatomy and may enable faster recovery. Robotic-assisted posterior spinal stabilization may be used as an adjunct to complex ASD reconstruction to facilitate a minimally invasive approach, reduce perioperative morbidity and physiological insult, and allow for the performance of procedures traditionally reserved for large academic centers to be effectively performed by qualified surgeons in optimized patients at smaller hospitals with fewer resources. The objective of this study is to assess realignment, perioperative complications, and patient-reported outcomes of complex, minimally invasive, robotic-assisted adult spinal deformity correction in a surgical specialty hospital. Methods: Demographic, surgical, and perioperative data were collected from the medical record. The Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS) for pain scores were collected preoperatively and at regular post-op visits. X-rays were captured preoperatively before hospital discharge and at follow-up visits. Results: Fifty consecutive deformity patients were corrected with a two-stage approach (anterior column reconstruction followed by posterior stabilization with robotic-assisted screw placement on the next day) at a 48-bed (eight operating rooms), surgeon-owned, subspecialty hospital. The average patient age was 70 years, and 64% were female. The average estimated blood loss (EBL) values for the first and second stages were 62 mL and 205 mL, respectively. The average operative time was 172 min during the first stage and 210 min for the second stage. Three interbody spacers (first stage) and 16 screws (second stage) were inserted on average in each procedure. The average length of stay (LOS) in the hospital was 5 days, and the average follow-up period was 10.6 months. No patients required a transfer to another facility with intensive care unit (ICU) capabilities, and none required a revision of hardware placement. There was an average reduction in the lumbar coronal scoliotic curve of 14.5° and an increase in lumbar lordosis of 14.8° at the latest follow-up (p < 0.01). The average mismatch between pelvic incidence and lumbar lordosis (PI-LL) preoperatively was 17.6°, which was reduced to 9.6° at the latest postoperative follow-up (p < 0.01). Mean ODI (%) and NRS scores were significantly improved by 33.8% (46.7 ± 13.3 to 30.9 ± 19.8; p < 0.01) and 55% (6.0 ± 2.2 to 2.7 ± 2.6; p < 0.01), respectively, at last follow-up. Conclusions: This study demonstrates the feasibility of performing complex, robotic-assisted ASD corrective surgery in a surgical specialty hospital, achieving significant correction of sagittal and coronal deformities, relieving patients’ symptoms, and offering efficiency and consistency to pedicle screw placement. This study demonstrates that a minimally invasive approach to complex deformity reconstruction reduces perioperative morbidity with decreased operative times, EBL, and LOS when compared to historic controls. This approach allows for the democratization of deformity care in that procedures typically reserved for large academic centers can be successfully accomplished at smaller institutions in optimized patients by qualified surgeons with appropriate perioperative support staff. Full article

Industrial electrical houses are engineered systems that transform and control electrical power to supply industrial loads. Preparing technical proposals for these rooms requires consistent engineering choices across multiple artifacts while drawing from heterogeneous client documents, historical projects, and supplier catalogs. This paper reports an industrial prototype that integrates generative AI, system modeling, and mathematical decision methods to support that workflow. We represent requested outputs as ordered sequences of functions and link those functions to candidate equipment blocks through functional and physical graphs that enable traceable retrieval and reuse. Using this representation, we compute a minimal internal-cost baseline by solving a mixed-integer assignment model with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL to derive criterion weights and TOPSIS to obtain an overall ordering under multiple criteria. The workflow is illustrated with an example and the prototype tool used in a company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and equipment-list extraction are integrated with human validation. The results illustrate how structured representations, optimization, and multi-criteria ranking can support auditable configurations for engineering review and commercial selection. Full article

While the global surgeon deficit continues to demand urgent action, traditional “over-the-shoulder” teaching is increasingly constrained by infection-control demands and crowded operating rooms. Over the past four years, we integrated head-mounted smart cameras into reconstructive-surgery workshops across East Africa. Utilizing voice-controlled, stabilized video technology, we provided trainees with a high-definition, wearer’s-perspective view that enhanced visualization without compromising the sterile field. Following remarkably high acceptance in Africa, we have initiated a pilot study at the National Burn Centre in Sweden to apply these lessons to a high-income setting. Our findings suggest that this technology improves surgical education while supporting infection-control stewardship through reduced overcrowding. This experience illustrates a reverse innovation, where tools refined under the logistical constraints of African operating theatres offer scalable solutions for universal challenges in surgical training and patient safety. Full article

Cold thermal energy storage (CTES) has emerged as a critical clean-energy technology for enhancing postharvest management in rural agricultural supply chains, where losses often exceed 20–40% due to inadequate cooling infrastructure and unreliable electricity. This review synthesizes the recent literature on CTES systems, including ice-, chilled-water-, and phase-change material (PCM)-based storage, with a focus on smart-farm integration, IoT-based monitoring, predictive control, and solar photovoltaic (PV) energy coupling. Trends in village-level cold rooms, micro-dairy milk cooling, and fruit–vegetable storage are critically examined, highlighting efficiency, resilience, and scalability relative to battery-dominant and conventional refrigeration systems. Current research gaps are identified in multi-scale modeling, PCM stability, state-of-charge estimation, techno-economic optimization, and AI-based operational strategies. Addressing these gaps is essential to realizing sustainable, low-carbon, and energy-efficient rural cold chains. Full article

Spin-exchange relaxation-free (SERF) atomic magnetometers have emerged as highly promising candidates for ultra-weak magnetic field detection, particularly in biomagnetic imaging, owing to their exceptional sensitivity, amenability to miniaturization, and near-room-temperature operation. While current miniaturized magnetometers typically employ laser chips with fixed optical power, the quantitative impact of laser power on critical performance metrics remains to be fully elucidated. This study systematically investigates the influence of laser power on sensitivity, bandwidth, and dynamic range by incorporating considerations of power broadening, saturation absorption, and noise constraints. A miniaturized probe, integrated with an actively controlled vertical-cavity surface-emitting laser (VCSEL), was developed for experimental validation. Theoretical and experimental results consistently demonstrate that as optical power increases, sensitivity exhibits a non-monotonic dependence, whereas both bandwidth and dynamic range manifest a monotonic upward trend, aligning well with theoretical simulations. The optimized sensor achieved a peak sensitivity of 16 fT/√Hz at 300 μW, while the bandwidth and dynamic range reached 230 Hz and ±5.4 nT at 500 μW, respectively. This work establishes a robust theoretical and experimental framework for the comprehensive performance optimization of laser-integrated miniaturized atomic magnetometers. Full article

The paper proposes an intelligent energy management system designed for a university laboratory room, centered on the user and based on the integration of smart sensors and fuzzy logic for the simultaneous optimization of thermal comfort and energy efficiency. The system architecture integrates three control methods, On/Off controller, Proportional Integral Derivative (PID) controller, and Fuzzy Logic, within a hybrid structure capable of managing multiple factors such as thermal comfort, energy consumption, and the availability of renewable energy sources. The system is implemented and tested using Zigbee 3.0 sensors, smart relays, and photovoltaic panels, while variables such as temperature, humidity, energy consumption, and user feedback are monitored. The simulation results, obtained in the MATLAB/Simulink development environment, demonstrate that the fuzzy algorithm reduces thermal oscillations, optimizes energy costs, and maintains perceived comfort within an optimal range. The main contribution of the study lies in the development of a user-centered, interpretable, and scalable architecture, along with a PowerApps application that records occupants’ feedback in real time, which can be implemented in smart buildings with limited computational resources. Two operating scenarios with different time periods were developed for the proposed system. The fuzzy controller maintained a mean temperature deviation below ±0.2 °C, reduced oscillatory behavior compared to PID controller, and enabled photovoltaic coverage of up to 29.97% during peak intervals, with an average daily contribution of 8.77%. The total simulated energy cost was 8.49 RON for the one-day scenario and 48.12 RON for the five-day interval. Full article

In fundamental physics, sensors operating below liquid helium temperatures are highly vulnerable to vibrations, which can affect the sensitivity, for example, of high-performance particle detectors. Pulse-tube refrigerators, while generating vibrations lower than those of conventional systems, may still introduce several disturbances. Hence, flexible thermal connections are a commonly used mechanical solution to mitigate these undesirable effects. Among the materials that can be used, ultra-high-purity aluminum (UHP-Al) has attracted the attention for low-amplitude-vibration cryogenic applications, including gravitational wave interferometry, quantum information systems, precision space instrumentation, and cryogenic resonators. Thus, the aim of the paper is the characterization of the mechanical and microstructure properties of three UHP-Als (i.e., 5N—99.999 wt%, 5N5—99.9995 wt% and 6N—99.9999 wt%) intended for the production of thermal flexible connections with low stiffness, specifically designed to reduce vibration transmission in cryogenic environments. Mechanical properties were evaluated through standard tensile tests from room (+25 °C) to low temperature (i.e., −150 °C), providing insights into yield strength, ultimate tensile strength, elongation and elastic modulus. In addition, the dynamic elastic modulus of material loads, at cryogenic conditions (i.e., about −180 °C), was determined by measuring the natural resonance frequency, thereby assessing the material’s response to vibrational. Moreover, an extensive microstructural analysis was conducted using electron backscatter diffraction and x-ray diffraction. The correlation between the observed microstructure and the elastic properties was systematically examined. The results underscore the pivotal role of microstructural characteristics in dictating the elastic behavior of UHP Als. Eventually, the analysis provides valuable guidelines for the materials employment inside cryogenic systems, where severe vibration control is critical to maintain high operational performance. Full article

Fiber-reinforced aerogel composites are attractive for thermal protection applications because porous polymer networks can suppress heat transfer while maintaining structural stability. In this study, carbon fiber felt was integrated with a polyimide aerogel via a freeze-drying-assisted multicycle impregnation process to achieve controlled formation of interconnected aerogel networks within the fibrous scaffold. With increasing impregnation cycles, the composites exhibited progressive microstructural densification and improved structural stability. Although bulk density increased, thermal protection performance under prolonged butane-torch exposure was significantly enhanced, showing delayed backside temperature rise and improved resistance to structural degradation compared with bare carbon felt. Post-ablation analyses revealed the formation of a micro-/nanoporous polymer-derived char layer and a multilayer thermal-resistance structure, which contributed to suppressed heat transfer during flame exposure. These results indicate that effective thermal protection in CF/PA composites is governed by dynamic microstructural evolution and char-layer formation rather than intrinsic room-temperature thermal conductivity alone. The proposed multicycle impregnation strategy provides a scalable approach for designing lightweight polymer-based thermal protection materials operating in high-temperature environments. Full article

Background and Clinical Significance: To our knowledge, there is a lack of electron microscopy studies in hematocornea since 1985, and more so for graft hematocornea after deep anterior lamellar keratoplasty (DALK). This study provides an ultrastructural characterization of hematocornea occurring in a DALK graft. Our study presents several limitations: single-case design and lack of control tissue. Case Presentation: The DALK graft with hematocornea was excised and introduced inside of the operating room in glutaraldehyde solution recipient. The graft was quickly cold-transported to light and transmission electron microscopy. Hematocornea in the DALK transplant graft resulted in features of stromal alteration and dysfunctional cellular clean-up response. The collagen lamellae ultrastructure was affected near electron-dense hem deposits. Two cellular aspects were observed: adaptation and degeneration. Electron-dense granules were found in keratocytes, which may exhibit cellular adaptations, such as vacuoles and phagosomes. Macropinocytosis may mechanistically explain ingestion of electron-dense granules, and dysfunctions in the macropinocytosis process may have led to cell degeneration. Cellular degeneration was marked by loss of organelle contour and loss of cellular membrane integrity (burst-cell aspect). Microscopic corneal alteration corresponded to macroscopic total loss of corneal transparency and elasticity. Conclusions: This study described lamellar ultrastructure alterations and dysfunctional cellular response in hematocornea of a DALK corneal transplant graft. Full article

To map and describe the scientific evidence on germicidal ultraviolet C (UV-C) light for hospital surface disinfection, this scoping review examined device types, reported operational parameters, microbiological and clinical outcomes, and implementation aspects. Primary studies conducted in hospital settings and evaluating UV-C or ultraviolet germicidal irradiation on environmental surfaces were searched in four databases without date restrictions. Data were synthesized descriptively in tables and narrative form following JBI and PRISMA-ScR guidance. Eleven studies (2007–2025) met the inclusion criteria. Reported microbial reductions ranged from 1 to ≥5 log₁₀. Higher and more consistent reductions were predominantly observed under laboratory or controlled experimental conditions, whereas reductions in real-world hospital surface sampling were more variable and influenced by pathogen type, surface material, room geometry, and shadowing. Integration of UV-C with manual cleaning and multi-position irradiation cycles was associated with greater effectiveness. Reporting of key radiometric parameters (dose, exposure time, and distance) was frequently incomplete, limiting reproducibility and cross-study comparability. Clinical findings were heterogeneous: some interrupted time-series analyses suggested reductions in healthcare-associated infections, although effects were not uniform across microorganisms. Implementation reports described room-level cycle times compatible with turnover, variable staffing requirements, and limited economic evaluation. Overall, UV-C appears to be a promising adjunct to standard cleaning practices in hospital environments. However, standardized radiometric reporting, multicenter studies, and robust clinical and economic evaluations are necessary to support safe, reproducible, and sustainable large-scale implementation. Full article

Power system control rooms are undergoing a profound transformation as renewable integration, distributed energy resources, sector coupling, and increasing operational uncertainty reshape the technical, organisational, and cognitive demands of grid operation. At the same time, Digital Twins and Agentic Artificial Intelligence offer new possibilities for monitoring, forecasting, reasoning, and decision support. However, existing control room architectures remain fragmented and insufficiently structured to support the coherent integration of digital models, intelligent reasoning systems, human operators, and regulatory accountability mechanisms in safety-critical power system environments. This article addresses that gap through a PRISMA ScR-informed scoping review combined with a structured architectural synthesis process. The study develops Infostructure as a reference architectural framework for situation awareness in future power system control rooms. The framework is derived from a synthesis of operational challenges, regulatory constraints, and human AI collaboration requirements identified across the scientific and regulatory literature. Infostructure formalises four interrelated architectural layers, Physical, Semantic, Orchestration, and Cognitive, constrained by cross cutting governance and compliance principles. The architectural coverage and internal coherence of the framework are illustrated through representative transmission and distribution system use cases, including wide area disturbance anticipation, distribution level congestion management, and cross organisational coordination during extreme events. A structured research and validation agenda is further outlined to support empirical evaluation and phased implementation. By transforming review-based synthesis into a coherent architectural formalisation, Infostructure contributes a rigorous foundation for the evolution of transparent, accountable, and resilient power system control rooms. Full article

Central business district (CBD) hospitals must sustain reliable pressure relationships in critical rooms while reducing whole-facility carbon under tight space and disruption constraints. We developed an ontology-grounded semantic digital twin that normalizes building automation system (BAS) and building management system (BMS) telemetry into a unified semantic store consistent with Brick Schema, enabling portable asset discovery via query and thereby supporting forecasting, anomaly detection, and multi-objective optimization without dependence on vendor point naming conventions. Whole-facility impacts were verified using International Performance Measurement and Verification Protocol Option C–style measurement and verification with an S0-calibrated baseline model and residual-based savings attribution. Relative to the baseline (S0), the intervention (S3) produced a step increase in the critical-room pressure-compliance pass rate, tighter room-to-corridor differential-pressure (ΔP) control across airborne infection isolation and open room strata, and intent-aligned ventilation delivery (air changes per hour ratio distribution concentrated near unity; p < 0.05 where letter groups differ). Operational-state discrimination improved (AUC 0.649→0.696) and issue-resolution times shortened (left-shifted cumulative distribution function), indicating reduced service burden. Option C verification showed energy residuals shifting negative under S3, consistent with net savings versus baseline expectations. Across progressive maturity (S0→S3), time-to-value and burden fractions decreased, carbon intensity (tCO₂e m⁻²) decreased, long-tail exposure compressed (log-scale horizon), and composite performance indices increased (p < 0.05). These results demonstrate a verifiable pathway to pressure-reliable, decarbonized hospital operations at the whole-facility boundary while making the semantic layer’s utility explicit through query-driven, ontology-grounded asset discovery. We present an IPMVP Option-C–verifiable semantic digital-twin governance framework that links audited operational evidence (telemetry → actions → verification) to whole-facility energy and carbon outcomes while maintaining critical-room pressure-relationship reliability. Optimization benchmarking (including quantum annealing) is used as supporting decision-support evaluation, rather than as the central contribution. Full article

The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload. This study introduces HRPM–IRC, an epidemiology-aware hyper-heuristic framework developed to optimize room–patient matching by minimizing the risk of nosocomial infections, reducing travel and specialty mismatch costs, and promoting equitable nurse workload distribution. A mixed-integer linear programming model is formulated to capture infection transmission probabilities, isolation and cohorting requirements, and multi-ward capacity constraints. On top of this model, a bio-inspired hyper-heuristic adaptively selects and refines low-level heuristics, including cohort-first greedy allocation, risk-gradient swaps, and pathogen-aware local MILP refinement, on the basis of contextual epidemiological indicators and reinforcement learning. The framework was validated using a real-world dataset obtained from a tertiary hospital in Lebanon, comprising 142 anonymized patient admissions, 35 rooms, and six nursing teams. Results demonstrate that HRPM–IRC consistently reduces modeled infection risk and workload imbalance by up to forty percent compared to conventional assignment heuristics while maintaining near-real-time decision-making capabilities suitable for dynamic hospital operations. These findings underscore the effectiveness of epidemiology-aware hyper-heuristics in enhancing hospital resilience, improving infection prevention, and supporting fair resource utilization in data-limited healthcare environments typical of Lebanon and other middle-income countries. Full article