Search Results (2,647)

With the advancement of the new-type power system, the exponentially growing high-frequency distribution and consumption data imposes heavy transmission and processing pressure on resource-constrained edge devices. Existing compression methods face two core limitations: static algorithm configurations that fail to adapt to dynamic time-varying power load characteristics, and complex computations that are difficult to deploy on resource-constrained edge terminals. To address these issues, this paper proposes a cloud-edge collaborative adaptive compression method based on CNN-LSTM load forecasting and Q-learning decision-making. A three-layer “cloud-edge-terminal” architecture is built to decouple compression decision-making from edge execution. The cloud employs a hybrid one-dimensional CNN and single-layer LSTM (1D-CNN-LSTM) for high-precision short-term load forecasting, and establishes an adaptive Q-learning decision mechanism to issue differentiated compression instructions according to varying load characteristics. The edge terminals receive these instructions and perform lightweight lossless compression accordingly. Simulation results show that the CNN-LSTM model achieves a MAPE of 7.59%. The Q-learning agent converges to an average reward of 65.35% during training and achieves a 66.73% overall compression ratio on the unseen test set, outperforming the fixed LZW baseline by approximately 6 percentage points. Furthermore, the proposed method improves the edge processing throughput by approximately 6.5 to 10.4 times compared to the comparative baselines. These results suggest that the cloud-edge collaborative approach offers a promising direction for alleviating edge pressure and balancing compression efficiency with computational overhead in massive power data transmission scenarios. Full article

Background/Objectives: The optimal surgical approach for Percutaneous KyphoPlasty (PKP) in patients with recent single-level Osteoporotic Vertebral Compression Fractures (OVCFs) remains controversial. Most available previous meta-analyses included studies with variable heterogeneity, often mixing unilateral and bilateral MIS approaches, differing surgical techniques, and various fracture patterns, which limited the reliability of their conclusions. This meta-analysis aimed to compare the efficacy and safety of unilateral versus bilateral PKP exclusively in patients with recent single-level OVCFs only. Methods: A systematic review was conducted according to the PRISMA 2020 guidelines. PubMed, Scopus, Cochrane Library, and ScienceDirect were searched for comparative studies published between 2000 and 2025. Randomized Controlled Trials (RCTs), prospective, and retrospective comparative studies comparing unilateral and bilateral PKP for recent single-level OVCFs were included. Clinical and radiological outcomes as well as perioperative complications and safety outcomes were analyzed using random-effects meta-analysis. Predefined subgroup analyses according to study design and sensitivity analyses were performed. Results: Eleven studies involving 1374 patients (705 unilateral and 669 bilateral PKP) met the inclusion criteria. No significant differences were observed between the two surgical approaches regarding short- or long-term pain relief, cement leakage, number of adjacent vertebral fractures, or overall clinical outcomes. Bilateral PKP demonstrated statistically significant, but clinically negligible, advantages in anterior vertebral body height restoration and kyphosis correction. Unilateral PKP required an insignificantly lower cement volume. For operative time, the overall pooled estimate favored unilateral PKP by approximately 10 min but showed extreme heterogeneity (I² = 98.5%). Importantly, the two RCTs showed no statistically significant between-group difference (MD = +1.2 min, 95% CI −4.5 to +6.8), indicating that the apparent overall effect was largely driven by observational evidence. Similar discrepancies between randomized and retrospective studies were observed for several other outcomes, underscoring the importance of considering study design when interpreting the results. Conclusions: Current evidence does not demonstrate clinically meaningful superiority of either unilateral or bilateral PKP for the treatment of recent single-level OVCFs. Bilateral PKP may provide small advantages in selected radiographic outcomes, whereas unilateral PKP uses modestly less bone cement; however, the relevance of these differences remains clinically uncertain. Surgical approach selection may therefore be individualized according to vertebral morphology, pedicle anatomy, fracture characteristics, surgeon experience, and technical feasibility rather than expectations of superior clinical outcomes. Further adequately powered randomized trials with standardized outcome reporting and long-term follow-up are warranted. Full article

Remote electroencephalography (EEG) systems require compact representations that remain useful when communication noise corrupts the transmitted message. We evaluated whether task-oriented residual refinement of filter bank common spatial pattern (FBCSP) features compressed by principal component analysis (PCA) improves four-class motor imagery (MI) decoding without increasing the transmitted dimension. The BNCI2014-001 dataset was assessed in nine subjects using bidirectional subject-specific cross-session evaluation. All methods transmitted $K\in \{16,32,64\}$ power-normalized real values through additive white Gaussian noise (AWGN) at seven signal-to-noise ratios (SNRs) and a noise-free reference. Balanced accuracy was averaged over 20 paired noise realizations per noisy condition, and paired subject-level differences were evaluated with exact joint sign-flip max-$\left|t\right|$ inference. At $K=32$, the proposed method achieved 41.10%, 49.46%, and 56.15% balanced accuracy at $-10$, $-5$, and 0 dB, compared with 38.63%, 46.28%, and 53.26% for conventional FBCSP–PCA transmission. Ten of the 24 semantic-versus-conventional comparisons were significant after family-wise max-$\left|t\right|$ correction, including all nine comparisons at $-10$, $-5$, and 0 dB. Receiver-only controls closely reproduced conventional performance at all three message dimensions, whereas alternative loss weights, uniform-SNR training and selection, and removal of the 0 dB/noise-free reference-condition guard retained positive low-SNR gains. Overall, baseline-preserving task-oriented refinement improved MI decision robustness under severe AWGN without increasing the number of transmitted values. Full article

The present systematic review set out to reassess whether claims of energy-efficient deep learning in transportation infrastructure are supported by direct sustainability evidence. Deep learning models run in transportation systems on edge devices with a limited energy budget, and the literature labels them “green” or “energy-efficient”; the share of studies that support the label with measurement had not been quantified. Following PRISMA 2020, the Scopus, IEEE Xplore, and Web of Science databases were searched for the period from January 2020 to June 2026. Included were 721 studies applying Green AI techniques: pruning, quantization, knowledge distillation, lightweight architectures, TinyML, and dedicated accelerators. The review covers the transport domains of roads and ADAS, railway, connected and autonomous vehicles, and sensor networks. Studies were classified by the strongest efficiency evidence they report: a direct sustainability metric (energy, power, power efficiency, battery life, CO₂) or computational proxies. A direct metric is reported by 58 studies (8.0%); the share is a lower-bound estimate. Full-text verification of a stratified random sample of 34 Tier 2 studies found one study with a direct metric not stated in its abstract (2.9%); the sample-adjusted estimate of the share is 10.7% (95% confidence interval 8.5 to 21.8%). The evidence levels differ: 48 studies (6.7% of the corpus) report power or energy measured on the target hardware, two derive battery life from a measured energy budget, seven report modelled or simulated values, and one a macro-level CO₂ estimate. Railway contributes three studies. The largest measured reduction in energy per inference is 1961.8-fold (0.005 J on an FPGA against 9.77 J on a 95 W CPU); the largest modelled factor in the corpus is approximately 2400-fold (a memristor accelerator against an embedded GPU). Measured and modelled values are distinguished throughout the text. The studies that measure show that rigorous reporting is feasible; from the evidence presented follows the recommendation that an efficiency claim in transportation AI be supported by a direct metric measured or explicitly modelled on a named target platform. Full article

Ovine coenurosis, caused by the larval stage of Taenia multiceps (Coenurus cerebralis), is a parasitic disease that affects the central nervous system of livestock. While diagnosis traditionally relies on clinical signs, imaging, and postmortem examination, electroencephalography (EEG) remains underutilized despite its potential to detect brain abnormalities. This case report describes EEG and quantitative EEG (qEEG) findings in a 2-year-old sheep presenting with clinical signs suggestive of a chronic focal encephalopathy (ataxia, head tilt and circling) associated with lethargy and progressive weight loss. Computed tomography (CT) confirmed a single, discretely bilobate intra-axial cystic lesion within the left cerebral hemisphere, with a severe mass effect, marked deformation and displacement of the ventricular system, compression of the thalamic structures, and contralateral deviation of the median line. A solitary parasitic cyst with no lesion in the right hemisphere was subsequently confirmed at necropsy and on histopathology. Under ketamine–diazepam sedation, both EEG recordings showed a continuous, delta-dominant background consistent with the anesthetic protocol. Epoch-wise asymmetry indices with bootstrap confidence intervals distinguished an unstable component (which reversed in hemispheric sign between recordings) from a reproducible one confined to the occipital derivations, where the asymmetry lay over the right hemisphere in both recordings in every band except delta. This reproducible preponderance was contralateral to the cyst, over a hemisphere in which no structural lesion was demonstrable either on imaging or at necropsy, and absolute powers indicated that it arose principally from attenuation of the signal recorded over the diseased hemisphere. These findings provide the first electrophysiological characterization of ovine coenurosis and show that in the presence of a severe mass effect, the side of the surface qEEG abnormality may not indicate the side of the lesion (an electrophysiological false localizing sign described in human patients but not previously in a ruminant). EEG therefore reflects the functional brain disturbance caused by Coenurus cerebralis but should be regarded as a complement to, rather than a substitute for, cross-sectional imaging in neurolocalization. Full article

The decarbonization of maritime transport demands propulsion and onboard energy systems that simultaneously achieve ultra-low emissions, high power density, and robust operational reliability. Nuclear propulsion, particularly when coupled with small modular reactors (SMRs), offers a compelling pathway due to its near-zero operational emissions and exceptional energy density; however, the efficient utilization of high-grade nuclear thermal energy under shipboard constraints remains a critical challenge. To address this issue, this study presents a comprehensive thermodynamic and exergy-based assessment of a novel cascaded waste heat recovery (WHR) architecture designed for a 100 MW class nuclear-powered vessel, integrating a supercritical carbon dioxide (sCO₂) cycle with downstream steam Rankine (SRC) and Kalina cycles. Detailed process modeling is performed using Aspen HYSYS to quantify energy and exergy performance at the component and system levels. The proposed cascade exploits the complementary thermodynamic characteristics of each cycle, enabling staged recovery of high-, medium-, and low-grade heat from the nuclear secondary loop. Results indicate that while the sCO₂ cycle dominates gross power generation due to its high power density and favorable high-temperature performance, its net efficiency is constrained by substantial compression work and associated auxiliary losses. In contrast, the SRC and Kalina cycles exhibit significantly higher energy efficiencies, demonstrating superior suitability for medium- and low-temperature waste heat utilization. Through thermodynamic synergy, the integrated sCO₂–SRC–Kalina configuration achieves an overall energy efficiency of 20.73%, representing a substantial improvement over a standalone sCO₂-based WHR system. Exergy destruction analysis reveals that system irreversibilities are primarily concentrated in heat exchangers, particularly the primary heat exchanger interfacing the nuclear heat source with the sCO₂ loop, whereas turbomachinery contributions are comparatively minor. These findings highlight heat exchanger design optimization and improved temperature matching as the most effective pathways for further performance enhancement. Overall, this study demonstrates that multi-cycle cascade integration provides a viable and high-efficiency solution for nuclear marine energy recovery, with strong implications for advanced hydrogen energy systems and other high-performance power generation applications requiring compactness, scalability, and thermodynamic robustness. Full article

An anthropomorphic patient-derived 3D-printed breast phantom (4.8 cm compressed thickness, 12.2% volumetric breast density) was evaluated for image-quality assessment in contrast-enhanced mammography (CEM). It comprises four 1.2 cm plates printed via fused filament fabrication (PLA/ABS). Five replicates of one of the plates (target) containing a glandular-tissue surrogate and four cavities for iodinated disks with same iodine area density per plate (0.2–0.5–0.75–1.0–1.5 mg/cm²) were fabricated. As a reference, a homogeneous PET-G phantom was printed. CEM images (low-energy (LE), high-energy (HE), and DES) were acquired in three different systems. Iodine signal linearity (mean pixel value, signal difference, signal-difference-to-noise ratio (SdNR)) and background cancellation (residual signals, texture cancellation coefficient (TCC), parameter β from noise power spectrum (1D-NPS)) were estimated. Evaluation across the three systems demonstrated the phantom provides appropriate measurements of iodine signal linearity (R² > 0.986, based on mean values across the four inserts) and background cancellation (TCC:0.04–0.11). Fitting 1D-NPS = αf^−β within 0.25–1.0 mm⁻¹ yielded β values ≈ 3.1–4.0, compatible with the literature, for patient LE and HE images, whereas DES reduced β to ≈1.1. In the homogeneous phantom, β remained invariant between LE and DES images (β ≈ 0.6). The local glandular ratio at each position of the iodinated disks enabled evaluating the impact of background on iodine signal. Full article

Seasonal energy storage effectively addresses seasonal electricity supply–demand imbalances. This study proposes a cross-seasonal compressed air energy storage system using a depleted gas reservoir as the storage reservoir. A six-stage compression–expansion system with intercooling and reheating is designed. Also, the heat of compression is recovered for district heating, and the expansion of cold energy is for cooling supply, thereby avoiding cross-seasonal heat storage costs. For the YD1 depleted gas reservoir case, the system achieves a round-trip efficiency of 57.85% and an exergy efficiency of 70.9%. The total energy utilization ratio, which represents the combined utilization of the electricity, heating, and cooling outputs relative to the corresponding energy input, reaches 166.6%. Incorporating revenue from the heating and cooling sales, the dynamic payback period is 2.38 years, and the investment recovery ratio reaches 4.40. Parametric analysis indicates that thermodynamic performance improves with the increase in discharge pressure and decrease in discharge power. Economic performance improves with longer daily operating hours and plant lifetime. This study demonstrates that depleted gas reservoirs combined with well-designed surface combined cooling, heating, and power systems offer a new research and development direction for large-scale, long-duration seasonal storage, facilitating renewable energy integration and grid stability. Full article

Passive Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) systems deployed in metallic commercial aircraft cabins suffer from severe multipath fading, non-stationary channel dynamics, and operator gait-induced signal jitter. Addressing these challenges without physical airframe modifications or regulatory recertification remains a critical operational bottleneck. This paper presents an edge-native, software-defined framework that integrates micro-electromechanical system (MEMS) inertial measurements with an IMU-assisted Adaptive Kalman Filter (AKF) and a distilled surrogate decision tree. The proposed algorithm extracts localized motion energy ($E_{\mathrm{IMU}}$) to dynamically scale the measurement noise covariance ($R_k$) prior to physical-layer signal corruption, thereby eliminating phase lag and power hunting. For deterministic edge execution on COTS handheld devices, surrogate model distillation compresses a parent Random Forest ensemble into an $8.2\mathrm{KB}$ 13-leaf decision tree (depth 5) yielding $0.12\mathrm{ms}$ inference latency. Empirical validation across 17 operational sessions in Airbus A320, Boeing 737, and Airbus A321 cabins (10,720 valid reads) demonstrates a $99.45\%$ mean RSSI jitter reduction ($95\%\mathrm{CI}:[99.21\%,99.63\%]$) and a $7.30\times$ suppression of transmit power oscillations. Statistically, asset detection completeness is fully preserved ($0.791$ vs. $0.795$ baseline, $z=0.281,p=0.779$). Operating entirely within standard handheld software runtimes, this approach bypasses Supplemental Type Certificate (STC) requirements while ensuring robust aerospace asset visibility. Full article

The environmental and land management issues associated with disposal of large quantity of pond ash (PA) from coal-fired thermal power plants are substantial, necessitating sustainable reuse strategies. The aim of this study is to investigate the performance of a ternary binder system of ordinary Portland cement (OPC), fly ash (FA) and ground granulated blast furnace slag (GGBS) for the engineering properties of PA for geotechnical applications. The Box–Behnken Design (BBD) approach in Response Surface Methodology (RSM) was used for the evaluation of the effect of binder composition on the unconfined compressive strength (UCS), California Bearing Ratio (CBR), and maximum dry density (MDD). The proportions of OPC, FA and GGBS were varied within predetermined limits, and 15 experimental mixtures were prepared. The experimental results indicated that the UCS was between 760 to 1260 kPa, soaked CBR was between 5.92% to 12.36%, and MDD ranged from 14.32 to 15.88 kN/m³. The developed quadratic models showed high statistical adequacy, with p-values below 0.0001 and coefficients of determination (R²) greater than 0.99 for all responses. The optimum combination of binders was obtained using multi-response optimization which yielded 2.45% OPC, 10.84% FA and 19.58% GGBS with a desirability index of 1.0, with a predicted UCS of 1272.81 kPa, soaked CBR of 12.63%, and MDD of 15.90 kN/m³. The validation experiments showed excellent agreement with the predicted results, with small deviations (less than 1%). SEM and EDS observations revealed a denser matrix with Ca–Si–Al-rich cementitious products. These features may indicate the development of C–S–H- and C–A–S–H-type phases, along with fewer visible voids and stronger bonding between the particles. The results demonstrate that the addition of OPC, FA, and GGBS is an effective and environmentally beneficial approach for improving the mechanical and compaction characteristics of PA, supporting its potential use in pavement foundations and other geotechnical engineering applications. Full article

The Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT), where intelligent data processing complements large-scale connectivity across applications ranging from smart homes to industrial automation. However, its rapid expansion and the increasing adoption of AI have led to growing environmental concerns, particularly increased energy consumption and electronic waste. These issues highlight the importance of Green AIoT practices, which extend Green IoT by combining energy-efficient communication, computing, and intelligent resource management to achieve energy-efficient and sustainable AIoT operation. This paper presents a comprehensive survey of techniques aimed at improving the energy efficiency and sustainability of Green AIoT systems. The focus is placed on networking aspects, particularly machine-to-machine (M2M) communications and wireless sensor networks (WSNs), alongside the roles of computing infrastructures, data centers, and energy-efficient processor architectures. The survey further examines how AI-assisted techniques, including TinyML, edge AI, and intelligent computation offloading, complement traditional Green IoT mechanisms to reduce energy consumption. Key approaches such as low-power communication protocols, energy-efficient data processing, data compression, smart energy management, and energy harvesting are reviewed and compared. Furthermore, the paper summarizes representative state-of-the-art solutions with quantitative insights and discusses open challenges and future research directions toward environmentally sustainable AIoT systems. Full article

To provide a unified description of the monotonic and cyclic compressive responses of concrete after freeze–thaw exposure, a one-dimensional phenomenological damage–plasticity constitutive framework is proposed. Freeze–thaw-induced pre-damage is quantified by the degradation of the initial static stiffness. A Mander-type equation is employed to describe the monotonic envelope, while residual strain is introduced to characterize plastic deformation. A signed stiffness variable is defined to distinguish pre-peak compaction from post-peak mechanical damage, and the unloading and reloading paths are represented by piecewise power-law functions for the pre-peak and post-peak regimes. The model is evaluated using 36 monotonic compression curves of recycled coarse aggregate self-compacting concrete subjected to sulfate freeze–thaw cycles and cyclic compression data for ordinary concrete subjected to seawater freeze–thaw cycles. After independent identification of the envelope-shape parameters, the monotonic responses yield an average R² of 0.978 and an average NRMSE of 0.041, whereas the complete cyclic responses yield an average R² of 0.941 and an average NRMSE of 0.066. Because the model parameters are identified separately using data from each freeze–thaw exposure level, these accuracy measures characterize parameter calibration and response reconstruction rather than independent prediction of untested exposure states. Freeze–thaw exposure causes substantially greater degradation of the initial static elastic modulus than reduction in peak stress, indicating that stiffness, strength, peak strain, and envelope shape should be treated as state variables at different hierarchical levels. The cyclic unloading stiffness exhibits both pre-peak enhancement and post-peak degradation, confirming the necessity of separately representing compaction and mechanical damage. The proposed model provides a unified representation of the monotonic envelope, residual deformation, and principal cyclic hysteretic characteristics of freeze–thaw-damaged concrete. Full article

This study developed alkali-activated binders based on biomass power plant ash (BPPA) and ground granulated blast furnace slag (GGBFS) for potential application in coal-mine goaf backfilling. Five precursor proportions, ranging from 100% BPPA to 100% GGBFS, were investigated to clarify the influence of precursor composition on fresh properties, mechanical performance, reaction-product evolution and microstructural development. Increasing the GGBFS content reduced slump and shortened both initial and final setting times, indicating accelerated precursor dissolution and early structural build-up. Compressive strength increased nonlinearly with GGBFS incorporation. Multiscale characterization consistently demonstrated that GGBFS promoted the transformation of the initially quartz-rich and weakly reactive BPPA system into a calcium-rich aluminosilicate binding matrix. This transformation was accompanied by changes in the Si-O-T bonding environment, increased formation of hydrated reaction products and progressive filling and bridging of the spaces between residual precursor particles. Consequently, the hardened matrix evolved from a porous particle-supported structure containing isolated reaction regions into a compact gel-supported network that contributed to more effective stress transfer within hardened matrix. Among the investigated mixtures, the formulation containing 25% BPPA and 75% GGBFS exhibited a favorable combination of BPPA utilization, processability, and mechanical performance, indicating its potential for further evaluation in coal-mine goaf backfilling applications. Full article

Urban-forest monitoring increasingly requires intelligent sensor-driven systems capable of characterizing short-term tree responses while operating efficiently within Internet of Things (IoT) and edge-computing environments. This study proposes a fusion-based artificial intelligence framework that integrates Quantization-Aware Training (QAT)-optimized PointNet++ models with machine-learning regression to predict a short-term dendrometer-derived stem-diameter response expressed in biomass-equivalent units. The framework combines 1024-point LiDAR tree representations, geometric measurements, and environmental sensor data through three components: QAT-optimized PointNet++ models for 34-species classification and trunk–crown part segmentation, frozen model-based prediction and geometric feature extraction, and MLP and XGBoost regression models for prediction of the short-term target. The dataset contained 2694 trees from five regions of South Korea, with the target derived from dendrometer-based stem-diameter measurements recorded over a 14-day interval between 8 September 2022 and 22 September 2022. Importantly, this short-term signal reflects both structural and reversible water-status-related stem dynamics and is therefore not interpreted as direct dry-biomass accumulation or carbon sequestration. The QAT-optimized models retained 92.52% segmentation accuracy (82.67% mIoU) and 80.46% species-classification accuracy, while the regression model reached R² = 0.9663 and RMSE = 0.4437 kg for the defined biomass-equivalent target. Quantization reduced the saved model size of both encoders by approximately 10.5× (21 MB → 2 MB) and accelerated CPU inference by up to 4.1×. These efficiency measurements were obtained on an ×86 desktop CPU and therefore characterize computational compression benefits rather than completed deployment or field validation on a low-power embedded device. These results demonstrate the computational feasibility of combining compressed point-cloud perception with multimodal prediction of short-term dendrometer-derived stem dynamics. Validation over seasonal and multi-year periods using independent biomass-reference measurements would be required before extending the framework to long-term biomass accumulation or carbon-sequestration assessment. Full article

Objectives: Manual surgical staplers remain widely used in minimally invasive lung surgery, while powered devices have been introduced to improve tissue compression, staple formation, and device handling. This study compared clinical outcomes associated with manual and powered endoscopic staplers in patients undergoing minimally invasive lung cancer surgery, with postoperative air leak as the main outcome of interest. Methods: We retrospectively reviewed 400 consecutive patients who underwent VATS segmentectomy or lobectomy for lung cancer between January 2023 and June 2025. After 20 exclusions, 380 patients were analyzed: 190 treated with manual staplers (Group A), representing a historical control cohort, and 190 with powered staplers (Group B). Postoperative air leak, operative time, intraoperative blood loss, postoperative complications, and length of stay were compared. Surgeon-perceived ergonomic benefit was assessed after each powered-stapler procedure using a dichotomous yes/no question. Results: Postoperative air leak occurred less frequently with powered than manual staplers (10.0% vs. 17.9%; p = 0.0382; OR = 0.510, 95% CI: 0.279–0.931). No patient developed prolonged air leak (>5 days) or required additional intervention for air leak. Operative time (65.32 ± 2.52 vs. 66.74 ± 3.07 min; p < 0.0001) and intraoperative blood loss (86.09 ± 4.12 vs. 88.3 ± 4.47 mL; p < 0.0001) were statistically lower with powered staplers, although absolute differences were small. No significant differences were observed in postoperative atelectasis or length of stay. All 190 surgeon assessments indicated a perceived ergonomic advantage with the powered stapler. Conclusions: Powered staplers were associated with a lower incidence of postoperative air leak. Operative time and blood loss were also statistically lower, although the absolute differences were of limited clinical relevance and did not translate into shorter hospitalization. Powered staplers were consistently perceived as ergonomically advantageous, although this assessment was subjective and non-validated. These findings warrant cautious interpretation given the retrospective, single-center design and historical-control comparison. Full article