Search Results (2,526)

Non-sinusoidal operating conditions are frequently present in modern power systems and can adversely affect the normal operation and performance of industrial equipment connected to the electrical grid. This paper presents a method for evaluating the relationship between the harmonic weights of voltage and current and the sensitivities of reactive and apparent power under these conditions. These conditions primarily arise from non-linear loads and circuit components, particularly power electronic converters and other power electronic devices. In practical industrial environments, the harmonic composition of voltage and current may vary considerably, leading to significant changes in their RMS values and consequently affecting power transfer and overall power quality. The proposed approach first establishes the mathematical dependencies between active, reactive, and apparent power and the harmonic components of voltage and current. Based on these relationships, the sensitivities of the power quantities can be determined when one or more system parameters undergo variations. To validate the proposed methodology, we developed and implemented a numerical algorithm in MATLAB/Simulink for a practical industrial case. We compare the sensitivities obtained using the proposed approach with those calculated directly from measured data. The small differences between the two sets of results confirm the method’s accuracy and show that it is suitable for assessing harmonic variations on power quantities in non-sinusoidal power systems. Full article

Countries operating major container ports without a domestic equipment supply base are pursuing self-reliance in smart port technology, but such programmes are hard to assess because their effects diffuse across the economy through inter-industry linkages. This study estimates inducement coefficients for the Korean Smart Port Technology Self-Reliance Equipment Development Project. Using the 2023 Input–Output Tables of the Bank of Korea, the programme is defined at the basic-sector level: 67 of the 380 basic sectors, spanning port equipment manufacture, construction, logistics services, software, and R&D, are extracted from their parent groups and consolidated into one sector, leaving the unrelated residuals endogenous. That sector is exogenously specified, capturing only repercussions on the remaining 33 sectors. A KRW 1 increase in programme output induces KRW 0.8358 of production and KRW 0.2958 of value added elsewhere, and KRW 1 billion induces 2.9758 jobs; on the domestic table these fall to KRW 0.5163, KRW 0.1872, and 1.9912 jobs, so about two-fifths of the gross inducement leaks abroad through imports. Production inducement falls upstream in materials, value-added, and employment inducement downstream in services. On the supply side, a KRW 1 shortfall in the sector’s domestic supply disrupts KRW 0.4013 of production among its users, and the sector shows the highest forward-linkage sensitivity of the 34-sector system. Eleven alternative delineations leave the structural findings unchanged. The coefficient vector is reported in full, so the estimates are reproducible. Full article

The rapid decarbonisation of urban districts is accelerating the deployment of rooftop photovoltaic (PV) systems, household battery energy storage systems (BESSs) and electric vehicles (EVs). However, the high variability of urban micro-generation creates substantial forecasting errors at the urban–grid interface, imposing high balancing costs on distribution system operators (DSOs) and local energy communities. Traditional metrics fail to assess how effectively internal peer-to-peer (P2P) flexibility offsets these forecast mismatches prior to grid settlement. To address this gap, this paper presents a novel methodological framework and a non-parametric indicator: the Community Imbalance Neutralisation Index (CINI). Formulated in a generalised, scalable matrix structure applicable to any heterogeneous urban neighbourhood (N households), CINI quantifies the relative reduction in net community-level imbalance relative to the cumulative sum of uncoordinated individual forecast errors. CINI ranges from 0 per cent (no collective mitigation) to 100 per cent (perfect internal neutralisation). Complementing this index, an adaptive day-ahead scheduling algorithm is introduced to determine the optimal community energy purchase requirement (E_forecast). The proposed framework is numerically evaluated using a high-resolution synthetic benchmark annual dataset with 15 min intervals (35,040 intervals) representing a Central European urban residential cluster equipped with diverse combinations of PV, BESS and managed EV charging infrastructure. The simulation results demonstrate that active cVPP coordination reduces annual grid-facing imbalance energy from 188.73 MWh to 143.88 MWh, increasing the annual CINI score from 57.22% to 67.39% (+10.17 percentage points) compared with the uncoordinated baseline. Notably, the framework reveals a ‘Winter Flexibility Paradox’, achieving its highest relative efficacy during the winter months (+13.88 percentage points in December). Furthermore, sensitivity analyses show that scaling flexibility up to 40 kW achieves a CINI score of 91.22%, revealing diminishing marginal returns and critical technological saturation thresholds. The proposed CINI metric and E_forecast dispatch algorithm provide city planners, municipal energy managers and DSOs with a transparent diagnostic tool to design dynamic socio-economic tariff incentives, optimise urban micro-grid sizing, prevent free-rider dynamics, and foster resilient, self-balancing smart cities. Full article

Macacine alphaherpesvirus 1 (McHV-1, commonly known as Monkey B Virus) is a fatal zoonotic pathogen naturally prevalent in macaques. In this study, we developed and conducted a proof-of-concept analytical validation of a HotStart Loop-Mediated Isothermal Amplification (LAMP) assay targeting a conserved, moderately GC-rich (61–62%) region of the UL1 gene in McHV-1. The reaction was optimized at 70 °C using engineered HotStart Bst 3.2/4.2 DNA Polymerase, tracked via real-time fluorescence and closed-tube visual colorimetric detection (pre-added L-HNB dye) without post-amplification tube opening to reduce the risk of post-amplification aerosol carry-over contamination. The assay achieved a preliminary practical analytical limit of detection (analytical LOD) of 100 copies/reaction (3/3 replicates) within a 15–30 min diagnostic cutoff window, matching the practical sensitivity of reference qPCR. Strict analytical specificity was demonstrated against a comprehensive panel of human herpesviruses (HHV-1 through HHV-5) and host macaque genomic DNA. As a single-target analytical prototype, this visual HotStart LAMP platform provides a rapid, equipment-free molecular tool with promising potential for McHV-1 surveillance in resource-limited settings and macaque breeding facilities upon further clinical validation. Full article

Multiphase pumps are key equipment for the efficient transport of multiphase fluids in the petroleum industry, and their transient stability under variable-speed conditions directly affects system reliability. By combining numerical simulation with experimental validation, this study systematically investigates the evolution of external characteristics, energy conversion mechanisms, and the dynamic response of the internal flow field during a 0.4 s variable-frequency speed regulation cycle at inlet gas volume fractions (IGVFs) of 10% and 20%. The numerical model was validated against experimental measurements of a four-stage multiphase pump under pure-water steady-state conditions, with deviations in head, efficiency, and power all within 5%. The results show that during acceleration, the increase in hydraulic efficiency at the lower IGVF is greater than that at the higher IGVF; once deceleration begins, IGVF has no significant effect on hydraulic efficiency. At the investigated IGVFs of 10% and 20%, a higher IGVF increases the transient sensitivity of the internal flow field to speed variation, and increasing IGVF suppresses energy conversion in the impeller. The principal novelty of this work lies in the temporal decomposition of impeller work into dynamic and static pressure components during transient speed variation, revealing that static pressure power consistently accounts for more than 50% of the total power throughout the speed regulation cycle. As rotational speed increases, dynamic pressure power rises because the circumferential velocity of the fluid increases with impeller peripheral speed, while static pressure power also increases continuously owing to the enhanced static pressure work of the blades. During deceleration, the impeller’s energy transfer capability weakens with decreasing rotational speed, and both dynamic and static pressure power decline. These findings elucidate the coupled evolution of gas–liquid two-phase flow under variable-speed conditions and provide a theoretical basis for the operational optimization and speed control of multiphase pumps. Full article

With the rapid development of the Internet of Medical Things (IoMT), wearable sensors and intelligent medical terminals continue to generate a large amount of sensitive medical data, which needs to be uploaded to the cloud platform for storage and sharing. However, IoMT devices usually have the characteristics of limited computing resources and limited energy, and it is difficult for traditional high-complexity encryption schemes to meet the requirements of security and efficiency. In addition, the existing Proxy Re-Encryption (PRE) scheme has the risk of authorization transfer, and it is difficult to achieve fine-grained and secure sharing of medical data. In response to the above problems, this paper proposes a lightweight non-pairing proxy re-encryption enhancement scheme SM2-PRE+ based on the SM2 algorithm, which is used for the secure sharing of IoMT medical sensing data. The scheme combines the SM2 elliptic curve cipher algorithm and the SM4 symmetric encryption algorithm to achieve data protection through a double-layer key structure, and it uses the re-encryption mechanism of message binding to enhance the authorization control ability. Compared with the traditional PRE scheme, the proposed scheme avoids bilinear pairing operations, reduces the computing overhead of resource-limited equipment, and supports collusion-resistant and authorization non-transferability. Security analysis shows that the scheme meets the security requirements of IND-CCA under the Random Oracle Model (ROM). Performance analysis results show that SM2-PRE+ has low computing overhead and storage burden, which is suitable for wearable medical devices, intelligent sensing terminals, and cloud-assisted IoMT data sharing scenarios. Full article

Gas-insulated switchgear (GIS) is an important component of power transmission systems. Accurate partial discharge (PD) pattern recognition is a key requirement for identifying internal insulation defects within the equipment. However, ultra-high-frequency (UHF) PD pulse sequences produced by different insulation defects usually contain complex nonlinear temporal structures and multi-scale periodic variations. These coupled characteristics are difficult to describe adequately via a single feature-mapping strategy. Thus, this paper proposes a temporal-frequency dual-branch stochastic configuration network (TF-SCN), which consists of two heterogeneous hidden-layer branches, for GIS PD pattern recognition. Specifically, in the temporal branch, the model uses a non-periodic, nonlinear activation function similar to that used in a conventional SCN to capture the nonlinear temporal characteristics. The frequency-sensitive branch introduces paired sine–cosine harmonic nodes with shared random projection parameters to capture frequency-sensitive features. The hidden outputs of the two branches are concatenated into a joint temporal-harmonic feature space, and the output weights are solved under the residual inequality constraints for GIS PD classification. To verify the superiority of the proposed model, comparative experiments are conducted on a dataset containing four PD patterns collected from the GIS PD experimental platform. Several baseline models, including 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, are selected for performance comparison. The results show that, compared to 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, TF-SCN effectively extracts distinguishable features in both the time and frequency domains, thereby achieving the best overall performance. Furthermore, its recognition performance remains consistently superior even on noisy data with signal-to-noise ratios ranging from 50 dB to 20 dB. By integrating highly sensitive UHF sensors with the proposed TF-SCN, this study presents a robust, AI-enhanced intelligent sensing and fault diagnosis system for continuous condition monitoring of power equipment. Full article

Planning charging facilities at expressway service areas requires joint consideration of traffic growth, charging behavior, temporal demand peaks, charger technology, existing equipment, and local power-supply capacity. However, existing planning approaches rarely integrate spatially heterogeneous charging behavior, peak-demand effects, charger-technology transition, existing infrastructure, and electrical-capacity constraints within a unified phased planning framework. This study develops a data-driven phased planning method that combines EV traffic forecasting, station-specific charging probability, peak-hour and holiday demand correction, charger-specific service rates, existing-capacity comparison, and transformer-capacity screening. Operational records from 2021 to 2024 were used to calibrate the main parameters for a provincial expressway network in China covering approximately 6000 km and 212 service areas. A baseline planning scenario and six single-parameter sensitivity cases were evaluated. Charging probability varied from 0.3% to 20.9%, with a network-weighted mean of 5.73%, demonstrating substantial spatial heterogeneity among service areas. Under the baseline scenario, the required network capacity reaches approximately 4520 conventional fast chargers and 1230 ultra-fast chargers by 2030, while the share of ultra-fast chargers in annual additions increases from 37.5% in 2025 to 86.9% in 2030. A retrospective comparison across 111 service areas yielded an overall discrepancy of approximately 15.5% and was interpreted as an engineering consistency assessment rather than independent predictive validation. Sensitivity analysis showed that EV traffic growth and the penetration of ultra-fast-compatible vehicles have the greatest influence on projected ultra-fast-charger requirements. The proposed framework provides a practical basis for differentiated and periodically updated expressway charging-infrastructure planning as traffic demand, charging behavior, vehicle technology, and electrical capacity evolve. Full article

The valorization of fish processing by-products, particularly fish heads, represents an opportunity to advance the circular economy and reduce the environmental burden associated with organic waste. This study presents a process simulation-based techno-economic assessment of industrial-scale oil extraction from fish heads using supercritical fluid extraction (SCFE) and enzymatic hydrolysis extraction (EHE). Process models were developed in SuperPro Designer (V9.0) for a 50,000 kg/batch facility, integrating mass and energy balances, equipment design, scheduling, and economic analysis. All the results are derived from process simulations and have not been validated experimentally. SCFE yields a relatively high annual oil output (43.03 million kg/year) but is constrained by high solvent costs. EHE produces less oil (34.06 million kg/year) yet shows superior economic indicators, including a higher return on investment (24.77%), a shorter payback period (4.04 years), and diversified co-product revenues (cake, lecithin, soapstock), although it requires relatively greater capital ($862.7 million compared with $846.2 million for SCFE). While a full life-cycle assessment was not conducted, a partial resource inventory assessment indicates that EHE enables a biorefinery approach that improves resource efficiency. A sensitivity analysis revealed that the most favorable batch capacities were 50,000 kg for the SCFE and 150,000 kg for the EHE under the assumptions and throughput range examined. Overall, this simulation-based study underscores the economic and resource-efficiency potential of integrated biorefinery strategies for fish waste valorization, while highlighting the need for experimental validation and comprehensive environmental evaluation in future work. Full article

Accurate pre-construction carbon accounting for power equipment is critical for grid decarbonization, yet a standardized methodology applicable to transmission and substation projects remains lacking. This study develops a pre-construction carbon accounting methodology for transmission and substation equipment and applies it to a 220 kV project in China, employing localized emission factors and investment-based screening to identify nine key equipment categories. The analysis quantifies carbon emissions in raw material extraction and manufacturing stages, yielding 23,154 tCO₂. Results show that substation equipment, despite its compact footprint, accounts for 35% (8087 tCO₂) of total emissions—disproportionately high relative to the 30 km transmission line. Emissions are highly concentrated: aluminum conductor steel-reinforced (ACSR), gas-insulated switchgear (GIS), and steel towers collectively contribute nearly 90%, with aluminum dominating ACSR and GIS emissions due to its high emission intensity (28.5 tCO₂/t). Sensitivity and scenario analyses identify aluminum and steel emission factors and GIS aluminum mass as the dominant uncertainty sources. The low-carbon scenario demonstrates a 34.7% abatement potential (8024 tCO₂) through hydropower-based aluminum, direct-reduced iron–electric arc furnace (DRI–EAF) steel, and compact GIS design, while the equipment-level emission hierarchy remains stable across all scenarios. These findings underscore that material decarbonization and compact design, pursued synergistically, offer high-impact pathways for pre-construction emission reduction. Full article

To meet the demands for high-density integration and broadband RF performance in next-generation electronic equipment, this paper investigates the structural design and process sensitivity of vertical interconnects in three-dimensional stacked system-in-package (3D-SiP). Based on quasi-coaxial matching and low-impedance compensation techniques, three typical vertical interconnect structures are designed: the PCB-BGA-SiP microstrip structure achieves S₁₁ better than −15 dB and S₂₁ < 0.3 dB within 2–20 GHz; the lower stripline–BGA–upper microstrip structure achieves S₁₁ better than −27 dB; the lower microstrip–BGA–upper microstrip structure achieves S₁₁ better than −18 dB within 1–23 GHz. Isolation simulation shows that the isolation between adjacent channels exceeds 55 dB within 25 GHz. For process sensitivity evaluation, physical samples of gold wire bonding parameters (length, diameter, number) were fabricated and tested for S₁₁, confirming that the dual-wire topology extends the effective bandwidth to 2–20 GHz (a 54% improvement over single wire), the third-wire marginal gain is only ~5%, and 25 μm diameter offers the best overall performance. For BGA ball radius and pad pitch, parametric sensitivity analysis via Ansys HFSS was performed (not experimentally validated process variation results), identifying the optimal BGA radius as 0.245 mm with an allowable variation of ±0.015 mm, and the pad center-to-center distance should be controlled near 0.8 mm. Based on these findings, process control strategies are proposed: low-loop wire bonding (loop height < 50–80 μm) with statistical process control; substrate warpage controlled through symmetric copper filling, a thick-middle dielectric stack-up, and distributed symmetric cavity layout, combined with eutectic pressure of 1.5 kPa, validated on 10 fabricated substrates with peak warpage consistently < 80 μm, void rate 4.75%, and solder overflow 94%; and BGA soldering using high-precision vision alignment and controlled collapse (20–35%). This work provides a theoretical basis and practical process pathway for transitioning 3D-SiP vertical interconnects from ideal design to mass production. Full article

Reliable electricity supply is essential for healthcare institutions, particularly where grid instability can disrupt service continuity, compromise patient safety, and affect the operation of critical medical equipment. In this context, selecting an appropriate photovoltaic (PV) proposal is a complex decision problem that requires assessing technical, economic, environmental, and regulatory factors jointly. This study develops a hybrid multi-criteria decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support PV proposal selection in healthcare institutions. The framework was applied to four competing proposals for a hospital case study in Barranquilla, Colombia. After integrating FAHP and DEMATEL, the economic, technical, and environmental criteria received balanced interdependence-adjusted weights of 0.324, 0.337, and 0.338, respectively. At the same time, DEMATEL identified the technical dimension as the main net influencing dimension within the expert-elicited influence network. The final ranking placed Proposal 1 first, followed by Proposal 4, Proposal 2, and Proposal 3, with closeness coefficients of 0.530, 0.518, 0.498, and 0.492, respectively. Additional comparative analysis showed that omitting DEMATEL changed the winning alternative, whereas preserving the FAHP–DEMATEL weighting structure and replacing TOPSIS with MARCOS yielded the same ranking. Robustness analyses further showed that the ranking remained stable in most supplier-exclusion and leave-one-expert-out scenarios. In contrast, bootstrap-based probabilistic sensitivity analysis showed that Proposal 1 ranked first in 96.2% of the replications. These results support the practical usefulness of the proposed framework for decision-making in healthcare energy planning. Full article

With the continuous increase in renewable energy penetration, the traditional distribution network is gradually evolving into the active distribution network. Facing increasingly severe regulation pressure, relying solely on resource allocation from a single side of the distribution network can no longer provide adequate flexibility support. As an effective carrier integrating sources and loads, collaborative mutual assistance between the microgrid and the distribution network has become an inevitable trend to tap the flexibility potential of multiple entities. However, the current insufficient coordination of multi-type flexibility resources and the lack of deep interaction between the distribution network and the microgrid limit system flexibility, affecting the secure operation of the power grid. Therefore, this paper proposes an active distribution network–microgrid collaborative planning method for flexibility enhancement. Firstly, a collaborative optimal allocation model of nodal and grid-level flexibility resources for the active distribution network is established. The upper tier minimizes the annualized comprehensive cost, while the lower tier minimizes the annual operational cost and optimizes flexibility indices, comprehensively considering constraints like equipment investment, system security, and flexibility supply–demand balance. Secondly, the coupling relationship between the distribution network and the microgrid is established through tie-lines to construct the active distribution network–microgrid collaborative planning model. Finally, an accelerated and robust analytical target cascading solution strategy is proposed. By constructing a balancing coefficient, it eliminates the algorithm’s sensitivity to initial penalty weights, effectively improving the stability and efficiency of the model solution. Case study analysis verifies the effectiveness of the proposed method. Full article

The global energy landscape is undergoing a low-carbon, diversified, and high-efficiency transition, creating an urgent need to develop intelligent operation and maintenance (O&M) technologies for critical nuclear power equipment to boost plant economic efficiency. As the core “heart” component of the primary loop, the reactor coolant pump (main pump) must meet extremely stringent reliability criteria to ensure safe and stable operation of nuclear facilities. Existing remaining useful life (RUL) prognostics for main pumps mostly output deterministic point estimates; they fail to quantify predictive uncertainties and cannot provide credible risk intervals to support maintenance decision-making. To fill this research gap, this study first performs coupled thermomechanical failure simulations for three vulnerable main pump components: the rotor shaft assembly, double-cone sealing structure, and motor shielding sleeve. Simulation results are validated via tests on a full-scale main pump prototype bench to extract sensitive degradation characteristic parameters. Accordingly, a hybrid Bayesian Transformer-LSTM prognostic framework is proposed for main pump RUL prediction with built-in uncertainty quantification. Data augmentation is utilized to expand multi-source degradation datasets of main pumps. The Mahalanobis distance is employed to build component-level health indicators (HIs), and a cloud barycenter weighted evaluation method fuses these sub-component HIs into a unified system-level comprehensive health index (CHI). Using the fused CHI as model input, the Bayesian Transformer-LSTM architecture incorporates probabilistic fully connected layers to simultaneously capture local time-series fluctuations and long-term global degradation trends, enabling joint RUL regression and uncertainty quantification. A full-scale main pump prototype from an in-service nuclear power plant is used to validate the multi-source data fusion strategy. Quantitative evaluation results show that the proposed method achieves a coefficient of determination R² = 0.997, root mean square error (RMSE) = 0.018, and prediction interval coverage probability (PICP) = 0.839. Comparative ablation experiments further confirm that the proposed model delivers outstanding fitting precision and reliable uncertainty quantification, enabling long-timescale full-lifecycle health characterization of main pumps. Full article

Metal-bearing tailings, slimes, metallurgical residues, spent catalysts, ashes, sludges, batteries, and electronic wastes are increasingly important secondary resources, but recovery is constrained by low and variable grades, fine particles, complex phase associations, passivation, and impurity-sensitive downstream processing. Hydrodynamic cavitation (HC) can modify selected flowsheet functions through interfacial renewal, localized mechanical action, gas–liquid transfer, fine-bubble generation, particle conditioning, and phase dispersion. The evidence was critically appraised across three independent dimensions: system relevance, causal attribution, and endpoint completeness. Application-level evidence is most developed for transport intensification in selected scheelite, uranium-bearing, and refractory-gold systems; particle conditioning and washing; spent-catalyst coating liberation; metal-bearing sludge treatment; copper cementation; and preparation of liquid emulsion membranes. Representative secondary-feed studies report conditioning, preconcentration, mobilization, and downstream separation responses, but complete feed-to-product recovery with controlled liquid and solid loops remains uncommon. Evidence is especially limited for battery black mass, electronic wastes, rare-earth-bearing residues, complex slags, metallurgical dusts, and multi-metal streams. HC is therefore most credible as a targeted module applied to a verified process limitation. A flowsheet advantage is established only when local gains persist through product recovery without offsetting increases in chemical use, water demand, energy consumption, equipment wear, or residual-stream burden. Full article