Search Results (262)

We studied the adsorption thermodynamics and mechanism behind the binding of nitric oxide (NO) in the interior surfaces and structural fragments of the high metal center density microporous Metal-Organic Framework (MOF) CPO-27-Cu, by gas sorption, at a series of temperatures. For the purpose of comparison, we also measured the corresponding CO₂ adsorption isotherms, and as a result, the isosteric heats of adsorption for the two studied adsorptives were derived, being in the range of 12–15 kJ/mol for NO at loadings up to 0.5 NO molecules per formula unit (f.u.) of the bare compound (C₄O₃HCu), and 23–25 kJ/mol CO₂ in the range 0–1 CO₂ per f.u. Microscopically, the mode of NO binding near the square pyramid Cu(II) centers was directly accessed with the use of in situ NO gas adsorption X-ray Absorption Spectroscopy (XAS). Additionally, during the vacuum/temperature activation of the material and consequent NO adsorption, the electronic state of the Cu-species was monitored by observing the corresponding X-ray Near Edge Spectra (XANES). Contrary to the previously anticipated chemisorption mechanism for NO binding at Cu(II) species, we found that at slightly elevated temperatures, under ambient, but also cryogenic conditions, only relatively weak physisorption takes place, with no evidence for a particular adsorption preference to the coordinatively unsaturated Cu-centers of the material. Full article

To enhance the operational stability of distribution networks during peak periods, this paper proposes a multi-timescale operational method considering load forecasting impacts. Firstly, the Crested Porcupine Optimizer (CPO) is employed to optimize the hyperparameters of long short-term memory (LSTM) networks for an accurate prediction of the next-day load curves. Building on this foundation, a multi-timescale optimization strategy is developed: During the day-ahead operation phase, a conservation voltage reduction (CVR)-based regulation plan is formulated to coordinate the control of on-load tap changers (OLTCs) and distributed resources, alleviating peak-shaving pressure on the upstream grid. In the intraday optimization phase, real-time adjustments of OLTC tap positions are implemented to address potential voltage violations, accompanied by an electrical distance-based control strategy for flexible adjustable resources, enabling rapid voltage recovery and enhancing system stability and robustness. Finally, a modified IEEE-33 node system is adopted to verify the effectiveness of the proposed multi-timescale operational method. The method demonstrates a load forecasting accuracy of 93.22%, achieves a reduction of 1.906% in load power demand, and enables timely voltage regulation during intraday limit violations, effectively maintaining grid operational stability. Full article

The increased uptake of Electric Vehicles (EVs) requires the installation of charging stations in parking lots, both to facilitate charging while running daily errands and to support EV owners with no access to home charging. Photovoltaic (PV) generation is ideal for powering up EVs, both for environmental reasons and for the benefit it creates for Charging Point Operators (CPOs). In this paper, we propose a centralised V1G Smart Charging (SC) algorithm for EV parking lots, considering real EV charging dynamics, which minimises both the EV charging costs for their owners and the CPO electricity provision costs or the related CO₂ emissions. We also introduce an innovative SC benefit-splitting algorithm that makes sure SC savings are fairly split between EV owners. Eight scenarios are described, considering costs or emissions minimisation, with and without a PV system. The centralised algorithm is benchmarked against a decentralised one, and tested in an exemplary workplace parking lot in Denmark, that includes includes 12 charging stations and one PV system, owned by the same entity. Reductions of up to 11% in EV charging costs, 67% in electricity provision costs for the CPO, and 8% in CO₂ emissions are achieved by making smart use of a 35 kWp rooftop PV system. Additionally, the SC benefit-splitting algorithm successfully ensures that EV owners save money when adopting SC. Full article

Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an excellent method to solve this problem; however, the standard CPO has limitations, such as the lack of adaptive parameter tuning to adapt to complex environments, slow convergence, and the tendency to fall into local optimal solutions. To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. Q-learning is employed to adaptively adjust the key parameters of the CPO, thereby overcoming the limitations of traditional fixed-parameter settings. Inspired by the porcupine’s defense mechanisms, a novel audiovisual coordination strategy is introduced to balance visual and auditory responses, accelerating convergence in the early optimization stages. A refined position update mechanism is designed to prevent excessive step sizes and boundary violations, enhancing the algorithm’s global search capability. A B-spline-based trajectory smoothing method is also incorporated to improve the feasibility and smoothness of the planned paths. In this paper, we compare QCPO with four outstanding heuristics, and QCPO achieves the lowest path cost in all three test scenarios, with path cost reductions of 30.23%, 26.41%, and 33.47%, respectively, compared to standard CPO. The experimental results confirm that QCPO offers an efficient and safe solution for UAV path planning. Full article

Competition in the industry forces palm oil producers to keep raising the caliber of their output. One of the businesses involved in the Crude Palm Oil sector is PT. XYZ. The quality of the CPO that PT. XYZ produces is a top priority. To ascertain the quality of the oil produced, crude palm oil (CPO) quality supervision is always conducted. At PT. XYZ, supervision is carried out to ascertain the degree of machine efficiency in addition to determining the oil quality. Additionally, this demonstrates PT. XYZ’s dedication to preserving the caliber of the oil produced. PT. XYZ consistently strives to produce oil that satisfies established criteria. A number of characteristics, including moisture content, loss content, and the value of FFA parameters, typically affect the quality of palm oil. The reduction in CPO quality caused by a rise in Free Fatty Acid (FFA) levels is a frequent problem in CPO mills, according to testing results on FFA levels in CPO from June to July 2024 because CPO storage is one of the key elements in assessing CPO quality and protecting it from impurities that can lower palm oil quality. A cause-and-effect diagram and Failure Mode and Effect Analysis are the methods used to examine the rise in FFA levels. Full article

A palm oil company based in Bangka is actively expanding its operations in the palm oil processing industry. The company specializes in producing crude palm oil (CPO) and palm kernel, with its production process encompassing five key stages: weighing, sterilization, threshing, pressing, and clarification. Oil loss, especially at the pressing station, is one of the company’s biggest problems. Nuts, fibers, empty bunches, and effluent are some of the sources of oil loss in CPO production. Since extreme losses that exceed set norms can cause serious inefficiencies and financial repercussions, it is imperative that the organization identifies and mitigates the underlying causes of oil loss. One option that the business could use is the fault tree analysis (FTA) method, which offers a methodical way to pinpoint the root causes of production inefficiencies to solve this problem. According to current assessments, empty bunches caused the largest average oil loss over a one-month period, with a loss rate of 0.11%. Oil loss at the pressing station is caused by a number of factors, such as inadequate maintenance practices, non-compliance with established work procedures, suboptimal ripeness levels of harvested palm fruit, and operator neglect in maintaining optimal machine pressure in accordance with company standards. To reduce oil loss in the production process and increase efficiency, these concerns need to be addressed. Full article

With the goal of addressing the difficulty of rapidly identifying the source of fire in commercial buildings, this study builds a numerical fire model based on the fire dynamics simulator (FDS) and combines it with a hybrid model to predict the location of a fire source. Different scenarios were built to simulate the spatial and temporal distributions of key parameters such as temperature, smoke, and CO concentration during the fire process. Combining convolutional neural networks (CNNs) and support vector machines (SVMs) for prediction, the fire-source location prediction model with temperature, smoke, and CO concentration as feature quantities was constructed, and the hyperparameters affecting the model accuracy and generalisation were optimised by the Crested Porcupine Optimizer (CPO) algorithm. The experimental results show that the positioning error of this method under the building plane is less than 0.95 m, the mean absolute error (MAE) is within 0.35, and the root-mean-square error (RMSE) is within 0.41, which are 43% and 82% higher than the unoptimised model, respectively. The localisation accuracy of the fire-source room is 97.61%. In addition, the model’s anti-interference performance was tested under various extreme conditions. The results show that the proposed model can ensure the accurate location of a fire source and can provide information in emergencies. Full article

In view of the difficulties encountered when tuning parameters and the lack of anti-interference capabilities exhibited by high-precision trajectory-tracking control of quadrotor UAVs in complex dynamic environments, this paper proposes a fusion control framework based on an improved crowned pig optimisation algorithm (ICPO) and preset performance anti-disturbance control (PPC-ADRC). Initially, this paper addresses the limited convergence efficiency of the traditional crowned pig algorithm (CPO) by introducing a dynamic time threshold mechanism and an adaptability-based directed elimination strategy to balance the algorithm’s global exploration and local development capabilities. This results in a significant improvement in the convergence speed and optimisation accuracy. Secondly, a hierarchical control architecture is designed, with the outer loop using a PPC-ADRC controller to dynamically constrain the tracking error boundary using an exponential performance funnel function and a combined state observer (ESO) to estimate the compound disturbance in real time. The inner-loop attitude control uses ADRC, and the 24-dimensional parameters of the ADRC (including the ESO bandwidth and non-linear feedback gain) are optimised autonomously using the ICPO to achieve efficient parameter tuning. The simulation experiments demonstrate that, in comparison with the original CPO, the ICPO attains an average fitness ranking that is superior in the CEC2014–2022 benchmark test, thereby substantiating its global optimisation capability. In the PPC-ADRC controller parameter optimisation, the preset performance of the ICPO-tuned PPC-ADRC controller (PPC-ADRC) is superior to that of the particle swarm optimisation (PSO), genetic algorithm (GA) and original CPO. The ICPO-based PPC-ADRC controller is shown to reduce the total error by more than 45.6% compared to the ordinary ADRC controller in the task of tracking a spiral trajectory, and it effectively reduces the overshoot. Its capacity to withstand complex wind disturbances is notably superior to that of the traditional PID and ADRC architectures. Stability analysis further proves that the system satisfies the Lyapunov convergence condition in a finite time. This research provides a theoretical foundation for the high-precision control of UAVs in complex dynamic environments. Full article

h-BN spherical nanoparticles, known as white graphene, have good anti-wear properties, long service life, chemical inertness, and stability, which provide superior lubricating performance as a solid additive item to nanofluids. However, the poor dispersion stability of h-BN nanoparticles in nanofluids is a bottleneck that restricts their application. Currently, to prepare h-BN nanofluids with good dispersion stability, a cold plasma (CP) modification of h-BN nanoparticles is proposed in this study. In this research, h-BN nanofluid with added surfactant (SNL), CP-modified h-BN nanofluid with N₂ as the working gas (CP(N₂)NL), and CP-modified h-BN nanofluid with O₂ as the working gas (CP(O₂)NL) were prepared, separately. The mechanism of the dispersion stability of CP-modified h-BN nanofluid was analyzed using X-ray photoelectron spectroscopy (XPS), and the performance of CP-modified nanofluid was analyzed based on static observation of nanofluid, kinematic viscosity, and heat transfer properties. Finally, friction and wear experiments were conducted to further analyze the tribological performance of h-BN nanofluids based on the coefficient of friction, 3D surface morphology, surface roughness (Sa), scratches, and micro-morphology. The results show that CP-modified h-BN nanofluid has excellent dispersed suspension stability and can be statically placed for more than 336 h. The CP-modified h-BN nanofluid showed stable friction-reducing, anti-wear, and heat transfer performance, in which the coefficient of friction of h-BN nanofluid was about 0.66 before and after 24 h of settling. The Sa value of the sample was reduced by 31.6–49.2% in comparison with pure cottonseed oil (CO). Full article

The battery management system (BMS) is crucial for the efficient operation of batteries, with state of health (SOH) prediction being one of its core functions. Accurate SOH prediction can optimize battery management, enhance utilization and range, and extend battery lifespan. This study proposes an SOH estimation model for lithium-ion batteries that integrates the Crested Porcupine Optimizer (CPO) for parameter optimization, Extreme Learning Machine (ELM) for prediction, and Adaptive Bandwidth Kernel Function Density Estimation (ABKDE) for uncertainty quantification, aiming to enhance the long-term reliability and sustainability of energy storage systems. Health factors (HFs) are extracted by analyzing the charging voltage curves and capacity increment curves of lithium-ion batteries, and their correlation with battery capacity is validated using Pearson and Spearman correlation coefficients. The ELM model is optimized using the CPO algorithm to fine-tune input weights (IWs) and biases (Bs), thereby enhancing prediction performance. Additionally, ABKDE-based probability density estimation is introduced to construct confidence intervals for uncertainty quantification, further improving prediction accuracy and stability. Experiments using the NASA battery aging dataset validate the proposed model. Comparative analysis with different models demonstrates that the CPO-ELM-ABKDE model achieves SOH estimation with a mean absolute error (MAE) and root-mean-square error (RMSE) within 0.65% and 1.08%, respectively, significantly outperforming other approaches. Full article

Currently, the central task of predicting rock fragmentation is becoming increasingly important in the field of rock mechanics and engineering blasting. This direction has been shown to be crucial to ensure the safety and durability of construction projects. In this study, a BP neural network is constructed to optimize the network weights and bias with the help of CPO algorithm, and its practicality and reliability are tested through the case of an iron ore mine in Hunan Province, China. The model is trained and tested using typical blasting data, and the results show that it performs efficiently, with a short prediction time and a high level of confidence. The predicted values were consistent with actual engineering measurements, achieving an RMSE of only 0.015813, which indicates strong potential for guiding practical blasting block size predictions. Full article

Photovoltaic (PV) energy has become a pillar of clean energy in rural areas. However, its extensive deployment in regions with geographically dispersed locations and limited road conditions has made efficient inspection a significant challenge. To address these issues, this study proposes a multi-regional PV inspection path planning method based on the crested porcupine optimization (CPO) algorithm. This method first employs a hybrid optimization framework combining a genetic algorithm, Simulated Annealing, and Fuzzy C-Means Clustering (GASA-FCM) to divide PV power stations into multiple regions, adapting to their dispersed distribution characteristics. Subsequently, the CPO algorithm is used to calculate obstacle-avoidance paths, replacing the Euclidean distance in the traditional Traveling Salesman Problem (TSP) with adaptive rural road constraint conditions to better cope with the geographical complexity in real-world scenarios. The simulation results verify the advantages of this method, achieving significantly shorter path lengths, higher computational efficiency, and stronger stability compared to the traditional solutions, thereby improving the efficiency of rural PV inspection. Moreover, the proposed framework not only provides a practical inspection strategy for rural PV systems but also offers a solution to the Multiple-Depot Multiple Traveling Salesmen Problem (MDMTSP) under constrained conditions, expanding its application scope in similar scenarios. Full article

The hypoxic microenvironment is the main challenge for the repair of damaged tissue, and oxygen supply is an effective means of alleviating hypoxia. In this study, a series of core–shell-structured calcium peroxide/poly(ethylene glycol)@silica (CPO@SiO₂) nanoparticles are prepared to generate oxygen steadily. The size of the CPO@SiO₂ nanoparticles ranges from 205 to 302 nm, with a narrow polydispersity index (PDI). In this system, the nano CPO core acts as the oxygen source to improve hypoxia, while the SiO₂ shell layer serves as the physical barrier to control the oxygen-generating rate and improve biocompatibility. The results suggest that the thickness of the SiO₂ shell layer can be modulated by adjusting the amount of tetraethyl orthosilicate (TEOS). The prepared CPO@SiO₂ nanoparticles show a controlled oxygen-generating rate. Moreover, compared with CPO, the CPO@SiO₂ nanoparticles have good biocompatibility. To assess the modulating effects for the hypoxic microenvironment, L929 cells are co-cultured with CPO@ SiO₂ nanoparticles under hypoxia. The results suggest that the CPO@ SiO₂ nanoparticles can support the cell survival under hypoxia. Moreover, they can effectively decrease oxidative stress damage and reduce the levels of expression of hypoxia-induced superoxide dismutase (SOD) and malondialdehyde (MDA). Therefore, the prepared CPO@ SiO₂ nanoparticles with controlled oxygen-generating properties could be a promising candidate for repairing damaged tissue. Full article

Oregano essential oil (OEO) has gained attention for its broad pharmacological activities, such as anti-inflammatory, antimicrobial, and anticancer properties. This study aimed to analyze the phytochemical composition and biological activity of OEO obtained from wild-growing Origanum vulgare L. in Romania. Gas chromatography–mass spectrometry (GC–MS) analysis identified p-cymene (43.98%), γ-terpinene (22.16%), and thymol (11.46%) as major constituents, with notable differences from previously reported chemotypes. Antioxidant activity was assessed using the DPPH, ABTS radical scavenging assay, and TPC. OEO has a moderate antioxidant activity, with IC₅₀ values of 134.67 ± 1.32 µg/mL (DPPH) and 88.15 ± 0.045 Inh% (ABTS) and a TPC of 159.63 mg GAE/g extract. The cytotoxicity of the simple water dispersion of OEO, OEO solubilized with polyethylene glycol 400 (OEO-PEG), and that solubilized with Tween 20 (OEO-Tw) was evaluated on human melanoma (A375) and human colorectal adenocarcinoma (HT-29) cancer cell lines, as well as on the normal human immortalized keratinocytes (HaCaT) cell line. The results demonstrated a significant inhibition of cancer cell viability with no recorded cytotoxic effect on normal cells. The highest inhibition of cell viability was recorded for OEO-PEG 200 µg/mL (7.22% ± 6.51 in A375 cell line and 22.25% ± 10.08 in HT-29 cell line). In cancer cells, OEO and its formulations significantly reduced malondialdehyde (MDA) levels (up to 41.24% in A375 cells and up to 48.58% in HT-29 cells), suggesting potent antioxidant activity. Moreover, treatment with OEO increased caspase 3/7 activation two-fold in treated A375 cells, while high-resolution respirometry studies revealed that OEO induces mitochondrial dysfunction by acting as a potential uncoupling agent. Molecular docking analysis suggested that β-caryophyllene oxide (CPO), a minor constituent of OEO, may act as a potential inhibitor of 3-phosphoinositide-dependent protein kinase-1 (PDPK1), indicating a possible mechanism of anticancer activity. Our findings highlight the potential of OEO as a natural anticancer agent, emphasizing the need for further investigations to elucidate its exact molecular mechanisms and therapeutic applicability. Full article

In actual operation, the output power of distributed marine photovoltaic monitoring faces challenges from wind, waves, and other dynamic motion factors. To address these challenges, this paper proposes a novel maximum power point inference method for distributed marine photovoltaic monitoring. First, a digital fusion model has been constructed to obtain a comprehensive dataset of the distributed marine photovoltaic monitoring system. Second, Multilayer Convolutional Neural Networks (CNN) are constructed to extract the local high-frequency motion characteristics, Squeeze and Excitation Attention (SE-Attention) is employed to capture the global low-frequency motion characteristics, and Long Short-Term Memory (LSTM) is utilized to perform temporal modeling of the motion characteristics. Subsequently, the Crested Porcupine Optimizer (CPO) algorithm is used to achieve high-precision recognition of the maximum power point in distributed marine photovoltaic monitoring. Finally, the effectiveness of the method is verified through experiments and simulations. The results indicate that the maximum power point of distributed marine photovoltaic monitoring exhibits multi-spectral motion characteristics, with the highest frequency at 335.2 Hz and the lowest frequency at 12.9 Hz. The proposed method enables efficient inference of the maximum power point for distributed marine photovoltaic monitoring under motion conditions, with an accuracy of 98.63%. Full article