Optimizing the image quality of peripancreatic blood vessels through the clinical application of single-energy spectral computed tomography (CT) imaging.

Background: With the increase of pancreatic tumor patients in recent years, there is an urgent need to find a way to treat pancreatic tumors. Surgery is one of the best methods for the treatment of pancreatic tumors, the success of which depends on the evaluation of peripancreatic vessels before surgery. Computed tomography (CT), as a non-invasive, fast, and economical auxiliary examination method, is undoubtedly one of the best means of clinical auxiliary examination. In this study, we investigated the impact of single-energy spectral CT imaging on the image quality of peripancreatic blood vessels and the clinical value of low-keV imaging in enhancing the image quality of peripancreatic arteriovenous vessels. Methods: We prospectively enrolled 103 patients who underwent abdominal vascular-enhanced CT examinations at the Affiliated Hospital of Hebei University between December 2022 and May 2023 and who were all scanned with the dual-energy feature on the United Imaging ATLAS scanner. The images were reconstructed at 70 keV, mixed energy, and optimized single energy in the post-processing station of United Imaging Healthcare Technology Co., Ltd. The CT value and contrast-to-noise ratio (CNR) of the superior mesenteric artery (SMA), gastroduodenal artery (GDA), inferior pancreaticoduodenal artery (IPDA), and superior mesenteric vein (SMV) were compared across energy levels, and then the image quality was subjectively evaluated. One-way analysis of variance and rank-sum tests were utilized for the statistical analysis. Results: The CT values of SMA, GDA, IPDA, and SMV in the optimal single energy group were 358.37±70.24, 323.36±88.23, 300.76±76.27, and 257.74±20.56 Hounsfield unit (HU), respectively, which were superior to those in the mixed energy (241.66±47.69, 235.17±53.71, 207.36±45.17, and 187.39±23.21 HU) and 70 keV groups (260.89±54.27, 252.41±58.87, 223.17±43.65, and 203.18±18.17 HU) (P<0.05). The diagnostic efficacy was greater in the optimal single energy group than in the other 2 groups (4.63±0.50, 3.91±0.57, and 4.23±0.83) (P<0.05). Conclusions: The optimal single energy for showing peripancreatic blood vessels is 62±7 keV when utilizing single-energy spectral CT imaging.

A Novel Framework of Developing a Predictive Model for Powder Bed Fusion Process.

The powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for predictive modeling of a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for studying the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters that is, laser specifications and mechanical properties, and how to obtain an optimum range of volumetric energy density for producing parts with high density (>99%), as well as better ultimate mechanical properties. In this article, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage (i.e., around 10%), which are used for process parameter selection in accordance with user or manufacturer part performance requirements. These models are based on techniques such as support vector regression, random forest regression, and neural network. It is shown that the intelligent selection of process parameters using these models can achieve a high density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

Efficient design of energy microgrid management system: A promoted Remora optimization algorithm-based approach.

Microgrids are a promising solution for decentralized energy generation and distribution, offering reliability, efficiency, and resilience. These small-scale power systems can operate independently or connect to the main grid, providing greater reliability and resilience. However, integrating renewable energy into microgrids presents challenges due to their unpredictable nature and fluctuating load of electricity. Energy management strategies play a crucial role in optimizing the operation of microgrids, aiming to balance electricity supply and demand, maximize renewable energy utilization, and minimize operational costs. Various approaches have been proposed for energy management in microgrids, including optimization algorithms, machine learning techniques, and intelligent control systems. This study proposes an optimized and efficient strategy for microgrids operating in both independent and grid-connected modes, focusing on microgrids that utilize a combination of solar and green energy sources. The proposed approach, based on the Promoted Remora Optimization (PRO) algorithm, aims to meet load power requirements at the lowest possible cost while ensuring constant DC bus voltage and safeguarding batteries against overcharging and depletion. The CRO method effectively optimized the charging process, maintaining a consistent level of charge and achieving a final SoC of 33.37 %-33.60 %. It also demonstrated high system efficiency, with an average of 87.99 %, and a range of 87.80 %-88.03 %. The optimizer efficiency ranged from 83.12 % to 86.52 %, with an average of 86.46 %. The CRO method also achieved reasonable operating costs, with a cost per power of $0.1687/kW to $0.1699/kW and a daily cost of $1,379,595 to $1,479,998. Overall, the CRO method showed promise in optimizing the charging process in terms of efficiency and cost-effectiveness. Comparative analysis with existing literature is conducted to evaluate the effectiveness of the proposed approach, demonstrating its superior results compared to other energy management strategies for microgrids. This study contributes to the field of microgrid energy management by providing a novel approach based on the PRO algorithm and demonstrating its effectiveness through comparative analysis.

Investigation on the proton range uncertainty with spectral CT-based virtual monoenergetic images.

OBJECTIVE: The stopping power ratio (SPR) prediction error will contribute to the range uncertainty of proton therapy. Spectral CT is promising in reducing the uncertainty in SPR estimation. The purpose of this research is to determine the optimal energy pairs of SPR prediction for each tissue and to evaluate the dose distribution and range difference between the spectral CT with the optimal energy pairs method and the single energy CT (SECT) method. METHODS: A new method was proposed based on image segmentation to calculate the proton dose with spectral CT images for the head and body phantom. CT number of each organ region were converted to SPR with the optimal energy pairs of each organ. The CT images were segmented into different organ parts with thresholding method. Virtual monoenergetic (VM) images from 70 keV to 140 keV were investigated to determine the optimal energy pairs for each organ based on Gammex 1467 phantom. The beam data of Shanghai Advanced Proton Therapy facility (SAPT) was employed in matRad (an open-source software for radiation treatment planning) for the dose calculation. RESULTS: The optimal energy pairs were obtained for each tissue. The dose distribution of two tumor sites (brain and lung) were calculated with the aforementioned optimal energy pairs. The maximum dose deviation between spectral CT and SECT at the target region was 2.57% and 0.84% for the lung tumor and brain tumor respectively. The range difference between spectral and SECT was significant with 1.8411 mm for the lung tumor. γ passing rate was 85.95% and 95.49% for the lung tumor and brain tumor with the criterion 2%/2 mm. CONCLUSIONS: This work presents a way to determine the optimal energy pairs for each organ and to calculate the dose distribution based on the more accurate SPR prediction.

Robotic arm trajectory optimization based on multiverse algorithm.

For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a trajectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are proposed. The multi-universe algorithm has better robustness and convergence accuracy in solving single-objective constrained optimization problems than other algorithms. In contrast, it has the disadvantage of slow convergence and quickly falls into local optimum. This paper proposes a method to improve the wormhole probability curve, adaptive parameter adjustment, and population mutation fusion to improve the convergence speed and global search capability. In this paper, we modify MVO for multi-objective optimization to derive the Pareto solution set. We then construct the objective function by a weighted approach and optimize it using IMVO. The results show that the algorithm improves the timeliness of the six-degree-of-freedom manipulator trajectory operation within a specific constraint and improves the optimal time, energy consumption, and impact problems in the manipulator trajectory planning.

The optimal energy management in multiple grids: Impact of interconnections between microgrid-nanogrid on the proposed planning by considering the uncertainty of clean energies.

Environmental pollution has attracted much attention in recent years. Many protocols have concluded between the major countries emitting these gases to reduce its consequences. Hence, governments and countries have considered various strategies to reduce it. One of the most important of these is the use of clean energies in the format of small networks called microgrids and nanogrids. So, this paper presents the optimal energy planning and management of the proposed test system, including microgrid, nanogrid, and the main grid. The whole grids are connected by transmission lines, enabling them to work in connected or islanding mode. Diverse scenarios are considered to investigate and cover all aspects of this. Thus, at first, the operation of each network is studied separately in the islanding mode, then the behavior of the proposed system in the connected mode is discussed. Moreover, uncertain parameters are involved in the programming to perform stochastic planning. The economic-ecological analysis is assumed as the objective function. The problem is modeled as MILP and solved simultaneously by augmented epsilon constrain (AEC) and LP-metric methods in GAMS software. The results show the effectiveness of the proposed planning to coordinate the function of the micro-nanogrid combination scheme.

Evaluation of different ablation strategies verifying the optimal overlap ratio in point-by-point laser balloon ablation for patients with atrial fibrillation.

BACKGROUND: Optimal overlap ratio remains unclear in point-by-point laser balloon (LB) ablation. OBJECTIVE: This study sought to determine the optimal overlap strategy with target energies on the acute and chronic outcomes in LB pulmonary vein (PV) isolation (PVI). METHODS: Consecutive 38 patients (148 PVs) with atrial fibrillation underwent the first-generation LB PVI with the following protocols based on the overlap ratios for each PV anterior/posterior wall: 50%/50% (13 patients [49 PVs], group A), 50%/25% (15 patients [60 PVs], group B), and 25%/25% (10 patients [39 PVs], group C). High energies (240-255 J: 12 W / 20 seconds, 8.5 W / 30 seconds), moderate energies (200-210 J: 10 W / 20 seconds, 7 W / 30 seconds), and low-to-moderate energies (low, 165-170 J: 5.5 W / 30 seconds, 8.5 W / 20 seconds) were targeted for left PV anterior walls, right PV anterior walls, and bilateral PV posterior walls, respectively. First-pass PVI, the other procedure-related data, and atrial tachyarrhythmia recurrences were analyzed. RESULTS: First-pass PVI rate per PV was higher in group A (94%) than in group B (88%) and group C (62%) (P < .001). All PVs were finally isolated. First-pass time, total LB PVI time, complications, and atrial tachyarrhythmia recurrences during a mean follow-up of 11 ± 5 months did not differ between the groups. A few residual gaps after first-pass LB ablations were found for PV anterior walls even in group A and group B. CONCLUSION: Sufficiently overlapped LB ablation promises a high rate of first-pass PVI without adverse outcomes. High energy could be required for PV anterior walls.

Research Trends and Future Perspectives in Marine Biomimicking Robotics.

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Higher Protein but Not Energy Intake Is Associated With a Lower Prevalence of Frailty Among Community-Dwelling Older Adults in the French Three-City Cohort.

BACKGROUND: The hypothesis that increasing protein and energy intakes may confer protection against frailty has been suggested, although few studies have examined these associations, especially regarding current protein energy recommendations in the older population. AIM: To assess the association between frailty and higher protein and energy intakes. METHODS: The present study is a secondary, cross-sectional analysis of the French Three-City cohort. Participants were community-dwelling older adults aged 65 and over. Frailty was defined as a score of 3/5 among the 5 Fried criteria: weight loss, exhaustion, muscle weakness, slowness, and physical activity. Protein intake was set at a daily intake ≥1 g/kg body weight and optimal energy intake defined as a daily intake ≥30 kcal/kg. Logistic regressions were performed while adjusting for several sociodemographic and clinical variables. RESULTS: The study sample consisted of 1345 participants [mean age (SD) 74.0 (4.9) years], of whom 55 (4.1%) were identified as frail. After adjusting for sociodemographic and clinical variables, higher protein intake was significantly associated with a lower frailty prevalence [odds ratio (OR) = 0.41, 95% confidence interval (CI) = 0.19-0.89; P = .024] whereas no significant association was observed between an optimal energy intake and the presence of frailty (OR = 0.70, 95% CI = 0.32-1.55, P = .38). CONCLUSIONS: A 1 g/kg protein intake was associated with a lower prevalence of frailty in French community-dwelling older subjects. This observation adds to the literature, suggesting increasing the daily protein intake to at least 1 g/kg for older adults aged 65 and more.

Optimal Energy Transfer in Light-Harvesting Systems.

Photosynthesis is one of the most essential biological processes in which specialized pigment-protein complexes absorb solar photons, and with a remarkably high efficiency, guide the photo-induced excitation energy toward the reaction center to subsequently trigger its conversion to chemical energy. In this work, we review the principles of optimal energy transfer in various natural and artificial light harvesting systems. We begin by presenting the guiding principles for optimizing the energy transfer efficiency in systems connected to dissipative environments, with particular attention paid to the potential role of quantum coherence in light harvesting systems. We will comment briefly on photo-protective mechanisms in natural systems that ensure optimal functionality under varying ambient conditions. For completeness, we will also present an overview of the charge separation and electron transfer pathways in reaction centers. Finally, recent theoretical and experimental progress on excitation energy transfer, charge separation, and charge transport in artificial light harvesting systems is delineated, with organic solar cells taken as prime examples.