Optimal Acquisition Research Articles

3D path planning for UAV based on A hybrid algorithm of marine predators algorithm with quasi-oppositional learning and differential evolution

3D path planning is a critical requirement for the autonomy of unmanned aerial vehicle (UAV) navigation systems. In this paper, we propose a novel approach, the differential evolution combined marine predators algorithm (DECMPA), specifically tailored to address the UAV 3D path planning problem in complex scenarios. To handle stringent constraints, DECMPA adopts a multi-step approach. Initially, it utilizes quasi-oppositional learning to generate a more uniformly distributed initial population. Throughout the iterative process, it probabilistically generates quasi-oppositional populations and merges them with existing populations, selecting the superior individuals for the next generation to mitigate local optima. Additionally, DECMPA integrates the variance, crossover, and selection processes of differential evolution into the marine predators algorithm iterations. The greedy selection of test and target vectors further accelerates population convergence. Numerical optimization experiments are conducted using the 10 and 20-dimensional CEC 2021 test suite. Compared to other algorithms, DECMPA exhibits superior solution accuracy and convergence speed, resulting in substantial performance enhancement, thus establishing itself as an efficient algorithm. Furthermore, the adoption of spherical vector coordinates in solution encoding ensures higher quality and facilitates the production of feasible solutions. To verify the effectiveness and practicality of the proposed algorithm, comprehensive path planning simulation experiments are conducted. DECMPA is compared against nine other state-of-the-art heuristic algorithms across six 3D scenarios of varying complexity. The experimental results demonstrate DECMPA’s superiority in terms of optimal cost acquisition, solution quality, and stability. In conclusion, DECMPA presents a promising solution for addressing the challenges of UAV 3D path planning in complex environments. Its innovative approach and superior performance underscore its potential for real-world application in autonomous UAV navigation systems.

Exploring the silence of Japanese EFL learners: Its relationship with the degree of willingness to communicate (WTC)

The concept of willingness to communicate (WTC) is pivotal in understanding student engagement in English as a foreign language (EFL) classrooms. As a dynamic and multifaceted construct, WTC is subject to continual fluctuations throughout the communication process, often changing situationally. Traditionally, learner silence in class or during second language (L2) conversations is perceived negatively, as it can signal a lack of motivation to speak, thereby impeding optimal language acquisition. However, silence can also function as a communicative strategy or reflect learners’ essential feelings and emotions. This study explored the relationship between Japanese EFL learners’ situational WTC and their instances of silence during L2 (English) conversations. Participants were recruited for the study and were asked to deliver short speeches and engage in conversations with a researcher, during which their periods of silence were observed and recorded. All participant utterances were meticulously transcribed and analysed. Additionally, participants self-assessed their WTC for each utterance, and a stimulated recall interview was conducted to gather in-depth qualitative data. The results generally indicated that participants’ WTC was somewhat decreased when silence occurred between and during utterances. However, the findings also revealed the intricate and multifaceted nature of silence in relation to WTC, underscoring the need for a deeper understanding and nuanced consideration of learners’ silence. This study highlights the complexity of the interplay between WTC and silence, suggesting that silence should not be solely viewed as a negative phenomenon but rather as an integral part of the communicative process that warrants careful analysis and understanding.

NIMG-37. “SYNTHETIC” PERFUSION MRI WITH ADJUSTABLE ACQUISITION PARAMETERS IN BRAIN TUMORS USING DYNAMIC SPIN-AND-GRADIENT-ECHO ECHOPLANAR IMAGING

Abstract INTRODUCTION Dynamic susceptibility contrast (DSC) perfusion MRI can assess normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) in brain tumors, which can aid in differential diagnosis, molecular profiling, and identification of disease progression. However, CBV-optimized and PSR-optimized protocols differ in terms of acquisition parameters. Additionally, DSC dependency on acquisition parameters limits the universalizability of diagnostic cutoffs for nrCBV and PSR across institutions and scanners. This study aims to generate “synthetic” DSC datasets with adjustable synthetic acquisition parameters using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) to: 1) simultaneously generate nrCBV and PSR with optimal acquisition parameters, 2) compare DSC datasets with heterogeneous external cohorts. METHODS Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection. Multiple synthetic DSC datasets with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo gradient-echo (GE) data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. For each patient, synthetic protocols were set to match guideline-compliant CBV-optimized protocols, PSR-optimized protocols, and protocols used in external cohorts from the literature, for comparison. RESULTS Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection. Conversely, suboptimal PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman’s ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. CONCLUSIONS Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with a single injection, facilitating use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion parameters across heterogeneous multicenter datasets.

Development of a user-friendly calculator for a pediatric split-bolus polytrauma computed tomography protocol

We present here a user-friendly calculator for the setting of a pediatric split-bolus polytrauma computed tomography (CT) protocol with a mixed arterial and venous phase, aiming to both reduce radiation dose and improve workflow while assuring optimal image quality. All the different parameters are calculated based on patient’s weight with rapid computation of the injected contrast media and saline volumes, injection’s flow rate, injection’s timing, and optimal acquisition time. The designed calculator is built in a widely available Google Sheets file, accessible by a quick response (QR) code. Although polytrauma imaging represents the main goal of the technique, it can be used in a wide variety of contexts, including oncological, infectious, and vascular pathologies.Graphical

Kinetic Analysis and Metabolism of Poly(Adenosine Diphosphate-Ribose) Polymerase-1-Targeted 18F-Fluorthanatrace PET in Breast Cancer.

The poly(adenosine diphosphate-ribose) polymerase inhibitors (PARPi) have demonstrated efficacy in ovarian, breast, and prostate cancers, but current biomarkers do not consistently predict clinical benefit. 18F-fluorthanatrace (18F-FTT) is an analog to rucaparib, a clinically approved PARPi, and is a candidate biomarker for PARPi response. This study intends to characterize 18F-FTT pharmacokinetics in breast cancer and optimize image timing for clinical trials. A secondary aim is to determine whether 18F-FTT uptake in breast cancer correlates with matched frozen surgical specimens as a reference standard for PARP-1 protein. Methods: Thirty prospectively enrolled women with a new diagnosis of breast cancer were injected with 18F-FTT and imaged dynamically 0-60 min after injection over the chest, with an optional static scan over multiple bed positions starting around 70 min. Kinetic analysis of lesion uptake was performed using blood-pool activity with population radiometabolite corrections. Normal breast and normal muscle reference tissue models were compared with PARP-1 protein expression in 10 patients with available tissue. Plasma radiometabolite concentrations and uptake in tumor and normal muscle were investigated in mouse xenografts. Results: Pharmacokinetics of 18F-FTT were well fit by Logan plot reference region models of reversible binding. However, fits of 2-tissue compartment models assuming negligible metabolite uptake were unstable. Rapid metabolism of 18F-FTT was demonstrated in mice, and similar uptake of radiometabolites was found in tumor xenografts and normal muscle. Tumor 18F-FTT distribution volume ratios relative to normal muscle reference tissue correlated with tissue PARP-1 expression (P < 0.02, n = 10). The tumor-to-normal muscle ratio from a 5-min frame between 50 and 60 min after injection, a potential static scan protocol, closely corresponded to the distribution volume ratio relative to normal muscle and correlated to PARP-1 expression (P < 0.02, n = 10). Conclusion: This study of PARPi analog 18F-FTT showed that uptake kinetics invivo corresponded to expression of PARP-1 and that 18F-FTT quantitation is influenced by radiometabolites that are increasingly present late after injection. Radiometabolites can be controlled by using optimal image acquisition timing or normal muscle reference tissue modeling in dynamic imaging or a tumor-to-normal muscle ratio. Optimal image timing for tumor-to-normal muscle quantification in humans appears to be between 50 and 60 min after injection. Therefore, a clinically practical static imaging protocol commencing 45-55 min after injection may sufficiently balance 18F-FTT uptake with background clearance and radiometabolite interference for quantitative interpretation of PARP-1 expression invivo.

Dynamic feed scheduling for optimised anaerobic digestion: An optimisation approach for better decision-making to enhance revenue and environmental benefits

Anaerobic digestion (AD) offers a sustainable solution for clean energy production, with the potential for significant revenue enhancement through enhanced decision-making. However, the complexity and limited flexibility of AD systems pose challenges in developing reliable optimisation methods. Changing feeding strategies provides opportunities for efficient feedstock utilisation and optimal gas production, especially in volatile gas markets.To provide better decision-making tools in AD for energy production, we propose an integrated site model for the dynamic behaviour of the AD process in a biomethane-to-grid system and optimise production based on predicted gas prices. The model includes methods for optimal feed co-digestion strategies and integrates these results into a scheduling model to identify the optimal feedstock acquisition, feeding pattern, and potential gas storage operation considering feedstock availability, properties, sustainability, and fluctuating gas demand under different pricing variations.The methodology was tested on a 150 tonnes per day farm-scale AD plant in the UK, processing energy crops and manure considering both environmental (global warming potential) and economic objectives. The results showed strong adaptability of the proposed feeding schedule to the general trend of gas prices over time. To address the challenge of immediate price peaks, typically unattainable due to the system's sluggish behaviour and high retention times, the impacts of on-site storage were explored, leading to annual revenue increases ranging from 2 % to 7.4 %, depending on the pricing scheme, which translates to a significant boost in terms of revenue.

Automatic assignment of optimal data acquisition protocols for MRI examinations in Japanese

Automatic assignment of optimal data acquisition protocols for MRI examinations in Japanese

After initial acquisition, problem-solving leads to better long-term performance than example study, even for complex tasks

BackgroundSuccessful implementation of effective acquisition strategies (e.g., example study, problem-solving) could help improve mathematics performance. However, it is not yet fully understood when each acquisition strategy should be used, despite the practical value of this knowledge for mathematics textbook authors, teachers, and students. AimsBuilding upon two recent perspectives on when example study and problem-solving are beneficial, we proposed that the optimal acquisition strategy could depend on both task complexity and retention interval (i.e., time between the final practice opportunity and the test). We conducted a multi-classroom experiment to test this proposition. Sample166 typically-developing Dutch fifth-grade students participated (Mage = 11.14 years; 42.2% boys). MethodsWe used a 2 (Task Complexity: simple vs. complex) x 2 (Acquisition Strategy: example study vs. problem-solving) x 2 (Retention Interval: 5 min vs. 1 week) between-subjects design with problem-solving performance as dependent variable. ResultsThere was no evidence for the hypothesised three-way interaction effect of task complexity, acquisition strategy, and retention interval. However, there was evidence for the hypothesised two-way interaction effect of acquisition strategy and retention interval, independent of task complexity. More specifically, after 5 min, there was no statistically significant performance difference between students studying worked examples and those solving practice problems, but after 1 week, students solving practice problems outperformed those studying worked examples. ConclusionsOur findings imply that, after initial acquisition, problem-solving leads to better long-term problem-solving performance than example study. This holds true even for a relatively complex task and with limited instruction.

Influence of device configuration and noise on a machine learning predictor for the selection of nanoparticle small-angle X-ray scattering models.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202-212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

The Impacts of Phenological Stages within the Annual Cycle on Mapping Forest Stock Volume Using Multi-Band Dual-Polarization SAR Images in Boreal Forests

SAR images with two polarizations show strong potential for mapping forest stock volume (FSV) combined with limited samples. However, accurately mapping FSV still presents challenges in selecting the optimal acquisition date to obtain the SAR images during specific phenological stages within the annual forest cycle (growth and dormant stages). To clarify the impacts of phenological stages within the annual cycle on FSV mapping, SAR images with various polarization models and bands (Sentinel-1(S), GaoFen-3(GF-3 (G)) and ALOS-2(A)) were acquired within the growth and dormant stages of an annual cycle in a boreal evergreen coniferous forest (Chinese pine) and a deciduous coniferous forest (Larch). Subsequently, single-band (G, S, and A) and multi-band combined alternative variable sets (A + G, A + S, S + G, and A + S + G) were extracted within the same stage, respectively. Finally, the forward selection approach was utilized in conjunction with four different models (MLR, KNN, RF, and SVR) to obtain the most suitable variable sets and generate FSV mapping. The results demonstrated a strong correlation between the intensity of backscattering coefficients and the phenological stages of the forest. Within the dormant stage, there was a significant decrease in the gaps of backscattering coefficients obtained from the same polarization compared to those within the growth stage. Furthermore, the results also revealed that more signals from inside the canopy could be detected during the dormant stage in both evergreen coniferous forests and deciduous coniferous forests. Subsequently, the accuracy in mapping FSV obtained from single-band SAR images within the dormant stage are slightly higher than that within the growth stage, and the accuracy was still significantly affected by both overestimation and underestimation. Moreover, the combined effects of different bands significantly improve the reliability of mapped FSV. The rRMSE values in four multi-band combinations ranged from 22.37% to 29.40% for Chinese pine forests and from 21.27% to 34.38% for Larch forests, and the optimal result was observed from combinations of A + S + G acquired within the dormant stage. It is confirmed that SAR signal and their sensitivity to FSV depends on the stages of forest annual growth cycle. In comparison to the growth period, dual-polarization SAR data acquired during the dormant stage is more suitable for estimating FSV in boreal forests.

Coronary Computed Tomography Angiography Using an Optimal Acquisition Time Window Based on Heart Rate Determined During Breath-Holding Following Free Breathing

Coronary Computed Tomography Angiography Using an Optimal Acquisition Time Window Based on Heart Rate Determined During Breath-Holding Following Free Breathing

Optimal acquisition time estimation method for CSEM with high-order pseudo-random signal

High-order pseudo-random signal is gradually being applied in controlled-source electromagnetic (CSEM) exploration. In contrast to the conventional single-frequency sweep mode, the high-order pseudo-random signal enables simultaneous transmission of multiple frequencies. However, estimating a fixed acquisition time based on observed noise levels often results in poor adaptability for high-order pseudo-random signal, which only require reception of one set of waveform. In this study, we presented an estimation method for acquisition time for CSEM with high-order pseudo-random signal using an improved logistic function. The improved logistic function was proposed to introduce a time-decay factor into the governing equation for the first time. By considering the transformation rule of noise statistical characteristics with time, the specific parameters of the function have been determined to better describe the dynamic evolution process of the signal quality. The effective frequencies were extracted at various acquisition times based on the noise evaluation number, and the resulting quantity of effective frequencies was used as the fitting target. Guidance for the fieldwork was determined based on the average time of the saturation period, in accordance with the properties of the function. The reliability of the improved logistic function was validated through a transmission current data simulation. The proposed method was demonstrated through the measured data from both strong and weak interference areas.

Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys

Atomistic simulations are crucial for predicting material properties and understanding phase stability, essential for materials selection and development. However, the high computational cost of density functional theory calculations challenges the design of materials with complex structures and composition. This study introduces new data acquisition strategies using Bayesian-Gaussian optimization that efficiently integrate the geometry of the convex hull to optimize the yield of batch experiments. We developed uncertainty-based acquisition functions to prioritize the computation tasks of configurations of multi-component alloys, enhancing our ability to identify the ground-state line. Our methods were validated across diverse materials systems including Co-Ni alloys, Zr-O compounds, Ni-Al-Cr ternary alloys, and a planar defect system in intermetallic (Ni1−x, Cox)3Al. Compared to traditional genetic algorithms, our strategies reduce training parameters and user interaction, cutting the number of experiments needed to accurately determine the ground-state line by over 30%. These approaches can be expanded to multi-component systems and integrated with cost functions to further optimize experimental designs.

Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review

Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning, realistic simulations, data-driven decision support, and adaptive assessment, along with further exploring the benefits and challenges of AI-powered EM training. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar to identify relevant studies focusing on AI applications in EM and EM training. The search terms included “artificial intelligence”, “emergency medicine”, “training”, “education”, “personalized learning”, “simulations”, “decision support”, and “assessment. Articles published in the past ten years were prioritized to ensure the inclusion of current advancements in the field. AI offers a plethora of opportunities to revolutionize EM training, including the following: Personalized learning: AI-powered systems can tailor educational content and pace to individual trainees’ needs, ensuring optimal instruction and knowledge acquisition. Realistic simulations: AI-powered simulations provide immersive experiences for trainees to practice clinical decision making under simulated pressure. Data-driven decision support: AI-powered systems analyze vast amounts of data to provide trainees with real-time recommendations and insights for informed clinical decisions. Adaptive assessment: AI-powered tools assess trainee progress dynamically, providing personalized feedback and identifying areas for improvement. Conclusions: AI integration into EM training holds immense promise for enhancing trainee learning and improving patient outcomes. By embracing AI, we can cultivate a new generation of EM physicians equipped to meet the ever-changing demands of this critical medical specialty.

Imaging and anesthesia protocol optimization in sedated clinical resting state fMRI.

The quality of resting-state functional MRI (rs-fMRI) under anesthesia is variable and there are no guidelines on optimal image acquisition or anesthesia protocol. We aim to identify the factors that may lead to compromised clinical rs-fMRI under anesthesia. In this cross-sectional study, we analyzed clinical rs-fMRI data acquired under anesthesia from 2009-2023 at Massachusetts General Hospital. Independent component analysis driven resting state networks (RSN) of each patient were evaluated qualitatively and quantitatively and grouped as robust or weak. Overall networks were evaluated using the qualitative method, and motor and language networks were evaluated using the quantitative method. RSN robustness was analyzed in 4 outcome categories: overall, combined Motor-Language, individual motor, and language networks. Predictor variables included rs-fMRI acquisition parameters, anesthesia medications, underlying brain structural abnormalities, age, and sex. Logistic regression was used to examine the effect of the study variables on RSN robustness. Sixty-nine patients were identified. With qualitative assessment, 40 had robust and 29 had weak overall RSN. Quantitatively, 45 patients had robust, while 24 had weak Motor-Language networks. Among all the predictor variables, only sevoflurane significantly contributed to the outcomes, with sevoflurane administration reducing the odds of having robust RSN in overall (Odds Radio (OR)= 0.2, 95% Confidence Interval (CI) = [0.05;0.79], p = .02), Motor-Language (OR = 0.18, 95% CI = [0.04;0.80], p = .02) and individual motor (OR= 0.1, 95% CI = [0.02;0.64], p= .02) categories. Individual language network robustness was not associated with the tested predictor variables. Sevoflurane anesthesia may compromise the visibility of fMRI resting state networks, particularly impacting motor networks. This finding suggests that the type of anesthesia is a critical factor in rs-fMRI quality. We did not observe the association of the MR acquisition technique or underlying structural abnormality with the RSN robustness. BOLD = Blood Oxygen Level-Dependent; ICA = Independent Component Analysis; Rs-fMRI = Resting-State Functional Magnetic Resonance Imaging; RSN = Resting-State Networks; SNR = Signal-to-Noise Ratio.

Technology in Forensic Sciences: Innovation and Precision

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor &amp; Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator’s prominence and responsibility in the resolution of cases.

Newcomer’s Guide into Optimal Data Acquisition for Electron Ptychography

Newcomer’s Guide into Optimal Data Acquisition for Electron Ptychography

Design Decisions for Wearable EEG to Detect Motor Imagery Movements.

The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG's ability to handle the algorithm parameters with good performance.

Optimizing Low-Dose [18F]FDG-PET/CT Scans: Ensuring Quality Amid Radiotracer Availability Challenges – Insights from a Peripheral Tertiary Care Center

Background: The introduction of positron emission tomography/computed tomography (PET/CT) has significantly advanced medical imaging. In oncology, 18F-fluorodeoxyglucose (18F-FDG) PET/CT is particularly crucial for staging, evaluating treatment response, monitoring follow-up, and planning radiotherapy. However, in resource limiting hospitals, the availability of fluorine-labeled 18F-FDG limits optimal scan acquisition. This study aims to determine the optimal dosage and acquisition time to maximize patient throughput during shortages. Aim and Objective: To optimize low-dose 18F-FDG scan protocols while maintaining high image quality despite radiotracer availability challenges. Materials and Methods: PET/CT scans were performed using GE’s Discovery IQ 5-ring, 16-slice system within 40–60 minutes of intravenous 18F-FDG injection. The protocol was adjusted to a low-dose (0.05 mCi/kg of 18F-FDG), and the PET data acquisition time was increased to 3 min per bed position to ensure image quality. Results: Notable differences were observed in image quality scores based on varying acquisition times, with the extended acquisition time helping maintain diagnostic standards despite reduced tracer doses. Conclusion: The high sensitivity and long axial length of the PET/CT system (with five rings spanning 26 cm AFOV) can significantly alleviate the challenges faced by cyclotron-dependent centers. By leveraging the increased sensitivity, we successfully reduced the injected activity rather than the scan time to address the tracer shortage at our institute. This approach proved to be effective in maintaining image quality and patient care standards.

How does the power structure affect the acquisition price and inventory decisions in the reverse supply chain

With growing concerns about environmental sustainability, reverse supply chains play a crucial role in recycling, remanufacturing, and reducing waste. Investigating the power structure’s impact on decisions related to acquisition prices and inventory management can provide insights into the integration of sustainable practices. This study analyzes a reverse supply chain with a supplier and a remanufacturer, in which the supplier provides the remanufacturer with sorted used cores. We investigate the acquisition pricing and inventory decisions under different power structures (supplierled Stackelberg model, remanufacturer-led Stackelberg model) through game theory analysis. We first establish the centralized model to explore optimal acquisition decisions and profit of the total system. Then we evaluate the acquisition quantity, wholesale prices, the profit of supplier and remanufacturer in the supplier-led model and in the remanufacturer-led model. Finally, the impact of power structure on the performance of supplier and remanufacturer are explored by numerical analysis, as well as the supply chain system. Our significant findings show that (i) the total profit of the supplier-led and remanufacturer-led reverse supply chain are less than the centralized model due to the double marginalization; (ii) the remanufacturer-led reverse supply chain is superior to the supplier-led model reverse supply chain; (iii) the selected grades in the supplier-led model is proved the same as that in the remanufacturer-led model, which could not been affected by the wholesale price; (iv) the greater the inventory risk (the uncertainty in demand), the greater the profit share for the remanufacturer (supplier) in the supplier-led (remanufacturer-led) model.