Resolution Performance Research Articles

Exploring the Relationship between Dispute Resolution and Organizational Performance: An Empirical Study on the Impact of Communication Climate

Ineffective communication is a dominant source of instability and significant worker dissatisfaction in the manufacturing industry, including the readymade clothing sector. Workers’ dissatisfaction at the workplace creates labor unrest. Consequently, productivity may be significantly affected by labor unrest, which is an inevitable component of any industrial operation. A harmonious work environment, free from conflicts among employees, should be a focus for every successful organization. This research suggests that the communication climate be implemented as a resolution technique for workplace disputes. Data was collected through structured questionnaire surveys and random sampling in this quantitative research. Data was obtained from the readymade garment factories in Bangladesh. One hundred twenty employees participated in the survey. Statistical Package for the Social Sciences (SPSS) was used to analyze the data. According to the research, the primary cause of poor business performance at the workplace in the sector is due to inadequate communication. Additionally, the investigation assessed the industry's employees' perspectives regarding the advantages of communication climate in dispute resolution. This study finds a positive correlation between dispute resolution techniques and the communication climate. Pluralist theory served as the theoretical foundation for the investigation in order to substantiate the notion that it provides resources such as knowledge, constructive criticism, and opportunities for growth of the sector.

Performance Simulation of Muon Detectors Based on Structural Design and Array Layout of Plastic Scintillators

The performances of muon detectors have prominent effects on the accuracy and application scenario of muon imaging. Previous studies have paid more attention to the performance improvements of muon detectors in the assembly of imaging systems and optimization of imaging algorithms, while, the structural design and array layout of plastic scintillators in muon detectors have received less attention. In this work, the simulation models of plastic scintillator, wavelength-shifting (WLS) fibers, and muon source are constructed in the Geant4 software. On this basis, different factors affecting light collection efficiency (LCE) have been investigated, including muon hitting position, plastic scintillator cross-sectional shape and size, and WLS fiber size and position. Meanwhile, the influences of scintillator array layout, average width, and muon energy on the position resolution performance of the detector are investigated. The constructive results have been listed as follows: (a) the longitudinal length of the plastic scintillator unit, the shape and size of the WLS fiber, and the position of the WLS fiber have a large impact on the LCE. (b) The Right-angled triangle staggered layout is suitable for small-sized scintillators with single-energy muon hitting, and the Rhombus staggered layout is suitable for large-sized scintillators with multiple-energy muon hitting. This work provides theoretical support for the structural design and array layout of plastic Scintillators, and it has an important significance as a guide for the design of the subsequent muography system.

A symmetric chiral aromatic polyamide modified silica material combining rigid-flexible and application in liquid chromatography

The development of new separation materials is an effective way to improve the separation efficiency of chiral and achiral analytes. Here, a C3-symmetric chiral tricarboxylic acid compound was prepared and successfully immobilized onto the surface of porous silica particles by a one-pot synthesis strategy. The resulting material (TPHs-SiO2) was composed of symmetric chiral aromatic polyamide with a combination of rigid-flexibility, which gave it both chiral and hydrophilic interaction/ion exchange (HILIC/IEC) characteristics. TPHs-SiO2 as a new stationary phase for liquid chromatography can separate 1-phenyl-ethanol, 1-phenyl-propanol, 1-phenyl-1,2-ethylene glycol, 4-chloro-a-methylbenzyl alcohol, ofloxacin and mandelic acid in normal phase chromatography (NPLC) with resolutions of 2.69, 5.89, 4.49, 1.04, 1.73 and 5.47, respectively, thus indicating that TPHs-SiO2 has chiral resolution performance. TPHs-SiO2 can also achieve the separation of achiral compounds, such as nucleosides, nucleotides, and aniline compounds in HILIC/IEC mode. Therefore, the prepared stationary phase can be used as an ideal multifunctional separation material with chiral separation capability and mixed-mode separation capability (HILIC/IEC), realizing the separation of chiral compounds and different types of compounds. This work provides a new idea and method for developing multifunctional separation materials with multi-selectivity and high efficiency, that has important theoretical and application value.

Image metric-based multi-observation single-step deep deterministic policy gradient for sensorless adaptive optics.

Sensorless adaptive optics (SAO) has been widely used across diverse fields such as astronomy, microscopy, and ophthalmology. Recent advances have proved the feasibility of using the deep deterministic policy gradient (DDPG) for image metric-based SAO, achieving fast correction speeds compared to the coordinate search Zernike mode hill climbing (ZMHC) method. In this work, we present a multi-observation single-step DDPG (MOSS-DDPG) optimization framework for SAO on a confocal scanning laser ophthalmoscope (SLO) system with particular consideration for applications in preclinical retinal imaging. MOSS-DDPG optimizes N target Zernike coefficients in a single-step manner based on 2N + 1 observations of the image sharpness metric values. Through in silico simulations, MOSS-DDPG has demonstrated the capability to quickly achieve diffraction-limited resolution performance with long short-term memory (LSTM) network implementation. In situ tests suggest that knowledge learned through simulation adapts swiftly to imperfections in the real system by transfer learning, exhibiting comparable in situ performance to the ZMHC method with a greater than tenfold reduction in the required number of iterations.

Square Parametric Excitation: A Digital Resonant Method for the Quadrupole Ion Trap.

Digital ion trap technology is an alternate method for driving quadrupole ion traps and mass filters using variable frequency, fixed amplitude RF square waves in place of variable amplitude, fixed frequency RF sine waves. This technique offers some advantages such as an increase in the high mass analysis range by varying frequency and lower overall voltage requirements. Here, we present a complex square waveform developed for resonant parametric excitation in a quadrupole linear ion trap. Unlike traditional resonance methods, the driving RF square wave and auxiliary square wave are coupled using the same digital circuitry without the need for transformer coupling. In this work, we use this complex waveform to selectively excite the first order parametric resonances of ion motion. The square parametric excitation method presented here employs a simple and repetitive circuit design consisting of a low-voltage waveform generator followed by a series of high-voltage MOSFET switches. This design allows for resonance methods to be easily implemented in the all-digital quadrupole. The complex square waveform can perform the same useful functions as sine wave auxiliary signals, such as selective mass elimination and mass isolation. We also demonstrate that the mass resolution performance and S/N of our digital mass spectrometer is improved by applying the complex square waveform during ion ejection.

Situational NMAC risk assessment using conditional random field for UAV tiered aerial passage operations

Complex urban unmanned aerial vehicle (UAV) operations necessitate near mid-air collision (NMAC) risk assessment to ensure safety. In this work, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. Firstly, a concept of tiered aerial passages is designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity. For UAV tiered aerial passage operations, a situational NMAC risk assessment framework is formulated, enabling the host UAV to evaluate and respond to real-time risks, ensuring avoidance of potential collisions. Specifically, a probabilistic risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the NMAC risks in overtaking, approaching head-on, and converging UAV situational encounters are quantified considering advisory, caution, and warning risk alert levels. To resolve risks assessed, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed method is validated by testing encounter situations and comparative study. The results show that the proposed method outperforms UAV NMAC risk assessment, effectively tailoring different risk requirements for diverse UAV operations.

TRENDS OF THE MICROWAVE PHOTONIC RADARS

Today, the world's leading countries are intensively working on the development of new generation radars - microwave photonic radars. Microwave photonic radars make it possible to significantly reduce the mass and size characteristics of radar stations, to increase the information capability and range of target detection due to the reduction of losses in long communication lines when using optical fiber, to ensure high immunity due to the significantly lower sensitivity of optical-electronic equipment and fiber-optic lines of communication connection to external electromagnetic influences. Microwave photonics provides wide bandwidth, flat response, low loss transmission, multi-dimensional multiplexing, ultra-fast analog signal processing and immunity to electromagnetic interference. Radar implementation in the optical domain can provide better resolution, coverage, and speed performance, which would be difficult to implement with traditional electronics. The review article examines the state of development and system architectures of such photonic radars as optoelectronic hybrid radars, all-optical radars, multifunctional microwave photonic radar systems, distributed microwave photonic radars, software-defined radars, and cognitive radars. New technologies in this field and possible future directions of research are discussed. As an example, a broadband microwave photon radar reproduced on the basis of a microcircuit is considered. The broadband signal generator and receiver are built into the silicon crystal on the insulator. A high-precision distance measurement with a resolution of 2.7 cm and an error of less than 2.75 mm was obtained. Visualization of multiple targets with complex profiles has been implemented. But the performance of most integrated microwave photonic chips is not yet satisfactory for practical radar applications. Monolithic integration of key microwave photonic subsystems is also not mature enough for practical applications, so hybrid integration of devices fabricated on their optimal integration platforms is of practical interest. At present, indium phosphide, silicon nitride and silicon on insulator are the three leading platforms for photonic integration

Enhancing Sustainable Transportation with Advancements in Photonic Radar Technology with MIMO and IIR Filtering for Adverse Weather Conditions

Sustainable transportation is crucial in addressing global road safety and environmental challenges. This study introduces a novel photonic radar system, leveraging Linear Frequency-Modulated Continuous Wave (LFMCW) technology for high-speed data transmission. Operating in a homodyne configuration, this system uses a single oscillator to generate both signal and reference waveforms. It incorporates mode division multiplexing (MDM) to enable the detection and ranging of multiple targets, even under adverse atmospheric conditions. To counter atmospheric attenuation, the system is equipped with a 2 × 2 MIMO technique and an Infinite Impulse Response (IIR) filter. Numerical simulations demonstrate the system’s superior performance in range resolution and target detection, achieving significant power improvements. The IIR filter further enhances detection, achieving a power improvement of 200% for target 1 and 276% for target 2. With low power requirements and enhancement through IIR filter equalization, this system presents a viable option for battery-operated vehicles. This innovative approach offers a low-power high-efficiency solution suitable for battery-operated vehicles, promoting safer and more reliable sustainable transportation.

Conflict Resolution and Organizational Performance of Oil and Gas Firms in South-South Nigeria

This study examined the relationship between conflict resolution and organizational performance oil and gas firms in south-south Nigeria. The study adopted correlational survey research design. Population of the study constitutes the elements within the study unit. Therefore, the population of the study comprised of twenty-five (25) oil and gas companies. Data for this study were collected through primary data source; the questionnaire was the instrument used in collecting primary data. A total of two hundred and fifty copies of the questionnaire were distributed in a frame of 10 copies per firm. Reliability analysis was performed on the data using the Cronbach Alpha coefficient and it stood at 0.98. The data collected for the study were analyzed through descriptive and inferential statistics. The multiple regression analysis was adopted. The findings revealed a significant relationship between compromising strategy and organisational performance of oil and gas firms in south-south Nigeria. Also, a strong significant linear relationship was revealed between collaboration strategy and organisational performance of oil and gas firms in south-south Nigeria. The study concludes that oil and gas firms should adopt collaboration and compromise strategy, given its effectiveness in conflict management, as demonstrated by this study and supported by numerous other research findings. Furthermore, the study recommends that organizations should focus on increasing the utilization of conflict management strategies like collaboration and compromise strategy to increase organizational performance.

Dual-channel end-to-end network with prior knowledge embedding for improving spatial resolution of magnetic particle imaging

Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth penetration, high sensitivity and contrast. Spatial resolution and signal-to-noise ratio (SNR) are key performance metrics for evaluating MPI, which are directly influenced by the gradient of the selection field (SF). Increasing the SF gradient can improve the spatial resolution of MPI, but will lead to a decrease in SNR. Deep learning (DL) methods may enable obtaining high-resolution images from low-resolution images to improve the MPI resolution under low gradient conditions. However, existing DL methods overlook the physical procedures contributing to the blurring of MPI images, resulting in low interpretability and hindering breakthroughs in resolution. To address this issue, we propose a dual-channel end-to-end network with prior knowledge embedding for MPI (DENPK-MPI) to effectively establish a latent mapping between low-gradient and high-gradient images, thus improving MPI resolution without compromising SNR. By seamlessly integrating MPI PSF with DL paradigm, DENPK-MPI leads to a significant improvement in spatial resolution performance. Simulation, phantom, and in vivo MPI experiments have collectively confirmed that our method can improve the resolution of low-gradient MPI images without sacrificing SNR, resulting in a decrease in full width at half maximum by 14.8%–23.8 %, and the accuracy of image reconstruction is 18.2 %–27.3 % higher than other DL methods. In conclusion, we propose a DL method that incorporates MPI prior knowledge, which can improve the spatial resolution of MPI without compromising SNR and possess improved biomedical application.

Cu(II) coordination MOF-303 based dual-functional sensor for precisely detecting and distinguishing humidity and temperature with multifunction applications

Humidity-temperature dual-functional sensors show great demand in healthcare and archaeology, requiring sensors with high sensitivity, high resolution and high stability. In this work, utilizing the post-coordination strategy, the uncoordinated N atoms in MOF-303 were used as the metal chelate sites to chelate CuCl2, and due to the accordion-like morphology, the obtained material shows potential in humidity sensing and temperature sensing. The CuCl2-MOF-303 based sensor shows whole range humidity sensing with response of 11.4 at 33 % RH and 56,700 at 95 % RH, 1 % RH of resolution and good long-term stability (>90 days), and excellent temperature sensing performance of high sensitivity (–3.3 %/°C), high linearity (R2>0.99) and high resolution (0.3 °C) in the temperature range of 35–38 °C. Furthermore, the application of sensor in healthcare, noncontact sensing and information transmission is verified. This study provides a preliminary exploration for the application of MOFs in humidity-temperature dual-functional sensors.

Evaluation of the safety, biodistribution, dosimetry of [18F]AlF-NOTA-LM3 and head-to-head comparison with [68Ga]Ga-DOTATATE in patients with well-differentiated neuroendocrine tumors: an interim analysis of a prospective trial.

18F-labelled somatostatin receptor (SSTR) analogs offer several advantages over 68Ga in terms of yield, cost, spatial resolution and detection rate. This study presents an interim analysis of a prospective trial designed to assess the safety, biodistribution and dosimetry of [18F]AlF-NOTA-LM3, and compare its diagnostic efficacy and clinical management outcomes with [68Ga]Ga-DOTATATE or [68Ga]Ga-NODAGA-LM3 in patients with well-differentiated NETs. Twenty-one patients with histologically confirmed well-differentiated neuroendocrine tumors (G1 and G2) were prospectively recruited. The first eight patients underwent serial PET scans at 5, 15, 30, 45, 60, and 120min after [18F]AlF-NOTA-LM3 injection to assess biodistribution and dosimetry. The remaining patients underwent whole-body PET/CT scans. [18F]AlF-NOTA-LM3 and [68Ga]Ga-DOTATATE PET/CT were done within a week, with a minimum 24-hour interval between the two scans. Focal uptake above the surrounding background activity and could not be explained by physiologic uptake was considered lesions of NETs. Lesion number, tumor uptake, and tumor-to-background ratio (TBR) were compared. In patients with discrepant findings, the size of the smallest lesions (measured on coregistered CT) detected on [68Ga]Ga-DOTATATE and [18F]AlF-NOTA-LM3 was compared. [18F]AlF-NOTA-LM3 was safe and well-tolerated. Physiological uptake of [18F]AlF-NOTA-LM3 was significantly lower than that of [68Ga]Ga-DOTATATE in abdominal organs and bone marrow, but higher in blood pool and lung. The mean effective dose was 0.024 ± 0.014 mSv/MBq. [18F]AlF-NOTA-LM3 detected significantly more liver lesions (457 vs. 291, P = 0.006) and lymph node lesions (30 vs. 22, P = 0.011) compared to [68Ga]Ga-DOTATATE. The tumor uptake was comparable, but TBR was significantly higher with [18F]AlF-NOTA-LM3 for lesions from all sites except for the duodenum. The size of the minimum liver lesions (0.54 ± 0.15 vs. 1.01 ± 0.49, P<0.001) and lymph node lesions (0.50 ± 0.19 vs. 1.26 ± 0.86, P = 0.024) detected on [18F]ALF-NOTA-LM3 were significantly smaller than those detected on [68Ga]Ga-DOTATATE. [18F]AlF-NOTA-LM3 shows favorable biodistribution, higher spatial resolution and superior performance than [68Ga]Ga-DOTATATE in detecting liver and lymph node metastases, with higher TBR. Notably, it is the first SSTR analog to show superiority in detecting lymph node lesions when compared to [68Ga]Ga-DOTATATE. ClinicalTrials.gov identifier NCT06056362.

Super-agile satellites imaging mission planning method considering degradation of image MTF in dynamic imaging

Super-agile satellites with dynamic imaging capabilities can significantly improve the imaging efficiency of space-borne Earth observation. However, the image modulation transfer function (image MTF) of dynamic imaging varies dramatically with changes in the attitude maneuver status while improving observation efficiency. Therefore, both the observation efficiency and image MTF should be considered in the imaging mission planning of super-agile satellites. Existing imaging mission planning methods are mainly aimed at traditional agile satellites that are not capable of dynamic imaging, and the mainly optimization objectives are coverage revenue, spatial resolution, and task performance time, without considering the observation efficiency and image MTF as the optimization objectives. To overcome the problem of image MTF degradation caused by dynamic imaging, a mission planning method which considers the degradation of the image MTF and observation efficiency was proposed. Firstly, a mapping relationship between the angular velocity of the attitude maneuvering and the image MTF was constructed. On this basis, a Pareto multi-objective imaging mission planning model of partial order based was established. Considering the particularity of the model, the non-dominated sorting strategy of the NSGA-II algorithm was redesigned when solving the model using NSGA-II algorithm. Finally, the imaging tasks of different scales in single satellite and multi-satellite simulation scenes were set, and the proposed imaging mission planning method was verified. The planning results showed that the proposed method can achieve a balanced optimization of the image MTF and task efficiency under the premise of ensuing maximum observation coverage revenue.

UPDs estimation and ambiguity resolution performance evaluation of LEO navigation system

Low-Earth orbit (LEO) satellites have fast motion and thus provide rapid geometric change relation to the ground stations in a short period of time, which are expected to improve the positioning performance. In this study, a LEO constellation of 160 LEO satellites was simulated and simulated LEO observation data at global, European, and American stations were used to estimate the uncalibrated phase delays (UPDs) and achieve static and kinematic precise point positioning (PPP) ambiguity resolution (AR). The quality of the estimated UPD products were evaluated. More than 89 % and 96 % of the wide- and narrow-lane UPDs had a posteriori residual of less than 0.15 and 0.25 cycles, respectively. In static PPP, all ambiguity fixing rates were larger than 63 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 8 %, 6 % and 7 %, respectively; the average root-mean-square errors (RMSEs) in the east, north, and up components at the global, European and American stations were decreased by (22 %, 20 %, 15 %), (36 %, 34 %, 28 %) and (44 %, 31 %, 29 %), respectively. In kinematic PPP, all ambiguity fixing rates were larger than 40 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 4 %, 19 % and 14 %, respectively; the average RMSEs in the east, north, and up components at the global, European and American stations were decreased by (10 %, 10 %, 7 %), (27 %, 22 %, 11 %) and (32 %, 20 %, 10 %), respectively. In both static and kinematic PPP, AR accelerated the convergence time and improved the positioning accuracy.

Feasibility of Endovascular Deep Brain Stimulation of Anterior Nucleus of the Thalamus for Refractory Epilepsy.

Deep brain stimulation (DBS) has developed into an effective therapy for several disease states including treatment-resistant Parkinson disease and medically intractable essential tremor, as well as segmental, generalized and cervical dystonia, and obsessive-compulsive disorder (OCD). Dystonia and OCD are approved with Humanitarian Device Exemption. In addition, DBS is also approved for the treatment of epilepsy in the anterior nucleus of the thalamus. Although overall considered an effective treatment for Parkinson disease and epilepsy, a number of specific factors determine the treatment success for DBS including careful patient selection, effective postoperative programming of DBS devices and accurate electrode placement. Furthermore, invasiveness of the procedure is a rate limiter for patient adoption. It is desired to explore a less invasive way to deliver DBS therapy. Here, we report for the first time the direct comparison of endovascular and parenchymal DBS in a triplicate ovine model using the anterior nucleus of the thalamus as the parenchymal target for refractory epilepsy. Triplicate ovine studies show comparable sensing resolution and stimulation performance of endovascular DBS with parenchymal DBS. The results from this feasibility study opens up a new frontier for minimally invasive DBS therapy.

Convolutional neural network application for supply–demand matching in Zhuang ethnic clothing image classification

This study aims to design a classification technique suitable for Zhuang ethnic clothing images by integrating the concept of supply–demand matching and convolutional neural networks. Firstly, addressing the complex structure and unique visual style of Zhuang ethnic clothing, this study proposes an image resolution model based on supply–demand matching and convolutional networks. By integrating visual style and label constraints, this model accurately extracts local features. Secondly, the model’s effectiveness and resolution performance are analyzed through various performance metrics in experiments. The results indicate a significant improvement in detection accuracy at different annotation points. The model outperforms other comparative methods in pixel accuracy (90.5%), average precision (83.7%), average recall (80.1%), and average F1 score (81.2%). Next, this study introduces a clothing image classification algorithm based on key points and channel attention. Through key point detection and channel attention mechanisms, image features are optimized, enabling accurate classification and attribute prediction of Zhuang ethnic clothing. Experimental results demonstrate a notable enhancement in category classification and attribute prediction, with classification accuracy and recall exceeding 90% in top-k tasks, showcasing outstanding performance. In conclusion, this study provides innovative approaches and effective solutions for deep learning classification of Zhuang ethnic clothing images.

An EEG channel selection method for motor imagery based on Fisher score and local optimization

Objective. Multi-channel electroencephalogram (EEG) technology in brain–computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness. Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection. Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%. Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

A model for Enhancing EI Performance in Conflict Resolution

This study explores a methodology to improve the performance of Emotional Intelligence (EI) in dispute resolution, utilizing quantitative analysis of 287 respondent's questionnaire data. This study examines how well active perception, Emotion understanding and task complexity predict outcomes of EI performance in conflict resolution, focusing on each of their roles and relationships. It is discovered that active perception significantly influences and strongly predicts EI performance in conflict resolution. Similar to active perception, Emotion understanding shows significance as mediator of the relationship between EI performance and active perception as well. In order to predict Emotional Intelligence, active perception and Emotion Understanding have various relationships. The study determines how important and influential these factors are in a relationship to each other. Although Emotion understanding and active perception have a high predictive value on EI performance in conflict resolution. These findings demonstrate how important it is for Emotional Intelligence (EI) training programs to emphasize active perception, emotion understanding and task complexity in order to promote successful conflict resolution outcomes. For professionals who want to develop competencies in Emotional Intelligence (EI) and advance organizational effectiveness in conflict resolution setting, the research offers insightful information. This research deploys transaction cost theory as the groundwork.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective&#xD;Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.&#xD;&#xD;Method&#xD;The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.&#xD;&#xD;Results&#xD;DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. &#xD;&#xD;Conclusion&#xD;DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Optimized quantification of fatty acids in complex media using advanced analytical techniques.

Various medium formulations contain essential fatty acids at concentrations ranging from 10 to 100 mg/L. Accurate and precise lipid measurement in media is crucial for monitoring media quality and conducting studies on lipids in the context of cell culture. This study employed two-dimensional gas chromatography (GC × GC) analyses to offer enhanced resolution, sensitivity, and separation performance compared to GC. Quantification of fatty acid methyl esters (FAMEs) in a medium was conducted using GC × GC combined with a high-resolution mass spectrometer and flame ionization detector, considering potential interference from nonionic surfactant Tween 80, which was precipitated and removed by optimizing the concentration of cobalt thiocyanate (CTA) solution during pretreatment. This advanced analytical approach enabled identification of cis and trans isomers of identical molecular weights and determination of the location and number of double bonds in the same carbon number structure. Our analysis identified 36 FAMEs within the C6-C24 region, and a 5% CTA solution was optimal for efficient removal of Tween 80 during lipid extraction. Additionally, this advanced method minimized FAME contamination and loss during pretreatment, thereby significantly reducing the sample volume required to detect trace levels of FAMEs. This improvement led to a fatty acid recovery rate of 106% while maintaining the average relative standard deviation for the target FAMEs of about 3%. Our research paves the way for future investigation into medium quality control and the role of fatty acids in cell culture. This offers the possibility for economical and effective trace quantification of fatty acids in complex media.