Reduction Approach Research Articles

Maximizing CO2 reduction efficiency: surface‐regulated highly porous Ag‐Cu alloy aerogel inserted with multi‐wall carbon nanotube achieving nearly complete CO selectivity

The electrochemical reduction of CO2 (eCO2RR) holds promise in mitigating atmospheric greenhouse gas levels but is hindered by low reaction kinetics, high energy barriers, and poor selectivity. To address these challenges, we developed a novel series of cost‐effective AgCu‐containing metal‐aerogel catalysts with high electrochemical surface areas (ECSA) using a top‐down reduction approach. The Ag85Cu15 aerogel, with an ECSA of 27.41 cm², achieved a Faraday efficiency (FE%) of 89.3% for CO production at ‐0.9 V vs. RHE. Increasing the Cu content to over 50% in the aerogel matrix produced small amounts of C2H4, with a maximum FE% of 12.9% at ‐1.0 V vs. RHE. To further enhance CO2 reduction efficiency, multi‐walled carbon nanotubes (CNT) were incorporated into the Ag85Cu15 alloy aerogel via a hydrothermal treatment. The highly dispersed CNTs within the aerogel matrix increased the ECSA to 57.00 cm² by forming a well‐defined porous structure through van‐der Waals interactions, improving CO selectivity, and achieving a FE% of 98.6% at ‐0.7 V vs. RHE and a partial current density of 9.6 mA/cm² in an H‐cell. 86% of the initial FECO% was maintained during an 18 h test with continuous electrolysis.

Adroit effect of copper nanoparticles and copper nanozyme and their effective decolorization of azo dyes

Copper nanoparticles (CuNPs) have garnered considerable attention owed to their straightforward and cost-effective synthesis techniques, making them suitable for various applications across several fields. This paper describes a straightforward method for synthesizing CuNPs using the aqueous chemical reduction approach. A unique copper nanozyme (CNZ) is also prepared to degrade dyes. The nanoparticles were analyzed using various techniques to examine their morphological structure, optical properties, functional groups, and crystallite size via SEM, UV–visible spectrophotometry, FTIR, XRD, and zeta potential with particle size analyzer. SEM analysis showed that the CuNPs have a cubical form, with particle size fluctuating from 250 to 300 nm. The CNZ has a flower-like structure with a typical 100 to 150 nm size. The zeta potential of CuNPs is measured to be + 23.4 mV, indicating a high level of stability for the nanoparticles. The XRD evaluation showed that the CuNPs displayed a prominent peak at an angle of 36.64°, indicating a crystallite size of 26.97 nm. The CNZ displayed a peak at 31.18 nm, corresponding to a crystallite size of 44.84 nm. The capacity of CuNPs and CNZ to degrade a combination of dyes (Methyl orange, Methyl red, Congo red, Tropaeolin-O, and Tartrazine) was investigated. The innovative approach utilizing CNZ and CuNPs resulted in a degradation percentage of 84.61 % for mixed colors. Experimental findings have demonstrated that the combined effect of CuNPs and CNZ is remarkably effective in catalytically degrading azo dyes, making it a highly efficient method for treating effluents from the textile sector and wastewater treatment.

Rapid detection of rare events from in situX-ray diffraction data using machine learning.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Identification of Infrastructure and Community Involvement Towards Improving the Quality of Roads in Beakang Padang District, Batam City

This research aims to identify the condition of road infrastructure, measure the level of community participation in efforts to improve the quality of environmental roads, and evaluate the impact on the aesthetics of the area in Behind Padang District, Batam City. Good infrastructure and active community participation are very important in creating a comfortable and aesthetic environment, which in turn can improve the quality of life and welfare of the community. The research method used is a descriptive method with a qualitative approach, which allows for in-depth data collection through field observations, in-depth interviews and documentation studies. The data obtained was analyzed qualitatively using a data reduction approach, data presentation and drawing conclusions. Field observations show that the physical condition of roads in the Rear Padang District varies greatly. Some main roads are in good condition with smooth asphalt surfaces, but many neighborhood roads are damaged, potholed and poorly maintained. This condition is exacerbated by supporting infrastructure such as drainage channels and street lighting which is still very limited in some areas. This deficiency has a negative impact on the comfort and safety of road users and reduces the aesthetics of the environment. This research found that improving road infrastructure and increasing community participation had a significant impact on the aesthetics of the area. Well-improved roads and well-organized neighborhoods create a more pleasant atmosphere and increase resident satisfaction. Active community participation in infrastructure planning also increases the sense of ownership and responsibility.

Induced-aggregates in photocatalysis: An unexplored approach to reduce the noble metal co-catalyst content

Photocatalysis has emerged as a promising and environmentally sustainable solution to produce high-purity hydrogen through ethanol photoreforming. It is commonly accepted that adding co-catalysts, especially noble metals, significantly enhances the catalytic activity of semiconductors. However, the high cost of noble metals such as Pt may limit the real application of this emerging technology. Here we evaluate the possibility of reducing the noble metal loading by creating the appropriate interface between pre-formed semiconductor nanoparticles. Commercial titania (P25) was selected as the semiconductor due to its commercial availability, facilitating the straightforward validation and corroboration of our results. Pt was selected as co-catalyst because one of the most efficient photocatalysts for the ethanol photo-reforming is still based on the use of P25 in combination with Pt. We report that the creation of induced aggregates dramatically improves the total hydrogen produced when very low loadings (≤0.05 wt%) of Pt are used. We have developed a pioneering reactor designed for conducting photoluminescence studies under authentic operational conditions of nanoparticle suspensions in the liquid phase. This approach allows us to obtain the average photoluminescence emission from the P25 agglomerates what it would be impossible to obtain by using standard solid samples holders. Thanks to this equipment, we can conclude that this remarkable improvement of the activity is mainly due to creation of an interface that favors the charge transfer between the particles of the aggregates. According to this, the titania nanoparticles of the agglomerates act as an antenna to collect the photons of the sun-light and produce the photo-excited electrons that will be transferred to the platinum nanoparticles located in the same agglomeration. In contrast, raw P25 with low loadings of Pt would have a high number of titania nanoparticles without platinum, and therefore, inactive. This result would be especially relevant in the case of immobilized photocatalytic systems for real future photocatalytic reactors because the immobilization of the semiconductors would generate similar interactions to the one created by our method. Consequently, the initial semiconductor immobilization followed by the subsequent photo-deposition of the co-catalyst emerges as a promising approach for a substantial reduction of the co-catalyst content.

Microbial upcycling of methane to phytoene using metabolically engineered Methylocystis sp. MJC1 strain

Methane, a potent greenhouse gas, requires sustainable mitigation strategies. Here, the microbial upcycling of methane to phytoene, a valuable colorless carotenoid with applications in the cosmeceutical industry was demonstrated. To achieve this goal, a stepwise metabolic engineering approach was employed in Methylocystis sp. MJC1, a methane-oxidizing bacterium. The incorporation of crtE and crtB genes from Deinococcus radiodurans R1 established the phytoene biosynthetic pathway. This pathway was fine-tuned through promoter optimization, resulting in a phytoene production of 450 μg/L from 37 mmol/L methane. Disrupting the ackA gene reduced a by-product, acetate, by 50 % and increased phytoene production by 56 %. Furthermore, overexpressing the dxs gene boosted phytoene titer 3-fold. The optimized strain produced 15 mg/L phytoene from 2 mol/L methane in fed-batch fermentation, a 4-fold increase in phytoene titer and 4-fold in yield. This demonstrates Methylocystis sp. MJC1′s potential for efficient phytoene production and presents a novel approach for greenhouse gas reduction.

Tensor loop reduction via the Baikov representation and an auxiliary vector

In this paper, we introduce a simple and efficient approach for the general reduction of one-loop integrals. Our method employs the introduction of an auxiliary vector and the identification of the tensor structure as an auxiliary propagator. This key insight allows us to express a wide range of one-loop integrals, encompassing both tensor structures and higher poles, in the Baikov representation. By establishing an integral-by-parts relation, we derive a recursive formula that systematically solves the one-loop reduction problem, even in the presence of various degenerate cases. Our proposed strategy is characterized by its simplicity and effectiveness, offering a significant advancement in the field of one-loop calculations. Published by the American Physical Society 2024

Precision reduction of femoral neck fractures: a novel strategy based on the femoral neck fracture morphology

In femoral neck fractures the secondary damage caused by repeated multiple reductions needs to be prevented. Accordingly, the aim of this study was to achieve an anatomical reduction in the first manipulation of reduction in femoral neck fractures. We propose a new reduction strategy using the x-ray morphology of femoral neck fractures for preoperative planning. In the present study we compared this approach to conventional operation procedures for the treatment of femoral neck fractures. From 2020 to 2021, 35 patients with femoral neck fractures were operated with this reduction strategy. Those were compared with 34 patients treated without the proposed preoperative method. All patients were compared for consistency between the preoperative assessment and the intraoperative execution of the reduction approach, number of reductions, operative time, number of fluoroscopies, intraoperative bleeding, and reduction quality. The operative time of patients in the study group was significantly shorter (38.40 ± 10.26 min) than that of the control group (47.26 ± 9.09 min), and the number of reductions [1.0 (1.0, 1.0)] was significantly less than that of the control group [2.0 (1.75, 2.25)]. The number of fluoroscopies (10.27 ± 2.84) was also significantly less than that of the control group (13.53 ± 2.59) times. The KAPPA value = 0.886 shows the good agreement between the preoperative proposed protocol and the intraoperative protocol about the study group .The bleeding, quality of repositioning, Harris Hip score, MOS 12-item Short Form Survey (SF-12), and early complication rate were not statistically different between the groups (P > 0.05). The selection of the appropriate repositioning method based on the analysis of femoral neck fracture X-ray morphology can improve the efficiency and accuracy of preoperative planning. This reduces the secondary damage that may be caused by multiple reduction, shortens the operation time and reduces the exposure to radiation.

An optimization framework with dimensionality reduction using Markov Chain Monte Carlo and genetic algorithms for groundwater potential assessment

Limited samples and high-dimensional feature spaces often hinder the accuracy of machine learning (ML) models in regional groundwater potential assessment (GPA). This study proposes a novel framework, the GPA with Dimensionality Optimization (GPADO), that optimizes feature dimension reduction to enhance prediction performance. Taking the Jianghan Basin as an example, data on nine continuous variables and five categorical variables influencing the region's GPA were gathered, expanding the feature set to 37 through One-hot encoding for categorical variables. Three scenarios were devised to assess prediction outcomes following various dimensionality reduction approaches. Comparative analysis revealed that a hybrid dimension reduction method, incorporating both continuous and categorical variables, yielded the highest validation set accuracy. Consequently, genetic algorithm and Markov Chain Monte Carlo methods were employed to determine the optimal solution and uncertainties associated with four unknown parameters: the chosen dimension reduction method for continuous and categorical variables, and the number of dimensions retained. Results indicated that utilizing singular value decomposition to reduce categorical variables to three dimensions, coupled with principal component analysis reducing continuous variables to three dimensions, produced the highest model validation accuracy of 0.834 within the GPADO framework. This optimal configuration facilitated automated ML training, resulting in a final validation set accuracy of 0.851 and a test set accuracy of 0.836. The resulting model provided a more precise spatial distribution of groundwater potential and demonstrated the GPADO framework's effectiveness in improving GPA accuracy, particularly in data-scarce regions. The GPADO framework offers a valuable approach for enhancing GPA studies.

Political constructions of people who use drugs in the Philippines: A qualitative content analysis

BackgroundThis paper examines the political constructions of people who use drugs in the Philippines throughout the presidency of Rodrigo Duterte (2016–2022), during which the government engaged in a ‘war on drugs’ and promoted a punitive drug regime. MethodsBuilding on and drawing inspiration from the global drug policy scholarship that has looked at the ways in which drugs are framed and problematised in various domains, this study used qualitative content analysis to review 96 documents from national government agencies - including strategic action plans, directives, memorandums, guidelines, annual reports, and legislative measures. ResultsForemost, the study finds that various terms were interchangeably used to refer to ‘drug users’ - dependent, offender, personality, abuser - and all of them contributed to the problematisation of people who use drugs as a societal “menace”. As “drug dependents”, they were likewise portrayed as necessitating treatment or rehabilitation. Moreover, presented as victims or passive subjects, their agency and subjectivity are not acknowledged in the documents, even as counter-discourses, mainly from opposition lawmakers, challenge these portrayals and call for people-centered, harm reduction approaches. ConclusionOverall, these overlapping framings cast people who use drugs simultaneously as victims, criminals, deviants, and sick individuals to the detriment of their security, health, and well-being - and to the retrogression of drug policy in the country.

Enhancing fusion welding in die-cast Mg alloys: A combined ultrasonic vibration and hot rolling approach for porosity reduction, microstructure refinement, and mechanical property improvement

Severe weld porosity has led the industry to believe that die-cast magnesium alloys cannot be fusion welded. To address this challenge, a new process of ultrasonic vibration coupled with hot rolling was employed to eliminate porosity, and the mechanism associated with weld porosity in die-cast magnesium alloys was analyzed in detail. Unlike conventional welding processes, ultrasonic vibration coupled with hot rolling is effective and efficient. In this study, a roller with ultrasonic vibrations is synchronized with the molten pool to apply pressure to the weld, which is in a semi-solid state on the upper surface. The results showed that hot rolling and ultrasonic vibration coupled with hot rolling could eliminate 78.12 % and 85.5 % of the porosity, respectively. Compared to hot rolling, the ultrasonic vibration coupled with the hot rolling process resulted in smaller and fewer porosities and finer β-Mg17Al12. This is attributed to the cavitation effect of ultrasonic vibration that promotes bubble overflow and breaks the β-Mg17Al12 dendrites in the melt pool, as well as the thermal effect that improves the plasticity of the weld. Furthermore, after closing the porosity under pressure, recrystallized grains will be produced around it. Tensile test results indicated that hot rolling was able to increase the ultimate tensile strength by 84.21 % and elongation by 44.03, whereas ultrasonic vibration coupled with hot rolling was able to increase the ultimate tensile strength by 244.46 % and elongation by 71.70 %. Combining the micron-scale industrial computed tomography results with theoretical calculations, it is found that the porosity of die-cast magnesium alloys is mainly caused by nitrogen, and they originate from gas entrapment during the die-casting process; further, the formation mechanism of die-cast magnesium alloys' fusion-welding porosity is revealed in detail. A physical model of the formation process of welded porosity morphology was established, and the analysis suggested that the pattern of the inner wall of the porosity was related to the Kelvin–Helmholtz instability. This research is expected to improve the weld quality of alloys manufactured by the die-casting process.

A Novel Noise Reduction Approach of Acoustic Emission (AE) Signals in the SiC Lapping Process on Fixed Abrasive Pads.

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time-domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.

Exploring object reduction approaches for optimizing decision-making in linguistic concept formal context

Exploring object reduction approaches for optimizing decision-making in linguistic concept formal context

A supervised multiclass framework for mineral classification of Iberian beads.

Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. As a proof of concept, we developed a multiclass model and evaluated its performance on two assemblages from different Portuguese sites with current mineralogical characterisation: Cova das Lapas (n = 15 samples) and Gruta da Marmota (n = 10 samples). Our results showed that decisión-tres based classifiers outperformed other classification logics given the discriminative importance of some chemical elements in determining the mineral phase, which fits particularly well with the decision-making process of this type of model. The comparison of results between the different validation sets and the proof-of-concept has highlighted the risk of using synthetic data to handle imbalance and the main limitation of the framework: its restrictive class system. We conclude that the presented approach can successfully assist in the mineral classification workflow when specific analyses are not available, saving time and allowing a transparent and straightforward assessment of model predictions. Furthermore, we propose a workflow for the interpretation of predictions using the model outputs as compound responses enabling an uncertainty reduction approach currently used by our team. The Python-based framework is packaged in a public repository and includes all the necessary resources for its reusability without the need for any installation.

Leaving Against Medical Advice: Current Problems and Plausible Solutions.

Leave against medical advice (LAMA) is defined as 'a decision to leave the hospital before the treating physician recommends discharge', and is associated with higher rates of readmission, longer subsequent hospitalization, and worse health outcomes. In addition to this, they also contribute to poor healthcare resource utilization. We conducted a single-center audit to establishpatient demographics and contributing factors of patients leaving against medical advice from our emergency department (ED). We benchmarked our data against locally available clinical policy guidelines.We interrogated our electronic health record system (known as Salamtak®), which is a Cerner-based platform (Cerner Corporation, Kansas City, MO 64138)for patients who signed LAMA from ED from 2018 to 2023. We selected a convenience pilot sample of 120 subjects. Based on a literature review, we identified patient demographics (age, gender, nationality, socioeconomic status, marital status, religion), possible contributing factors (time of attendance, insurance status, length of ED stay), and patient outcomes (reattendances within 1 week and mortality) to evaluate. Based on locally available guidance, we formulated six criteria to audit with a standard set at 100% for each. A team of emergency medicine residents collected data that was anonymized on an Excel spreadsheet (Microsoft Excel,Microsoft Corporation. (2018). Basic descriptive statistics were used to collate results. About 93 patients (77.5%) were 16 years and above, and 27 patients (22.5%) were below 16 years. There was a slight preponderance of males (64 patients, 53.3%) than females (56 patients, 46.6%). The majority of LAMA cases presented in the evening and night (97 patients, 80.8%). About 57 (47.5%) patients had an ED length of stay of 3 hours or more. The average ED length of stay for these patients was 3.4 hours. About 73 patients (60.3%) were insured. Out of 120 patients, only 12 (10%) had a mental capacity assessment documented. The commonest reason for signing LAMA was a social reason in 45 (37.5%) cases. In the remaining cases, the causes were a combination of family, financial, waiting, or other/undocumented reasons). When faced with a decision to LAMA, the involvement of a Public Relationship Officer (PRO) was only documented to be consulted in seven (5.8%) cases. About 14 cases were re-attended within 1 week (11.6%) and no mortalities were reported in any of the reattendances. LAMA is a not-so-rare phenomenon often occurring in EDs, and often a cause of trepidation for healthcare workers. Treating this as an aberrant behavior on the part of the patient, or laying the responsibility for this action on the healthcare provider is primitive, counter-productive, and not patient-centric. Familiarity with local guidelines around this contentious area is essential. Revised nomenclature like 'premature discharge' may be less stigmatizing for the patient.Where possible, a harm reduction approach should be used and frontline healthcare workers must be prepared with an escalation plan.In the United Arab Emirates, familiarity with Wadeema's Law as a child protection measure is essential.

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET

BackgroundResidual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering.MethodsAltogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ([18F]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{18}\ ext {F}]$$\\end{document}FDG, [18F]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{18}\ ext {F}]$$\\end{document}L-DOPA, [68Ga]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{68}\ ext {Ga}]$$\\end{document}DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the “optimal” filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.ResultsThe automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SUV}_\ ext {max}$$\\end{document} values of the unfiltered images with a low mean ± SD difference of δSUVmaxCNN,STD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {CNN},\ ext {STD}}$$\\end{document} = (−3.9 ± 5.2)% and δSUVmaxBF,STD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {BF},\ ext {STD}}$$\\end{document} = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SUV}_\ ext {max}$$\\end{document} values in the vast majority of cases with an overall average difference of δSUVmaxCNN,BF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {CNN},\ ext {BF}}$$\\end{document} = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δNoiseCNN,BF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {Noise}^{\ ext {CNN},\ ext {BF}}$$\\end{document} = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed.ConclusionsOur results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using POD-based reduced-order modeling

AbstractThis study explores reduced-order modeling for analyzing time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which the chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using the finite element method. We employ proper orthogonal decomposition as a model order reduction approach. The reduced-order model performance is tested through a benchmark problem on hydrogel swelling and a case study simulating co-axial printing. Then, we embed the reduced-order model into an optimization loop to efficiently identify the coupled problem’s material parameters using full-field data. Finally, a study is conducted on the uncertainty propagation of the material parameter.

Combined Impact of Multidelay Feedback Number and Interval: A Novel Mechanism for Controlling the Stability of Stochastic Duffing Systems

In this paper, we study a class of nonlinear stochastic Duffing oscillators with multidelay feedback. We propose an effective reduction approach with the help of center manifold theory and stochastic averaging method. Taking the initial time-delay [Formula: see text] as the parameter, we reduce the original system to a one-dimensional averaged Itô equation. Our analysis reveals that the original system exhibits stochastic bifurcations, including stochastic D and P bifurcations. Once we have a clear understanding of the bifurcation structure, we can use this knowledge to choose appropriate system parameters and place the system in the desired state. For instance, by adjusting the initial time-delay [Formula: see text] of the control system, we can stabilize the system and achieve the desired outcome. Numerical simulations also verify the theoretical results. With appropriate parameter choices, multiple time delays can destabilize the equilibrium and promote chaotic behaviors, and can also lead to more stable dynamical behavior. Remarkably, we discovered that increasing the interval of time delays and feedback numbers can enhance system stability. It may potentially serve as a novel mechanism for stabilizing stochastic systems. The study provides a solid theoretical foundation for exploring stochastic systems subject to complex time-delay feedback control, and offers a valuable framework for related fields.

A New Torque Control Approach for Torque Ripple Minimisation in Switched Reluctance Drives

The switched reluctance motor (SRM) has many merits, such as robustness, a simple construction, low cost, and no permanent magnets. However, its deployment in servo applications is restrained due to acoustic noise and torque ripple (TR). This paper presents a new torque control approach for TR reduction in switched reluctance drives. The approach is based on the maximum utilisation of the available dc-link voltage, hence extending the zero torque-ripple speed range. The approach is suitable for an SRM with any number of phases and stator/rotor poles. Soft switching control is deployed, which reduces switching losses. At any instant (regardless of the number of phases being conducted simultaneously), only one phase current is controlled. The well-established torque-sharing function concept is adapted and generalised to cater for more than two phases conducting simultaneously. MATLAB/Simulink confirmation simulations are based on the widely studied four-phase 8/6, 4 kW, 1500 rpm SRM.

Building resilience: Insights into ecosystem-based integrated environmental risk reduction approaches for urban environments in coastal Bangladesh

The intricate relationship between environmental quality, ecosystem services, and human well-being underscores the importance of understanding and addressing environmental complexities. Considering these issues, this research aims to propose ecosystem-based approaches for mitigating environmental risks within the Khulna City Corporation (KCC) area, while also elucidating the connection between ecosystem services and these risks. By doing so, it indirectly addresses disaster risk reduction approaches, acknowledging the relationship between environmental degradation and increased disaster occurrences. Leveraging multi-spectral Landsat images from 2001 (Landsat-5), 2011 (Landsat-5), and 2021 (Landsat-8), the study evaluates LULC dynamics and quantifies ecosystem service values (ESVs) using the Benefit Transfer Method. Additionally, seven key indicators, including land use-based carbon emissions, elevation, LST, NDVI, NDWI, NDSI, and NDBI, were analyzed to monitor spatiotemporal changes. The weighted overlay method was employed to integrate raster data and delineate environmental risk zones driven by deteriorating ESVs in KCC. Notably, the study reveals a significant increase in highly environmental risk areas from 7 % to 34 % and a decrease in medium-risk areas from 92 % to 60 % of the total area between 2001 and 2021 due to the decline in ecosystem services resulting from unplanned rapid urbanization. Furthermore, a moderate negative correlation (R2 values of 0.56, 0.67, and 0.69) between ESVs and the environmental risk index underscores the linkage between deteriorating ecosystem services and the expansion of high-risk zones. Furthermore, this study advocates policies such as drought-resistant tree plantations, community involvement, post-disaster resource provision, funding allocation, and stakeholder knowledge dissemination to enhance the resilience of KCC.