Conventional Methods Research Articles

A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization

Objective. Accurate delineation of organs-at-risk (OARs) is a critical step in radiotherapy. The deep learning generated segmentations usually need to be reviewed and corrected by oncologists manually, which is time-consuming and operator-dependent. Therefore, an automated quality assurance (QA) and adaptive optimization correction strategy was proposed to identify and optimize ‘incorrect’ auto-segmentations. Approach. A total of 586 CT images and labels from nine institutions were used. The OARs included the brainstem, parotid, and mandible. The deep learning generated contours were compared with the manual ground truth delineations. In this study, we proposed a novel contour quality assurance and adaptive optimization (CQA-AO) strategy, which consists of the following three main components: (1) the contour QA module classified the deep learning generated contours as either accepted or unaccepted; (2) the unacceptable contour categories analysis module provided the potential error reasons (five unacceptable category) and locations (attention heatmaps); (3) the adaptive correction of unacceptable contours module integrate vision-language representations and utilize convex optimization algorithms to achieve adaptive correction of ‘incorrect’ contours. Main results. In the contour QA tasks, the sensitivity (accuracy, precision) of CQA-AO strategy reached 0.940 (0.945, 0.948), 0.962 (0.937, 0.913), and 0.967 (0.962, 0.957) for brainstem, parotid and mandible, respectively. The unacceptable contour category analysis, the (FI,AccI,Fmicro,Fmacro) of CQA-AO strategy reached (0.901, 0.763, 0.862, 0.822), (0.855, 0.737, 0.837, 0.784), and (0.907, 0.762, 0.858, 0.821) for brainstem, parotid and mandible, respectively. After adaptive optimization correction, the DSC values of brainstem, parotid and mandible have been improved by 9.4%, 25.9%, and 13.5%, and Hausdorff distance values decreased by 62%, 70.6%, and 81.6%, respectively. Significance. The proposed CQA-AO strategy, which combines QA of contour and adaptive optimization correction for OARs contouring, demonstrated superior performance compare to conventional methods. This method can be implemented in the clinical contouring procedures and improve the efficiency of delineating and reviewing workflow.

Evaluation of electrochemical performance of supercapacitors from equivalent circuits through cyclic voltammetry and galvanostatic charge/discharge

Conventional methods used to analyze electrochemical performance of supercapacitors are complex and cannot illustrate the asymmetrical behavior of charge and discharge curves and the variation of resistance with scan rate and current density. In this work, we propose a simple method to calculate total capacitance and internal resistance of supercapacitors with equivalent circuits and discuss the mechanisms responsible for the asymmetry of galvanostatic charge discharge (GCD) curves. Using the equivalent circuits, we demonstrate the feasibility of analyzing electrochemical performance of supercapacitors from the current-voltage curve for cyclic voltammetry (CV) and the GCD curves for galvanostatic cycling. A series of supercapacitors are constructed, in which the electrodes are pre-compressed under a compressive stress in a range of 1– 7.5 MPa. The experimental results reveal that the total capacitance of the supercapacitors increases with increasing the pre-compressive stress and validated the proposed method. Increasing the pre-compressive stress likely can improve the electrochemical performance of supercapacitors. The results from the analysis with the analytical relations for the resistance of the resistor in parallel connection in the three-element circuit approaching infinity exhibit similar trends to the corresponding ones from the conventional methods in contrast to the results from the three-element circuit, which exhibit different trends. The three-element circuit with the resistor in parallel connection approaching infinity can be used to analyze the electrochemical performance of supercapacitors for both the CV and GCD operational conditions.

Development of an action classification method for construction sites combining pose assessment and object proximity evaluation

AbstractAddressing the inherent hazards of on-site construction work and stagnant labor productivity is crucial in the construction industry. To tackle these challenges, automated monitoring of construction sites and analysis of workers' actions play a pivotal role. In this study, we developed a method for classifying actions at a construction site from video, using deep learning. Specifically, we used two image processing techniques, pose assessment and object detection, and found that the accuracy of action classification was improved by extracting information on the proximity of workers to equipment installed at the construction site, and also by considering the pose information. For classification, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and XGBoost models were used, and the presence of proximity information improved average recall by 7.0% to 8.5% for all models used. The final model was developed as an ensemble of these methods, offering accuracy and average recall that are higher than with conventional methods. The methodology developed in this research enables quantification and visualization of work content at construction sites, contributing to the overall enhancement of safety and productivity within the construction industry.

Antibiotic prescribing patterns and carriage of antibiotic-resistant Escherichia coli and Enterococcus species in healthy individuals from selected communities in Lusaka and Ndola districts, Zambia.

This study assessed antibiotic prescribing patterns in primary healthcare facilities and antimicrobial resistance (AMR) profiles of commensal Escherichia coli and enterococci isolated from pregnant women and children under 5 years of age. This cross-sectional study was conducted in Lusaka and Ndola districts of Zambia. Prescription pattern data were obtained from hospital pharmacies. Identification and antimicrobial susceptibility profiles of E. coli and enterococci were determined by conventional methods, while confirmation of both pathogens and AMR genes were determined by PCR. Data were analysed using WHONET and SPSS version 25.0. Most prescribed antibiotics at the primary healthcare facilities belonged to the Access group of the WHO Access, Watch and Reserve (AWaRe) classification. All the primary healthcare facilities adhered to the AWaRe framework of ≥60% prescribed antibiotics belonging to the Access group. However, resistance was highest in the Access group of antibiotics. E. coli resistance to ampicillin ranged from 71% to 77% and to co-trimoxazole from 74% to 80%, while enterococcal resistance to tetracycline was 59%-64%. MDR was highest in E. coli (75%) isolates, while XDR was highest in enterococcal isolates (97%). The identified AMR genes in E. coli included blaCTX-M, sul2 and qnrA, while those of enterococci included erm(B), erm(C) and erm(A). Resistance was highest in the prescribed WHO Access group of antibiotics. These findings highlight the need to use local susceptibility data to formulate country-specific treatment guidelines in line with WHO AWaRe classification and enforce regulations that prohibit easy access to antibiotics.

V2X assisted co‐design of motion planning and control for connected automated vehicle

AbstractThe rapid development of vehicle‐to‐everything (V2X) communication technologies significantly promotes the revolution of intelligent transportation systems. V2X communication is expected to play a critical role in enhancing the safety and efficiency of connected and automated vehicles (CAVs), especially for mixed traffic scenarios. Additionally, the computational and storage capabilities of roadside units (RSUs) will be harnessed to effectively enhance the motion planning and control performance of CAVs within the constraints of limited on‐board computational resources. Thus, a V2X assisted co‐design of motion planning and control algorithm for CAVs to improve their situational awareness and computational efficiency is proposed. Under this architecture, a pre‐planning algorithm is proposed first to utilize the computational and storage capabilities of RSUs and generate feasible trajectories for different driving tasks. By analysing the relationship between driving risk index and motion planning performance, an online‐planning algorithm is derived to modify the pre‐planned trajectories in real‐time with static or dynamic obstacles. Furthermore, the lateral and longitudinal control of the vehicle using the Frenet coordinate system is decoupled. The lateral control employs an offline linear quadratic regulator (LQR) from RSUs to control the steering angle of the vehicle. The longitudinal control employs a dual‐loop PID to control the throttle opening of the vehicle. The performance of the proposed framework is evaluated and demonstrated by a Carsim‐Prescan simulation study in different mixed traffic scenarios. Compared with conventional methods, the proposed method improves the computational efficiency by 23% and reduces the collision rate by 13%.

A dual-mode portable platform with spatiotemporal temperature–pressure signal readouts for ultrasensitive quantitative determination of cancer cells

Early precise diagnosis of cancer is a prerequisite for cancer-related mortality and improved therapeutic outcomes. Direct quantitative cancer cell detection technologies show a great potential for early screening and diagnosis of cancer. However, the reported techniques generally involve exorbitant costs, lengthy procedures, and inevitable use of large-scale apparatus. We develop herein a spatiotemporal dual-mode sensing platform based on temperature and pressure signals for rapid, sensitive, and straightforward quantitative detection of cancer cells. Specifically, this platform is constructed by a dual-signal response probe consisting of hyaluronic acid (HA)-modified hollow copper sulfide nanoparticles (CuS@HA NPs), which not only elicits a robust and spatiotemporal photothermal effect via localized surface plasmon resonance (LSPR) but also promotes a rapid photothermal decomposition of NH4HCO3. This dual-signal response probe demonstrates a broad detection range from 10 to 105 cells/mL for sensing triple-negative breast cancer cells (MDA-MB-231) due to the specific recognition between HA and CD44 receptors. Notably, the remarkable sensitivities, with detection limits of 9 and 4 cells/mL for temperature and pressure signal readouts respectively, display about one order of magnitude lower than conventional immunoassay methods. The practicability and high reliability of the proposed strategy are further validated via analyzing clinic serum samples. Moreover, the localized photothermal therapy induced by the specific binding of CuS@HA NPs can realize targeted elimination of cancer cells. Overall, the developed dual-mode sensing platform demonstrates significant potential as a facile yet robust strategy for real-time and on-site determination of cancer cells in actual samples.

An Updated Overview of Magnetic Composites for Water Decontamination.

Water contamination by harmful organic and inorganic compounds seriously burdens human health and aquatic life. A series of conventional water purification methods can be employed, yet they come with certain disadvantages, including resulting sludge or solid waste, incomplete treatment process, and high costs. To overcome these limitations, attention has been drawn to nanotechnology for fabricating better-performing adsorbents for contaminant removal. In particular, magnetic nanostructures hold promise for water decontamination applications, benefiting from easy removal from aqueous solutions. In this respect, numerous researchers worldwide have reported incorporating magnetic particles into many composite materials. Therefore, this review aims to present the newest advancements in the field of magnetic composites for water decontamination, describing the appealing properties of a series of base materials and including the results of the most recent studies. In more detail, carbon-, polymer-, hydrogel-, aerogel-, silica-, clay-, biochar-, metal-organic framework-, and covalent organic framework-based magnetic composites are overviewed, which have displayed promising adsorption capacity for industrial pollutants.

Holistic coordination of transactive energy and carbon emission right trading for heterogenous networked multi-energy microgrids: A fully distributed adaptive consensus ADMM approach

The paradigm of multi-energy microgrids (MEMGs) with internal energy sharing and trading is considered a promising option to empower low-carbon energy transitions. In this work, a holistic and efficient optimization framework is proposed to coordinate electricity, heat, and carbon emission right (CER) trading activities among multiple MEMGs. The proposed framework captures the heterogeneity of MEMGs, peer-to-peer transactive energy of different forms, and CER trading and network constraints together in a holistic fashion. To preserve the privacy of MEMGs and ensure transparency of CER trading, a fully distributed adaptive consensus alternating direction method of multipliers (AC-ADMM) is proposed, based on the combination of parallel computation capability of C-ADMM and the penalty parameters adaptive tuning mechanism. Case studies demonstrate the effectiveness of the proposed AC-ADMM method and reveal significant economic and environmental benefits for MEMGs by coordinating different forms of energy and CER trading in local transactive markets, while satisfying the network constraints. The superior convergence performance of the proposed method is also validated against conventional ADMM and C-ADMM methods.

Synthesis of 1H-benzimidazoles via the condensation of o-phenylenediamines with DMF promoted by organic acid under microwave irradiation

An effective, green and eco-friendly method was developed to synthesize 1H-benzimidazole under microwave irradiation. The reactions of substituted o-phenylenediamine with DMF were efficiently promoted by butanoic acid, and the desired product 1H-benzimidazole derivatives were obtained in good to excellent yields. Compared with the conventional heating method, microwave irradiation significantly reduced the reaction time and improved the yields of the target products.

An Electrochemical Sensor Based on Simple Nano-Modification for Sensitive Detection of Ofloxacin Residue in Animal Derived Food

The residue of ofloxacin in animal derived food would seriously affect human health. This study reports an inexpensive, fast, and simple electrochemical sensor for the sensitive detection of OFL in animal derived food samples. A poly-L-glutamic acid/poly(sulfobutyl-β-cyclodextrin) CdTe quantum dot-modified electrode P(L-Glu)/SEB-β-CD@CdTeQDs/GCE was prepared by an electrochemical polymerization reaction. We investigated the electrochemical behavior of OFL on the sensing electrode by cyclic voltammetry and square wave voltammetry. The results showed that the sensitivity of the sensor was significantly improved under the synergistic effect of L-glutamic acid, sulfobutyl-β-cyclodextrin, and CdTe quantum dots. In addition, the electrochemical reaction process of OFL on the sensing electrode was investigated and the results showed that the transfers of two protons and two electrons were involved in the reaction. Subsequently, the electrochemical sensing method based on P(L-Glu)/SEB-β-CD@CdTeQDs/GCE was developed and validated for the determination of OFL, showing a good linear response in the range of 0.07 ∼ 1 μg·ml−1 with a detection limit of 3.4 ng·ml−1, and the precision and accuracy were high enough. Compared with the conventional HPLC method, the established electrochemical sensing method shows the merits of being eco-friendly, shorter detection time and being much more simple, sensitive, and convenient.

Study on time-varying characteristics and flow behavior of microencapsulated polymer

Due to the challenges posed by high injection pressure and significant viscosity shear loss, conventional polymer flooding methods are unable to effectively meet the urgent demand for enhanced oil recovery in medium- and low-permeability reservoirs. Microencapsulated polymer flooding is a promising solution to address the aforementioned challenges due to its advantages of easy wellbore injection, shear resistance, and delayed thickening. However, there is a lack of research on the release behavior and time-dependent characteristics of microencapsulated polymer and its impact on flow behavior in reservoirs. To address this gap, we conducted a series of experiments, including thermal stability tests, microfluidic chip experiments, and micro-scale oil displacement experiments, to reveal the time-varying behavior generated by the triggered release of microencapsulated polymers. The results showed that microencapsulated polymer can be fully triggered after 8 days of aging at 85 °C, with a viscosity exceeding the initial viscosity by more than 25 times. The release mechanism of the polymer aligned with the Ritger-Peppas model, transitioning from super Case-II transport to anomalous transport, and finally to Fickian diffusion with increasing temperature. During the triggering process, the presence of particles stabilized shear and improved oil displacement, particularly in small pore throats. Higher triggering degrees of the solution resulted in greater flow resistance within the microchannel, effectively suppressing fluid fingering. The oil displacement effectiveness of fully triggered microencapsulated polymer was comparable to that of conventional polymers. This study provides insights into the flow behavior of microencapsulated polymer at a microscopic level, offering valuable references for its application in the field.

Controlled Formation of Nanoribbons and Their Heterostructures via Assembly of Mass-Selected Inorganic Ions.

Control of nanomaterial dimensions with atomic precision through synthetic methods is essential to understanding and engineering of nanomaterials. For single-layer inorganic materials, size and shape controls have been achieved by self-assembly and surface-catalyzed reactions of building blocks deposited at a surface. However, the scope of nanostructures accessible by such approach is restricted by the limited choice of building blocks that can be thermally evaporated onto surfaces, such as atoms or thermostable molecules. Herein this limitation is bypassed by using mass-selected molecular ions obtained via electrospray ionization as building blocks to synthesize nanostructures that are inaccessible by conventional evaporation methods. As the first example, micron-scale production of MoS2 and WS2 nanoribbons and their heterostructures on graphene are shown by the self-assembly of asymmetrically shaped building blocks obtained from the electrospray. It is expected that judicious use of electrospray-generated building blocks would unlock access to previously inaccessible inorganic nanostructures.

Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives

AbstractInfectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface‐enhanced Raman Spectroscopy (SERS), as an innovative non‐invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost‐effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label‐free SERS strategy, reporting on the latest advancements of SERS technique in detecting bacteria, viruses, and fungi in clinical settings. Furthermore, we emphasize the application of machine learning algorithms in SERS spectral analysis. Finally, challenges faced by SERS application are probed, and the prospective development is discussed.

Some lithogeochemical and isotope-geochemical features of clay rocks and hiatuses in the Riphean stratotype

Research subject. Clay rocks of the Lower, Middle, and Upper Riphean (respectively, the Burzyan, Yurmatau, and Karatau groups) of the Western slope of the Southern Urals. Changes in their characteristic values of a number of ratios of oxides and some trace elements that are indicators of rock composition in provenances are considered, taking into account the breaks of different duration present in the Riphean stratotype. Methods. Calculation of t he average values of TiO2/Al2O3, Th/Sc, La/Co and (La/Yb)N, typical for shales, fine-grained clay siltstones, and mudstones of the main lithostratigraphic units of the Riphean stratotype (formation and, in some cases, subformation) and analysis of their changes taking into account the hiatuses known in the Riphean stratotype (Pre-Mashak/Pre-Zigalga, Pre- Zilmerdak, Pre-Minyar, Pred-Uk). Results. It was established that throughout the entire Riphean, regardless of breaks in sedimentation, presumably associated with certain transformations in the areas of erosion and sedimentation, a number of lithogeochemical characteristics of fine-grained clastic rocks of the Burzyan, Yurmatau, and Karatau groups (such as TiO2/Al2O3, Th/Sc, La/Co, and, to a significant extent, (La/Yb)N) practically did not experience significant changes. Conclusions. The data obtained give reason to think that there were no fundamental changes in the composition of the sources of fine-grained aluminosiliciclastics for the Riphean sedimentary sequences for more than 1 Ga, and contradict the rather active scenario of tectonic processes at the end of the Early Precambrian and the entire Late Precambrian. Conversely, the data on the values of tNd(DM) and eNd(t) in 37 samples of clay rocks of the Riphean stratotype nevertheless reflect a change in the composition of the rocks of the feeding provinces and, one way or another, fit into the outline of subglobal events established by conventional geological methods in the stratotype area of the Riphean. In accordance with them, in the Ai and Mashak times, an increase in the proportion of juvenile material took place in the areas of erosion. Apparently, the Pre-Zilmerdak hiatus also led to the appearance of a significant proportion of basic igneous rocks among the rocks of the provenances.

An optimal sparse sensing approach for scanning point selection and response reconstruction in full-field structural vibration testing

Non-contact vibration measurements, such as 3D scanning laser Doppler vibrometry (3D SLDV), are becoming more prevalent in testing next-generation lightweight aerospace structures. This approach reduces the impact of attached sensors and improves measurement reliability. Acquiring precise measurement data for the whole area is feasible, albeit it would demand a considerable amount of time and storage space for testing. The concept of compressed sensing has been recently approved as an effective way to exploit signal sparsity and achieve full response reconstruction with very few measurements. The objective of this work is to enhance the efficiency of non-contact vibration testing by utilizing the state-of-the-art compressive sensing approach. In contrast to conventional sensor placement methods that rely on effective independence, modal kinetic energy, or modal assurance criterion matrix as targets, this paper proposes a novel sensor placement methodology from the perspective of dynamic response reconstruction. The scanning points are chosen with a minimal number to reduce testing time and are well-placed such that full-field vibration responses of the test structure can be reconstructed accurately. This allows for the spatially-detailed vibration responses to be obtained efficiently and accurately with optimal sparse sensing placement and effective response reconstruction through ℓ1 algorithm. Two case studies will be presented in this work to demonstrate and validate the methodology. The first case study is focused on a simplified cantilever beam using the numerical data from the FE analysis to demonstrate the methodology. The second case study is focused on using 3D SLDV experimental testing data from a full-scale industrial fan blade. Based on the results, it is evident that the proposed approach can significantly decrease the scanning points required for a full-field dynamic response reconstruction during full-field vibration testing.

Building initial model for seismic inversion based on semi‐supervised learning

AbstractSeismic inversion is an important tool for reservoir characterization. The inversion results are significantly impacted by a reliable initial model. Conventional well interpolation methods are not able to meet the needs of seismic inversion for lateral heterogeneous reservoirs. Inspired by the sequence modelling network and seismic inversion in the Laplace–Fourier domain, we propose an initial model‐building method using semi‐supervised learning strategy. The proposed method considers spatial information to ensure the horizontal continuity of the initial model. Based on the fact that the low‐frequency components of seismic signals in the Laplace–Fourier domain are easier to obtain, we use the forward model in the Laplace–Fourier domain to replace the time‐domain forward model. The proposed workflow was validated using the Marmousi II model. Although the training was carried out on a small number of low‐frequency impedance traces, the proposed workflow was able to build low‐frequency model for the entire Marmousi II model with a correlation of 98%. Field data examples demonstrate the feasibility and effectiveness of the proposed method. For lateral heterogeneous reservoirs, the proposed method performs better than the well interpolation method. By utilizing the model obtained by the proposed method as the initial low‐frequency model of the conventional inversion method, it is possible to estimate better inversion results. The results of different combinations of training sets demonstrate the stability of the proposed method. This method may still be a viable choice if there is lateral heterogeneity underground but not much well‐logging label data.

Mid-Long Chain Dicarboxylic Acid Production via Systems Metabolic Engineering: Progress and Prospects.

Mid-to-long-chain dicarboxylic acids (DCAi, i ≥ 6) are organic compounds in which two carboxylic acid functional groups are present at the terminal position of the carbon chain. These acids find important applications as structural components and intermediates across various industrial sectors, including organic compound synthesis, food production, pharmaceutical development, and agricultural manufacturing. However, conventional petroleum-based DCA production methods cause environmental pollution, making sustainable development challenging. Hence, the demand for eco-friendly processes and renewable raw materials for DCA production is rising. Owing to advances in systems metabolic engineering, new tools from systems biology, synthetic biology, and evolutionary engineering can now be used for the sustainable production of energy-dense biofuels. Here, we explore systems metabolic engineering strategies for DCA synthesis in various chassis via the conversion of different raw materials into mid-to-long-chain DCAs. Subsequently, we discuss the future challenges in this field and propose synthetic biology approaches for the efficient production and successful commercialization of these acids.

Adaptive estimation of the Gutenberg–Richter b value using a state space model and particle filtering

Earthquakes follow an exponential distribution referred to as the Gutenberg–Richter law, which is characterized by the b value that represents a ratio of the number of large earthquakes to that of small earthquakes. Spatial and temporal variation in the b value is important for assessing the probability of a larger earthquake. Conventionally, the b value is obtained by a maximum-likelihood estimation based on past earthquakes with a certain sample size. To properly assess the occurrence of earthquakes and understand their dynamics, determining this parameter with a statistically optimal method is important. Here, we discuss a method that uses a state space model and a particle filter, as a framework for time-series data, to estimate temporal variation in the b value. We then compared our output with that of a conventional method using data of earthquakes that occurred in Tohoku and Kumamoto regions in Japan. Our results indicate that the proposed method has the advantage of estimating temporal variation of the b value and forecasting magnitude. Moreover, our research suggests no heightened probability of a large earthquake in the Tohoku region, in contrast to previous studies. Simultaneously, there is the potential of a large earthquake in the Kumamoto region, emphasizing the need for enhanced monitoring.

Hyper ensembled extreme gradient boosting techniques via novel triple algorithms to appraise the hardened properties of modern ternary admixed concrete

Mechanical parameters used in many design codes can be achieved by expensive and time-consuming experiments or by non-destructive approaches such as estimative modelling. This investigation proposed Extreme Gradient Boost (XGB) for estimating the slump (SL) and compressive strength (CS) of high-performance concrete (HPC). In addition, to bring the results of the models closer to the experimental data and increase the accuracy, algorithms were combined with the model, including Sunflower Optimizer (SFO) and Jellyfish Search Optimize (JSO). The relevant models have been examined in three frameworks: individual, hybrid, and ensemble-hybrid. For this purpose, several evaluators were provided to determine the errors, compare, and accuracy of the presented models. The XGFJ model has demonstrated exceptional performance, achieving remarkable results in terms of RMSE (Root Mean Square Error) and R2 (R-squared) values. Specifically, it has attained an exceptionally small RMSE value of 1.785 for CS and 5.183 for SL, indicating the model’s high precision in predicting these parameters. Additionally, it has achieved the biggest R2 values of 0.9960 for CS and 0.9949 for SL. Additionally, it is worth noting that the XGSF model closely matches the performance of the ensemble form of XGFJ, as evident from its R2 values of 0.9956 for CS and 0.9934 for SL. Based on the study, it was observed that using machine learning to anticipate the mechanical characteristics of concrete is valuable and efficient and can be considered an alternative method instead of time-consuming laboratory methods. This research addresses challenges in predicting HPC properties fueled by the need to overcome drawbacks in traditional methods. Costly and time-intensive laboratory experiments prompted the exploration of alternatives, leading to the proposal of XGB combined with optimization algorithms (SFO and JSO). The study aims to enhance prediction accuracy while tackling broader concerns such as construction costs, material efficiency, and environmental impact. The resource-intensive nature of conventional methods, along with inaccuracies due to material variations, serves as a primary challenge. The proposed resolution advocates for a paradigm shift to machine learning, exemplified by the XGFJ model, showcasing exceptional precision and efficiency in predicting HPC properties.

Modern acceleration in musculoskeletal MRI: applications, implications, and challenges.

Magnetic resonance imaging (MRI) is crucial for accurately diagnosing a wide spectrum of musculoskeletal conditions due to its superior soft tissue contrast resolution. However, the long acquisition times of traditional two-dimensional (2D) and three-dimensional (3D) fast and turbo spin-echo (TSE) pulse sequences can limit patient access and comfort. Recent technical advancements have introduced acceleration techniques that significantly reduce MRI times for musculoskeletal examinations. Key acceleration methods include parallel imaging (PI), simultaneous multi-slice acquisition (SMS), and compressed sensing (CS), enabling up to eightfold faster scans while maintaining image quality, resolution, and safety standards. These innovations now allow for 3- to 6-fold accelerated clinical musculoskeletal MRI exams, reducing scan times to 4 to 6min for joints and spine imaging. Evolving deep learning-based image reconstruction promises even faster scans without compromising quality. Current research indicates that combining acceleration techniques, deep learning image reconstruction, and superresolution algorithms will eventually facilitate tenfold accelerated musculoskeletal MRI in routine clinical practice. Such rapid MRI protocols can drastically reduce scan times by 80-90% compared to conventional methods. Implementing these rapid imaging protocols does impact workflow, indirect costs, and workload for MRI technologists and radiologists, which requires careful management. However, the shift from conventional to accelerated, deep learning-based MRI enhances the value of musculoskeletal MRI by improving patient access and comfort and promoting sustainable imaging practices. This article offers a comprehensive overview of the technical aspects, benefits, and challenges of modern accelerated musculoskeletal MRI, guiding radiologists and researchers in this evolving field.