bluuebunny/crossref_metadata_2025 huggingface dataset:
Laboratory assessment of shear strength parameters of sand amended via subsequent biopolymer-based soil treatment and enzyme-induced calcite precipitation combinations
Authors: Park Suhyuk , Park Jinwoo , Chang Ilhan
Published: May 2024

Current ground improvement practices predominantly rely on cement; however, interest in strengthening the ground in an environmentally sustainable manner is growing due to concerns regarding the environmental impact, such as the high CO2 footprint associated with cement usage. Biopolymer-based soil treatment (BPST) and enzyme-induced calcite precipitation (EICP) have emerged as promising methods for environmentally friendly ground improvement. While several studies have demonstrated the effectiveness of BPST and EICP individually, both methods have their limitations, which can be seen as complementary (for instance, EICP is more effective for coarse soils, whereas BPST is more effective for clayey soils). Recently, efforts have been made to combine BPST with EICP to address these limitations. This study considered four consecutive BPST-EICP combinations: 1) EICP alone, 2) BPST alone, 3) pre-EICP followed by post-BPST, and 4) pre-BPST followed by post-EICP. Laboratory experiments were conducted to assess the shear strength parameters (i.e., direct shear cohesion and friction angle) of sand treated using these four methods. The peak and residual shear strength parameters of the pre-EICP followed by post-BPST soil were the highest among all cases. However, each combination is suited to specific site conditions, such as soil type, groundwater conditions, and the purpose of ground improvement.
Analysis and intelligent prediction of subway train-induced vibration in the building with pile foundation
Authors: Tu W B , Shen L Q , Zhang X L , Liu L Y , Li J P , Zhang P F
Published: May 2024

The rapid development of subway construction inevitably leads to the subway lines traversing through or adjoining the buildings. However, current research primarily focuses on the train-induced ground-borne or building with shallow foundation vibrations, with a relatively limited investigation into the vibration pertaining to the building with pile foundation. A vibration analysis model of a building with pile foundation adjacent to a subway is established in this study using the finite element method, and the effects of various factors on the vibration of the building are analyzed, then the prediction of building vibration characteristics is also carried out by combining artificial intelligence methods. The results show that the building floor has an amplifying effect on the vibration acceleration level and Z-vibration level in the high frequency range, and the vibration energy attenuation at high frequencies is faster in the building with pile foundation compared to the building with shallow foundation. In the meantime, it shows that the GA-BP model is capable of effectively capturing the vibration characteristics of the building with pile foundation within the dominant frequency range. and the vibration of the building with pile foundation can be predicted not only by parameters such as train speed, soil and structure parameters but also by train-induced ground-borne vibration, which provides the possibility of simplifying the process of vibration prediction process and enhancing the practical application.
Numerical modelling of downward progressive landslides in sensitive clay
Authors: Zhang Y , Zhang X , Li X
Published: May 2024

Landslides in sensitive clays are catastrophic events that threaten life and property. When plastic strain occurs in sensitive clays, there is a significant reduction in the undrained shear strength. Consequently, even minor triggers can lead to multiple progressive landslides, causing extensive devastation. Investigating landslides in sensitive clay presents challenges, necessitating robust numerical methods to comprehend failure modes and collapse behaviors, particularly in the post-failure phase. Although several numerical methods have been utilized for simulations, most have primarily focused on upward progressive landslides. In this study, we adopted the nodal integration-based particle finite element method to simulate downward progressive landslides triggered by surcharge loads from an embankment near the crest in sensitive clay on a long slope. We also considered nonlinear strain softening in sensitive clays. The effects of the strain softening rate and strength gradient of the sensitive clay layer on the failure mechanisms and destruction extent were investigated. The simulation results reveal that a downward progressive landslide initiates with a localized flow slide surrounding the embankment and usually involves a combination of various failure modes.
Research and application of sinking-aided methods for circular open caisson under complex formation conditions: case study
Authors: Wang J , Zhen L , Fang T , Tao Y
Published: May 2024

Open caissons have been widely used owing to their good structural integrity and waterproofing performance, convenient construction, and low cost. However, sinking construction is affected by many factors, such as geological conditions, groundwater, designed depth, and self-weight. Improper measures may lead to severe subsidence of the surface and adjacent structures of an open caisson and even adversely affect its structure. This study using a large-diameter circular open caisson in the Jinshan Lake, Zhenjiang, first examines the causes of its substantial sinking and stagnation during construction. Owing to the different leading causes, sinking-aided methods differ. Therefore, these methods and their subsequent optimization schemes are established, and their effects are analyzed to provide a reliable reference for future related projects.
Experimental study on the stability of dike reinforced by steel sheet pile under storm surge
Authors: Zhang J L , Xie K L , Peng M
Published: May 2024

Over recent decades, upgrading dikes that defend against storm surges increasing in both intensity and frequency resulting from global warming, face challenges such as environmental impact and susceptibility to brittle failure. To address this issue, the steel sheet pile can be employed to improve the dike stability due to its resilience in withstanding substantial deformation. To investigate the reinforcement mechanism of the steel sheet pile, this study conducted three wave flume tests including the dike without configuration, the dike with single sheet pile, and the dike with double sheet pile. The dike stability and pile bending moment were analyzed based on the monitoring data. The experimental results showed that the single pile significantly improved the dike stability by reducing the seepage. The maximum bending moment for the single sheet pile occurred at approximately 1/3 of the pile depth, primarily due to its proximity to the wave impact area on the dike. The double pile further enhanced the dike stability primarily through the reduction of wave scour. The maximum bending moment for the double pile occurred at a lower position (2/3 of the pile depth) and was smaller due to the external force distribution and tie rod limitation.
Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles
Authors: Lv You , Zhang Ru , Zhang Anlin , Shen Yanjun , Ren Li , Xie Jing , Zhang Zetian , Zhang Zhilong , An Lu , Sun Junlong , Yan Zhiwei , Mi Ou
Published: May 2024

The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.
Approach for Data-driven Controlling of Construction Projects using the example of Claim Management
Authors: Lenz L T , Schmidt J-L , Riechmann A , Gralla M
Published: May 2024

Current methods for controlling of construction projects are characterized by manual processes, which are failure-prone, tedious and for that time-consuming and cost-intensive. Furthermore, an additional challenge is caused by the absent dynamic in conventional controlling processes, which is of high importance to avoid damages and ensure a proactive management of a construction project. As construction projects are highly complex due to various influences which imply an enormous amount of information and are designated by dynamic processes, conventional controlling methods are insufficient and not able to meet these requirements. Therefore, current data-driven developments which can deal with an enormous amount of information are indispensable for a sufficient controlling of complex and dynamic projects. Accordingly, within this paper an approach for a data-driven controlling solution is presented, in which common data environments, databases and business intelligence applications are combined to ensure an efficient and proactive controlling. To investigate the advantages of business intelligence solutions for data-driven controlling the exemplary scenario of claim management controlling is used. It is shown that the user is able to speed up the processes and secure a proactive controlling by using a consistent database throughout the project. To achieve these targets an automatic data orchestration and visualization of the claim offers is exploited. Consequently, the claim manager is able to predict developments, identify risk potential, investigate KPIs or take actions to minimize negative effects e.g., time effort and costs. The requirements for these purposes are considered in a recommendation for action differentiated regarding the individual elements of a socio-technical system (human, organization, technology). The outlook includes an overview of potential technologies to ensure real-time analytics to shorten reaction and damage times related to the claims by immediately initiating countermeasures.
A Numerical Study on the Influence of External Magnetic field on Hydrogen Electric Arc Flow
Authors: Al Nasser M , Karimi-Sibaki E , Wu M , Redl C , Ishmurzind A , Voller N , Hackl G , Kharicha A
Published: May 2024

We present a 3D numerical model capable of simulating hydrogen electric arc. The numerical model couples the electromagnetic field with hydrogen plasma arc dynamics (flow and heat transfer). Electric arc operates under the conditions of atmospheric pressure using direct current (DC) in the presence of external magnetic field. It is found that arc instabilities emerge spontaneously near the cathode before propagating downwards along the arc jet. Also, spontaneous splitting into several arcs is observed before merging again into one arc. The horizontal magnetic field induces arc skewing and directional flow perpendicular to the applied magnetic field. However, the axial magnetic field induces a rotational flow and arc swirling.
Corrosion properties and mechanism of Ti43Zr27Mo5Cu10Be15 amorphous composites in various conditions
Authors: Lin Dan , Shang Jian , Zheng Pan
Published: May 2024

The in situ Ti43Zr27Mo5Cu10Be15 amorphous composites were investigated for their corrosion properties in solutions of NaCl, HCl, H2SO4 and NaOH. Electrochemical testing, SEM, EDS and XPS analyses revealed that pitting in NaCl and HCl solutions caused local surfaces damage. Amorphous matrix corrodes slightly in H2SO4 solution. Uniform corrosion occurred in NaOH solution without passive film formed, leading to the worst corrosion resistance. The optimal corrosion performances for NaCl and HCl solution are achieved at 0.5 mol l−1 and 0.75 mol l−1 separately which is related to the shortage of oxygen content in the solutions. While, the best corrosion performances for H2SO4 and NaOH solution are at 0.25 mol l−1. Moreover, the research on the effect of temperature was conducted in 3.5 wt% NaCl solution, it was found that Ti43Zr27Mo5Cu10Be15 amorphous composites exhibited good corrosion resistance at 298 K, while the E
corr declined with increasing solution temperature.
Enhancing photocatalytic performance of BiOI/TiO2 NTs nanocomposites through SILAR optimization for methylene blue degradation
Authors: Zaghdoudi M , Bennaceur J , Ouertani R
Published: May 2024

BiOI nano-leaves were deposited on to TiO2 nanotubes (NTs) using the Successive Ionic Layer Adsorption and Reaction (SILAR) technique, which was developed using the electrochemical anodization method. Various SILAR cycle numbers (three, five, and seven cycles) were employed in the experiment. The as-prepared nanocomposites (NCs) were characterized by several technique, the morphology of the elaborated NCs samples was examined using a S − 4800 field emission scanning electron microscope (SEM), the Reflectance and diffuse reflectivity of the NCs samples were measured by a Shimadzu UV-3100S spectrophotometer in the spectral range [300 –1200 nm] and the XRD diffraction was used to identify the crystalline structure of the processed BiOI/TiO2 NTs nanocomposites. A diffractometer with a Cu Kα anode (λ = 0.1542 nm) operating at 40 kV and 30 mA was used. The as-prepared NCs, specifically BiOI/TiO2 NTs, were designed for the photocatalytic degradation of methylene Blue (MB). X-ray diffraction analysis revealed the formation of the TiO2 anatase phase and polycrystalline BiOI films in all processed NCs. UV/Vis measurements indicated a shift in the nanocomposite’s active region from UV to visible light. The highest absorption and the lowest bandgap energy (Eg value, ∼2 eV) were observed in the NCs with 5 BiOI cycles. The photocurrent density reached 27 μA cm−2, approximately three times higher than the photocurrent density exhibited by TiO2 nanotubes under similar conditions. The optimal photocatalytic rate was achieved with BiOI/TiO2 NCs processed after five SILAR cycles.
Towards a realistic model for cavity-enhanced atomic frequency comb quantum memories
Authors: Taherizadegan Shahrzad , Davidson Jacob H , Kumar Sourabh , Oblak Daniel , Simon Christoph
Published: May 2024

Atomic frequency comb (AFC) quantum memory is a favorable protocol in long distance quantum communication. Putting the AFC inside an asymmetric optical cavity enhances the storage efficiency but makes the measurement of the comb properties challenging. We develop a theoretical model for cavity-enhanced AFC quantum memory that includes the effects of dispersion, and show a close alignment of the model with our own experimental results. Providing semi-quantitative agreement for estimating the efficiency and a good description of how the efficiency changes as a function of detuning, it also captures certain qualitative features of the experimental reflectivity. For comparison, we show that a theoretical model without dispersion fails dramatically to predict the correct efficiencies. Our model is a step forward to accurately estimating the created comb properties, such as the optical depth inside the cavity, and so being able to make precise predictions of the performance of the prepared cavity-enhanced AFC quantum memory.
Laser-forged transformation and encapsulation of nanoalloys: pioneering robust wideband electromagnetic wave absorption and shielding from GHz to THz
Authors: Zhang Shizhuo , Rao Senlin , Li Yunfan , Wang Shuai , Sun Dingyue , Liu Feng , Cheng Gary J
Published: May 2024

The emergence of the internet of things has promoted wireless communication’s evolution towards multi-band and multi-area utilization. Notably, forthcoming sixth-generation (6G) communication standards, incorporating terahertz (THz) frequencies alongside existing gigahertz (GHz) modes, drive the need for a versatile multi-band electromagnetic wave (EMW) absorbing and shielding material. This study introduces a pivotal advance via a new strategy, called ultrafast laser-induced thermal-chemical transformation and encapsulation of nanoalloys (LITENs). Employing multivariate metal-organic frameworks, this approach tailors a porous, multifunctional graphene-encased magnetic nanoalloy (GEMN). By fine-tuning pulse laser parameters and material components, the resulting GEMN excels in low-frequency absorption and THz shielding. GEMN achieves a breakthrough of minimum reflection loss of −50.6 dB in the optimal C-band (around 4.98 GHz). Computational evidence reinforces GEMN’s efficacy in reducing radar cross sections. Additionally, GEMN demonstrates superior electromagnetic interference shielding, reaching 98.92 dB under THz band (0.1–2 THz), with the mean value result of 55.47 dB. These accomplishments underscore GEMN’s potential for 6G signal shielding. In summary, LITEN yields the remarkable EMW controlling performance, holding promise in both GHz and THz frequency domains. This contribution heralds a paradigm shift in EM absorption and shielding materials, establishing a universally applicable framework with profound implications for future pursuits.
Multi-stage manufacturing process parameter optimization method based on improved marine predator algorithm
Authors: Jiang Xiaojun , Zhan Hongfei , Yu Junhe , Wang Rui
Published: May 2024

Product quality is a critical factor in manufacturing industry competition, and mechanical processing technology has been widely applied in manufacturing, directly affecting product quality. Therefore, it is very important to find the appropriate optimal parameters to improve the impact of processing on product quality. However, modern production processes are characterized by complex mechanisms and the mutual influence of multiple processes, which poses higher challenges for optimizing processing technology parameters. In this regard, the thesis proposes a method for optimizing process parameters in multi-process manufacturing based on an improved marine predator algorithm, aiming to optimize and improve process parameters in multi-process manufacturing processes. Firstly, a multi-process modeling strategy is adopted to explore the nonlinear relationship between process parameters and quality indicators based on multi-gene genetic planning, establishing a multi-process parameter optimization objective model. This effectively solves the problem of modeling difficulty caused by severe coupling of multiple processes. Then, to improve the efficiency of solving the optimization objective model, an improved marine predator algorithm is proposed, utilizing reverse learning strategies and mixed control parameters to enhance optimization capability, thereby obtaining the global optimal solution. Finally, using production process data from a certain factory as an example, the feasibility of the proposed method is verified, achieving the goal of multi-process process parameter optimization and ensuring the stability of product quality.
Economic complexity and the sustainability transition: a review of data, methods, and literature
Authors: Caldarola Bernardo , Mazzilli Dario , Napolitano Lorenzo , Patelli Aurelio , Sbardella Angelica
Published: May 2024

Economic Complexity (EC) methods have gained increasing popularity across fields and disciplines. In particular, the EC toolbox has proved particularly promising in the study of complex and interrelated phenomena, such as the transition towards a more sustainable economy. Using the EC approach, scholars have been investigating the relationship between EC and sustainability, and identifying the distinguishing characteristics of green activities and to assess the readiness of productive and technological structures for the sustainability transition. This article proposes to review and summarize the data, methods, and empirical literature that are relevant to the study of the sustainability transition from an EC perspective. We review three distinct but connected blocks of literature on EC and environmental sustainability. First, we survey the evidence linking measures of EC to indicators related to environmental sustainability. Second, we review articles that strive to assess the green competitiveness of productive systems. Third, we examine evidence on green technological development and its connection to non-green knowledge bases. Finally, we summarize the findings for each block, while identifying criticalities and avenues for further research in this recent and growing body of empirical literature.
Reinforcement learning pulses for transmon qubit entangling gates
Authors: Nam Nguyen Ho , Motzoi Felix , Metcalf Mekena , Birgitta Whaley K , Bukov Marin , Schmitt Markus
Published: May 2024

The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.
Estimating and presenting hazard ratios and absolute risks from a Cox model with complex nonlinear interactions
Authors: Bellavia Andrea , Melloni Giorgio E M , Park Jeong-Gun , Discacciati Andrea , Murphy Sabina A
Published: May 2024

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.
A critique and examination of the polysocial risk score approach: predicting cognition in the Health and Retirement Study
Authors: Jawadekar Neal , Zimmerman Scott , Lu Peiyi , Riley Alicia R , Glymour M Maria , Kezios Katrina , Al Hazzouri Adina Zeki
Published: May 2024

Polysocial risk scores were recently proposed as a strategy for improving the clinical relevance of knowledge about social determinants of health. Our objective in this study was to assess whether the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13 773 individuals aged ≥50 years at baseline from the 2006-2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort study. Four linear mixed models were compared: 2 simple models including a priori–selected covariates and 2 polysocial risk score models which used least absolute shrinkage and selection operator (LASSO) regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R2 and root mean-squared prediction error (RMSPE) using training/test split validation and cross-validation. For predicting cognition, the simple model including age, race, sex, and education had an R2 value of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R2 = 0.35 and RMSPE = 0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, sex, and education had an area under the receiver operating characteristic curve (AUROC) of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC = 0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social “risk factors.”
Racial and ethnic differences in epithelial ovarian cancer risk: an analysis from the Ovarian Cancer Association Consortium
Authors: Meagher Nicola S , White Kami K , Wilkens Lynne R , Bandera Elisa V , Berchuck Andrew , Carney Michael E , Cramer Daniel W , Cushing-Haugen Kara L , Jordan Susan , Kaufmann Scott H , Le Nhu D , Pike Malcolm C , Riggan Marjorie , Qin Bo , Rothstein Joseph H , Titus Linda , Winham Stacey J , Anton-Culver Hoda , Doherty Jennifer A , Goode Ellen L , Pearce Celeste Leigh , Risch Harvey A , Webb Penelope M , Cook Linda S , Goodman Marc T , Harris Holly R , Le Marchand Loic , McGuire Valerie , Pharoah Paul D P , Sarink Danja , Schildkraut Joellen M , Sieh Weiva , Terry Kathryn L , Thompson Pamela J , Whittemore Alice S , Wu Anna H , Peres Lauren C , Merritt Melissa A
Published: May 2024

Limited estimates exist on risk factors for epithelial ovarian cancer (EOC) in Asian, Hispanic, and Native Hawaiian/Pacific Islander women. Participants in this study included 1734 Asian (n = 785 case and 949 control participants), 266 Native Hawaiian/Pacific Islander (n = 99 case and 167 control participants), 1149 Hispanic (n = 505 case and 644 control participants), and 24 189 White (n = 9981 case and 14 208 control participants) from 11 studies in the Ovarian Cancer Association Consortium. Logistic regression models estimated odds ratios (ORs) and 95% CIs for risk associations by race and ethnicity. Heterogeneity in EOC risk associations by race and ethnicity (P ≤ .02) was observed for oral contraceptive (OC) use, parity, tubal ligation, and smoking. We observed inverse associations with EOC risk for OC use and parity across all groups; associations were strongest in Native Hawaiian/Pacific Islander and Asian women. The inverse association for tubal ligation with risk was most pronounced for Native Hawaiian/Pacific Islander participants (odds ratio (OR) = 0.25; 95% CI, 0.13-0.48) compared with Asian and White participants (OR = 0.68 [95% CI, 0.51-0.90] and OR = 0.78 [95% CI, 0.73-0.85], respectively). Differences in EOC risk factor associations were observed across racial and ethnic groups, which could be due, in part, to varying prevalence of EOC histotypes. Inclusion of greater diversity in future studies is essential to inform prevention strategies.

This article is part of a Special Collection on Gynecological Cancers.
Do Absorbable Sutures Work for Rectus Diastasis Repair in Abdominoplasty Patients?
Authors: Jackson Brandon T , Moradian Simon , Bricker Jonathan T , Termanini Kareem M , Ferenz Sarah , Bai Jennifer , Kim John Y
Published: May 2024

Background

The standard treatment for rectus diastasis is rectus sheath plication during abdominoplasty. Lasting correction of diastasis is essential, but there is currently a debate as to whether absorbable or nonabsorbable rectus plication achieves a lower rate of recurrence.

Objectives

The goal of this study is to assess long-term patient outcomes and the recurrence of rectus diastasis after plication with long-lasting absorbable sutures.

Methods

A retrospective study of abdominoplasties performed by the senior author between 2018 and 2022 was performed. Only female patients with >6 months of follow-up were included. Plication of the rectus muscles was performed with a combination of interrupted, buried, figure of eight #0 polydioxanone suture and running #0 Maxon (Covidien, Mansfield, MA). Outcomes were assessed by physical examination at postoperative visits. A retrospective chart review was used to obtain demographic and perioperative information.

Results

Seventy-one patients underwent abdominoplasty with an average follow-up of 21.1 months. The average age was 43 years, and the average BMI was 27 kg/m2. Correction of rectus diastasis was performed using absorbable sutures in all patients with no recurrence of diastasis in any patient (0% diastasis recurrence rate). Complications included delayed wound healing (11%), seroma (8.5%), hematoma (2.8%), and deep vein thrombosis/pulmonary embolism (2.8%). No patients needed reoperation.

Conclusions

Abdominal wall plication using a double-layered, long-lasting absorbable suture closure is a safe, reliable, and effective method to address rectus diastasis during abdominoplasty. Our technique achieved no recurrence of diastasis in any patient and a low complication profile.

Level of Evidence: 3
Predicting tall-cell subtype of papillary thyroid carcinomas independently with preoperative multimodal ultrasound
Authors: Ye Bei-Bei , Liu Yun-Yun , Zhang Ying , Liu Bo-Ji , Guo Le-Hang , Wei Qing , Zhang Yi-Feng , Xu Hui-Xiong
Published: May 2024

Objectives

This study aimed to explore the differences between tall-cell subtype of papillary thyroid carcinoma (TCPTC) and classical papillary thyroid carcinoma (cPTC) using multimodal ultrasound, and identify independent risk factors for TCPTC to compensate the deficiency of preoperative cytological and molecular diagnosis on PTC subtypes.

Methods

Forty-six TCPTC patients and 92 cPTC patients were included. Each patient received grey-scale ultrasound, colour Dopplor flow imaging (CDFI) and shear-wave elastography (SWE) preoperatively. Clinicopathologic information, grey-scale ultrasound features, CDFI features and SWE features of 98 lesions were compared using univariate analysis to find out predictors of TCPTC, based on which, a predictive model was built to differentiate TCPTC from cPTC and validated with 40 patients.

Results

Univariate and multivariate analyses identified that extra-thyroidal extension (odds ratio [OR], 15.12; 95% CI, 2.26-115.44), aspect ratio (≥0.91) (OR, 29.34; 95% CI, 1.29-26.23), and maximum diameter ≥14.6 mm (OR, 20.79; 95% CI, 3.87-111.47) were the independent risk factors for TCPTC. Logistic regression equation: P = 1/1+ExpΣ[−5.099 + 3.004 × (if size ≥14.6 mm) + 2.957 × (if aspect ratio ≥ 0.91) + 2.819 × (if extra-thyroidal extension). The prediction model had a good discrimination performance for TCPTC: the area under the receiver-operator-characteristic curve, sensitivity, and specificity were 0.928, 0.848, and 0.954 in cohort 1, and the corresponding values in cohort 2 were 0.943, 0.923, and 0.926, respectively.

Conclusion

Ultrasound has the potential for differential diagnosis of TCPTC from cPTC. A prediction model based on ultrasound characteristics (extra-thyroidal extension, aspect ratio ≥0.91, and maximum diameter ≥14.6 mm) was useful in predicting TCPTC.

Advances in knowledge

Multimodal ultrasound prediction of TCPTC was a supplement to preoperative cytological diagnosis and molecular diagnosis of PTC subtypes.
Synthetic data in radiological imaging: current state and future outlook
Authors: Sizikova Elena , Badal Andreu , Delfino Jana G , Lago Miguel , Nelson Brandon , Saharkhiz Niloufar , Sahiner Berkman , Zamzmi Ghada , Badano Aldo
Published: May 2024

A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions, or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized.
Potential for in vivo visualization of intracellular pH gradient in the brain using PET imaging
Authors: Yamasaki Tomoteru , Mori Wakana , Ohkubo Takayuki , Hiraishi Atsuto , Zhang Yiding , Kurihara Yusuke , Nengaki Nobuki , Tashima Hideaki , Fujinaga Masayuki , Zhang Ming-Rong
Published: May 2024

Intracellular pH is a valuable index for predicting neuronal damage and injury. However, no PET probe is currently available for monitoring intracellular pH in vivo. In this study, we developed a new approach for visualizing the hydrolysis rate of monoacylglycerol lipase, which is widely distributed in neurons and astrocytes throughout the brain. This approach uses PET with the new radioprobe [11C]QST-0837 (1,1,1,3,3,3-hexafluoropropan-2-yl-3-(1-phenyl-1H-pyrazol-3-yl)azetidine-1-[11C]carboxylate), a covalent inhibitor containing an azetidine carbamate skeleton for monoacylglycerol lipase. The uptake and residence of this new radioprobe depends on the intracellular pH gradient, and we evaluated this with in silico, in vitro and in vivo assessments. Molecular dynamics simulations predicted that because the azetidine carbamate moiety is close to that of water molecules, the compound containing azetidine carbamate would be more easily hydrolyzed following binding to monoacylglycerol lipase than would its analogue containing a piperidine carbamate skeleton. Interestingly, it was difficult for monoacylglycerol lipase to hydrolyze the azetidine carbamate compound under weakly acidic (pH 6) conditions because of a change in the interactions with water molecules on the carbamate moiety of their complex. Subsequently, an in vitro assessment using rat brain homogenate to confirm the molecular dynamics simulation-predicted behaviour of the azetidine carbamate compound showed that [11C]QST-0837 reacted with monoacylglycerol lipase to yield an [11C]complex, which was hydrolyzed to liberate 11CO2 as a final product. Additionally, the 11CO2 liberation rate was slower at lower pH. Finally, to indicate the feasibility of estimating how the hydrolysis rate depends on intracellular pH in vivo, we performed a PET study with [11C]QST-0837 using ischaemic rats. In our proposed in vivo compartment model, the clearance rate of radioactivity from the brain reflected the rate of [11C]QST-0837 hydrolysis (clearance through the production of 11CO2) in the brain, which was lower in a remarkably hypoxic area than in the contralateral region. In conclusion, we indicated the potential for visualization of the intracellular pH gradient in the brain using PET imaging, although some limitations remain. This approach should permit further elucidation of the pathological mechanisms involved under acidic conditions in multiple CNS disorders.
A comparative study of bifacial versus monofacial PV systems at the UK’s largest solar plant
Authors: Badran Ghadeer , Dhimish Mahmoud
Published: May 2024

This paper presents an extensive analysis of the UK’s largest bifacial photovoltaic (PV) power plant, located in North Yorkshire. Commissioned in January 2020, this trailblazing facility, with a total installed capacity of 34.7 MW, is a benchmark for the evaluation of bifacial solar technology within the region. This pioneering study provides a thorough comparative assessment of bifacial and monofacial PV systems through a methodical investigation of their energy production, degradation rates, and spectral responses over a 4-year operational period. Our findings reveal that bifacial PV modules, distributed across four segments of the power plant, demonstrate a remarkable average power gain ranging between 15.12% and 17.31% compared with monofacial modules. Despite experiencing marginally higher annual degradation rates—1.17% for bifacial compared with 0.91% for monofacial systems—bifacial modules show superior resilience and energy yield, particularly during winter months when albedo effects are pronounced due to snow coverage. The study also highlights the strategic importance of spectral response analysis in optimizing PV performance. Bifacial modules have shown greater efficiency in capturing infrared radiation—a property that could be exploited to enhance overall energy yield under specific environmental conditions. The empirical data indicate a consistent performance of bifacial modules with an average normalized energy output clustering around the expected efficiency level. Therefore, the results of this study are pivotal for understanding the practical implications of deploying bifacial PV technology on a large scale. They provide valuable data for stakeholders in the solar energy sector, guiding future installations and innovations in solar panel technology.
Telangiectasia of the lower limbs in a 40-year-old man
Authors: Damani Namiz , Gunavardhan Anu , Waas Rachel L V
Published: May 2024

A 40-year-old man presented to the dermatology clinic with a 3-year history of erythematous changes to both of his lower legs. These changes began at his feet before spreading proximally.
An Analytical Approach by Tri-Combination of Gradient UFLC, Response Surface Methodology and Green Chemistry Principle for Simultaneous Quantification of Azelnidipine and Chlorthalidone in Rabbit Plasma
Authors: Bhagyalakshmi C , Rekha T N , Longkumer Piyongsola , Dutta Koushik Nandan , Sahariah Bhargab Jyoti , Ramesh B , Majumder Manish
Published: May 2024

In this study, a sustainable and eco-friendly method is developed to quantify azelnidipine and chlorthalidone in rabbit plasma by gradient liquid chromatography based on green chemistry principle and analytical quality by design. The separation was achieved on a Shim pack C18 (25 cm × 5 cm × 4.6 μm) column with L1 packing. The mobile phase compromised of ethanol and 50-Mm ammonium acetate buffer (pH.6) at flow rate of 0.6 mL/min with 25-min runtime. The resolution and asymmetric factor were identified as critical analytical attributes (CAAs). The screening studies employing Control Noise Experimentation revealed that mobile phase pH, flow rate and ethanol concentration at 6 and 15 min significantly affected the CAAs method. The critical method parameters were optimized using Central Composition design. Chromatogram showed peak of the drugs at retention time of 9.03 min for chlorthalidone and 16.83 min for azelnidipine. The greenness score of the analytical method was found to be 1876.43 using analytical method greenness score calculator. The validation of the developed method was done which showed linearity at the range of 16–520 ng/mL, with R2 of 0.9992 and 0.9996 for azelnidipine and chlorthalidone, respectively, furthermore accuracy, precision, recovery and stability studies are carried out.
Optimization of Extraction of Four Components from Radix Scrophulariae with Natural Deep Eutectic Solvents and Evaluation of Extract’s Antioxidant Activity
Authors: Huang Ping , Zhou Yanxia , Ruan Fei , Sun Jianyu , Shen Jinglin , Chen Hongmei
Published: May 2024

In this research, eight natural deep eutectic solvents (NaDESs) consisting of food-grade ingredients were screened for the extraction of four bioactive compounds (acteoside, cinnamic acid, angoroside C and harpagoside) from radix scrophulariae (RS). Among these NaDESs, Proline-Glycerol NaDES with higher comprehensive score was selected. The Criteria Importance Through Intercriteria Correlation (CRITIC) was applied to calculate the information entropy and the weight of indexes, and figured out a comprehensive score. The weights of acteoside, cinnamic acid, angoroside C and harpagoside were 0.369, 0.172, 0.241 and 0.218, respectively. Response surface methodology (RSM) mathematical model was used to optimize the extraction parameters. The optimal extraction parameters were as follows: extraction time with 42.21 min, NaDES concentration with 52.89%, solid-to-liquid ratio with 1 : 37.05 g/mL and the predicted value of comprehensive score was 0.885. Under the optimal condition, the comprehensive score was 0.903 ± 0.005. Finally, the antioxidant activity experiment revealed that the 1,1-Diphenyl-2-picrylhydrazyl · radical scavenging activity and hydroxyl radical scavenging activity of the extract at 2.0 mg/mL and 1.5 mg/mL were approximately equal to those of ascorbic acid, respectively. The results showed that the extraction condition optimized by RSM combined with CRITIC was reasonable and dependable, and the extract of radix scrophulariae exhibited good antioxidant activity.
Streamlining atrial fibrillation ablation management using a digitization solution
Authors: O’Brien Jim , Valsecchi Sergio , Seaver Fionnuala , Rosalejos Lorena , Arellano Diana , Laurilla Kristine , Jauvert Gael , Fitzpatrick Noel , Tahin Tamas , Keelan Ted , Galvin Joseph , Szeplaki Gabor
Published: May 2024

Aims

Catheter ablation is a widely accepted intervention for atrial fibrillation (AF) management. Prior to undertaking this procedure, thorough patient education on its efficacy and potential complications is crucial. Additionally, educating patients about stroke risk management and anticoagulant therapy is imperative. At Mater Private Hospital in Dublin, we implemented a solution, integrating a customized treatment pathway and a mobile application. This patient-centred approach aims to optimize the clinical management of AF catheter ablation candidates, focusing on knowledge gaps and adherence to guideline-based care to enhance overall outcomes.

Methods and results

The application automates pre-operative assessments and post-operative support, facilitating seamless patient–clinician communication. During the observation period (September 2022–April 2023), 63 patients installed the app. Patient adherence to the pathway was strong, with 98% of patients actively engaging in the treatment pathway and with 81% completing all pre-operative tasks. The average enrolment-to-admission duration was 14 days, and post-ablation tasks were fulfilled by 62% of patients within an average of 36 days. Operators perceived the solution as user-friendly and effective in enhancing patient connectivity. Patient satisfaction was high, and knowledge about AF improved notably through the solution, particularly concerning the recognition of symptoms and anticoagulation therapy-related complications.

Conclusion

Our findings demonstrate the successful implementation of the app-based Ablation Solution, showcasing widespread patient use, improved adherence, and enhanced understanding of AF and its treatments. The system effectively connects healthcare providers with patients, offering a promising approach to streamline AF catheter ablation management and improve patient outcomes.
Cardiovascular risk factors and aortic valve stenosis: towards 10-year absolute risk charts for primary prevention
Authors: Kjeldsen Emilie W , Thomassen Jesper Q , Rasmussen Katrine L , Nordestgaard Børge G , Tybjærg-Hansen Anne , Frikke-Schmidt Ruth
Published: May 2024

Aims

Due to ageing populations, the incidence of aortic valve stenosis (AVS) is increasing steeply. Since no medical therapy is available but only surgical interventions, it is highly warranted to identify modifiable risk factors for early prevention. The aim of the study was to investigate the associations of cardiovascular risk factors with AVS and to create 10-year absolute risk scores for use in primary prevention.

Methods and results

In the Copenhagen General Population Study (n = 93 979), lifestyle data, biochemical measures, and confounders were assessed at baseline. Risk factors with the strongest association with AVS from Cox regression analyses were included in 10-year risk prediction models. Ten-year absolute risk scores were conducted using the method of Fine–Gray proportional sub-hazards models, accounting for competing events. One thousand one hundred and thirty-two individuals developed AVS during follow-up. Of well-known cardiovascular risk factors, those that associated with AVS included increasing levels of remnant cholesterol, triglycerides, lipoprotein(a) [Lp(a)], systolic blood pressure, and body mass index, low adherence to Danish dietary guidelines, current smoking, high alcohol consumption, lipid-lowering therapy, and diabetes mellitus. Ten-year absolute risk scores increased when compiling the most important risk factors for AVS: age, sex, body mass index, systolic blood pressure, Lp(a), and diabetes. Ten-year absolute risk increased from <1 to 19%.

Conclusion

The presence of cardiovascular risk factors is associated with AVS, supporting that this disease, at least partly, may be modifiable through lifestyle changes. Risk charts combining cardiovascular risk factors have the potential to identify high-risk individuals, offering opportunities for preventive strategies.