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[Display omitted] Alkyl levulinates are a class of compounds that have diverse applications and show great promise as environmentally friendly fuel additives. These compounds are renewable, have a high energy density, and can easily be used with existing infrastructure. Therefore, they can play a significant role in contributing to the ongoing endeavors toward more sustainable and efficient energy solutions. In this study, alkyl levulinates (ALs) as fuel additives were synthesized using phosphomolybdate g-C 3 N 4 (PMo x /g-C 3 N 4) as an efficient catalyst for the esterification reaction of levulinic acid (LA) with three diverse alcohols, namely ethanol, 1-butanol, and 1-hexanol. An experimental investigation assessed the impact of several key factors, such as temperature, the volumetric proportion of LA to alcohol, reaction time, catalyst loading, and catalyst amount, on the chemical process under study. For ethyl levulinate (EL) a 76 % yield was obtained at 130 °C, 8 h reaction time, LA: ethanol 1:12, and 20 mg catalyst. Also, a > 99 % yield was obtained for the butyl levulinate (BL) production at 130 °C, 8 h reaction time, LA: alcohol proportion of 1:12, and 10 mg catalyst. Hexyl levulinate (HL) was achieved with 71 % yield under the optimal condition of 230 °C, 8 h reaction time, LA: hexanol 1:20, and 10 mg catalyst. [ABSTRACT FROM AUTHOR]

Purpose: Human papillomavirus (HPV) is a pervasive sexually transmitted infection associated with various cancers, including cervical, anogenital, and oropharyngeal carcinomas. While the majority of HPV infections are transient and self-limiting, persistent infections with high-risk HPV strains play a critical role in carcinogenesis. Herein, the current knowledge on HPV’s involvement in cancer, the pivotal role of the tumor microenvironment in HPV-related malignancies, and the emerging potential of organoid models in research and therapeutic development were reviewed. Methods: A comprehensive analysis of recent studies and advances in the field was conducted based on Google Scholar, PubMed, and Web of Science searching, focusing on the interaction between HPV and the tumor microenvironment and the application of organoid technology. Results: The tumor microenvironment significantly influences the growth and progression of cancers linked to HPV. Organoid models have shown to be useful resources for researching HPV-driven carcinogenesis. Conclusion: Organoid technology’s application in HPV research enhances understanding of tumor microenvironment function, paving the way for specialized treatments and improved management of HPV-associated cancers. Lay Summary: HPV is a prevalent virus associated with a number of malignancies. Scientists are gaining a better understanding of the development of HPV-related malignancies and how they could be treated through new study employing 3D models called organoids. This review emphasizes how crucial these models are to advancing cancer therapy.

Pulmonary arterial hypertension (PAH) is a vascular disease characterised by restricted flow and high pressure through the pulmonary arteries, leading to progressive right heart failure and death. This study reports the global burden of PAH, leveraging all available data and using methodology of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to understand the epidemiology of this under-researched and morbid disease.

Differential disease control may contribute to racial disparities in outcomes of childhood‐onset systemic lupus erythematosus (cSLE). We evaluated associations of race and individual‐ or neighborhood‐level social determinants of health (SDoH) with achievement of low lupus disease activity state (LLDAS), a clinically relevant treatment target. In this cSLE cohort study using the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, the primary exposure was self‐reported race and ethnicity, and collected SDoH included insurance status and area deprivation index (ADI). Outcomes included LLDAS, disease activity, and time‐averaged prednisone exposure. Associations among race and ethnicity, SDoH, and disease activity were estimated with multivariable regression models, adjusting for disease‐related and demographic factors. Among 540 children with cSLE, 27% identified as Black, 25% identified as White, 23% identified as Latino/a, 11% identified as Asian, 9% identified as more than one race, and 5% identified as other. More Black children (41%) lived in neighborhoods of highest ADI compared to White children (16%). Black race was associated with lower LLDAS achievement (adjusted odds ratio 0.56, 95% confidence interval [CI] 0.38–0.82) and higher disease activity (adjusted β 0.94, 95% CI 0.11–1.78). The highest ADI was not associated with lower LLDAS achievement on adjustment for renal disease and insurance. However, renal disease was found to be a significant mediator (P= 0.04) of the association between ADI and prednisone exposure. Children with cSLE who identified as Black are less likely to achieve LLDAS and have a higher disease activity. Living in areas of higher ADI may relate to renal disease and subsequent prednisone exposure. Strategies to address root causes will be important to design interventions mitigating cSLE racial disparities.

Introduction: Opioid overprescription after colorectal surgery can lead to adverse events, persistent opioid use, and diversion of unused pills. This study aims to assess the extent to which opioids prescribed at discharge after elective colorectal surgery are consumed by patients. Methods: This prospective cohort study included adult patients (≥ 18 yo) undergoing elective colorectal surgery at two academic hospitals in Montreal, Canada. Patients completed preoperative questionnaires and data concerning demographics, surgical details, and perioperative care characteristics (including discharge prescriptions) were extracted from electronic medical records. Self-reported opioid consumption was assessed weekly up to 1-month post-discharge. The total number of opioid pills prescribed and consumed after discharge were compared using the Wilcoxon signed-rank test. Negative binomial regression was used to identify predictors of opioid consumption. Results: We analyzed 344 patients (58 ± 15 years, 47% female, 65% laparoscopic, 31% rectal resection, median hospital stay 3 days [IQR 1–5], 18% same-day discharge). Most patients received a TAP block (67%). Analgesia prescription at discharge included acetaminophen (92%), NSAIDs (38%), and opioids (92%). The quantity of opioids prescribed at discharge (median 13 pills [IQR 7–20]) was significantly higher than patient-reported consumption at one month (median 0 pills [IQR 0–7]) (p< 0.001). Overall, 51% of patients did not consume any opioids post-discharge, and 63% of the prescribed pills were not used. Increased opioid consumption was associated with younger age (IRR 0.99 [95%CI 0.98–0.99]), higher preoperative anxiety (1.02 [95%CI 1.00–1.04]), rectal resections (IRR 1.45 [95%CI 1.09–1.94]), and number of pills prescribed (1.02 [95%CI 1.01–1.03]). Conclusion: A considerable number of opioid pills prescribed at discharge after elective colorectal surgery are left unused by patients. Certain patient and care characteristics were associated with increased opioid consumption. Our findings indicate that post-discharge analgesia with minimal or no opioids may be feasible and warrants further investigation. Graphical Abstract:

Introduction Premature rupture of membranes (PROM) is one of the most common and important causes of premature births and peripartum mortality. Maternal antibiotic treatment affects the infantile prognosis. This study was conducted to compare the efficacy of Ampicillin and Cefotaxime administered for the parturients on neonatal outcomes.Material and Methods A comparison between the effects of Cefotaxime and Ampicillin on infantile complications of PROM was done in this clinical trial. Two hundred and twenty parturients with PROM who needed antibiotic therapy were randomized in two groups of control (Ampicillin) and intervention (Cefotaxime) treatments. The maternal/fetal statuses up to accouchement and the infants’ status up to transfer to neonatal intensive care unit, death, or discharge from hospital were followed. The Apgar score, cardiac, respiratory and nervous systems, infection, immaturity, asphyxia, and mortality rates were compared in both groups.Results The differences between the two groups were significant in: Apgar score min1 and min5, need for resuscitation, asphyxia, need for hospitalization, infection, and mortality rate.Conclusion Administration of Cefotaxime in parturients with PROM improved the Apgar scores and decreased respiratory complications, infection, asphyxia, mortality rate, and need for ICU hospitalization in infants.

AbstractMetallo-β-lactamases (MBLs) are a group of enzymes that hydrolyze the most commonly used β-lactam-based antibiotics, leading to the development of multi-drug resistance. The three main clinically relevant groups of these enzymes are IMP, VIM, and NDM. This study aims to introduce potent novel overlapped candidates from a ZINC database retrieved from the 200,583-member natural library against the active sites of IMP-1, VIM-2, and NDM-1 through a straightforward computational workflow using virtual screening approaches. The screening pipeline started by assessing Lipinski’s rule of five (RO5), drug-likeness, and pan-assay interference compounds (PAINS) which were used to generate a pharmacophore model using D-captopril as a standard inhibitor. The process was followed by the consensus docking protocol and molecular dynamic (MD) simulation combined with the molecular mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method to compute the total binding free energy and evaluate the binding characteristics. The absorption, distribution, metabolism, elimination, and toxicity (ADMET) profiles of the compounds were also analyzed, and the search space decreased to the final two inhibitory candidates for B1 subclass MBLs, which fulfilled all criteria for further experimental evaluation.Communicated by Ramaswamy H. Sarma

Judgment Post-Stratification (JPS) is a sampling method that uses extra rank information in a simple random sampling (SRS) to stratify the sample and increase the efficiency of the estimators of the population parameters. In this paper, we consider the kernel estimation of the probability density function (pdf) using JPS sample. The properties of JPS estimator of pdf and the asymptotic mean integrated squared error of this estimator are obtained. We find a condition which guarantees that JPS density estimate performs better than its simple random sampling counterpart. To implement the kernel density estimator, it is required to specify a bandwidth. We use a Bayesian approach to find an estimate of the bandwidth. To compare the JPS density estimator with SRS estimator and also Bayesian bandwidth with other existing bandwidths, we use an extensive simulation study. Results are applied to the bone mineral density (BMD) data from the third National Health and Nutrition Examination Survey to estimate pdf of BMD.

Purpose: This study presents a novel approach to enhancing hyper-reduction in nonlinear structural dynamics by utilizing the predictive capabilities of stacked Long Short-Term Memory (LSTM) neural networks. Hyper-reduction methods are crucial for overcoming the limitations of traditional model order reduction techniques, particularly in accurately capturing the nonlinear behavior of internal force vectors in complex structures. Method: The proposed technique employs stacked LSTM neural networks to generate training snapshots for the Energy Conserving Mesh Sampling and Weighting (ECSW) hyper-reduction method. By training the model on a well-defined dataset, we achieve an impressive accuracy of 97.5%. The effectiveness of our method is demonstrated through a geometrically nonlinear dynamic analysis of a leaf spring, resulting in only a 3.24% error when compared to full simulation results. This study emphasizes the potential of deep learning techniques in improving hyper-reduction methods and underscores the importance of computational efficiency in simulations of complex structural dynamics. Results: The findings reveal significant advancements in the application of deep learning for hyper-reduction methods, showcasing the ability to accurately model nonlinear structural behaviors while maintaining computational efficiency. This research contributes valuable insights into the integration of advanced machine learning techniques within the field of structural dynamics.

In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.

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Caring for patients with hard-to-heal (chronic) wounds requires a multifaceted approach that addresses their diverse needs, which can contribute to the complexity of care. Wound care providers must have a comprehensive understanding of the patient's comorbid conditions and psychosocial issues to provide personalised and effective treatment. Key quality indicators for effective wound care involves not only selecting appropriate local wound care products, such as foam dressings, but also addressing individual patient experiences of wound-related pain, odour, itch, excessive wound drainage, and self-care needs. The purpose of this review is to inculcate the wound care practice community, research scientists and healthcare industry with a sense of accountability in order to work collaboratively in addressing these unmet care needs.

Current recommendations for weight loss in individuals with prediabetes come from individual trials and are derived from older data.

Concrete is the most widely used construction material in the world. Therefore, the production of concrete with characteristics such as high strength and durability has received the attention of researchers. In alignment with sustainable development objectives, a pivotal focus within the construction industry has been the exploration of viable alternatives to conventional cement. In this study, the mechanical characteristics and permeability of the prepared samples containing pozzolanic materials (natural and synthetic) have been investigated by using the experimental method. To achieve the objectives of this research, six unique concrete mix formulations were developed, each incorporating silica fume, metakaolin (as a synthetic pozzolanic additive), and zeolite (as a natural pozzolanic substance). The performance outcomes of these mixes were then systematically evaluated against a baseline mixture that did not contain any pozzolanic components. Four distinct curing methods were employed: a humid environment (referred to as group A), a dry environment (designated as group B), and two corrosive environments (denoted as groups C and D). Compressive strength tests were conducted at 7, 14, 28, and 90 days, alongside indirect tensile strength tests at 28 and 90 days. Additionally, samples subjected to sulfuric solution (H2SO4) curing with a controlled pH of 1 at 90 days were compared against standard curing conditions. The permeability of the samples was evaluated through initial and final water absorption measurements, as well as penetration of water under pressure tests. Substituting 10% of the cement content with metakaolin and silica fume in the concrete mixing design enhanced the 28-day compressive strength under both humid and dry curing conditions, as compared to the control mixture. Incorporating a 10% substitution of cement with both natural and synthetic pozzolanic additives can beneficially preserve a portion of the compressive and tensile integrity that is otherwise diminished by sulfuric acid exposure, relative to the standard mix. Furthermore, this substitution enhances the mechanical robustness of the concrete. Replacing 10% of cement with natural and synthetic pozzolanic materials has a positive effect on maintaining part of the compressive and tensile strength loss due to sulfuric acid attacks compared to the control mixture, and the use of these materials improved the concrete’s mechanical performance. The results indicate that incorporating pozzolanic materials (such as silica fume, metakaolin, and zeolite) leads to a reduction in initial water absorption compared to the control mix. Notably, this reduction is more pronounced in samples containing silica fume than in those containing metakaolin and zeolite.

Assessing physical frailty (PF) is vital for early risk detection, tailored interventions, preventive care, and efficient healthcare planning. However, traditional PF assessments are often impractical, requiring clinic visits and significant resources. We introduce a video-based frailty meter (vFM) that utilizes machine learning (ML) to assess PF indicators from a 20 s exercise, facilitating remote and efficient healthcare planning. This study validates the vFM against a sensor-based frailty meter (sFM) through elbow flexion and extension exercises recorded via webcam and video conferencing app. We developed the vFM using Google’s MediaPipe ML model to track elbow motion during a 20 s elbow flexion and extension exercise, recorded via a standard webcam. To validate vFM, 65 participants aged 20–85 performed the exercise under single-task and dual-task conditions, the latter including counting backward from a random two-digit number. We analyzed elbow angular velocity to extract frailty indicators—slowness, weakness, rigidity, exhaustion, and unsteadiness—and compared these with sFM results using intraclass correlation coefficient analysis and Bland–Altman plots. The vFM results demonstrated high precision (0.00–7.14%) and low bias (0.00–0.09%), showing excellent agreement with sFM outcomes (ICC(2,1): 0.973–0.999), unaffected by clothing color or environmental factors. The vFM offers a quick, accurate method for remote PF assessment, surpassing previous video-based frailty assessments in accuracy and environmental robustness, particularly in estimating elbow motion as a surrogate for the 'rigidity' phenotype. This innovation simplifies PF assessments for telehealth applications, promising advancements in preventive care and healthcare planning without the need for sensors or specialized infrastructure.

Thin film nanocomposite (TFN) reverse osmosis (RO) and nanofiltration (NF) membranes have attracted considerable attention for industrial applications in recent years. Among recent nanomaterials used in the fabrication of TFN membranes, carbon nanotubes (CNTs) have gained significant interest due to their unique structure (e.g., tubular shape, mechanical strength, porosity, etc.). Here, the effects of multi-walled carbon nanotubes (MWCNTs) with different functional groups as one of the well proven nanchannel structures and their interaction with two typical monomers, MPD and PIP, were thoroughly investigated. All fabricated membranes were analysed using SEM, FTIR, AFM and contact angle analyzer. Pressure variation significantly affected the performance of membranes over 24 hours of testing. The membranes incorporated with polypyrrole (PPy) modified MWCNTs showed promising results with nearly 37 % and 99 % water flux improvement for the TFN-RO and NF membranes, respectively, compared to the bare (unmodified) TFC RO/NF membranes. Specifically, the water flux of the RO OX-MWCNTs-PPy membrane increased from 18.9 to 25.5 L.m−2.h−1, while the NF OX-MWCNTs-PPy membrane's water flux rose from 45.2 to 90.1 L.m−2.h−1. The salt rejection of RO and NF remained relatively high, with over 90 % salt rejection against NaCl and Na2SO4for RO and NF membranes. The NF OX-MWCNTs-PPy membrane exhibited a 98.2 % rejection rate for Na2SO4, and the RO OX-MWCNTs-PPy membrane showed around a 98.8 % rejection rate for NaCl. The TFN membranes showed less fouling tendency compared to the TFC membranes. In general, the integration of MWCNTs with various functional groups significantly enhanced the water flux and salt rejection properties of these membranes. Specifically, the modified membranes showed considerable improvements in water flux and maintained high salt rejection rates. Additionally, the interaction between MWCNTs and PIP monomers in TFN-NF membranes exhibited a stronger affinity compared to MPD monomers in TFN-RO membranes, indicating a more promising performance for NF applications. The findings suggest that TFN-NF membranes incorporating MWCNTs hold great promise for future industrial use, offering enhanced performance and reduced fouling rates.

ABSTRACTHigh costs primarily pose challenges to forest management in planning and executing the repair of forest roads. With budget limitations and inadequate oversight, it has become critically essential to monitor the state of these roads. Monitoring the condition of forest roads has become imperative, driven by budget constraints and a lack of effective supervision. While smartphones have proven effective in detecting road defects on public roads, their application on forest roads is hindered by the absence of suitable indices and software infrastructure. Addressing this gap, this research focuses on the development of the Forest Road Pavement Condition Index (FRPCI) to facilitate smartphone-based monitoring. We collected and compared data from 4 kilometers of forest roads, employing two traditional harvesting methods alongside smartphone sensor data. Utilizing deep learning methods, including Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and CNN-LSTM, we processed the collected data. Signal processing using GPS data, coupled with wavelet transformation, demonstrated promising results with an accuracy and recall exceeding 80%. The proposed system functions as a distributed information system, transitioning data from organizational mode to field mode. It measures damage, assesses forest road conditions, and leverages image processing and GPS technologies. This monitoring system technology offers capabilities for preparing, storing, updating, maintaining, and analyzing diverse information. Importantly, adopting this method can significantly reduce operating costs, making forest road monitoring for maintenance purposes more feasible.

Research on host-pathogen interactions (HPIs) has evolved rapidly during the past decades. The more humans discover new pathogens, the more challenging it gets to find a cure and prevent infections caused by those pathogens. Many experimental techniques have been proposed to predict the interactions but most of them are highly costly and time-consuming. Fortunately, computational methods have been proven to be efficient in overcoming such limitations. In this study, we propose utilizing Deep Learning methods to predict HPIs using protein sequences. We use the monoMonoKGap (mMKGap) algorithm with K = 2 to extract features from the sequences. We also used the Negatome Database to generate negative interactions. The proposed method was performed on three separate balanced human-pathogen datasets with 10-fold cross-validation. Our method yielded very high accuracies of 99.65%, 99.52%, and 99.66% (mean accuracy of 99.61%). To further evaluate the performance of the deep Network, we compared it with other classification methods, which were the Random Forest (RF) as multiple Decision Tree, the Support Vector Machine (SVM), and Convolutional Neural Network (CNN). We also tested the Dipeptide Composition algorithm as another feature extraction method to compare the results with the mMKGap method. The experimental results prove that the proposed method is very accurate, robust, and practical and could be used as a reliable framework in HPI research.

Corrugated metal pipes (CMPs) corrode over time. To maintain structural performance, deteriorated CMPS must be replaced or rehabilitated. Spray-applied pipe liners (SAPLs) are one of the quickest ways to rehabilitate deteriorated CMPs among other ways. Only a few lab tests and finite element studies from the past have been done on this new method. The calibration of a three-dimensional (3D) full-scale finite element method model using test results obtained at the Center for Underground Infrastructure Research and Education laboratory at the University of Texas at Arlington is covered in this paper. The tests were carried out on circular invert cut CMPs that had been rehabilitated with polymeric SAPLs. To repair the invert-cut CMPs, three different thicknesses were used: 0.25, 0.5, and 1-in. The removal of an 18-in. invert from the intact CMP represented the deterioration of the CMP. A full 3D corrugated model was developed to represent the test setup in the FE model using ABAQUS. To perform the calibration process, the load–displacement curves, earth pressure distribution, and strain around the liner were compared to the test results. The comparison of these parameters showed the capability of the model for verification. The verified FE model was used to generate the load–displacement graphs for other thicknesses and elastic modulus of the liner. In addition, the role of the embedment depth is also considered in the analyses in which the maximum deformation of the rehabilitated pipe has decreased by 58.9% with increasing the burial depth of the pipe from 0.4D (D = external pipe diameter) to 1.0D.

Stream processing acceleration is driven by the continuously increasing volume and velocity of data generated on the Web and the limitations of storage, computation, and power consumption. Hardware solutions provide better performance and power consumption, but they are hindered by the high research and development costs and the long time to market. In this work, we propose our re-configurable stream processor (Diba), a complete rethinking of a previously proposed customized and flexible query processor that targets real-time stream processing. Diba uses a unidirectional dataflow not dedicated to any specific type of query (operator) on streams, allowing a straightforward placement of processing components on a general data path that facilitates query mapping. In Diba, the concepts of the distribution network and processing components are implemented as two separate entities connected using generic interfaces. This approach allows the adoption of a versatile architecture for a family of queries rather than forcing a rigid chain of processing components to implement such queries. Our experimental evaluations of representative queries from TPC-H yielded processing times of 300, 1220, and 3520 milliseconds for data streams with scale factor sizes of one, four, and ten gigabytes, respectively.

Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension.

The objective of this study was to compare the effectiveness of a second tumor necrosis factor inhibitor (TNFi) versus a non‐TNFi biologic following discontinuation of a TNFi for patients with polyarticular‐course juvenile idiopathic arthritis (pJIA). Using the Childhood Arthritis and Rheumatology Research Alliance Registry, patients with pJIA who started receiving a second biologic following a first TNFi were identified. Patients were required to have no active uveitis on the index date and a visit six months after the index date. Outcome measures included Clinical Juvenile Arthritis Disease Activity Score with a maximum of 10 active joints (cJADAS10), cJADAS10 inactive disease (ID; ≤2.5) and cJADAS10 minimal disease activity (MiDA; ≤5). Multiple imputation was used to account for missing data. Adjusted odds ratios (aORs) were calculated using propensity score quintiles to compare outcomes at six months following second biologic initiation. There were 216 patients included, 84% initially received etanercept, and most patients stopped receiving it because of its ineffectiveness (74%). A total of 183 (85%) started receiving a second TNFi, and 33 (15%) started receiving a non‐TNFi. Adalimumab was the most common second biologic received (71% overall, 84% of second TNFi), and tocilizumab was the most common non‐TNFi second biologic received (9% overall, 58% of non‐TNFi). There was no difference between receiving TNFi versus non‐TNFi in cJADAS10 ID (29% vs 25%; aOR 1.23, 95% confidence interval [CI] 0.47–3.20) or at least MiDA (43% vs 39%; aOR 1.11, 95% CI 0.47–2.62) at six months. Most patients with pJIA started receiving TNFi rather than non‐TNFi as their second biologic, and there were no differences in disease activity at six months.

The utilization of iron powder as a sustainable energy carrier, conducive to a carbon-free future, has garnered substantial attention due to its commendable attributes such as high energy density, widespread availability, and absence of emissions. To harness its potential optimally, a comprehensive understanding of the combustion behavior of iron powder and the development of corresponding combustion technologies are imperative. This study endeavors to investigate the influence of iron powder particle size, as well as the flow rate of air and iron powder, on the temperature at the exit of the ignition chamber. Experimental trials were conducted utilizing a metal cyclonic combustor (MC2) equipped with a system for feeding iron powder. The findings reveal that an increase in the diameter of iron particles corresponds to an elongation of the path from the ignition chamber to the outlet. Consequently, this elongation induces prolonged ignition delay time and burning duration. Notably, larger particles exhibit enhanced combustion efficiency in comparison to their smaller counterparts. The outcomes demonstrate that particles approximately 50 µm in size achieve an efficiency of 94%, as opposed to 72% for particles below 20 µm. Temperature measurements and spectrometric analysis expose a discernible relationship between particle size and temperature during combustion, elucidating that larger particles yield higher temperatures. Comprehending the intricate correlation between particle size and combustion behavior is crucial for optimizing combustion systems when utilizing iron powder as an energy carrier. By controlling particle size and combustion conditions, the efficiency and efficacy of iron powder combustion processes can be enhanced, thereby contributing to cleaner and more sustainable energy solutions. The implications of this study extend to the enhancement of burner system design and functionality, along with an overall improvement in combustion efficiency. These findings hold significance within the realm of combustion science, presenting opportunities for the development of more sustainable and environmentally friendly energy solutions. Implementing the insights derived from this research empowers researchers to harness the potential of iron powder as an energy carrier, thereby advancing progress toward a greener future through environmentally conscious combustion processes.

Gamma-valerolactone (GVL) is a useful chemical with various applications obtained from the hydrolysis of lignocellulosic biomass. The present study aims to explore the synthesis of GVL through the hydrogenation of ethyl levulinate (EL) using a palladium–manganese bimetallic catalyst immobilized on a dendritic fibrous nanosilica (Pdx%–Mny%/DFNS). The key strategy in this reaction involves utilizing the formic acid (FA) decomposition reaction to generate indirect hydrogen and catalytic transfer hydrogenation (CTH) process to convert EL to GVL, a safe and environmentally friendly method. As an economical and available source, EL has less acidity than LA and is more easily separated from the reaction medium. Also, FA (as a byproduct in various processes) can be used as a liquid medium to store hydrogen gas (without the risk of explosion). It can be a potential solution for long-term energy storage by considering the necessary infrastructure. This research showed that the catalytic activity of Pd has developed in the presence of Mn and DFNS and can be affordable. In this reaction, various parameters were investigated. Under the best conditions (1 mL of ethyl levulinate, 3 mL of formic acid, 3 mL of deionized water, 2 g of sodium formate, 50 mg of Pd6%–Mn3%/DFNS catalyst, and 8 h), the yields obtained for GVL are 70 and 99.5% at 180 and 230 °C, respectively. Meanwhile, the yield of GVL under the same conditions (without formic acid and sodium formate) using direct molecular hydrogen (2 MPa) at 180 °C was 76%. Also, various methods were used to characterize the catalysts, including Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction, Brunauer–Emmett–Teller, inductively coupled plasma mass spectroscopy, X-ray photoelectron spectroscopy, transmission electron spectroscopy, field emission scanning electron microscopy, and elemental mapping.

Purpose: Transmetatarsal amputation (TMA) with primary closure has long been an option for limb salvage in patients with advanced chronic limb-threatening ischemia (CLTI) with extensive tissue loss of the forefoot. However, TMA healing and closure techniques are challenging, specifically in high-risk patients. Guillotine transmetatarsal amputations (gTMA) with staged closure may provide an alternative treatment in this population. We report long-term outcomes of such technique.Materials and Methods: A single-center retrospective cohort study of CLTI patients undergoing gTMA between 2017 and 2020 was performed. Limb salvage, wound healing, and survival rates were quantified using Kaplan-Meier (KM) analysis. Multivariate regression was used to identify the effect of patient characteristics on the outcomes.Results: Forty-four gTMA procedures were reviewed. Median follow-up was 381 (interquartile range [IQR], 212–539.75) days. After gTMA, 87.8% (n=36) of the patients were able to ambulate after a median interval of 2 (IQR, 1–3) days. Eventual coverage was achieved in a personalized and staged approach by using a combination of skin substitutes (88.6%, n=39) ± split thickness skin grafts (STSG, 61.4%, n=27). KM estimates for limb salvage, wound healing, and survival were 84.1%, 54.5%, and 88.6% at 1 year and 81.8%, 63.8%, and 84.1% at 2 years. Wound healing was significantly associated with STSG application (p=0.002, OR=16.5, 95% CI 2.87–94.81).Conclusion: gTMA resulted in high limb salvage rates during long-term follow-up in CLTI patients. Adjunctive STSG placement may enhance wound healing at the gTMA site, thus leading to expedited wound closure. Surgeons may consider gTMA as an alternative to reduce limb loss in CLTI patients at high risk of major amputation.Clinical Impact Currently, the clinical presentation of CLTI is becoming more complex to deal with due to the increasing comorbidities as the society becomes older. The data shown in this article means for clinicians that when facing diffused forefoot gangrene and extensive tissue loss, limb preservation could still be considered instead of major amputation. Guillotine transmetatarsal amputations in the setting of an aggressive multidisciplinary group, can be healed by the responsibly utilization of dermal substitutes and skin grafts leading to the preservation of the extremity, allowing mobility, avoiding of sarcopenia, and decreasing frailty. This will equate to maintenance of independent living and preservation of lifespan.

Measuring neuronal electrical activity, such as action potential propagation in cells, requires the sensitive detection of the weak electrical signal with high spatial and temporal resolution. None of the existing tools can fulfill this need. Recently, plasmonic-based electrochemical impedance microscopy (P-EIM) was demonstrated for the label-free mapping of the ignition and propagation of action potentials in neuron cells with subcellular resolution. However, limited by the signal-to-noise ratio in the high-speed P-EIM video, action potential mapping was achieved by averaging 90 cycles of signals. Such extensive averaging is not desired and may not always be feasible due to factors such as neuronal desensitization. In this study, we utilized advanced signal processing techniques to detect action potentials in P-EIM extracted signals with fewer averaged cycles. Matched filtering successfully detected action potential signals with as few as averaging five cycles of signals. Long short-term memory (LSTM) recurrent neural network achieved the best performance and was able to detect single-cycle stimulated action potential successfully [satisfactory area under the receiver operating characteristic curve (AUC) equal to 0.855]. Therefore, we show that deep learning-based signal processing can dramatically improve the usability of P-EIM mapping of neuronal electrical signals.

What defines a regional water distribution system? An AWWA committee sorted through the characteristics of systems and their respective models to come up with a clear classification. Regional water distribution models are broken into two categories: regional transmission and regional emergency supply, with many models landing somewhere along the spectrum. A close examination of four regional water systems revealed the unique attributes and numerous considerations that make developing and maintaining hydraulic models complex.

AbstractChildhood maltreatment (CM) and its associated cognitive vulnerabilities, such as early maladaptive schemas (EMSs), have been identified as dysfunctional and risk factors for obsessive-compulsive disorder (OCD) and its related clinical outcomes, including obsessive-compulsive (OC) symptoms, depression, and suicidality. However, the combined effects of these variables on suicidal risk have not been investigated yet. This study aimed to examine the relationships between CM and suicidal risk through the paths of EMS domains, OCD symptoms, and depression in patients with OCD (n = 300). Participants completed a series of self-report scales assessing the psychological and clinical variables. The results showed a significant relationship between childhood emotional abuse and suicidal ideation, which was mediated by the paths of disconnection/rejection, unacceptable obsessional thoughts, and depression. These findings suggest that individuals with OCD who have experienced childhood emotional abuse may exhibit heightened levels of disconnection/rejection, frequent repugnant obsessions, and depressive mood, possibly contributing to an increased risk of suicide. Therefore, it is crucial for therapeutic settings, such as schema therapy, to carefully assess and monitor these risk factors in order to prevent suicidal risk in individuals with OCD.

Nanoporous materials in the form of metal–organic frameworks such as zeolitic imidazolate framework-8 (ZIF-8) are promising membrane materials for the separation of hydrocarbon mixtures. To compute the adsorption isotherms in such adsorbents, grand canonical Monte Carlo simulations have proven to be very useful. The quality of these isotherms depends on the accuracy of adsorbate–adsorbent interactions, which are mostly described using force fields owing to their low computational cost. However, force field predictions of adsorption uptake often show discrepancies from experiments at low pressures, providing the need for methods that are more accurate. Hence, in this work, we propose and validate two novel methodologies for the ZIF-8/ethane and ethene systems; a benchmarking methodology to evaluate the performance of any given force field in describing adsorption in the low-pressure regime and a refinement procedure to rescale the parameters of a force field to better describe the host–guest interactions and provide for simulation isotherms with close agreement to experimental isotherms. Both methodologies were developed based on a reference Henry coefficient, computed with the PBE-MBD functional using the importance sampling technique. The force field rankings predicted by the benchmarking methodology involve the comparison of force field derived Henry coefficients with the reference Henry coefficients and ranking the force fields based on the disparities between these Henry coefficients. The ranking from this methodology matches the rankings made based on uptake disparities by comparing force field derived simulation isotherms to experimental isotherms in the low-pressure regime. The force field rescaling methodology was proven to refine even the worst performing force field in UFF/TraPPE. The uptake disparities of UFF/TraPPE improved from 197% and 194% to 11% and 21% for ethane and ethene, respectively. The proposed methodology is applicable to predict adsorption across nanoporous materials and allows for rescaled force fields to reach quantum accuracy without the need for experimental input.

Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. Sixty‐five studies were included. Standard MRI sequences such as T1‐weighted (T1w), T2‐weighted (T2w) and fluid‐attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion‐weighted imaging (PWI), diffusion‐weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring. In this systematic review, we explore the latest advancements in MRI techniques used to detect and characterise brain metastases in lung cancer patients. We focus on MRI and machine learning methods, which have shown potential in enhancing diagnosis, prognosis and personalised treatment for those with lung cancer and brain metastases.