Efficient Approach Research Articles

Adaptation of an evidence-based, preventive intervention to promote mental health in Hispanic adolescents: eHealth Familias Unidas Mental Health.

Youth internalizing symptoms (i.e., depression and anxiety), suicide ideation and attempts have been rising in recent years, including among Hispanics. Disparities in mental healthcare are concerning and require intervention, ideally prevention or early intervention. Familias Unidas is a culturally-syntonic, family-centered intervention effective in reducing youth drug use and sexual risk, with evidence of unanticipated effects on internalizing symptoms. This paper describes the systematic process used to adapt the eHealth version of the Familias Unidas intervention to more directly address internalizing symptoms and suicide risk in preparation for an effectiveness-implementation hybrid trial for youth with elevated internalizing symptoms, a history of suicide ideation/attempts, or poor parent-youth communication. The resulting eHealth Familias Unidas Mental Health intervention is described. Guided by a 4-phase framework, the steps in the adaptation process involved: assessment of the community and intervention delivery setting (pediatric primary care clinics); integration of previous intervention research, including intervention mechanisms of action; and expert and community consultation via focus groups. Focus group analyses showed that youth and parents perceived that the intervention was helpful. Their feedback was categorized into themes that were used to directly target mental health by addressing technology use, parent mental health, and social support. Effective and scalable preventive interventions are needed to address mental health disparities. The systematic adaptation process described in this paper is an efficient approach to expanding interventions while maintaining known, empirical and theoretical mechanisms of action. Findings from the ongoing effectiveness-implementation trial will be critical.

Women’s perspectives on the acceptability of risk-based cervical cancer screening

BackgroundThe increased knowledge of cervical cancer (CC) risk factors and suboptimal performance of present screening programs has generated interest in shifting from a universal screening approach to one based on individual risk assessment. To inform the future development of risk-based CC screening programs, it is crucial to gain insight into the factors influencing the acceptability of such approach among screening target group women. The aim of this study was to prospectively investigate the acceptability of risk-based CC screening and to identify potential barriers.MethodsIn this qualitative study, one-to-one semi-structured interviews were conducted with a purposeful sample including women aged 30–65 years to explore women’s perspectives on the acceptability of risk-based CC screening. The study was conducted in Estonia, and interviews were conducted from March to September 2023. Potential participants were approached in person by a member of the study team or by their healthcare providers at primary care or gynaecology clinics. The interview guides were developed based on the concept of acceptability of healthcare interventions.ResultsTwenty participants (mean age 44.5, SD = 8.6) with diverse backgrounds were interviewed. The seven components of acceptability (affective attitude, burden, ethicality, opportunity costs, perceived effectiveness, self-efficacy, and intervention coherence) were explored as key themes. Generally, women supported risk-based screening. However, we identified several factors that may compromise the acceptability of risk-based screening. The participants were reluctant to accept less intense screening for low-risk women and anticipated that if risk-based approach was implemented, more frequent testing would remain an option. Providing in-person clinician support was expected, requiring additional healthcare resources. Knowledge gaps in CC prevention highlighted the need for accessible information and education. Most women were unworried about sensitive data inclusion in risk score calculations. However, some participants were concerned about potential confidentiality breaches by healthcare workers.ConclusionThis study indicates that risk-based CC screening is acceptable, except for testing low-risk women less frequently. Our findings underscore the necessity for comprehensive understanding of the needs and concerns of the target group women for program development. Healthcare organizations are required to proactively address these needs by implementing comprehensive information dissemination and efficient communication approaches.

An Efficient System for Agrobacterium-Mediated Transformation of Elite Cultivars in Brassica juncea

Efficient genetic transformation approaches play pivotal roles in both gene function research and crop breeding. However, stable transformation in mustard, particularly for different horticultural types, has not been systematically studied and well-established so far. In this study, we optimized the key factors in the genetic transformation of mustard, including the optical density value of Agrobacterium suspension, the age of explants, and the combination of phytohormones at different concentrations. As a result, the optimal conditions for the genetic transformation of leaf and stem mustard included hypocotyl explants derived from 4-day-old seedlings, infection by 0.8 OD600nm Agrobacterium suspension, and then re-differentiation on a medium containing 4 mg/L trans-zeatin (tZ) and 0.1 mg/L indoleacetic acid (IAA) for leaf mustard, and for stem mustard, re-differentiation on a medium containing 2 mg/L tZ and 0.4 mg/L IAA, with positive rates of 4.74% and 5.26%, respectively. Those for root mustard were hypocotyl explants derived from 8-day-old seedlings, infection by 0.2 OD600nmAgrobacterium suspension, and a medium containing 2 mg/L tZ and 0.1 mg/L IAA, with a positive rate of 4.42%. Overall, this work provides an effective tool for both the theoretical study and genetic improvement of Brassica juncea.

Boosting BOD/COD biodegradability of automobile service stations wastewater by electrocoagulation

ABSTRACT This study investigates the application of electrocoagulation for enhancing the biodegradability of organic matter in automobile service station wastewater, notorious for its contamination with polyaromatic hydrocarbons, heavy metals, and surfactants. Optimization of key variables such as electrode material, current density, and electrical consumption is conducted, with correlation analysis assessing their impact on water quality. Experimental setups utilize a vertical configuration comprising eight monopolar steel electrode plates as cathodes and eight counter electrodes (either iron or aluminum) connected in parallel. Results indicate that employing iron as the sacrificial electrode significantly increases the biochemical oxygen demand/chemical oxygen demand (BOD/COD) ratio, highlighting the efficacy of heightened power levels in enhancing organic matter degradation. Optimal removal efficiencies, including 87.5 for COD, 96.01 for BOD, and 92.2% for total solids, are achieved at a current density of 42 A/m2 and energy consumption of 360 kWh/m3, while maintaining pH levels between 6 and 9. The findings underscore the potential of electrocoagulation with Fe, Al as anodes, and stainless-steel cathodes as an efficient wastewater treatment approach, particularly for COD, BOD, and solid particle removal, thus contributing significantly to environmental sustainability.

An Efficient Hybrid STO-MFDNN Approach for Induction Effect in Nanoparticle with Magneto-Convective Nanofluid Boundary Layer Flow

An Efficient Hybrid STO-MFDNN Approach for Induction Effect in Nanoparticle with Magneto-Convective Nanofluid Boundary Layer Flow

Divergent Synthesis of Benzo[4,5]imidazo[2,1-b][1,3]thiazines and α-Trifluoromethyl-β-arylthio Tertiary Alcohols from 2-Mercaptobenzimidazoles and α-CF3 Alkenes.

An efficient and metal-free approach for the divergent synthesis of 2-fluoro-3-aryl-4H-benzo[4,5]imidazo[2,1-b][1,3]thiazines and α-trifluoromethyl-β-arylthio tertiary alcohols from 2-mercaptoimidazoles and α-CF3 alkenes has been developed. The chemoselectivity was well controlled by base or light; a series of 2-fluoro-3-aryl-4H-benzo[4,5]imidazo[2,1-b][1,3]thiazines were afforded via base-mediated sequential SN2'- and SNV-type reactions. Meanwhile, α-trifluoromethyl-β-arylthio tertiary alcohols could be selectively achieved through visible-light-driven and electron donor-acceptor (EDA) complex-initiated radical cascade thiolation/hydroxylation in the absence of base, transition metal, and external photocatalyst.

Coral-like Ti3C2Tx/PANI Binary Nanocomposite Wearable Enzyme Electrochemical Biosensor for Continuous Monitoring of Human Sweat Glucose

With the continuous advancement of contemporary medical technology, an increasing number of individuals are inclined towards self-monitoring their physiological health information, specifically focusing on monitoring blood glucose levels. However, as an emerging flexible sensing technique, continuous and non-invasive monitoring of glucose in sweat offers a promising alternative to conventional invasive blood tests for measuring blood glucose levels, reducing the risk of infection associated with blood testing. In this study, we fabricated a flexible and wearable electrochemical enzyme sensor based on a two-dimensional Ti3C2Tx MXene nanosheets and coral-like polyaniline (PANI) binary nanocomposite (denoted as Ti3C2Tx/PANI) for continuous, non-invasive, real-time monitoring of sweat glucose. The exceptional conductivity of Ti3C2Tx MXene nanosheets, in conjunction with the mutual doping effect facilitated by coral-like PANI, significantly enhances electrical conductivity and specific surface areas of Ti3C2Tx/PANI. Consequently, the fabricated sensor exhibits remarkable sensitivity (25.16 μA·mM−1·cm−2), a low detection limit of glucose (26 μM), and an extensive detection range (0.05 mM ~ 1.0 mM) in sweat. Due to the dense coral-like structure of Ti3C2Tx/PANI binary nanocomposite, a larger effective area is obtained to offer more active sites for enzyme immobilization and enhancing enzymatic catalytic activity. Moreover, the sensor demonstrates exceptional mechanical performance, enabling a 60° bend in practical applications, thus satisfying the rigorous demands of human sweat detection applications. The results obtained from continuous 60 min in vitro monitoring of sweat glucose levels demonstrate a robust correlation with the data of blood glucose levels collected by a commercial glucose meter. Furthermore, the fabricated Ti3C2Tx/PANI/GOx sensor demonstrated agreement with HPLC findings regarding the actual concentration of added glucose. This study presents an efficient and practical approach for the development of a highly reliable MXene glucose biosensor, enabling stable and long-term monitoring of glucose levels in human sweat.

Heterogeneous visible-light photoredox catalysis with NiCl2@g-C3N4 for induced C(sp2)-H/N[sbnd]H cross-dehydrogenative coupling

A novel photoactive NiCl2@g-C3N4 catalyst system, which effectively drives C(sp2)-H/NH bond formation between quinolin-2(1H)-ones, primary and secondary aliphatic amines under blue light irradiation. The NiCl2@g-C3N4 composite with a NiCl2 loading of 0.7wt% had the highest photoactivity, and the calculated band gap energy value is 2.56 eV The nickel ion could act as an electron acceptor enhancing carrier separation and transfer efficiency, greatly enhanced the photoreaction efficiency of g-C3N4. Preliminary mechanistic studies indicate upon visible light irradiation, the photo-generated electrons and holes act as oxidants, thereby generating amine radicals in the absence of external chemical oxidants. This provides a straightforward and efficient approach for directly 3-aminating quinoxalines-2(1H)-ones. Moreover, the heterogeneous catalyst can be recycled at least six times without significant activity loss.

Reference-based image super-resolution of hyperspectral and red-green-blue image for determination of wheat kernel quality using deep learning networks

In the process of cultivation and harvest, wheat kernel quality is highly susceptible to various factors, such as disease, mildew, atrophy and impurities, and detection of kernel quality is essential to avoid hazard proliferation, facilitate product grading, and ensure food safety. Possessing abundant image and spectral characteristics, hyperspectral imaging (HSI) has gained impressive achievements in kernel quality analysis, but its low spatial resolution limits its detection accuracy. In this study, reference-based image super-resolution (RefSR) of HSI and Red-Green-Blue image was adopted to improve resolution to determine wheat kernel quality using deep learning networks. Firstly, RefSR was conducted by the improved transformer network with dual-branch feature extraction and weighted fusion operation and achieved excellent RefSR with significant resolution improvement, peak signal to noise ratio of 35.521 and structural similarity index of 0.97, outweighing the existing state-of-the-art networks. Then, the reflectance images (RIs) of effective wavelengths (EWs) from generated HSI images were combined with the residual network with a spatial, channel attention and multi-scale residual to determine wheat kernel quality. Precise analysis was achieved with the accuracy in calibration, validation and prediction sets of 100.00%, 95.26% and 92.78%. RefSR provides a novel and efficient approach for obtaining HSI images of high spatial resolution and facilitates the application of HSI in analysis of crop kernels. RIs of several sporadic EWs can be easily acquired and processed, achieving field and rapid kernel detection. Therefore, the proposed method furnishes the efficient, accurate and applicable determination of wheat kernel quality.

Modeling rate of penetration using hybridization of Artificial Neural Network and Artificial Fish Swarm algorithm

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the Artificial Fish Swarm Algorithm (AFSA) with the Back Propagation (BP) algorithm in an Artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young’s model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model’s applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

Research progress of nanoscale zero-valent iron in synergistic combinations of denitrifying bacteria for nitrate removal

Nitrate (NO3--N) is a common inorganic nitrogen pollutant in water. Excessive NO3--N can lead to water eutrophication and threaten human health. Nanoscale zero-valent iron (nZVI) has attracted much attention in NO3--N removal due to its high specific surface and excellent electron donor properties. The combination of nZVI and denitrifying bacteria (DNB) demonstrates high efficiency in converting NO3--N into N2. This approach not only substantially enhances the removal rate of NO3--N but also exhibits superior environmental sustainability compared with conventional chemical denitrification methods. Accordingly, it holds substantial promise for mitigating NO3--N pollution and warrants further exploration in the pollution control. Therefore, it is necessary to understand the interaction mechanism between nZVI and DNB for NO3--N removal. This paper details the factors affecting the removal of NO3--N by nZVI combined with DNB, reviews the latest research progress in this field, elaborates on the interaction mechanism between nZVI and DNB for NO3--N removal, and discusses the challenges and future research directions of NO3--N removal by nZVI combined with DNB. This review aims to provide a theoretical basis for the development of efficient approaches for the remediation of NO3--N pollution.

Insights into the influence of anions on the syngas production behavior of bio-wastes during molten salt thermal treatment

Thermal treatment of bio-wastes in molten nitrate at low temperatures has emerged as an efficient approach for producing syngas. Molten salt facilitates the thermal conversion of bio-wastes due to its superior heat transfer and catalytic properties. To enhance the depolymerisation of macromolecules in bio-wastes for the production of high-quality syngas, molten NaNO3-NaNO2 with varying anion ratios was employed in this study, and the effects of these anions on bio-wastes with diverse properties were thoroughly investigated. The results indicated that NO3− tended to be significantly involved in the cleavage of carbonyl groups and C–C/CC bonds, which dominated the high CO2 production. NO2− tended to be significantly involved in the cleavage of ether and C–C/CC bonds, which dominated the high CO production. The elevated NO2− ratio in molten salt significantly facilitated the depolymerisation of bio-wastes, increasing the yield of combustible gases (CO, CH4, H2) while decreasing CO2 yield in the syngas. This resulted in a notable enhancement of the lower heating value (LHV) of syngas, reaching up to 11.55 MJ/Nm3. This behavior also led to the formation of substantial quantities of nitro-phenols and amines containing nitro/nitroso groups in the tar. The nitrosation reaction of NO2− with bio-wastes was the primary factor in this process and took precedence over the nitrification of NO3−. This study elucidated the reaction pathways of NO3−/NO2− with bio-wastes and their contributions to the production of high-quality syngas at low temperature.

FeTiO3: A low-cost and efficient photocatalytic mineral for sustainable NOx abatement

This study investigates the potential of FeTiO3 as a photocatalyst for the removal of nitrogen oxides (NOx) from gas streams, a critical environmental and health concern. The photocatalytic activity of FeTiO3 was evaluated across a range of operational parameters, with initial NO concentrations set from 5 to 50 ppmv and gas hourly space velocity (GHSV) values of 18, 36, and 54 h−1. The findings demonstrate that FeTiO3 effectively converts NOx, even at room temperature and under saturated atmosphere, with a clear correlation between the photocatalytic removal efficiency and both the initial NO concentration and GHSV. A rigorous mechanism for the photooxidation of NO to nitrate via NO2 was proposed based on these experimental results and the surface characterization of raw and used ilmenite, corroborated by DFT calculations. These outcomes would allow widening the application of air pollution control strategies to flue gas with relatively low NOx concentration and low temperature, where other NOx control techniques show severe drawbacks. Therefore, this research contributes to the development of efficient and sustainable approaches for air quality improvement using photocatalytic systems.

Discovering and Ranking Urban Social Clusters Out of Streaming Social Media Datasets

ABSTRACTUrban social media mining is the process of discovering urban patterns from spatio‐temporal social media datasets. Urban social clusters are the clusters formed by the social media posts of users living in cities at a certain time and place. Discovering and identifying urban social clusters is of great importance for urban and regional planning, target audience identification, a better understanding of city dynamics and so forth. Discovering and ranking urban social clusters out of streaming social media datasets require efficient filtering approaches and mining algorithms. In the literature, there are several studies performed that address the discovery of the importance of urban clusters. Most of these studies take into account the spatial expansions over time and the changes in the numbers of elements within clusters when identifying the significance of urban clusters. However, in contrast to these studies, we have also considered cluster temporal formation stability, spatial density variation, and the impact of meta‐information on urban social clusters. In this study, Temporal, Spatial, and Meta Important Urban Social Clusters Miner (TSMIUSC‐Miner) algorithm is proposed. In the proposed algorithm, urban social clusters are discovered, and their importance relative to each other are compared and ranked. The temporal, spatial and meta importance scores of the clusters are calculated and then, the clusters that satisfy predefined score thresholds are discovered. The performance of the proposed TSMIUSC‐Miner algorithm compared with that of a naive approach using real‐life streaming Twitter/X dataset. The results showed that the proposed TSMIUSC‐Miner algorithm outperforms the naive approach in terms of execution time.

Analysis of chemosensitivity of tumor spheroids exposed to two-dimensional gradient of combination drugs in a hydrogel-based diffusion microfluidic platform

BackgroundLiver cancer stands as a leading cause of cancer-related deaths globally, challenging conventional treatments due to resistance to chemotherapy and targeted therapy. Although frontline medications show initial efficacy, prolonged use often leads to resistance and harm. Current clinical strategies rely on combination therapies, but evaluating their effectiveness remains challenging. ResultsTo address this, we developed a hydrogel-based diffusion microfluidic platform for assessing chemosensitivity. This platform features a hydrogel-filled diffusion layer linked to liquid wells, allowing the creation of drug gradients. Tumor spheroids, cultured on the non-adhesive hydrogel surface, were exposed to single or combination drug gradients. Analysis revealed that drug efficacy, quantified by IC50 values, could be determined from responses to single drug gradients. A novel method was introduced to assess spheroid circularity as a time-invariant index of drug efficacy. Furthermore, exposing spheroids to 2D combination drug gradients allowed intuitive visualization of their responses via a color map. This analysis identified optimal drug combinations, exhibiting superior efficacy to monotherapy. SignificanceThe microfluidic platform enables assessment of synergistic effects and replicates in vivo conditions, enhancing the relevance of test results. By offering a streamlined, fast, and efficient drug screening approach, this platform aims to provide insight into tumor spheroid responses to varying drug combinations, facilitating more effective clinical applications.

Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.

The purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future. In two acts, we describe the efforts made by our research group to improve breast cancer screening by introducing digital breast tomosynthesis (DBT), all the way from initial studies to a large prospective population-based screening trial and beyond. Our studies have shown that DBT has significant advantages over digital mammography (DM), the current gold standard method for breast cancer screening in Europe, in many aspects except a major one-the increased radiologist workload introduced with DBT compared with DM. It is foreseen that AI could be a viable solution to overcome this problem. We have proved that one-view DBT is a highly efficient screening approach with respect to diagnostic performance.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98±0.02 for lung cancer subtype differentiation and 0.83±0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Sensitivity analysis of a dual-continuum model system for integrated CO2 sequestration and geothermal extraction in a fractured reservoir

Depleted hydrocarbon reservoirs are considered as the most feasible option for CO2 geological sequestration and utilization. Most of the hydrocarbon reservoirs are naturally fractured. Simulation of fluid flow and heat transfer in these fractured formations remains a significant challenge in reservoir engineering. In this study, a dual-continuum model is developed to simulate integrated CO2 sequestration and CO2-circulated geothermal extraction in a fractured reservoir block in North Oman. The high-dimensional sensitivity of key parameters controlling CO2-brine flow and heat transfer in this matrix-fracture system is quantitatively evaluated by an efficient surrogate modeling approach. The surrogate models are constructed and validated based on a suite of physics-based model simulations. It is found that fracture permeability dominates the CO2 injectivity, storage, circulation and associated geothermal extraction. Response surface analysis shows that the flow area density between matrix-fracture and matrix block length controls the flux interaction between matrix and fracture formations. In contrast, the fracture aperture shows negligible influence in the dual-continuum modeling system. Particularly, sensitivity varying with locations on the response surface is analyzed for defined performance indicators.

Mesoscale Reorganization of the Depolymerized Aluminosilicate into Single-Crystalline Hierarchical Zeolite.

Controllable synthesis of hierarchical zeolites from natural aluminosilicate minerals is considered an efficient and eco-friendly approach for the production of high-performance zeolites, but its synthesis mechanism is still obscure. Herein, we take the synthesis of a single-crystalline hierarchical NaA zeolite using submolten salt depolymerized kaolin (SMS-K) as the sole source of silicon and aluminum via a mesoscale reorganization strategy as an example to elucidate the reorganization process. Comprehensive morphological and structural analyses reveal that sodium-rich voids in SMS-K facilitate concurrent assembly both within the interior and at the interface of the amorphous gel, leading to the formation of numerous nanoparticles with short-range order which assemble into single-crystal nanocube NaA zeolites with intracrystalline mesopores. By harnessing confinement effects, SMS-K modulates the growth of nanoparticle sizes and enhances the intimate interconnection of nanocubes, thereby yielding NaA zeolite aggregates that exhibit hierarchical porosity, encompassing micro-, meso-, and macropores. This study offers the potential for designing and precisely controlling the fabrication of hierarchical zeolites derived from natural minerals.

Finite-order method to calculate approximate density matrices in the Fock-space multireference coupled cluster theory

An efficient approach to calculate approximate pure-state and transition reduced density matrices in the framework of the multireference relativistic Fock-space coupled cluster (FS CC) theory is proposed. The method is based on the effective operator formalism and consists of the direct substitution of the FS CC Ansatz for a wave operator into the effective operator expression with the subsequent truncation of expansion at the terms quadratic in cluster amplitudes. The final density matrix is defined by active-space density matrices of different ranks ‘dressed’ with contributions from cluster operators. The method gives a connected expression for pure-state density matrices, provided that the intermediate normalisation condition is fulfilled. Moreover, under some additional assumptions, the connectivity can also be ensured for calculated transition property matrix elements and natural transition spinors. The developed technique allows for fast and accurate calculations of one-particle reduced density matrices for a wide range of electronic states. A pilot application of the new technique to construct averaged atomic natural orbital (ANO) basis sets for fully relativistic electronic structure calculations is presented.