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This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]

The wide range of pressure detection and the exceptional linearity are essential performance parameters for flexible pressure sensors, enabling them to adapt to diverse scenarios and acquire information accurately. However, currently available “ultra‐wide range” piezoresistive sensors lack an optimal solution that effectively balances sensing properties, device thickness, and process cost. This study proposes a distinctive approach by introducing a Janus conductive structure assembled with dual resistive sensitive layers. The design allows for a pressure‐induced staged transformation of the current transport path, effectively mitigating variations and saturation in sensor resistance over a wide pressure range. The resulting piezoresistive sensor demonstrates an unprecedented detection range of 0–3800 kPa, showcasing remarkable sensitivity of 4.11 kPa−1 and outstanding linearity of 99.9% within the range of 0–1000 kPa. Additionally, the sensor boasts a thickness of only ≈200 µm, made possible through the utilization of a cellulose nanofibers material matrix. These performance achievements stand at the forefront when compared to existing reports. This research explores the potential applications of these sensors and extended arrays in the domains of human health and motion monitoring. It investigates their utility in gait analysis for assisted posture correction, as well as in the assessment and rehabilitation of gait instability. [ABSTRACT FROM AUTHOR]

Mapping high dynamic range (HDR) images to low dynamic range (LDR) display devices while performing enhancement processing is a challenging task. To address the issues of chromatic drift, the imbalance between preserving naturalness and enhancing details during the mapping process, this paper proposed a tone mapping operator (TMO) based on an improved guided filter with multi‐layered processing. Firstly, the operator tackled the problem of insufficient extraction of detail layer information in complex scene images by redefining the formula of the guided filter and introducing adaptive regularization parameters. This enhancement enabled more effective extraction of details compared to traditional fixed‐parameter guided filters. Secondly, the algorithm employed a centre‐surround approach to subdivide and processed the detail layers individually, effectively suppressing halos and artefacts. Thirdly, image entropy was utilized to guide the fusion and enhancement processing of the detail layers, preventing excessive or insufficient enhancement. Finally, this paper proposed an adaptive gamma correction algorithm and a chromatic consistency algorithm to improve overall contrast and hue saturation. The results demonstrate convincing performance in terms of subjective visual evaluation and image quality assessment. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

Gas reactant transport plays a crucial role in various gas‐consumption reaction‐based electrochemical devices, but the pivotal performance limitation still centers on gas diffusion electrodes (GDEs). To this end, natural cross‐cut wood to prepare GDEs for aluminum–air batteries is introduced by utilizing the ordered structure with microchannels. With cobalt–nitrogen co‐doped carbon nanotubes as the oxygen reduction reaction catalysts grown inside the channels of carbonized wood slice and wettability gradient modification, cherry‐based GDE achieves higher power density (267 vs. 236 mW cm−2) than the commercial carbon fiber paper‐based electrode. By bridging the identified characteristics of pore structure via deep‐learning image recognition technology with the permeability of other three typical kinds of (ash, pine, and oak) wood‐based GDEs, it is revealed that the ratio of effective porosity to the average pore size is key to the performance. This work demonstrates the feasibility of bio‐derived wood‐based materials for fabricating high‐performance GDEs and provides insights into pore structure for the rational design of structured electrodes. [ABSTRACT FROM AUTHOR]

Real‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real‐time performance and accuracy due to the post‐processing required to rectify anomaly detection results during data pre‐processing. This paper presents a novel traffic flow prediction model, termed the With‐Anomaly Detection Probability (ADP) Attention‐Bidirectional Long‐Short Term Memory (BiLSTM) model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework. Additionally, the model incorporates an attention mechanism within the long‐short term memory network. Through a comprehensive simulation study utilizing actual measured traffic flow data from the Shanghai‐Chongqing Expressway, the effectiveness of the proposed model is rigorously evaluated. The prediction results show that the model proposed in this paper is a real‐time and accurate short‐time traffic flow prediction model compared with the basic models. [ABSTRACT FROM AUTHOR]

Aim: To explore and summarise published literature with regards to secondments to clinical research and to identify the gaps in research to inform further work. Design: Systematic scoping review. Method: A scoping review was undertaken in accordance with the Patterns, Advances, Gaps, Evidence and Research framework. Databases searched included CINAHL, PubMed, Medline and Embase. Inclusion/exclusion criteria were applied by two independent reviewers. Two reviewers independently retrieved full‐text studies for inclusion and applied the framework as a tool for synthesising Patterns, Advances, Gaps, Evidence and Research recommendations. Results: Six papers and one abstract published between 2003 and 2018 were included. All secondees (n = 34) were released from NHS posts, with secondments (where specified) ranging in duration from 0.25 to 2 years and for 40%–100% of their working hours. All seven papers reported benefits for personal and professional development, predominantly in the form of personal reflections. Few described involvement with research delivery teams. Conclusion: Published initiatives vary in nature and lack standardised reporting and measurement of impact. Further research is required to identify benefits at a departmental or organisational level, the facilitators for setting up secondments and the application of knowledge gained from secondment opportunities. Implications for the Profession: Undertaking a research secondment is reported to offer professional and personal benefit for clinical staff. Research secondments are one way in which a research culture can practically be embedded within clinical settings. Impact: This scoping review identified a lack of published empirical research seeking to understand research secondments as a tool to enhance research and evidence engagement. Although there is a suggestion that secondments could positively impact staff retention, there is limited evidence about the benefit for the organisation or for patient care. These findings have implications for staff, managers and their organisations. Reporting Method: The PRISMA‐ScR guidelines were used to guide reporting. No Patient or Public Contribution: This was not relevant to the research design. [ABSTRACT FROM AUTHOR]

Gamma spectrum denoising can reduce the adverse effects of statistical fluctuations of radioactivity, gamma ray scattering, and electronic noise on the measured gamma spectrum. Traditional denoising methods are intricate and require analytical expertise in gamma spectrum analysis. This paper proposes a segmentation‐enhanced Convolutional Neural Network‐Stacked Denoising Autoencoder (CNN‐SDAE) method based on convolutional feature extraction network and stacked denoising autoencoder to achieve gamma spectrum denoising, which adopts the idea of data segmentation to enhance the learning ability of the neural network. By dividing the complete gamma spectrum into multiple segments and then using the segmentation‐enhanced CNN‐SDAE method for denoising, the method can achieve adaptive denoising without manually setting the threshold. The experimental results show that our method can effectively achieve gamma spectrum denoising while retaining the characteristics of the gamma spectrum. Compared with traditional methods, the denoising speed and effectiveness have been significantly improved, and the proposed method demonstrates an approximately 1.72‐fold enhancement in smoothing performance than the empirical mode decomposition method. Furthermore, in terms of retaining gamma spectrum characteristics, it also achieves a performance improvement of approximately three orders of magnitude than the wavelet method. [ABSTRACT FROM AUTHOR]

Traumatic haemorrhage is a prevalent clinical condition, and effective and timely haemostasis is crucial for the preservation of patients' lives. In recent years, injectable hemostatic materials have gained significant attention due to their excellent hemostatic efficacy, biocompatibility, and biodegradability, making them widely applied in the treatment of incompressible traumatic haemorrhage. Systematic analysis of injectable hemostatic materials is crucial for research in this area. This article provides a comprehensive review of the development and research trends of injectable hemostatic materials over the past 20 years using visualization techniques. Analysis of collaboration and co‐citation networks revealed localized research collaboration networks, highlighting the need for enhanced international collaboration in the field of injectable hemostatic materials. Current research focuses primarily on hemostatic materials, hemostatic processes, and hemostatic mechanisms. Injectable hemostatic materials with excellent performance offer promising strategies for wound healing. This review provides a comprehensive and systematic summary of injectable hemostatic materials, offering valuable guidance for the development and clinical application of novel injectable hemostatic materials. Additionally, visualized methodology and mapping analysis are effective data mining methods that provide approaches and strategies for clear knowledge network analysis. These methods facilitate better understanding and interpretation of research dynamics in the field of injectable hemostatic materials, thereby guiding and inspiring future research. [ABSTRACT FROM AUTHOR]

The May 2024 issue of the Journal of Cosmetic Dermatology offers cutting-edge research papers and review articles in the field of cosmetic dermatology. The journal prides itself on its efficient peer-review process, with associate editors assigning papers to reviewers who provide recommendations for acceptance, editing, or rejection. The journal aims to provide authors with timely responses and invites readers to get involved in the review process. The journal also acknowledges the hard work of its publishers and the team that keeps everything organized. [Extracted from the article]

The Ecological Society of America (ESA) has been publishing journals for over 100 years, including the Bulletin of the Ecological Society of America, Ecology, Ecological Monographs, and others. In 2022, the ESA journals published 1,034 new papers, with over 5.7 million cumulative paper downloads. The acceptance rates for the journals range from 20 to 49%, and the average time from submission to acceptance is 155 days. Publishing in ESA journals supports critical programs for science communication, training, and diversity, and the revenue generated from publishing in these journals is essential for supporting ESA programs for students, early career researchers, and underrepresented groups. The ESA continues to study the publishing landscape and may consider future partnerships with other publishers. [Extracted from the article]

In this paper, the wake characteristics of Zell 2000 wind turbine under different tip velocity ratios are studied by using the lattice Boltzmann method and large eddy simulation. The adaptive mesh refinement method is performed to capture the fine flow structure and wake characteristics development. In this paper, we mainly focus on the effect of the tip speed ratio on the flow structure and unsteady characteristics of wind turbine wake. The three‐dimensional flow vorticity structures, the section vorticity diagram, the pressure fluctuation of wake and the lift coefficient of wind turbine wake are utilized to explore the effect of the tip speed ratio on the unsteady physics mechanism of wind turbine wake. With the increase of the tip speed ratio, the distance between two adjacent vortex rings along the axial direction gradually decreases, as the position of the broken vortex circles gradually approaches the center of the blade, separated vortexes are rapidly generated, and the coherent structure appears closer to the wind turbine. A relationship is established between the tip speed ratios and the positions of the broken vortex circles. It is further found that the dominant frequency amplitude gradually increases with the increase of tip speed ratio and the pressure amplitude spectra of vortex increases with the decrease of the distance between the wake and the center of the blade axis. The above series of studies can provide significant physical insight into deep understanding the influence of the tip speed ratio on the wake characteristics of wind turbines. [ABSTRACT FROM AUTHOR]

An important aspect of Space Situational Awareness is to estimate the intent of objects in space. This paper discusses how discriminating features can be obtained from Inverse Synthetic Aperture Radar images of such objects and how these discriminators can be used to recognise the objects or to estimate their intent. If the object is, for example, a satellite of a known type, the scheme proposed is able to recognise it. The ability of the scheme to detect damage to the object is also discussed. The focus is on imagery obtained in the sub‐terahertz band (typically 300 GHz) because of the greater imaging capability given by the diffuse scattering which is observed at these frequencies. The paper also discusses the importance of being able to use images obtained by electromagnetic simulation to be able to train the subsystem which recognises features of the objects and describes a practical scheme for creating these simulations for large objects at these very short wavelengths. [ABSTRACT FROM AUTHOR]

With the development of detrital zircon U–Pb ages and Hf isotope analysis as conventional sedimentological research methods, a large number of studies have been conducted in the Tarim, West Kunlun, Tajik, Pamir and Tianshuihai regions and abundant data have been accumulated. Summarizing these data to characterize sedimentary units and source regions is more important than local studies of specific geological epochs; therefore, we compiled a database of detrital zircon U–Pb ages and Hf isotope analyses from the Tarim, West Kunlun, Pamir, Tajik and Tianshuihai terranes. The database contains data from 90 papers, including 35,281 individual U–Pb ages and 4,181 Hf isotope analyses. The database records the literature source, petrologic sample information, regional geological information, geographic coordinates and analytical parameters for each data point in as much detail as possible to allow readers to review, process, analyse and use the data. The database relies on the Deep‐time Digital Earth (DDE) platform and uses an open database update system to enable sharing and collaborative building. We encourage other researchers to contribute relevant published data to facilitate wider use. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive control strategy for a battery energy storage system (BESS) participating in primary frequency modulation (FM) while considering the state of charge (SOC) recovery. On the one hand, when the frequency fluctuations are outside the dead zone of the FM, variable coefficient virtual sag control and variable coefficient virtual inertia control are coordinated for more accurate primary FM. Also, an assisted FM strategy is proposed, where the temporal alignment of assisted FM is determined by the regional segmentation of the frequency deviation and SOC, and it enables the BESS to recover SOC concurrently during its involvement in primary FM. On the other hand, when the frequency fluctuations are inside the dead zone of the FM, typical S‐type functions are deployed to dynamically establish the mutual constraints relationships of SOC recovery demand coefficient and frequency deviation. Subsequently, the recovery control coefficients are derived, and accordingly, a SOC recovery strategy is proposed. Finally, simulation results verify the efficacy of the proposed control strategy in the grid primary FM system and demonstrate that the strategy expedites the SOC recovery and concurrently diminishes the conventional unit output. [ABSTRACT FROM AUTHOR]

The coupling capacitance between the line and the transducer cannot be ignored when making non‐contact voltage measurements on low‐voltage three‐phase power lines. This leads to an increase in the number of unknown capacitances, and the number of correlatable response voltage equations is less than the number of unknown coupling capacitances. In this paper, a simplified decoupling method is proposed based on the characteristics of triangular three‐phase lines erected in distribution areas. Firstly, the equivalent circuit measured by the voltage sensing mechanism during three‐phase operation is established, and the three‐phase line response voltage equation is derived. Secondly, this paper proposes a three‐phase line response voltage decoupling algorithm after designing the decoupling matrix. Then, this paper uses the least squares method to solve the coefficients in the equations. Finally, the three‐phase voltage fluctuations of the actual low‐voltage stations are simulated by simulation. The measured sensor voltage values are substituted into the algorithm to invert and calculate the three‐phase line voltage. The three‐phase phase error is less than 2°. The amplitude error is less than 1.77%. The experiment verifies the stability and accuracy of the proposed decoupling method. [ABSTRACT FROM AUTHOR]

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking. [ABSTRACT FROM AUTHOR]

Background: From menarche until menopause, the average menstruator will use over 11 000 tampons or sanitary pads. Vaginal and vulvar tissue is highly permeable, and chemicals are absorbed without undergoing first‐pass metabolism. Objectives: To conduct a review of the literature to determine exposure to environmental chemicals in menstrual products. Search strategy: This review identified 15 papers over the past 10 years. Selection criteria: Papers that measured chemicals in menstrual products and that measured human biomarkers of chemical exposure were included. Papers had to also be available in English. Data collection and analysis: Reviewers assessed the articles and data provided. Multiple chemical groups were found. Main results: Phthalates, volatile organic compounds, parabens, environmental phenols, fragrance chemicals, dioxins and dioxin‐like compounds were detected in menstrual products. Research gaps were identified, including the lack of studies on newer products such as menstrual underwear and cups/discs. In addition to measuring chemicals in these products, future research should focus on clarifying the exposure per menstrual cycle to these chemicals to understand how menorrhagia and cycle length influence exposure from menstrual products. Conclusion: Menstrual products contained measurable levels of a range of endocrine disrupting chemicals including phthalates, phenols and parabens. This reflects a potentially important route of exposure to chemicals that can impact women's reproductive health. [ABSTRACT FROM AUTHOR]

This paper concentrates on presenting a reliable procedure to compute the stabilizing region of fractional‐order proportional integral derivative (FOPID) controllers for interval fractional‐order plants having an interval time delay. An interval fractional‐order plant is defined as a fractional‐order transfer function whose denominator and numerator coefficients are all uncertain and lie in specified intervals. Also, an interval time delay points to a delay term whose value varies in a specific interval. The D‐decomposition technique and the value set concept are employed to determine the stabilizing region of FOPID controllers. In this study, first, a theorem is presented to compute the boundary of the value sets of systems having interval time day. Then, a lemma is provided for robust stability analysis of the given closed‐loop control system. For a convenient use of the paper results, an algorithm is proposed to solve the problem of robustly stabilizing interval fractional‐order plants with an interval time delay using FOPID controllers. Finally, four examples are provided to illustrate the proposed procedure. [ABSTRACT FROM AUTHOR]

Background: The physical challenges faced by adolescents and young adults (AYA) after a cancer diagnosis may be different from those experienced by paediatric and older adult cancer patients. Patient‐reported outcome measures (PROMs) are valuable tools that can be useful in exploring the experiences of AYAs and identifying important issues, recurrent themes and areas to potentially improve quality of life. Objective: We compared patient‐reported physical function outcomes between AYAs diagnosed with cancer and non‐cancer controls. Method: This paper builds on a scoping review published in early 2023 and focuses on PROMs related to physical function. Results: This systematic review includes 16 studies that measured and reported on physical function PROMs in AYA cancer survivors compared with their cancer‐free peers. Of these studies, 14 found that physical function in AYA survivors was significantly worse. This paper also includes a meta‐analysis conducted on 5 studies using the EORTC‐QLQ‐C30 to measure physical function, which found that physical function score was an average of 7.03 (95% CI: −10.21, −3.86) points lower in the AYA cancer group, compared to their cancer free‐peers, a difference that is clinically meaningful. Conclusions: The results overwhelmingly demonstrate that AYAs post a cancer diagnosis have worse health‐related quality of life from a physical function perspective than their cancer‐free peers, providing a compelling argument for the need to address this issue. All but one of the studies were cross‐sectional, which highlights the need for further assessment of this group longitudinally throughout their cancer journey. [ABSTRACT FROM AUTHOR]

This article explores the impact of electric vehicle (EV) charging on power systems and emphasizes the importance of careful analysis and planning. It discusses various topics related to EV charging, including EV hosting capacity, optimal placement of EVs and charging stations, technical impacts of Vehicle-to-Grid (V2G) technology, shared vehicle rental, decentralised collaboration frameworks, intelligent energy management schemes, EV demand response, and optimal sizing and siting of public EV charging stations. The article provides valuable insights and solutions for utilities, academia, and policymakers to address the challenges and opportunities associated with EV charging. Additionally, the article introduces a new approach for determining the limits of transformers and overhead lines in relation to EV charging, using Monte Carlo simulations and proposing new parameters for evaluating maximum demands. The authors stress the need for further analysis and attention to the impact of EVs on power systems. [Extracted from the article]

China's social safety net is still underdeveloped, hence family support in the form of intergenerational transfers often serves as a substitute for the public transfer system. Using data from the China Health and Retirement Longitudinal Study, this paper finds that both upstream inter‐vivos transfers (from children to parents) and downstream inter‐vivos transfers (from parents to children) are prevalent in urban China. Moreover, the relative income status of the parent and children has an impact on inter‐vivos transfers. To investigate what economic factors generate the observed patterns of inter‐vivos transfers, this paper adopts a general equilibrium life‐cycle model in which overlapping generations are altruistically linked and calibrates the model to match data from urban China. Counterfactual experiments of removing one source of economic risk or modifying the social security replacement rate from the baseline model at a time reveal that intergenerational transfers mainly serve as informal insurance against the income risk of the children. [ABSTRACT FROM AUTHOR]

Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set. [ABSTRACT FROM AUTHOR]

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real‐world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real‐time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta‐Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full‐text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross‐frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1‐weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI‐PAD combinations. These combinations enable in‐depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain–behavior models that extend beyond the scanner and into real‐world contexts. [ABSTRACT FROM AUTHOR]

Background: The increasing popularity of cosmetic injections using various fillers and neuromodulators for facial rejuvenation has brought both new opportunities and challenges to this field. Aim: Our study was designed to employ bibliometric and visual analysis for a qualitative and quantitative evaluation of facial cosmetic injections, as well as to identify research trends and hotspots in this field. Methods: All publications covering facial cosmetic injection during 2002–2023 were retrieved and extracted from the Web of Science database. The VOSviewer 1.6.18 software and the online tool (http://bibliometric.com/) were applied to analyze the publication trend. Results: A total of 3797 articles related to facial cosmetic injection were identified during the period 2002–2023. The United States had the largest volume of publications (1520, 40.0%), followed by China (333, 8.8%) and Germany (282, 7.3%). Among the institutions and journals, the University of California system and Plastic and Reconstructive Surgery accounted for the most papers related to facial cosmetic injection, respectively. Facial anatomy and injection techniques, prevention and management of complications, regenerative medicine, efficacy and safety of various soft‐tissue fillers, as well as botulinum toxin injections for facial rejuvenation were identified as hotspots for facial cosmetic injections. Conclusions: Facial cosmetic injections are showing an increasing trend in terms of both the number of published papers and operations performed. Despite the notable advancements in this field, numerous challenges persist, including safety concerns and the level of research evidence. With the emergence of novel technologies and materials, scholars from diverse countries and institutions should engage in more extensive collaboration, thereby directly expediting the progress of this field. [ABSTRACT FROM AUTHOR]

Due to their chemical and physical properties, rare earth elements (REEs) are essential in modern applications such as energy conversion or IT technology. The increasing demand for these elements leads to strong incentives for REE recovery and induces the exploration of new, alternative sources for REEs. Accessing REEs from clay minerals, in our case kaolinite, by an elution process is a promising method. The present study investigates the potential application of REE recovery through elution with different mineral acids (HNO3, H2SO4, and HCl) in a microwave process. The material used in this study—residues from an industrial kaolin production process—contained 2.47 g/kg REEs which is a significant amount for REE recovery. The ability of various mineral acids to solubilize metals was studied to assess the REE content of this residual resource. Around 1.87 g/kg of REEs was eluted from industrial kaolinite residues in hydrochloric acid, 1.71 g/kg in sulfuric acid, and 1.13 g/kg in nitric acid. [ABSTRACT FROM AUTHOR]

Endoscopic submucosa dissection (ESD) has been applied extensively in the treatment of large intestine tumours due to its high total excision ratio. Nevertheless, there is a high incidence of adverse reactions in colon ESD, and the efficacy of prophylactic ESD following ESD in prevention of postoperative haemorrhage is still disputed. The purpose of this meta‐analysis is to evaluate the effectiveness of prophylaxis of wound closure in large intestine ESD after operation. For eligibility, we looked through three databases: PubMed, Embase and Cochrane Library. Heterogenity was measured by means of a chi‐square method of Q‐statistic and an I2 test. Fixed or random effects models were used for data processing. Based on the retrieval policy, we found a total of 1286 papers, and then we collected nine papers to extract the data. Regarding postoperative haemorrhage, there was a significant reduction in the risk of wound haemorrhage in the wound closure group than in the control group (OR, 0.29; 95% CI, 0.19–0.44 p < 0.0001). No statistical significance was found in the incidence of perforation in the wound closure and the control group (OR, 0.45; 95% CI, 0.19–1.03 p = 0.06). There was a significant reduction in the incidence of postoperation fever among those in the wound closure group than in the control group (OR, 0.37; 95% CI, 0.15–0.93 p = 0.04). Preventive endoscopic closure decreased the rate of ESD in colon disease, but did not significantly decrease the rate of postoperation perforation and postoperative fever. Future research will be required to clarify the risk factors and classify high‐risk individuals in order to formulate a cost‐effective prevention strategy. [ABSTRACT FROM AUTHOR]

A simple fluorescent probe, (E)‐N‐(3‐methoxy‐4‐methylphenyl)‐1‐(quinolin‐2‐yl)methanimine (L), was synthesized via Schiff base condensation reaction between quinoline‐2‐carboxaldehyde and 3‐methoxy‐4‐methylaniline. L can be used as a ′turn‐on′ fluorescent probe for the selective and sensitive detection of Zn2+ ions in a CH3OH/HEPES medium. The detection limit was 1.04×10−7 M. Job plot analysis shows that the binding stoichiometry of L and Zn2+ is 1 : 1, further verified by the single crystal structure. The fluorescence‐enhanced response of L towards Zn2+ ions may be related to the chelation‐enhanced fluorescence (CHEF) supported by theoretical calculations. By adding EDTA, the reversibility of the probe binding to Zn2+ ions is demonstrated, enabling the construction of a functional molecular logic circuit. In addition, probe L was successfully incorporated into test strips for the quantitative detection of Zn2+. [ABSTRACT FROM AUTHOR]

This research attempts to fabricate a novel type of pressure‐sensitive adhesive (PSA) tape utilizing epoxidized linseed oil (ELO) as the primary component in an effort to reduce reliance on petrochemical polymers. This raw material comes from the natural renewable resources, making this tape a more environmentally friendly choice in reducing impact on the environment and alleviating resource pressure. The ester groups on ELO are subjected to hydrolysis in order to yield a combination of epoxy linseed oil fatty acids (ELFAs) containing both epoxy and carboxyl groups. By controlling the temperature and catalyst conditions, the epoxy and carboxyl groups undergo a ring‐opening reaction, resulting in the self‐polymerization of the ELFA mixture and the formation of hydroxy‐functionalized polymers. These polymers are then applied as a coating on kraft paper and subsequently cut to produce PSA tapes. The tapes produced using this method exhibit a peak peel strength of 20.538 N per 25 mm. Moreover, the incorporation of glycerol ester of rosin serves to enhance the peel strength, resulting in a higher value of 21.883 N per 25 mm. The utilization of sealing the cardboard box by the tapes also serves as validation for the immense capabilities of the tapes in practical applications. [ABSTRACT FROM AUTHOR]

It has come to our attention that there was a mistake regarding the authors' affiliations in our published article.[1]The affiliation Medical School University of Crete, Heraklion, Greece is missing. The two institutions have contributed equally to this publication.The online version of the article has been corrected accordingly. [Extracted from the article]

The Limnology and Oceanography Bulletin published a list of the top three accessed articles for each issue in the third quarter of 2023. The articles cover a range of topics including siderophore production by marine bacteria in the North Pacific Ocean, variability in pH, dissolved oxygen, and temperature in an upwelling dominated ecosystem, and spatial and temporal variability in carbon dioxide and methane in ponds and shallow lakes. Other topics include the support of the mesopelagic zooplankton food web by small particles and heterotrophic protists in the subarctic northeast Pacific Ocean, low carbon accumulation in a macro-tidal mangrove forest on the Amazon coast, and shifts in inorganic carbon and ecosystem state in a temperate estuary. The final articles discuss the distinct habitat and biogeochemical properties of low-oxygen-adapted tropical oceanic phytoplankton, the recurrence of diatom communities in a temperate estuary, and the mechanisms of particle flux attenuation using nitrogen isotope analyses of amino acids. The authors are commended for their contributions to the field of aquatic sciences. [Extracted from the article]

To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy. [ABSTRACT FROM AUTHOR]

Bi‐mode Insulated Gate Transistor (BIGT), owing to its notable attributes in power density and compact dimensions, holds promising applications in the realm of high power converters. However, its characteristic of the voltage drop in diode mode controlled by gate‐emitter voltage poses significant challenges for low‐loss operation. This paper investigates the relationship between BIGT's on‐state voltage drop in diode mode and its gate‐emitter voltage in detail. By combining simulation and experimental analysis, recommended the gate‐emitter voltage values are provided. Furthermore, a novel driver optimization scheme is proposed to automatically lower the gate voltage during reverse conduction, which can effectively reduce the reverse on‐state losses of the BIGT. Experimental validation demonstrates a 10.6% reduction in reverse on‐state voltage drop, showcasing the efficacy of the proposed driver optimization scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a nonparametric point cloud filter to address the issue that existing point cloud filtering methods cannot retain important point cloud features after filtering and often require complex parameter adjustments. Firstly, a nonparametric clustering method is proposed to cluster various features of the point cloud and filter out isolated outliers. Then, the manifold distance truncation method is adopted to remove the outlier cluster generated by the point cloud clustering to complete point cloud filtering. Additionally, the proposed nonparametric clustering algorithm is compared with four of the latest clustering algorithms, including K‐means clustering and OPTICS clustering to verify the rationality of the clustering features. Finally, the filtering results of the nonparametric point cloud filter are compared with those of statistical filtering and two other recently proposed point cloud filters to demonstrate its improved filtering effect and algorithm stability. The experimental results indicate that the proposed nonparametric point cloud filter can achieve better‐filtering results and retain more point cloud features without adjusting parameters. [ABSTRACT FROM AUTHOR]

The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at developing an open software ecosystem for model agnostic data assimilation. This paper considers implementation of the model‐agnostic family of the local volume solvers in the JEDI framework. The implemented solvers include the Local Ensemble Transform Kalman Filter (LETKF), the Gain form of the Ensemble Transform Kalman Filter (GETKF), and the optimal interpolation variant of the LETKF (LETKF‐OI). This paper documents the implementation strategy for the family of the local volume solvers within the JEDI framework. We also document an expansive set of localization approaches that includes generic distance‐based localization, localization based on modulated ensemble products, and localizations specific to ocean (based on the Rossby radius of deformation), and land (based on the terrain difference between observation and model grid point). Finally, we apply the developed solvers in a limited set of experiments, including single‐observation experiments in atmosphere and ocean, and cycling experiments for the atmosphere, ocean, land, and aerosol assimilation. We also illustrate how JEDI Ensemble Kalman Filter solvers can be used in a strongly coupled framework using the interface solver approximation, which provides increments to the ocean based on observations from the ocean and atmosphere. Plain Language Summary: The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at reducing the time it takes to transition research on initialization of the Earth system models to operation. The JEDI framework is designed to be agnostic of the specific numerical model and, hence, can facilitate collaboration between research institutions and operational centers. This paper documents implementation of the Ensemble Kalman filtering framework within JEDI. The implementation strategy supports a variety of algorithmic approaches to the Ensemble Kalman Filtering and is appropriate for multiple Earth system applications. Specifically, we demonstrate applications for atmosphere, atmospheric composition, ocean, and land data assimilation. Key Points: Local volume solvers are developed in the Joint Effort for Data assimilation Integration (JEDI) softwareLocalization operators for various components of the Earth system are developedCase studies are presented with various components of the Earth system [ABSTRACT FROM AUTHOR]

Introduction: This paper provides a critical reflection from both the researcher and public contributor (PC) perspective on the benefits and the learnings taken from involving PCs in research related to Parkinson's. Approach to Patient and Public Involvement (PPI): This paper reports on how PCs shaped the design and development of the PRIME‐UK research programme study materials through input into information leaflets, consent forms and other patient‐facing documents used across three studies within the PRIME‐UK research programme. The PRIME‐UK research programme is designed to improve the quality of life of people with Parkinson's and this project included three studies: a cross‐sectional study, a randomised control trial and a qualitative study. We captured these impacts using Public Involvement Impact Logs, which provide a framework allowing researchers and PCs to report on the learnings, immediate outcomes and impacts from PPI. For this project, the impact logs enabled us to provide reflections from PCs and researchers on the process of involving 'the public' in Parkinson's research. Findings: This paper builds on existing evidence of the range of benefits and challenges that emerge from working with patients and the public in Parkinson's research; this includes reflecting on the changes made to the study materials and benefits for the people involved. Four themes emerged from the reflections that were common to the researchers and PCs; these were the importance of providing a supportive environment; recognition of the benefit of the evaluation of the impact of PPI; acknowledgement that engagement of PPI can make a positive difference to the research process and that timely communication and the use of face‐to‐face communication, where available, is key. Furthermore, we demonstrate how impact logs provide a useful and straightforward tool for evaluating public involvement practices and supporting the feedback process. Conclusion: We offer key recommendations for involving patients and the public in Parkinson's research and suggest approaches that could be implemented to capture the impacts of public involvement. Public Contribution: Public contributors (PCs) were involved in the design and development of the participant information leaflets, consent forms and other patient‐facing documents used for studies within the PRIME‐UK research programme. In addition, PCs evaluated their involvement using impact logs and co‐authored this paper. [ABSTRACT FROM AUTHOR]

Background: Liver disease is a growing health concern and a major cause of death. It causes multiple symptoms, including financial, psychological and social issues. To address these challenges, palliative care can support people alongside active treatment, and towards the end of life, but little is known about the care experiences of individuals with liver disease in the United Kingdom. This review aimed to explore the palliative and end‐of‐life care experiences of people with liver disease in the United Kingdom. Method: A systematic review was conducted using a five‐stage process and following Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines. Searches were across Web of Science, Scopus, EBSCO and grey literature until 10 May 2023. The review was registered through International Prospective Register of Systematic Reviews (PROSPERO). NVivo 12.5 was used to facilitate data analysis (systematic review registration: PROSPERO CRD42022382649). Results: Of 6035 papers (excluding duplicates) found from searches, five met the inclusion criteria of primary research related to adults with liver disease receiving palliative and/or end‐of‐life care in the United Kingdom, published in English. Reflexive thematic analysis of the data was conducted. The themes identified were the experiences of people with liver disease of relating to healthcare professionals, using services, receiving support, and experiences of information and communication. These were connected by an overarching concept of disempowerment versus empowerment, with the notion of person‐centred care as an important feature. Conclusion: This review has found variations in the care experiences of people with advanced liver disease towards the end of life and an overall lack of access to specialist palliative care services. Where services are designed to be person‐centred, experiences are more empowering. Further research is needed but with recognition that it is often unclear when care for people with liver disease is palliative or end‐of‐life. Patient and Public Contribution: An online public involvement workshop was held on 18 April 2023 through Voice (2023). This included four people with liver disease and four carers to discuss the review findings and to design a qualitative research study to further explore the topic. [ABSTRACT FROM AUTHOR]

Background: Achieving universal health coverage (UHC) in the context of limited resources will require prioritising the most vulnerable and ensuring health policies and services are responsive to their needs. One way of addressing this is through the engagement of marginalised voices in the priority setting process. Public engagement approaches that enable group level deliberation as well as individual level preference capturing might be valuable in this regard, but there are limited examples of their practical application, and gaps in understanding their outcomes, especially with rural populations. Objective: To address this gap, we implemented a modified priority setting tool (Choosing All Together—CHAT) that enables individuals and groups to make trade‐offs to demonstrate the type of health services packages that may be acceptable to a rural population. The paper presents the findings from the individual choices as compared to the group choices, as well as the differences among the individual choices using this tool. Methods: Participants worked in groups and as individuals to allocate stickers representing the available budget to different health topics and interventions using the CHAT tool. The allocations were recorded at each stage of the study. We calculated the median and interquartile range across study participants for the topic totals. To examine differences in individual choices, we performed Wilcoxon rank sum tests. Results: The results show that individual interests were mostly aligned with societal ones, and there were no statistically significant differences between the individual and group choices. However, there were some statistically significant differences between individual priorities based on demographic characteristics like age. Discussion: The study demonstrates that giving individuals greater control and agency in designing health services packages can increase their participation in the priority setting process, align individual and community priorities, and potentially enhance the legitimacy and acceptability of priority setting. Methods that enable group level deliberation and individual level priority setting may be necessary to reconcile plurality. The paper also highlights the importance of capturing the details of public engagement processes and transparently reporting on these details to ensure valuable outcomes. Public Contribution: The facilitator of the CHAT groups was a member from the community and underwent training from the research team. The fieldworkers were also from the community and were trained and paid to capture the data. The participants were all members of the rural community‐ the study represents their priorities. [ABSTRACT FROM AUTHOR]

With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting. [ABSTRACT FROM AUTHOR]

The consumption of coal in winter heating period in northern China is large, and the combustion generates greenhouse gases that pollute the environment. At the same time, wind abandonment is widespread in northern China, causing waste of energy. In order to solve these problems, this paper proposes to apply clean energy heating and waste wind power generation for heating, and build a multi‐objective optimal dispatching model under the goal of considering customer satisfaction and operating costs. Finally, taking a region in the north of China as an example, the improved genetic algorithm is used to solve the model, the improved genetic algorithm ensures the survival rate of excellent genes, which is more efficient than the traditional genetic algorithm. The example results verify that the use of clean energy heating can increase the wind power consumption space and reduce the heating cost in winter. [ABSTRACT FROM AUTHOR]

With the successful implementation of Software‐Defined Networking (SDN) in data center networking, the way forward for its deployment in the ISP/Telco network is becoming prominent. Small and medium‐sized networks may easily adopt SDN. The research on SDN deployment and implementation for a large‐scale network is continuing. This paper properly presents the current research status of Controller Placement Problem (CPP) and Multi‐CPP (MCPP) over SDN with their specific challenges and provides a comprehensive review of the major performance metrics, that is, latency, and controller load balancing techniques. This survey highlights the use of network partitioning‐based CPP and clustering approaches and their benefits in the context of SDN deployment. Moreover, this paper highlights the importance of implementing SDN and SDN security issues into ISP/Telco networks. Finally, we provide some key areas of ongoing research and discuss the future research direction regarding the various SDN‐based Controller Placement (CP) issues in the next‐generation IP and advanced networking technologies. [ABSTRACT FROM AUTHOR]