Precise Evaluation Research Articles

Typhoon strikes, political costs and earnings management

AbstractThis study examines how firms in typhoon‐prone areas respond to the political costs of typhoon strikes. We found that such firms tend to engage in downward earnings management after a typhoon strike. Various robustness tests support our findings: cross‐sectional variation tests indicate that this tendency is strong in cities with high governmental intervention and incentives for political leaders and in firms with high political connections and visibility. Further analysis reveals that typhoon strikes enhance earnings management by intensifying fiscal pressure. Finally, this study provides a precise evaluation of the adverse effects of typhoons and complements the existing literature on the political cost hypothesis.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

A Novel Evaluation for Vertebral Artery Course Using 3D Magnetic Resonance Imaging with Computed Tomography -like Bone Contrast and Magnetic Resonance Angiography: A Proof of Concept Study

Vertebral artery (VA) injury poses a significant risk in cervical spine surgery, necessitating accurate preoperative assessment. This study aims to introduce and validate a novel approach that combines the Fast field echo that resembles a computed tomography using restricted echo spacing (FRACTURE) sequence with Time of Flight (TOF) Magnetic Resonance Angiography (MRA) for comprehensive evaluation of VA courses in the cervical spine. A total of eight healthy volunteers and two patients participated in this study. The FRACTURE sequence provided high-resolution bone images of the cervical spine, while TOF MRA offered non-invasive vascular imaging. Fusion images were created by merging FRACTURE and MRA modalities to simultaneously visualize cervical spine structures and VA courses. Board-certified orthopedic spine surgeons independently evaluated images to assess the visibility of anatomical characteristics of the VA course by Likert-scale. The FRACTURE-MRA fusion images effectively depicted the extraosseous course of the VA at the craniovertebral junction, the intraosseous course of the VA at the craniovertebral junction, the VA entrance level to the transverse foramen, and the side-to-side asymmetry of bilateral VAs. Additionally, clinical cases demonstrated the utility of the proposed technique in identifying anomalies and guiding surgical interventions. The integration of the FRACTURE sequence and TOF MRA presents a promising methodology for the precise evaluation of VA courses in the cervical spine. This approach improves preoperative planning for cervical spine surgery with detailed anatomy and is a valuable alternative to conventional methods without contrast agents.

Distinguishing the effects of micro‐ and macro‐habitats on plant‐associated microbiomes in the Qinghai‐Tibetan Qaidam Basin

AbstractAlthough microbiomes associated with plants and the environment are intricately intertwined, it remains unclear how various environmental scales affect these relationships differently. We employed high‐throughput amplicon sequencing to examine the plant‐associated bacterial and fungal communities across three different micro‐habitats (leaf, root, and root zone soil) in 16 shrubs distributed across a >500 km transect of the eastern Qinghai‐Tibetan Qaidam Basin. Among the three microhabitats, the diversity and composition of plant‐associated bacterial and fungal communities differed significantly. Although micro‐habitats play a crucial role in shaping plant‐associated microbial communities, geographic and climatic variables largely influenced the community compositions across the three micro‐habitats. Notably, the microbial communities in all three micro‐habitats showed significant distance‐decay patterns. Furthermore, fungi exhibited less variation across the three microenvironments, and were more sensitive to environmental changes than bacteria. Moreover, microbiome complexity and stochasticity gradually increased from soils to roots and finally to leaves. The microbial communities in soils and epiphytes (leaves and roots) were predominantly shaped by determinism and stochasticity, respectively. These results highlight that plant‐associated microbiomes are primarily shaped by the microenvironments associated with plants; however, their compositions, dynamics, and assembly are further influenced by geographic and climatic factors. Overall, our findings greatly increase our understanding of the intricate dynamics between plant‐associated microbiomes and their surrounding environment in drylands. This knowledge contributes to ongoing research on microbial ecology and provides substantial supports for the precise evaluation of desert ecosystem function. The findings are helpful for biodiversity conservation and sustainable management of desert shrublands.

Computational study of Jeffrey Hybrid nanofluid flow over on a non-uniformly heated permeable exponentially stretching surface with Arrhenius activation energy and inclined magnetic field

This study delves into the intricate dynamics of magnetohydrodynamic (MHD) flow, investigating the behaviour of a TiO2–Cu/H2O Jeffrey hybrid nanofluid cascading over a porous exponentially stretching surface. Employing MATLAB's bvp4c solver, we address a complex interplay of factors including aligned magnetic fields, radiation effects, non-uniform thermal fluxes, and the influence of Arrhenius activation energy. Our findings reveal a nuanced landscape wherein heightened Jeffrey fluid parameters amplify thermal and concentration gradients, yet diminish heat and mass transfer rates. Interestingly, variations in the Deborah number yield subtle effects, enhancing thermal and concentration profiles while moderating transfer rates. Augmented magnetic and porosity parameters deepen profiles but reduce velocity and transfer rates. Notably, magnetic field alignment reduces velocity while accentuating thermal and concentration gradients. Furthermore, non-uniform thermal fluxes elevate temperatures but restrain heat transfer rates. Activation energy augments concentration profiles distinctly from chemical reaction parameters. The comparison of Jeffrey hybrid nanofluid and its conventional counterpart reveals intriguing disparities: the former exhibits a notable 30 % increase in absolute skin friction while increasing heat transfer rate by 4.5 %. These insights underscore the pragmatic significance of our model for precise prognostication and evaluation across various applications.

Robust high-performance thin-layer chromatography (HPTLC) method for stability assessment and simultaneous quantification of epigallocatechin-3-gallate and rosmarinic acid in lipid-based nanoparticles and biological matrices.

Skin cancer poses a significant health risk globally, necessitating effective and safe therapeutic interventions. Epigallocatechin-3-gallate (EGCG) from green tea and rosmarinic acid (RA) from herbs like rosemary offer promising anticancer properties. Combining these compounds may enhance their effectiveness, prompting the need for a reliable analytical method to quantify them. Herein, we present the development and validation of a high-performance thin-layer chromatography (HPTLC) method for concurrent quantification of EGCG and RA in lipid-based nanoparticles and biological samples. The method underwent optimisation through design of experiments (DoE), resulting in the establishment of robust chromatographic conditions. The separation process utilised aluminium HPTLC plates coated with silica gel 60 F254 as the stationary phase, with the mobile phase comprising ethyl acetate, toluene, formic acid, and methanol in a ratio of 4:4:1:1 v/v. The retention factor (Rf) values obtained were 0.38 for EGCG and 0.61 for RA. The method demonstrated linearity over a range of 100-500 ng/band for both compounds with excellent correlation coefficients. Limits of detection and quantification were determined, indicating high sensitivity. Precision evaluations revealed relative standard deviation below 2%, ensuring method reproducibility. Recovery assays in lipid-based nanoparticles, plasma, and urine samples demonstrated excellent recoveries (96.2%-102.1%). Forced degradation studies revealed minimal degradation under various stress conditions, with photolytic degradation showing the least impact. The developed HPTLC method offers a rapid, sensitive, and reliable approach for quantifying EGCG and RA, laying the groundwork for their further investigation as anticancer agents alone and in combination therapies.

Studies on the performance of FASnI3:Zn2+-based lead-free perovskite solar cells: A numerical simulation

The search of stable, lead-free absorber material for perovskite solar cells (PSC) resulted in many alternatives, particularly, zinc (Zn) doped formamidinium tin iodide (FASnI3). This work facilitates in investigating the behavior of four different Zn doping ratios 0 %, 2 %, 5 %, and 10 % (say Zn-0, Zn-2, Zn-5, and Zn-10) on the performance of FTO/PCBM/FASnI3:Zn2+/MoOx/Au solar cell structure. A precise evaluation on the impact of the four various Zn doping ratios in the mentioned solar structure in terms of thickness of perovskite absorber layer, hole transport layer, and electron transport layer were executed. The investigation through numerical study further deals with the role of interfacial defect density and capture cross-section of charge carriers on the performance of suggested PSCs with varying Zn doping ratios. It is evident from the optimization results that PSC having absorber layer thickness of 400 nm yields better power conversion efficiency (PCE) of 11.49 % for Zn-5. The PCE is further improved to 13.98 % for capture cross section of 2 ×10−15 cm2. The entire work witnessed distinguishable superior behavior from FASnI3-based PSC doped with Zn ratio: Zn-5 in comparison to the PSCs doped with other Zn ratios. Furthermore, impact of the series and shunt resistances on performance parameters are also examined.

An IoT‐based multiple teaching quality evaluation method for English translation with improved deep learning

AbstractTo enhance the teaching quality (TQ) of English translation (ET) courses, multivariate evaluation methods have garnered significant attention for their ability to identify deficiencies in the teaching process and subsequently improve instructional standards. Traditional evaluation techniques have shown limited effectiveness. Therefore, this paper proposes an ET in multiple teaching quality (MTQ) evaluation method with improved deep learning in internet of things (IoT). First, the paper establishes evaluation indicators for ET in MTQ, which mainly encompass ET teaching effectiveness, pedagogic competency in ET, teaching methods, content, as well as attitudes towards ET teaching. It then utilizes IoT technology to preliminarily collect data on these indicators, and using clustering algorithm based on weighted attributes and density (CABWAD) algorithm to mine data on English teaching MTQ evaluation indicators for ET. The extracted evaluation indicator data is then denoised using a probabilistic undirected graph model. Ultimately, the multilayer perceptron in deep learning is improved through Wolfe line search optimization, and this enhanced multilayer perceptron is employed to construct an ET in MTQ evaluation model. The denoised indicator data is inputted into the model, which then outputs precise MTQ evaluation results. The results show that the absolute value of the average Pearson correlation coefficient of this method is the highest, the Spearman correlation coefficient is the lowest, the mean average precision value is 0.965, and the positive category imbalance degree and the negative category imbalance degree are the lowest, indicating that the proposed method has outstanding performance in all aspects, and has certain application value in the field of TQ evaluation.

Advancing Post‐Secondary Batteries under Lean Electrolyte Conditions through Interfacial Modification Strategies

AbstractIn response to the growing global demand for portable electronics and electric vehicles, there is an escalating interest in developing advanced battery technologies with superior energy density. Research efforts are focused on unveiling post‐lithium‐ion batteries (LIBs) that outperform the performance of current LIBs through the use of innovative active electrode materials. Yet, these technological advancements face significant hurdles, primarily due to intricate interfacial issues within battery components. In laboratory‐scale studies, these challenges often lead to the utilization of excess electrolytes, which complicates the precise evaluation of battery performance. This review emphasizes the significance of designing future batteries that operate effectively under lean electrolyte usage conditions. It discusses essential principles, obstacles, and diverse strategies for interfacial modification, including in situ growth, coating of supportive layers, and embedding of active substances in pre‐structured templates. Furthermore, it compiles and examines data on the lean electrolyte conditions achieved in various battery systems, contrasting their energy densities with those of commercially established batteries. Ultimately, the potential of future batteries to achieve or even exceed the energy densities of existing commercial batteries is assessed, thereby offering a strategic roadmap for the progression of next‐generation battery technologies.

Multi-criteria constrained interval type-2 fuzzy decision-making: A space analysis perspective

Practical decision-making problems invariably involve inherent uncertainty in information, making it challenging to obtain precise evaluations. In this research, the utilization of constrained interval type-2 fuzzy sets (CIT2 FSs) enables the representation of complex uncertainty in decision information, and a space analysis approach, called CIT2 fuzzy multi-criteria acceptability analysis (CIT2FMAA), is proposed for conducting reliable decision-making. The CIT2FMAA introduces a novel paradigm for type-2 fuzzy decision-making, wherein CIT2 fuzzy evaluations are acknowledged as spaces of type-1 fuzzy evaluations and processed using type-1 fuzzy operations and methodologies. First, from a space analysis perspective, the type-2 fuzzy measures of logical rank expressions are formalized as CIT2 fuzzy rank acceptability indexes, and the CIT2 fuzzy rank acceptability analysis is developed to rank CIT2 fuzzy numbers. Second, the weighted average of the CIT2 fuzzy numbers is defined, and the CIT2FMAA is proposed to support uncertain multi-criteria decision-making. Subsequently, a sampling algorithm is designed for implementing CIT2 fuzzy rank acceptability analysis and CIT2FMAA. Finally, a linguistic disease diagnosis is performed to demonstrate the efficiency of CIT2FMAA. Compared to traditional type-2 fuzzy decision-making methods, CIT2FMAA is more efficient because it not only derives ranks of alternatives but also provides confidence degrees for those ranks.

Nonlinear effects of climate change on tourism development: evidence from China

PurposeThe purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.Design/methodology/approachBased on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.FindingsThe study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.Originality/valueFirst, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

A Study on Interobserver and Intraobserver Reliability of the Huashan Radiologic Classification System for Cervical Spinal Cord Injury Without Fracture and Dislocation.

Observational study. To assess the reproducibility and reliability of the system. The Huashan radiologic classification system for cervical spinal cord injury without fracture and dislocation (CSCIWFD) was recently proposed and found useful for clinical practice. Patients diagnosed with CSCIWFD between 2015 and 2021 were recruited. Six spine surgeons from different institutions, three experienced and other inexperienced respectively, were trained as observers of the system, and these surgeons classified the recruited patients using the system. Then, 8 weeks later, they repeated the classification on the same patients in a different order. The interobserver and intraobserver agreement between the results was analyzed using percentage agreement, weighted kappa, and Cohen kappa (κ) statistics. A total of 60 patients were included in the analysis. Type I was the most frequent type (29 cases, 48.3%), followed by type II (13 cases, 21.7%), type III (12 cases, 20%), and type IV (6 cases, 10%). For all the observers, experienced observers, and inexperienced observers, the overall agreement percentages were 77.6% (κ = 0.78), 84.4% (κ = 0.84), and 72.8% (κ = 0.74), respectively, indicating substantial to nearly perfect interobserver reproducibility. A higher level of agreement was found for differentiating type I from other types, with the percentage agreement ranging from 87.8% to 94.4% (κ= 0.74-0.88). For distinguishing compression on the spinal cord (types I and II vs types III and IV) among the different groups of observers, the percentage agreement was 97.8% (κ = 0.94), indicating nearly perfect reproducibility. As for intraobserver agreement, the percentage agreement ranged from 86.7% to 96.7% (κ = 0.78-0.95), indicating at least substantial reliability. The Huashan radiologic classification system for CSCIWFD was easy to learn and apply in a clinical environment, showing excellent reproducibility and reliability. Therefore, it would be promising to apply and promote this system for the precise evaluation and personalized treatment strategy.

Spacer rotation technique allows precise evaluation of gap balance in total knee arthroplasty.

The symmetry of the flexion and extension gap influences the functional and long-term outcome after total knee arthroplasty (TKA). Most surgeons check it by applying varus and valgus stress using spacers. This technique has limited accuracy and could be easily extended by rotational movement of the spacer. The objective was to determine the detection threshold and interobserver reliability of this technique. In an in vitro setting with a human cadaveric knee, gap asymmetries were simulated by different medially and laterally applied forces. Using an optical measurement system, the pivot point of the spacer was calculated as a function of the gap symmetry in the first part of the experiment. In the second part, the detection threshold and interobserver reliability of 4 surgeons were determined. For this purpose, gap asymmetries were adjusted to between 0 and 120N in a blinded trial. With a symmetrical gap, the centre of rotation of the spacer was located in the centre of the tibia. With increasing gap asymmetry, the centre of rotation of the spacer shifted to the tight side. This shift was approximately linearly dependent on the force difference. A perfectly balanced gap was detected by the examiners in 50% of the cases. From a force difference of 40N, all examiners identified the gap asymmetry in all cases (ICC = 1.0). The method of spacer rotation described is suitable for reliably detecting gap differences at ≥ 40N, independently of the examiner.

A Finite Element Analysis-Unascertained Measure Theory-Based Hybrid Approach to Safety Assessment for Pipelines Subject to Landslide Disasters

Abstract Pipeline safety faces a prevalent threat in mountainous areas due to landslides. The advent of landslides introduces the risk of pipeline leaks or ruptures, posing a significant threat to the environment, with the potential for casualties. Throughout the occurrence of landslides, uncertainties abound, yet few studies have addressed the incorporation of uncertainties in assessing pipeline safety. This work proposes a novel hybrid approach to the safety assessment for pipelines under landslides. The use of finite element analysis (FEA) models the pipeline under the action of landslides. The numerical outcomes, combined with unascertained measure theory (UMT), develop a multi-indicator unascertained measure (UM) matrix. Random forest (RF) algorithm is employed to determine the weight of indicators in the matrix. The hybrid application of set pair theory and the UM evaluation vector finally determine the pipeline safety degree and level. The proposed methodology has been well-validated through a case study on an in-service pipeline. The results indicate that the case pipeline safety degree is 2.777, 2.132, 3.132, 3.904, and 2.240, respectively. The corresponding safety level is III, II, III, IV, and II, respectively, which is consistent with the pipeline's actual condition. Different from the conventional safety assessment approach, the proposed methodology demonstrates the enhanced effectiveness, facilitating a more precise evaluation of the pipeline's safety condition.

Evaluation Model of Rice Seedling Production Line Seeding Quality Based on Deep Learning

A critical precondition for realizing mechanized transplantation in rice cultivation is the implementation of seedling tray techniques. To augment the efficacy of seeding, a precise evaluation of the quality of rice seedling cultivation in these trays is imperative. This research centers on the analysis of rice seedling tray images, employing deep learning as the foundational technology. The aim is to construct a computational model capable of autonomously evaluating seeding quality within the ambit of intelligent seedling cultivation processes. This study proposes a virtual grid-based image segmentation preprocessing method. It involves dividing the complete image of a rice seedling tray into several grid images. These grid images are then classified and marked using an improved ResNet50 model that integrates the SE attention mechanism with the Adam optimizer. Finally, the objective of detecting missing seeding areas is achieved by reassembling the marked grid images. The experimental results demonstrate that the improved ResNet50 model, integrating the SE attention mechanism and employing an initial learning rate of 0.01 over 50 iterations, attains a test set accuracy of 95.82%. This accuracy surpasses that of the AlexNet, DenseNet, and VGG16 models by respective margins of 4.55%, 2.07%, and 2.62%. This study introduces an innovative model for the automatic assessment of rice seeding quality. This model is capable of rapidly evaluating the seeding quality during the seedling phase; precisely identifying the locations of missing seeds in individual seedling trays; and effectively calculating the missing seed rate for each tray. Such precision in assessment is instrumental for optimizing seedling processes

Random Forest-Based Prediction Models for Assessing Cardiovascular Disease Risk: Integrating Clinical and Genetic Factors

This research investigates the adequacy of Irregular Forest-based expectation models in evaluating cardiovascular disease (CVD) hazards by joining clinical and hereditary variables. Leveraging a dataset comprising comprehensive clinical profiles and hereditary data, we prepared and assessed Arbitrary Woodland models near other machine learning calculations. Ours comes about illustrates that the Random Forest show outperformed Logistic Relapse, Support Vector Machine, and Slope Boosting in precision (85%), affectability (82%), specificity (88%), and zone beneath the recipient working characteristic bend (AUC-ROC) (0.92). Furthermore, the consideration of hereditary highlights altogether progressed the prescient execution, with the show accomplishing a precision of 88%, affectability of 86%, specificity of 90%, and AUC-ROC of 0.94. This study highlights the significance of coordination hereditary data for personalized hazard evaluation in CVD. The comparison with related works underscores the progressions made in leveraging machine learning for cardiovascular hazard forecast and conclusion. Our discoveries recommend that Irregular Forest-based models offer a promising approach for upgrading quiet results through precise chance evaluation, early location, and focused on preventive intercessions. Moving forward, encourage inquiry about is justified to illustrate fundamental pathophysiological components and direct accuracy medication approaches in cardiovascular wellbeing.

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

An Integrated Model of the Sustainable Consumer

The term ‘sustainable consumer’ (SC) is used across various knowledge domains, from sustainable consumption, green marketing, sustainability, and social change to social marketing for sustainability issues. However, the term SC lacks a precise definition, which leads to the inaccurate evaluation and measurement of the impact of green marketing or social marketing campaigns on consumption—sustainable or otherwise. This paper develops a framework to clarify the term ‘sustainable consumer’ to assist both scholars and practitioners. The application of systems thinking was applied to the extant literature to theorise the SC. This conceptual paper provides a new framework for theorising SCs: the integrated model of the sustainable consumer (ISMC). This framework emphasises the interconnected relationships of influences within the SC profile to assist scholars in examining SCs within these systems with precision. We contend that, to promote and maintain the desired sustainable consumption for long-term effects, researchers and practitioners should consider the impact not only of the socio-psycho-demographic characteristics but also the connection of the person to the environment and their community, in addition to their worldviews. The framework presented here challenges linear models by proposing a nested, dynamic structure that recognizes the interconnected influences within the sustainable consumer’s ecosystem. The framework also enables a targeted intervention design according to the layer and element and permits more precise evaluations of behaviour change campaigns’ effectiveness.

Improved Tensor Current Limit from ^{8}B β Decay Including New Recoil-Order Calculations.

A precision measurement of the β^{+} decay of ^{8}B was performed using the Beta-decay Paul Trap to determine the β-ν angular correlation coefficient a_{βν}. The experimental results were combined with new abinitio symmetry-adapted no-core shell-model calculations to yield the second-most precise measurement from Gamow-Teller decays, a_{βν}=-0.3345±0.0019_{stat}±0.0021_{syst}. This value agrees with the standard model value of -1/3 and improves uncertainties in ^{8}B by nearly a factor of 2. By combining results from ^{8}B and ^{8}Li, a tight limit on tensor current coupling to right-handed neutrinos was obtained. A recent global evaluation of all other precision β decay studies suggested a nonzero value for right-handed neutrino coupling in contradiction with the standard model at just above 3σ. The present results are of comparable sensitivity and do not support this finding.

Hybrid and Asymmetric Supercapacitors: Achieving Balanced Stored Charge across Electrode Materials.

An electrochemical capacitor configuration extends its operational potential window by leveraging diverse charge storage mechanisms on the positive and negative electrodes. Beyond harnessing capacitive, pseudocapacitive, or Faradaic energy storage mechanisms and enhancing electrochemical performance at high rates, achieving a balance of stored charge across electrodes poses a significant challenge over a wide range of charge-discharge currents or sweep rates. Consequently, fabricating hybrid and asymmetric supercapacitors demands precise electrochemical evaluations of electrode materials and the development of a reliable methodology. This work provides an overview of fundamental aspects related to charge-storage mechanisms and electrochemical methods, aiming to discern the contribution of each process. Subsequently, the electrochemical properties, including the working potential windows, rate capability profiles, and stabilities, of various families of electrode materials are explored. It is then demonstrated, how charge balancing between electrodes falters across a broad range of charge-discharge currents or sweep rates. Finally, a methodology for achieving charge balance in hybrid and asymmetric supercapacitors is proposed, outlining multiple conditions dependent on loaded mass and charge-discharge current. Two step-by-step tutorials and model examples for applying this methodology are also provided. The proposed methodology is anticipated to stimulate continued dialogue among researchers, fostering advancements in achieving stable and high-performance supercapacitor devices.