Performance Advantages Research Articles

Combined effect of gilsonite, sasobit, and soft binder on performance characterization of high RAP binder

The purpose of this research is focused on the performance evaluation of Gilsonite-modified binders (GMBs) and high-aged binder (HAB) content (more than 40 %) with and without the use of WMA additive. Accordingly, GMBs and GMB-WMA samples were prepared by combining PG52–28 softer binders and 4 %, 8 %, 12 %, and 16 % of Gilsonite with and without Sasobit. The results indicated that increasing the Gilsonite increases the viscosity, improves the rheological behavior, reduces the rutting, and increases the fatigue life Although higher Gilsonite content has performance advantages, it also has negative effects in terms of performance and low temperature/fatigue resistance. While the use of a composite-modified binder showed an effective way to overcome these limitations and optimize the overall performance characteristics of HAB. Adding 4, 8, 12, and 16 Gilsonite combined with Sasobit increased the rutting resistance by 46 %, 64 %, 80 %, and 87 % respectively, compared to the base binder. Combining Sasobit with 8 and 12 Gilsonite upgraded the performance of the base binder from PG58–22 to PG70–16 and PG76–10. The fatigue life of the AB/G+WMA containing 4, 8, 12, and 16 Gilsonite was 2.11, 2.25, 2.52, and 2.38 times greater than the base binder, respectively. Based on optimization results, Gilsonite/Sasobit combinations were recommended for different traffic conditions: G15.3+S1.8 for heavy traffic areas, G11.6+S1.9 for tropical regions, and G6+S2 for cold climates. The goal was to optimize the blends to leverage the benefits of Gilsonite while mitigating any drawbacks, depending on the specific climate and traffic conditions. The study demonstrates that the synergistic use of Gilsonite and Sasobit can significantly enhance the performance characteristics of asphalt binders, enabling tailored solutions for different applications and environmental conditions.

Dynamic performance analysis and optimization research on solar dual-stage evaporation multigeneration system

A novel solar dual-stage evaporation multigeneration system is proposed, with analysis on the impact of the parabolic trough collector area (APTC) and the volume of the heat storage tank (VHST) on performance under dynamic conditions. The particle swarm optimization- genetic algorithm and the minimum path method optimized the system’s overall performance. Results show that reducing APTC and increasing VHST can enhance energy performance within a certain range, but beyond specific thresholds, changes in APTC and VHST no longer affect it. As APTC and VHST increase, economic performance initially improves but then declines. The particle swarm optimization- genetic algorithm increased optimization efficiency and avoided local optima. When using the minimum path method, the dimensionless cumulative error was 0.63, balancing thermal and economic performance. Using toluene as the working fluid, the optimized system achieved thermal and exergy efficiencies of 56.06 % and 9.15 %, respectively, with a net present value of 58.12 M$, a payback period of 5.23 years, and an equivalent CO2 reduction of 24.8 kt, outperforming the solar organic Rankine cycle system in energy efficiency, economic performance, and carbon reduction potential. The solar dual-stage evaporation multigeneration system achieved the best performance with toluene as the working fluid, while heptane provided longer running time and greater carbon reduction potential. At low rated power generation, performance differences between toluene and heptane were minimal, while as the rated power generation increased, toluene showed significant advantages in thermal and economic performance.

Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications.

Application of polymer adhesive hydrogel in wound healing

As the global adhesive industry continues to grow, researchers are discovering that polymer adhesives are playing an increasing role in the medical field, polymer adhesives have attracted more and more attention from the medical industry due to their unique advantages, such as hydrogels, have gradually appeared, which can effectively help wound healing. Most of the hydrogels have good biocompatibility and water absorption, and have the advantages of controllable structure, adjustable performance and easy degradation. Compared with traditional wound healing methods, hydrogels effectively reduce additional trauma and leakage and allow for greater tissue integration. This paper reviews the types of polymeric adhesive hydrogels and examples of some of them being artificially modified to be applied to the field of wound healing. Analyzes their mechanism of action and the advantages and shortcomings of their clinical applications, and finally looks into the potential functions and clinical efficacy of hydrogels in the future.

Phase Regulation Promotes High Rate‐Long Term Na4Fe3(PO4)2P2O7 Cathode for Sodium‐Ion Batteries

AbstractSodium‐ion batteries (SIBs) have evoked much attention, benefiting from the advantages of low cost, high safety and excellent performance at low temperature. Especially, Na4Fe3(PO4)2P2O7 (NFPP) cathode is considered to be one of the best candidates for SIBs cathode with abundant resources and long‐term cycling stability. However, the impurities of NaFePO4 (NFP) and Na2FeP2O7 (NFPO) formed synchronously with NFPP which restrict the further application of NFPP. It is meaningful to clear the formation process and regulate the contents of NFP and NFPO. Therefore, NFPP cathodes with different contents of NFP and NFPO were prepared through high energy ball milling cooperated with post‐heat treatment by controlling the Fe concentration in reactants. The NFPP‐2.85 showed the best electrochemical performance because of the high content of NFPP and transition zone between NFPP and NFPO which fasts the Na+ transport kinetics. When employed as cathode for SIBs, the as‐prepared NFPP‐2.85 showed a specific capacity of 111.8 mAh g−1 at 0.1 C and maintained at 68.9 mAh g−1 even at 100 C. The retention ratio was as high as 93.6 % after 1500 cycles at 20 C, implying superior high rate‐long term cycling stability. This work provides a new way for impurities regulation and the improvement of NFPP electrochemical performance.

Additional Advantages for Agronomic Performance and Fruit Quality in Tomato Hybrids of the Saladette Type Derived from a Dwarf Male Parent

Tomatoes have tremendous economic, social, and nutritional importance. Among the various types of tomatoes, the Saladette/Italian stands out as an important cultivar for both fresh consumption and industrial processing. The production of this vegetable requires investments exceeding USD 30,000.00. Strategies that increase productivity to offset these costs are fundamental. One proposal to increase yield potential in tomato is the production of new hybrids using dwarf male parents as donors through an organized backcross breeding scheme. The present study, therefore, evaluated possible improvements in the agronomic performance and fruit quality of Saladette hybrids obtained from crosses between normal and dwarf inbred lines. Seventeen tomato hybrids obtained from three backcrosses (BC1, BC2, and BC3) and two commercial cultivars as a control (cv. Bento and Vivacy) were evaluated. The data were analyzed using the Scott–Knott test, and artificial neural networks were used to study the dissimilarities among the hybrids. The hybrids obtained from the dwarf male parent exhibited morphological changes in the plants, including a reduction in internodes, a greater number of bunches per linear meter of plant, and biofortification of the fruits. Notably, from the second backcross onwards, increases in fruit productivity and quality were observed when creating hybrid combinations from dwarf male parents.

Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.

Implementation of Strategic Human Resource Management Practices: A Review of the Organisational Performance and New Research Paths

This study aims to discover new avenues for research in the field of Strategic Human Resource Management (SHRM) by investigating the effects of SHRM practices on organizational performance. This study investigates how well HR policies and procedures mesh with overarching company objectives by means of a quantitative methodology and an exhaustive literature analysis. Quantitative data will be gathered through structured surveys aimed at 500 HR managers and executives from different industries. The purpose of these surveys is to measure the implementation and outcomes of SHRM practices. A systematic review of the existing literature will also be conducted to synthesize current knowledge and identify research gaps. In addition to discussing new trends like technology, globalization, sustainability, employee happiness, and agile work methods, important results show that SHRM improves financial performance, employee engagement, and competitive advantage. This study seeks to contribute to the understanding of SHRM and its crucial role in improving organizational resilience and performance by pointing out major research gaps and offering future approaches.

Small-scale cross-layer fusion network for classification of diabetic retinopathy

Deep learning-based automatic classification of diabetic retinopathy (DR) helps to enhance the accuracy and efficiency of auxiliary diagnosis. This paper presents an improved residual network model for classifying DR into five different severity levels. First, the convolution in the first layer of the residual network was replaced with three smaller convolutions to reduce the computational load of the network. Second, to address the issue of inaccurate classification due to minimal differences between different severity levels, a mixed attention mechanism was introduced to make the model focus more on the crucial features of the lesions. Finally, to better extract the morphological features of the lesions in DR images, cross-layer fusion convolutions were used instead of the conventional residual structure. To validate the effectiveness of the improved model, it was applied to the Kaggle Blindness Detection competition dataset APTOS2019. The experimental results demonstrated that the proposed model achieved a classification accuracy of 97.75% and a Kappa value of 0.971 7 for the five DR severity levels. Compared to some existing models, this approach shows significant advantages in classification accuracy and performance.

Construction of ESG Performance and Corporate Competitive Advantage: A Case Study of Poly Development Holding Group Co., Ltd.

In recent years, the attention to Environmental, Social, and Governance (ESG) issues has been increasing, accompanied by the enrichment and improvement of related policy systems. The advantages brought to enterprises by corresponding ESG policies have gradually become evident. Therefore, this paper takes the development of Poly Development Group from 2021 to 2023 as a sample to study the advantages that enterprises have gained in response to ESG-related policies. Through the analysis of existing data, it is found that the advantages established by ESG for enterprises mainly include: (1) enhancing corporate economic benefits, (2) reducing corporate operating risks, and (3) promoting corporate innovation and development. Based on these findings, it is proposed to accelerate the improvement of the ESG-related policy system, increase the attention of enterprises to ESG performance and disclosure, and contribute to the construction of a favorable development ecosystem for both academia and industry, thereby effectively improving production and development efficiency.

A review on depth perception techniques in organoid images

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

Plasmonic modulators based on enhanced interaction between graphene and localized transverse-electric plasmonic mode

Active plasmonic modulators with high modulation depth, low energy consumption, ultra-fast speed, and small footprint are of interest and particular significance for nanophotonics and integrated optics. Here by constructing a transverse-electric (TE) plasmonic mode and maximizing the in-plane component localized on the graphene surface, we propose a high-performing plasmonic modulator based on a graphene/split ring-like plasmonic waveguide (SRPW) system with a record high modulation depth (20.46 dB/µm) and suppressed insertion loss (0.248 dB/µm) at telecom wavelength 1310 nm, simultaneously possessing pronounced advantage in broadband ability (800-1650 nm) and superior electrical performance with energy consumption of 0.43 fJ/bit and modulation speed of 200 GHz. This innovative design provides a novel approach and idea for enhancing the interaction between light and matter in the waveguide system and will certainly inspire new schemes for the development of on-chip integrated optoelectronic devices.

Modification and Functionalization of Separators for High Performance Lithium–Sulfur Batteries

Lithium–sulfur batteries (LSB) have been recognized as a prominent potential next-generation energy storage system, owing to their substantial theoretical specific capacity (1675 mAh g−1) and high energy density (2600 Wh kg−1). In addition, sulfur’s abundance, low cost, and environmental friendliness make commercializing LSB feasible. However, challenges such as poor cycling stability and reduced capacity, stemming from the formation and diffusion of lithium polysulfides (LiPSs), hinder LSB’s practical application. Introducing functional separators represents an effective strategy to surmount these obstacles and enhance the electrochemical performance of LSBs. Here, we have conducted a comprehensive review of recent advancements in functional separators for LSBs about various (i) carbon and metal compound materials, (ii) polymer materials, and (iii) novel separators in recent years. The detailed preparation process, morphology and performance characterization, and advantages and disadvantages are summarized, aiming to fundamentally understand the mechanisms of improving battery performance. Additionally, the development potential and future prospects of advanced separators are also discussed.

Aligning Large Language Models for Enhancing Psychiatric Interviews Through Symptom Delineation and Summarization: Pilot Study.

Recent advancements in large language models (LLMs) have accelerated their use across various domains. Psychiatric interviews, which are goal-oriented and structured, represent a significantly underexplored area where LLMs can provide substantial value. In this study, we explore the application of LLMs to enhance psychiatric interviews by analyzing counseling data from North Korean defectors who have experienced traumatic events and mental health issues. This study aims to investigate whether LLMs can (1) delineate parts of the conversation that suggest psychiatric symptoms and identify those symptoms, and (2) summarize stressors and symptoms based on the interview dialogue transcript. Given the interview transcripts, we align the LLMs to perform 3 tasks: (1) extracting stressors from the transcripts, (2) delineating symptoms and their indicative sections, and (3) summarizing the patients based on the extracted stressors and symptoms. These 3 tasks address the 2 objectives, where delineating symptoms is based on the output from the second task, and generating the summary of the interview incorporates the outputs from all 3 tasks. In this context, the transcript data were labeled by mental health experts for the training and evaluation of the LLMs. First, we present the performance of LLMs in estimating (1) the transcript sections related to psychiatric symptoms and (2) the names of the corresponding symptoms. In the zero-shot inference setting using the GPT-4 Turbo model, 73 out of 102 transcript segments demonstrated a recall mid-token distance d<20 for estimating the sections associated with the symptoms. For evaluating the names of the corresponding symptoms, the fine-tuning method demonstrates a performance advantage over the zero-shot inference setting of the GPT-4 Turbo model. On average, the fine-tuning method achieves an accuracy of 0.82, a precision of 0.83, a recall of 0.82, and an F1-score of 0.82. Second, the transcripts are used to generate summaries for each interviewee using LLMs. This generative task was evaluated using metrics such as Generative Evaluation (G-Eval) and Bidirectional Encoder Representations from Transformers Score (BERTScore). The summaries generated by the GPT-4 Turbo model, utilizing both symptom and stressor information, achieve high average G-Eval scores: coherence of 4.66, consistency of 4.73, fluency of 2.16, and relevance of 4.67. Furthermore, it is noted that the use of retrieval-augmented generation did not lead to a significant improvement in performance. LLMs, using either (1) appropriate prompting techniques or (2) fine-tuning methods with data labeled by mental health experts, achieved an accuracy of over 0.8 for the symptom delineation task when measured across all segments in the transcript. Additionally, they attained a G-Eval score of over 4.6 for coherence in the summarization task. This research contributes to the emerging field of applying LLMs in psychiatric interviews and demonstrates their potential effectiveness in assisting mental health practitioners.

ESG Performance and Enterprise Competitive Advantage Construction: A Case Study of Walmart

This essay opens with an in-depth discussion of the growing importance of ESG concepts in modern corporate management and highlights their significant role in promoting long-term corporate value creation. With the increasing global focus on sustainability, ESG concepts have become an integral part of corporate strategic planning and operational decision-making. Next, this article selects the global retail giant Walmart as a case study and uses SWOT analysis and other management tools to analyse in detail how Walmart has effectively constructed and consolidated its competitive advantages in the highly competitive market through the implementation of an active ESG strategy. The article specifically describes Walmart's practices on several ESG dimensions, including but not limited to the implementation of environmental protection measures, active support to the community, the enhancement of employee welfare, and the optimisation of corporate governance structure. In terms of environmental protection, Walmart has significantly reduced the environmental impact of its operations through measures such as reducing carbon emissions, improving energy efficiency and promoting sustainable products. In terms of community support, the company has strengthened its ties with local communities and enhanced its brand image through various community programmes and charitable activities. Employee well-being has been achieved through improved working conditions and career development opportunities, which have not only increased employee satisfaction but also enhanced the company's attractiveness. The article concludes by summarising the important contribution of Walmart's ESG practices to the company's sustainability and long-term competitive advantage and suggests possible future strategic directions and recommendations for Walmart in ESG based on current market trends and company performance. These recommendations are intended to provide valuable references and insights for Walmart and other companies in their future strategic planning and decision-making processes, especially in terms of how to balance economic efficiency and social responsibility to achieve long-term sustainable development. However, due to the limitation of data and the depth of research on Walmart ESG, the article still has limitations.

The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

Development and performance evaluation of a novel solar mid-and-low temperature receiver/reactor with packed metal foam

Solar-driven hydrogen production from methanol decomposition (MD) is an important method of storing solar energy as clean fuels and improving the solar energy utilization efficiency. Catalyst preparation is one of the key steps, but conventional Cu-ZnO-Al2O3 particle catalysts lead to uneven reactor temperature, reducing the efficient conversion of solar energy into fuels. To alleviate this challenge, a monolithic catalyst utilizing packed foamy copper as a carrier is developed with exceptional thermal and mass transfer properties. The experimental results validate its excellent performance in hydrogen production using MD. The absolute conversion increases by an average of 12.11 % at 250 °C and further by 14.43 % at 270 °C compared to the particle catalyst. Based on these findings, a novel solar reactor is proposed, and a multi-field coupling model is built. The comparative results verify the performance advantage of the developed solar thermochemical receiver/reactor with packed metal foam-based catalyst. It maintains high conversion rates and solar-to-fuel energy efficiency, while alleviating overheating problems caused by traditional particle catalysts. Under the design conditions, the new reactor significantly improves the absolute conversion of methanol and the energy efficiency of solar-to-fuel conversion by 4.57 % and 3.00 %, respectively, resulting in 92.64 % and 67.56 %. This study provides a theoretical basis and technical solution for more stable and efficient use of mid-and-low temperature solar energy.

On prior visual experience in mental map navigation using allocentric and egocentric perspectives in the visually impaired.

Using new developments in the mental comparison task paradigm, this study addresses the question of the influence of prior visual experience in the natural use of mental perspective to achieve mental spatial tasks without any protocol-imposed perspective. During the experiment, 39 participants (11 early blind, 13 late blind, and 15 blindfolded-sighted) explored two corridor maps to memorise the spatial arrangement of 10 objects disposed along corridors. After the learning phase, several tasks addressing spatial memory and reasoning used in the mental spatial representation were performed. Blindfolded-sighted participants preferred an egocentric perspective, while the two visually impaired groups showed no overriding preference between egocentric and allocentric perspectives. Results showed a performance advantage for egocentric over allocentric perspectives, regardless of visual experience. Our results shed light on previous assumptions regarding cognitive mental map construction, suggesting the need to reflect on previous results and their dependence on imposed mental perspectives.

Single layer vanadium oxide assisted switchable metasurfaces for transmissive and reflective polarization conversion

Polarization control using metasurfaces is highly advantageous; however, most metasurfaces operate exclusively in either transmission or reflection mode. Additionally, achieving both wide bandwidth and high efficiency simultaneously in their design remains a significant challenge. Here, we design a high-efficiency and wideband switchable metasurface that can switch between transmissive and reflective polarization conversion modes by utilizing a single layer of I-shaped resonators and gold-vanadium dioxide (VO2) alternating gratings printed on a quartz substrate. When VO2 transitions to its metallic phase, the continuous metal layers of gold-VO2 eliminate transmission and the designed metasurface operates as a reflective cross-polarization converter with a polarization conversion ratio (PCR) exceeding 0.9 over a broad frequency range of 1.4–3.3 THz. Conversely, when VO2 is in its insulator phase, the THz incoming wave can transmit through the gratings, and the design behaves as a transmissive cross-polarization converter with a PCR above 0.99 in the 0.75-4.0 THz range for a forward y- polarized wave. The working mechanism for both transmissive and reflective polarization conversion modes is explained through theoretical analysis and surface current distributions. With the advantages of facile design, high performance, and dual functionality, our design is expected to be applied in the fields of imaging, sensing, and communication.

FDT‐Net: Frequency‐Aware Dual‐Branch Transformer‐Based Optic Cup and Optic Disk Segmentation With Parallel Contour Information Mining and Uncertainty‐Guided Refinement

ABSTRACTAccurate segmentation of the optic cup and disc in fundus images is crucial for the prevention and diagnosis of glaucoma. However, challenges arise due to factors such as blood vessels, and mainstream networks often demonstrate limited capacity in extracting contour information. In this paper, we propose a segmentation framework named FDT‐Net, which is based on a frequency‐aware dual‐branch Transformer (FDBT) architecture with parallel contour information mining and uncertainty‐guided refinement. Specifically, we design a FDBT that operates in the frequency domain. This module leverages the inherent contextual awareness of Transformers and utilizes Discrete Cosine Transform (DCT) transformation to mitigate the impact of certain interference factors on segmentation. The FDBT comprises global and local branches that independently extract global and local information, thereby enhancing segmentation results. Moreover, to further mine additional contour information, this study develops the parallel contour information mining (PCIM) module to operate in parallel. These modules effectively capture more details from the edges of the optic cup and disc while avoiding mutual interference, thus optimizing segmentation performance in contour regions. Furthermore, we propose an uncertainty‐guided refinement (UGR) module, which generates and quantifies uncertainty mass and leverages it to enhance model performance based on subjective logic theory. The experimental results on two publicly available datasets demonstrate the superior performance and competitive advantages of our proposed FDT‐Net. The code for this project is available at https://github.com/Rookie49144/FDT‐Net.