Enhance Human Research Articles

Simulation of e-learning visualization technology based on image enhancement in the communication mode of public performance art videos

With the simulation of public performing arts video communication modes, this study aims to explore the application of image-enhanced electronic learning visualization techniques in this field. Through the introduction of this technology, it aims to improve the effect of e-learning and enhance learners' understanding and perception of public performing arts videos. Through the acquisition and processing of public performing arts videos, the image enhancement technology is used to improve the image clarity and color saturation. Based on the e-learning platform, the enhanced video will be transmitted online, so that learners can watch through electronic devices and carry out corresponding learning operations. Finally, the learning effect of traditional learning and image-based e-learning. The results show that e-learning visualization technology based on image enhancement can significantly improve learners' understanding and feelings of public performing arts videos. Through the enhanced video images, learners can more clearly observe the actors' performance movements, facial expressions and other details, so as to better understand the connotation and communication meaning of the public performing arts.

Analysis of factors influencing market participation among orange‐fleshed sweet potato smallholder farmers in southern region of Malawi: A case of RTC project

AbstractAgricultural markets remain major issue downgrading root and tuber crop farmers who form the mainstream of the agricultural producers in Malawi. Recent evidence however reveals that orange‐fleshed sweet potato (OFSP) (Ipomea batatas) is currently being sold in produce markets along with the non‐orange varieties, sometimes in differentiated form in Malawi. The main objective of this study was to examine factors affecting market participation of sweet potato farmers in southern region of Malawi. Using a double‐hurdle model, this paper analyzed both the factors affecting farmer decision to participate and the extent/intensity of participation in market. The first tier of the double hurdle focused on decision to participate in the market using probit model and the second tier addressed factors affecting intensity of participation using truncated normal regression model. The paper used data collected from 360 farmers in southern districts of Malawi. Out of 360, 146 farmers had grown OFSP in the season preceding the survey. Double hurdle results indicate that farm size, output, age of a farmer (household head), and early maturing trait significantly determined decision to participate in the market. Further, results on intensity of participation indicate that access to market training, farm size, asset value, and output positively determined the intensity and negatively determined by distance to main market and gender of a farmer (household head). Based on the research findings, higher output levels and early maturing trait were found to increase smallholder farmer market participation. It is therefore recommended that strategies aimed at increasing household capacity to produce surplus produce through market tailored extension messages and productivity enhancement technologies could be highly effective in fostering smallholders' participation in commercial markets. Again, enhanced capacity building of research institutions to breed sweet potato varieties based on market demand is pivotal for increased market participation. Furthermore, research suggests that improving smallholder farmers' access to market price information, asset value, and farm size can help increase their intensity in the market. Therefore, it is critical for policymakers to holistically comprehend these socioeconomic factors in order to address the issue of smallholder farm households' decisions about market participation and the degree to which they participate.

Human Augmentation in the Context of Health Equality

Human Augmentation in the Context of Health Equality

Chemical space deconstruction-based dynamic model ensemble architecture for molecular property prediction

Green solvents, catalysts, functional materials, drugs, and other chemical products now have a quick engine for design thanks to machine learning (ML) based prediction of molecular properties. However, the accuracy and stability of ML-based models can be impeded by poor data quality, which is rarely studied in chemical product discovery and design. Inspired by the dynamic ensemble selection (DES), an improved DES based on chemical space deconstruction is proposed in this work to accommodate the prediction task of molecular properties. We innovatively developed a chemical space representation and deconstruction model based on a self-organizing mapping (SOM) neural network, facilitating the rapid implementation of the improved DES on molecular samples. Consequently, a novel dynamic model ensemble architecture (SOM-DES) is proposed as a model enhancement technology to build a more accurate and stable ensemble model, aiming to improve the predictive performance on the chemical subspace within poor-quality data. To achieve the architecture, a supervised dimensionality reduction algorithm has been improved to enhance the deep mining of molecular feature information for DES optimization. Additionally, a novel resampling strategy based on the combination of the geometric synthetic minority oversampling technique (G-SMOTE) algorithm and chemical space deconstruction, as a data augmentation technology, has been proposed for mitigating the disadvantage of unbalanced data during DES training. The prediction task for enthalpy of formation of the ideal gas is applied as a case study to demonstrate the superiority of the proposed SOM-DES. The results indicate that the proposed SOM-DES (R2 = 0.9731, RMSE = 55.4639) outperforms the traditional static ensemble strategy (SES, R2 = 0.9552, RMSE = 71.5045) in terms of global chemical spatial precision. More importantly, for chemical subspaces that are difficult to predict due to low data quality, SOM-DES shows a significant reduction in prediction errors compared to SES.

Addressing Challenges: Adopting Blockchain Technology in the Pharmaceutical Industry for Enhanced Sustainability

The growing importance of sustainability in organizational success, particularly in the pharmaceutical industry, underscores the need for leveraging technologies such as blockchain methods to enhance sustainability indicators across environmental, social, and economic pillars. This study aims to identify and understand the challenges hindering the adoption of blockchain technology in the pharmaceutical sector for improving sustainability performance, addressing two research topics: the specific challenges faced by blockchain adoption in this context and the interdependencies among these challenges. Employing a two-step approach, the study compiles challenges through a literature review, refines them via expert opinions, and establishes their interrelationships using methodologies like fuzzy interpretive structural modeling (FISM) and cross-impact matrix multiplication applied to classification (MICMAC). The research contributes to unraveling the complex relationships and dependencies within the system, providing a structured framework for improved decision making and strategic planning. It fills a literature gap as the first attempt to outline driving and dependent factors related to the challenges of adopting blockchain technology for sustainability enhancement in the pharmaceutical sector, offering insights that can significantly impact brand image, company perception, and consumer value.

Stacked U-Net with Time–Frequency Attention and Deep Connection Net for Single Channel Speech Enhancement

Deep neural networks have significantly promoted the progress of speech enhancement technology. However, a great number of speech enhancement approaches are unable to fully utilize context information from various scales, hindering performance enhancement. To tackle this issue, we introduce a method called TFADCSU-Net (Stacked U-Net with Time-Frequency Attention (TFA) and Deep Connection Layer (DCL)) for enhancing noisy speech in the time–frequency domain. TFADCSU-Net adopts an encoder-decoder structure with skip links. Within TFADCSU-Net, a multiscale feature extraction layer (MSFEL) is proposed to effectively capture contextual data from various scales. This allows us to leverage both global and local speech features to enhance the reconstruction of speech signals. Moreover, we incorporate deep connection layer and TFA mechanisms into the network to further improve feature extraction and aggregate utterance level context. The deep connection layer effectively captures rich and precise features by establishing direct connections starting from the initial layer to all subsequent layers, rather than relying on connections from earlier layers to subsequent layers. This approach not only enhances the information flow within the network but also avoids a significant rise in computational complexity as the number of network layers increases. The TFA module consists of two attention branches operating concurrently: one directed towards the temporal dimension and the other towards the frequency dimension. These branches generate distinct forms of attention — one for identifying relevant time frames and another for selecting frequency wise channels. These attention mechanisms assist the models in discerning “where” and “what” to prioritize. Subsequently, the TA and FA branches are combined to produce a comprehensive attention map in two dimensions. This map assigns specific attention weights to individual spectral components in the time–frequency representation, enabling the networks to proficiently capture the speech characteristics in the T-F representation. The results confirm that the proposed method outperforms other models in terms of objective speech quality as well as intelligibility.

Study on the Change Law of Pore Structure and Permeability of Coal Under Chemical Permeability Enhancement Technology

In order to deeply explore the permeability enhancement effect and permeability enhancement mechanism of the three chemical permeability enhancement technologies, the pore structure change and permeability enhancement mechanism of anthracite coal were studied from the perspective of coal pore structure. The following conclusions are drawn: the permeation effect of the four solutions from large to small is 99% CS2, 10% NaClO, 8% HF, 8% The total pore volume of HCl and HCl solution decreases after acidification, the pore volume of HF solution increases after acidification, the number of pores in the coal sample after oxidation of NaClO solution increases, and the micropores are transformed into mesopores, and the pore volume of CS2 solution increases after extraction, and the pore expansion effect is better than that of the other three solutions, and the pore volume with a pore diameter greater than 2 nm affects the flow of gas between pores, and the larger the pore volume, the higher the permeability. The pores with a pore diameter of less than 2 nm have little correlation with the permeability of coal and rock, and the micropores do not affect the gas flow

The effects of seed enhancements on plant establishment in native grasses: A meta‐analysis

AbstractQuestionsNative grasses are widespread with high commercial value and demand across the restoration sector, though their uptake is often hampered by seed‐use challenges. Seed enhancement technologies (SETs) provide a valuable tool for improving plant establishment outcomes for species (such as native grasses) where seed‐based approaches are essential to achieve large‐scale targets. However, due to the increased investment in resources and time associated with application of SETs, their adoption is dependent on the ability of these treatments to consistently provide benefits to one or more demographic life stages, particularly under field conditions. This meta‐analysis addresses the following research question: what are the overall effects of SETs on plant establishment outcomes compared to untreated florets or seeds in native grasses globally?LocationGlobal.MethodsThe effects of six major SET categories (acid treatment, coating, pelleting, flash flaming, priming, and treatment combinations) on germination, emergence, and shoot and root length and biomass were explored. Furthermore, the consistency of the effects of SETs across study types (i.e., laboratory, glasshouse, and field) was investigated.ResultsThe overall effects of SETs on native grasses in this meta‐analysis were positive for germination (16% improvement), emergence (22% improvement), and growth (6.42–8.86 cm and 2.26–2.77 g increase in seedling length and biomass, respectively). However, effects ranged from neutral to positive when data were grouped by SET type, with coating, pelleting and priming delivering consistent benefits across multiple life stages, and acid treatments, flash flaming, and combination treatments having mixed effects. This analysis also provided evidence that benefits observed from SET application in laboratory and glasshouse studies are not translating to field environments.ConclusionsOverall, SETs generally had positive effects and can therefore provide a relatively low‐risk approach to improving seed‐based restoration outcomes in native grasses. Continued SET research focussed on long‐term plant establishment outcomes in field environments is needed to further advance SET uptake.

Policy of Academic Revitalization through Information Technology for Quality Management Enhancement in Private Higher Education Institutions in Indonesia

Policy of Academic Revitalization through Information Technology for Quality Management Enhancement in Private Higher Education Institutions in Indonesia

Study of a vortex-induced vibration piezoelectric wind energy harvester based on the synergy of multi-degree-of-freedom technology and magnetic nonlinear technology

A novel multi-degree-of-freedom and magnetic piezoelectric wind energy harvester (MNPWEH) is proposed in this work to improve the working bandwidth and output power for the wind energy harvesting based on vortex-induced vibration. In the process of studying the presented collaborative strategy utilizing multi-degree-of-freedom and nonlinear force enhancement technologies, four response states of the harvester are observed and named as inter-well state, intra-well state, pulse state, and intermittent state. The key factors determining these four response states are firstly obtained through a series of numerical simulations and pre-experiments. Then, a thorough investigation into the influence of structural parameters on these four response states and the output performance of the system has been conducted. The findings from the experimental results indicate that greater tip mass of the outer beams results in higher output voltage generated by the same state, while the excitation magnet spacing can be adjusted for meeting different requirements in power and working bandwidth. In the optimal configurations, the fabricated prototype finally can obtain a maximum average power of 1.83 mW under the condition of v = 5 m/s and s1 = 15 mm, and maintain a wide working bandwidth of 2 m/s −6 m/s with an average output power above 0.66 mW when s1 = 25 mm, respectively. The main contribution of this study has been to confirm the effectiveness of the collaborative use of the multi-degree-of-freedom technology and magnetic nonlinear technology in piezoelectric wind energy collection based on vortex-induced vibration. It also provides a deeper new insight into the performance improvement method for vortex-induced vibration piezoelectric wind energy harvester.

Peculiarities of Translating an Oral Text in the Field of Neuro-Legal and its Transformation into Written Text

Due to the practical usage of neuro-technologies in all areas of our life, lawyers make attempts to provide a legal definition of such a philosophical category as mind, which expresses the highest type of thinking activity, and to develop legal norms and standards in this field. The area of law that deals with the impact of neuroscience discoveries on the regulation of social relations is referred to as neurolaw. This branch of law is considered to be interdisciplinary establishing close connections with a spectrum of sciences such as neuropsychology, neurogenetics, neurobiology, neurosociology and many other neurosciences and links a brain with law. As a result this area of law often employs categories and concepts of those sciences. This is a reason for legal terminology in this field of law to be constantly broadened. New legal terms are of great interest to translators who create and analyze conceptual fields in the area of “neuro-law”. For translators it is a real challenge to deal with specialized legal texts when they can apply not only their translation skills, techniques and methods but also their knowledge in a particular field of neuro-technology application in the real sector of economy. The author of the article makes an attempt to reveal the peculiarities and features of translation of the texts which deal with human body enhancement technologies and legal issues related to their application. New legal terms and concepts are of great interest to understand a developing area of law.

Deep learning based next word prediction aided assistive gaming technology for people with limited vocabulary

AimVocabulary is the base for communication and vocabulary deficits reduce effective social communication. This study aims to develop and analyze English language-based assistive gaming technology to support individuals with vocabulary deficits. Materials and methodsThe developed intervention is based on deep learning technology for next word prediction, and it is equipped with web and mobile application interaction support. Various deep learning models, including long short-term memory, bidirectional long short-term memory, and bidirectional encoder representations from transformers, were learned and deployed using an open-source dataset corpus. The proposed intervention was analyzed using a specified task of a 5-minute lecture on a simple technical topic. The completed activities were also evaluated by professionals using vocabulary-related expert characteristics. ResultsThe results of the conducted 5-minutetask on social domain without intervention and later with intervention improved. The results were assessed by counting vocabulary words as well as by vocabulary specialists. The levels of centraltendencies and deviations changed as the task improved with the proposed intervention. Mean, median, and standard deviation scores of the experimental group improved significantly (189.1, 194.0, and 67.61) in comparison to the control group (125.4, 127.0, and 43.55). Furthermore, language specialists observed significant differences in the mean and median without intervention (12.84 and 13.0) compared to the intervention group (24.23 and 24.0, respectively). ConclusionsThe outcome of the experiments appears to be in favor of the suggested intervention. As a result, the suggested deep learning based next word prediction aided assistive gaming technology could support persons in overcoming their limited vocabulary disadvantages. In the future, the integration of the proposed technology into existing communication enhancement technology could achieve better results.

Combustion and plume-plasma characteristics of cesium-based solid propellant

The high-temperature plume of solid propellant has spawned considerable amount of highly ionized plasma, which can be used to increase the specific impulse of propellant with the external physical field, as well as serve as the charged particles for the magnetohydrodynamic (MHD) generator on the thruster. Unfortunately, the deficiency of workable deal to promote the electrical performance of the plume hinders the development of the above technologies. In this work, cesium-based composite solid propellants (CCSP) were designed and fabricated by introducing cesium-based oxidants into the AP/HTPB/Al propellant as the highly ionized seed. The propulsion properties and plume conductivity of CCSP were preliminarily calculated through theoretical method. The comprehensive combustion-plasma characteristics (plume conductivity, flame morphology and combustion temperature) of CCSP were further investigated, utilizing plume-plasma diagnostic system. Profiting from the high electron derailment capacity of cesium atom with high temperature, the density of charged particles in the plume of CCSP significantly multiplied. The conductivity of CCSP plume with a value of >1000 S/m presented several orders of magnitude enhancement compared with the plume of baseline propellant (3.9 S/m). Additionally, partially replacing AP with CN/CP contributed to the combustion adequacy of Al MPs. The investigation on the burning rate pressure-index of CCSP and baseline using the high-pressure combustion chamber indicated that 0<nbaseline<nCCSP<1, proving the application ability on SRM and the ability to regulate the burning rate over a wide pressure range for CCSP. Overall, the presented work provided a novel method for improving the plume-plasma performance of propellant, exhibiting the admirable application prospects for Rocket-MHD generator technology and enhancement technology of thrust/specific impulse by physical energy field.

Analysis of the Role of Information and Communication Tech-nology (ICT) in Enhancing the Managerial Capacity of SMEs for Achieving Local Economic Sustainability

Analysis of the Role of Information and Communication Tech-nology (ICT) in Enhancing the Managerial Capacity of SMEs for Achieving Local Economic Sustainability

SCAPSM: attenuated phase-shift mask structure for EUV lithography.

The attenuated phase-shift mask (Att. PSM) is proven to be a promising resolution enhancement technology (RET) to improve the imaging performance in extreme ultraviolet (EUV) lithography. However, due to the reflective nature of the mask structure, the serious shadowing effect can affect the diffraction near field of the mask intensely and further impact the lithography imaging. With the purpose of improving the contrast of lithography imaging, a novel structure of the Att. PSM, to the best of our knowledge, is proposed in this paper. By introducing an absorbent sidewall along the edge of the mask absorber, the diffraction and shadowing effects can be mitigated. By applying the Kirchhoff approximation of mask diffraction, the ability of the novel structure to improve imaging performance is theoretically analyzed. Additionally, these analyses are confirmed by rigorous lithography simulations. The simulation results demonstrate that the proposed mask structure can improve the imaging contrast of EUV lithography, which has potential usage in advanced integrated circuit (IC) manufacturing.

Analysis of Image Data Augmentation in Smart Agriculture

The demand for agricultural production automation in smart agriculture has made deep learning widely used in tasks, e.g., disease identification, weed-crop segment and fruit detection based on image processing. However, in agricultural production environments, there are limitations in data acquisition, so data enhancement technology has also been widely used. This study introduces two data enhancement methods commonly used in smart agriculture, namely image transformation and GAN-based, and analyzes their specific applications and roles in three common task scenarios: disease identification, weed-crop segment, and fruit detection. According to the analysis, the data enhancement is mainly used to deal with class imbalance problems and over-fitting problems, and can significantly improve model performance in specific scenarios; the complexity of tasks and image structures has an impact on the application effects of different methods, and image segmentation tasks. It is necessary to increase the number of image modes, so methods such as GAN-based are adopted. However, due to the complexity of the image, fruit detection uses simple transformation methods to achieve better results. In addition, data enhancement methods designed for specific tasks, such as simulating synthetic images, can also achieve good results. However, data augmentation technology still has mode collapse problems and is too cumbersome in its application, which points out the direction for subsequent research. Overall, this research sorts out the applications of common data enhancement methods, analyzes the characteristics and limitations of these methods, and hopes to provide guidance for subsequent research.

Application of voice enhancement based on virtual sensor network in enterprise financial management

Based on virtual sensor network, this paper explores the application of voice enhancement technology in enterprise financial management. Virtual sensor network is a low cost, easy to expand and flexible network system based on simulation and simulation technology. In this network system, the virtual voice sensor node can collect and process the voice information inside the enterprise. Speech enhancement technology uses signal processing and machine learning algorithms to reduce noise, enhance and clarify speech signals, thereby improving the accuracy and reliability of speech data. By constructing virtual voice sensor nodes in virtual sensor networks and applying voice enhancement technology, the research results show that enterprises can collect and process voice data more accurately in the process of financial management. Voice enhancement technology can reduce the impact of environmental noise on financial data and improve the clarity of voice signals, so as to effectively improve the quality of data. Through the voice enhancement technology based on virtual sensor network, high-quality voice data acquisition and processing can be realized in the financial management of enterprises, improve the accuracy and effect of financial decision-making, and reduce the cost and complexity of enterprises.

Impact of flow intermittency on heat transfer enhancement in serpentine channels

With liquid cold plates being widely applied in industries such as battery energy storage systems, advanced heat transfer enhancement technologies are urgently needed to efficiently dissipate the ever-increasing heat load. The present work numerically and experimentally explores the potential of flow intermittency in a laminar serpentine channel for thermal performance improvement. The numerical analysis shows that the dynamic Dean vortex evolution induced by the intermittent mainstream disrupts the thermal boundary layer more effectively than the steady-flow vortices and enhances local Nusselt number at the U-turns by 117% maximally. Such secondary vortices are transported intermittently to the straight segments, resulting in a 55% increase in the area-averaged heat transfer by promoting mainstream-boundary flow mixing. The optimization of the flow intermittency profile is achieved by matching the pulse-on and deceleration stage durations with the characteristic times of secondary vortex growth and transport. The numerical results are qualitatively validated by the experimental measurement conducted in a water bath. The current study novelly demonstrates the design concept of enhancing heat transfer in curved channels by actively controlling the intermittent flow and proposes the design criteria for the intermittency profile to achieve optimal performance.

Diagnostic performance of linked color imaging for gastric cancer by Helicobacter pylori infection status: A subanalysis of the large-scale, multicenter randomized controlled trial LCI-FIND.

Linked color imaging (LCI) is a new image enhancement technology that facilitates the recognition of subtle differences in mucosal color. In the large-scale, multicenter randomized controlled trial LCI-FIND, LCI demonstrated good diagnostic performance for the detection of tumor lesions in the upper gastrointestinal tract. The aim of the present study was to exploratively evaluate the diagnostic performance of LCI according to H. pylori infection status as a subanalysis of LCI-FIND trial. The patients were randomly allocated to receive white light imaging (WLI) first, followed by LCI (WLI group), or vice versa (LCI group), and the two groups were compared for the detection of tumors. Data from this trial were analyzed by the presence/absence of H. pylori infection and further analyzed by successful or unsuccessful eradication in the H. pylori infection group. The 752 patients in the WLI group and 750 patients in the LCI group who had participated in the LCI-FIND trial were included. In the successful eradication group, more gastric lesions were detected by primary mode in the LCI group than in the WLI group, indicating that more lesions were missed by WLI. Fisher's exact probability test for the comparison of the WLI and LCI groups yielded a p-value of 0.0068, with missed gastric lesions being detected 0.136 times (95% confidence interval: 0.020-0.923), significantly less with LCI than with WLI. The current study suggests that LCI should be used for gastric cancer screening, particularly in patients with successful H. pylori eradication.

Discriminative elastic-net broad learning systems for visual classification

The broad learning system (BLS) has garnered significant attention in the realm of visual classification due to its exceptional balance between accuracy and efficiency. However, the supervision mechanism in BLS typically relies on strict binary labels, limiting the approximation freedom and failing to represent the data distribution adequately. Furthermore, the inadequacy of the guidance mechanism for the output weights hinders the precise approximation of the target spaces by the input features. To tackle these issues, in this paper, we propose two discriminative elastic-net regularized BLS models with label enhancement, achieving the following significant objectives: Firstly, the proposed label enhancement technologies can substantially augment the margins between different classes while enhancing the diversity within label spaces. Secondly, the compactness and effectiveness of the output weights can be further improved via the guidance of elastic-net regularization of singular values. Thirdly, our proposed algorithms can be efficiently optimized with the augmented Lagrangian method, whose convergence and calculation complexity can be guaranteed well with solid theoretical analysis. Extensive experiments are intended on various popular databases to compare our proposed models with numerous other state-of-the-art recognition algorithms. The numerical results indicate that our proposed algorithms can achieve the best face recognition accuracy of 99.67%, and 97.54% for object recognition. Even in challenging recognition tasks, our methods can still yield an average improvement of 0.8 percentage points.