Search

Polymer blends are critical in many commercial products and industrial processes and their phase behavior is therefore of paramount importance. In most circumstances, such blends are formulated with samples of high dispersity, which have generally only been studied at the mean-field level. Here, we extend the renormalized one-loop theory of concentration fluctuations to account for blends of disperse polymers. Analyzing the short and long length-scale fluctuations in a consistent manner, various measures of polymer molecular weight and dispersity arise naturally in the free energy. Thermodynamic analysis in terms of moments of the molecular weight distribution(s) provides exact results for the inverse susceptibility and demonstrates that the theory is not formally renormalizable. However, physically motivated approximations allow for an "effective" renormalization, yielding (1) an effective interaction parameter, χe, which depends directly on the sample dispersities (i.e., Mw/Mn) and leaves the form of the mean-field spinodal unchanged, and (2) an apparent interaction parameter χa that depends on higher-order dispersity indices, for instance Mz/Mw, and characterizes the true limits of blend stability accounting for long-range off-critical fluctuations. We demonstrate the importance of dispersity on several example systems, including both "toy" models that may be realized in computer simulation and more realistic industrially relevant blends. We find that the effects of long-range fluctuations are particularly prominent in blends where the component dispersities are mismatched, especially when there is a small quantity of the high-dispersity species. This can be understood as a consequence of the shift in the critical concentration(s) from the monodisperse value(s). [ABSTRACT FROM AUTHOR]

Knowledge sharing is considered an important promotor of new product development (NPD) performance. However, studies have shown that making experts share knowledge in new product development (NPD) projects is often a challenging task. Consequently, this topic has attracted substantial attention in academic research. However, although studies have produced valuable insights, more detailed accounts of the mechanisms shaping knowledge sharing processes in NPD projects are sparse. To add to the knowledge on this topic, this study conducts a series of interviews with NPD professionals at manufacturers of high-tech industrial products to identify 'factors influencing knowledge sharing' (FIKS). The identified FIKS are explained through the socio-physiological theory, the 'reasoned action approach' (RAA). This involves several contributions. First, the study provides a detailed explanation of the role of FIKS by describing how they affect the behaviour of knowledge providers and receivers in NPD processes. Second, by identifying 161 FIKS in NPD processes, organised under 18 categories, the study offers the so far most extensive account of FIKS in NPD processes. Third, the findings suggest a special characteristic of knowledge sharing in NPD contexts concerning the relatively high importance of knowledge receiver behaviour. [ABSTRACT FROM AUTHOR]

This study reports an alkali-activated binder including blast furnace slag (BFS) together with marble waste (MW). Cement is an industrial product that emits a significant amount of CO2 during its production and incurs high energy costs. MW is generated during the extraction, cutting, and processing of marble in production facilities, where dust mixes with water to form a settling sludge. This sludge is an environmentally harmful waste that must be disposed of in accordance with legal regulations. In this study, a substantial amount of MW, a by-product with considerable environmental and economic impacts worldwide, was utilized in the production of a binder through the alkaline activation of BFS. In doing this, different experimental parameters were tested to obtain the best binder samples according to workability and mechanical properties. Then, some experiments such as drying shrinkage determination, strength testing, and microstructure analyses were fulfilled through samples with the best values. The findings supported the improvement of the rapid-setting property of BFS by means of the addition of MW. MW reduced the time-dependent drying shrinkage values of BFS by 55%, especially in slag alkaline activation systems with a low or moderate alkali activator content. The substitution of MW (≤50%) in BFS increased flexural and compressive strengths (4.5 and 61.7 MPa), while a reference sample contained BFS only. Although the use of MW did not create a new phase, it contributed to a C-S-H bonding structure during the alkali activation of BFS in a microstructure analysis. [ABSTRACT FROM AUTHOR]

A modified two-step coprecipitation method has been formulated to prepare a series of model CuZnO/Al2O3 catalysts for methanol synthesis from syngas. Evaluation at industrially relevant condition showed slightly higher activity and much higher stability for model catalysts than for a commercial catalyst. Due to the same CuZn-binary precursor, the model catalysts had similar structural properties with large specific surface area and pore volume, small CuO crystallites and good reducibility, resembling that of the commercial catalyst. However, the model catalysts showed much better uniformity of alumina distribution, which accounted for their better performance. Strong positive correlation between the deactivation rate and the coefficient of variation of Al/Zn indicates the uniformity of alumina distribution plays an important role in determining stability. Specifically, CuZnO/Al2O3 catalysts with alumina distributed more uniformly had higher stability. This work demonstrated that the formulated modified two-step coprecipitation could provide excellent CuZnO/Al2O3 catalysts for industrial applications. Additionally, it also affords new clues for the development of even better industrial catalysts. [ABSTRACT FROM AUTHOR]

• Proposed fuzzy small displacement torsor model for tolerance classification representation. • Established an assembly reliability index for evaluating assembly precision. • Developed constrained transformation method for efficient and high-accuracy calculation. • Proposed a novel fuzzy algorithm for assembly precision management. The assembly precision of a specified product is subject to epistemic uncertainty due to manufacturing and measurement errors. The efficient and accurate assembly precision management is essential for realizing smart production lines, which rely on the robust assembly precision analysis model and calculation method. Parametric models are extensively used for assembly precision analysis in industrial products, typically integrating worst-case and statistical methods for calculations. Nevertheless, the combined application of these methods presents inherent limitations. Therefore, a novel fuzzy algorithm for assembly precision management is proposed in this paper by using fuzzy sets to quantify epistemic uncertainty in assembly precision, which integrates the proposed fuzzy-based assembly precision analysis model and fuzzy-based calculation method. The proposed fuzzy small displacement torsor model is a fuzzy-based model for deviation representation and tolerance classification using decomposed fuzzy numbers, where an assembly reliability index is always established for the hierarchical evaluation and management of assembly precision. Subsequently, a comprehensive assembly precision analysis model is developed for precision prediction and contribution quantification by integrating the Jacobian model for deviation propagation. A new constrained transformation method is proposed as a fuzzy-based calculation method, offering efficient and highly accurate assembly precision computation. It accounts for torsor parameter constraints to ensure greater prediction accuracy than the worst-case method and improves computational efficiency compared to statistical methods. An assembly case of the centering pin mechanism is applied to verify the superiority of the proposed novel fuzzy algorithm compared with the Jacobian-Torsor model with worst-case or statistical methods. [ABSTRACT FROM AUTHOR]

With the current demand of wood products and industrial requirements, plantation owners need to re-think ways in which harvesting is carried out and identify the most efficient harvesting system that also utilizes small diameter parts of stem per industry's product specifications. This paper contributes to the discussion by comparing the harvesting productivity and stem recovery between conventional harvesting (CH) and integrated harvesting (IH) systems. CH is harvesting which does not necessarily align with the log dimensional requirements of the customer and only recovers logs with big diameters while IH is based on harvesting and sorting logs according to need in a single operation so that as much volume and value as possible of the stands is realized. The study was carried out at the Sao Hill Forest Plantation in the Southern Highlands of Tanzania. The harvested compartment was purposively selected and divided into IH and CH plots. A total of 39 trees were randomly selected to be harvested from each plot. The findings portray a significant difference in the stem recovery of 75% for IH and 60% for CH. CH had an average cutting productivity of 16 m3/h as compared to 14 m3/h for IH while skidding productivity was 15.3 m3/h and 24.9 m3/h for IH and CH, respectively. In this study, the IH system resulted in higher production in terms of volume recovered, meaning IH had a significant impact on the production and stem recovery of the standard stem-wood for use in different industries. [ABSTRACT FROM AUTHOR]

In industrial product development with customers, it is well‐known that lead userness is associated with novel and commercially attractive products. We take a next step by analyzing if lead userness is related to product development that potentially affects industry‐level practices. Our main hypothesis is that lead userness results in the pursuit of Schumpeterian product opportunities: disequilibrating, radical, rare, and based on new knowledge and creative activities. In contrast, at low lead userness, more Kirznerian product opportunities are expected—which maintain industry standards. The hypothesis is supported by data from 139 high‐tech small firms. Next, we anticipate that factors related to the selection of lead users, and the effective processing of their inputs, moderate the connection between lead userness and Schumpeterian opportunity. As for selection, we find a stronger connection when firms collaborate with "new" customers. Conversely, when involved customers have been business partners already for a long time, the pursuit of Schumpeterian opportunities is mitigated. Also, tentative evidence is found that the connection between lead userness and Schumpeterian opportunity amplifies when involved customers are less dominant market players. Finally, when it comes to processing lead users' input, we report tentative evidence that firms' R&D intensity should be higher (indicating ability to process lead users' inputs and make continued development efforts). Our findings help to explain why lead users have been associated with industry dynamics, and provide guidance to identify "relevant" lead users when firms seek product opportunities that potentially disequilibrate their industry. [ABSTRACT FROM AUTHOR]

The product quality is the key index to measure the process of the industrial manufacture. Thanks to the ever-expanding scale of time-series data, the deep learning technology can be regarded as the effective approach to predict the future product quality accurately. In this article, the product quality with time series data is considered and an attention-based convolutional recurrent neural network (ACRNN) is proposed for the prediction of the product quality. Firstly, by reconstructing the time series data into the two dimensions the convolutional layers are built to compress the information of the product quality data, and the more comprehensive features can be extracted. Furthermore, to ensure the prediction accuracy of the time series data process and extract the 2-dimmension feature of the data, the long short-term memory (LSTM) based on recurrent neural network (RNN) layers are constructed. After that, the fully connection layers with attention mechanism is applied to improve the calculation efficiency and accuracy of the models. Finally, the test experiments on the time series data of the industrial product are given and the comparisons show that the effectiveness of the proposed algorithm framework. [ABSTRACT FROM AUTHOR]

Material identification based on R_value (Transparency natural logarithm ratio of low‐energy to high‐energy.) of line‐scan dual‐energy X‐ray transmission (DE‐XRT) has a good prospect for industrial application. Unfortunately, the DE‐XRT signals before attenuation within the material range cannot be directly measured, whereas their precision has essential effect on R_value. Therefore, a vertical‐horizontal‐context‐based signal synthesis method was proposed to rebuilt incomplete/masked image, which takes the filtered signals outside the material range as the reference context, and takes into account the vertical (forward/column/Y) and horizontal (scanning/row/X) anisotropy. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The vertical curves are synthesized one by one, thus extending to the whole surface. The special rigorous synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only around 0.0007, and it only takes 35 ms to complete the surface synthesis of 119 × 119 pixels on the CPU with 3.4 GHz main frequency. This high numerical precision can match the similarly filtered signals after attenuation so as to improve the accuracy of R_value. And this, together with calculation real‐time, can promote the application of industrial inline material identification. [ABSTRACT FROM AUTHOR]

Cu immobilized onto N-doped carbon spheres (Cu/N-doped CS) derived from soybean flour was synthesized via the hydrothermal method and certified as a green high-efficiency catalyst for the regioselective synthesis of 1,4-disubstituted 1H-1,2,3-triazoles. The obtained N-doped carbon spheres from a combination of glucose and soy flour have a larger size and suitable affinity for load copper species. The morphology and structure of the as-prepared catalyst have been confirmed based on FT-IR, XRD, FE-SEM, EDX, BET, and ICP-OES characterizations. The catalytic performance of the Cu/N-doped carbon spheroids was investigated experimentally. It was found that the Cu/N-doped CS nanocomposite has a high efficiency and recyclability for 5 cycles without any appreciable loss in its catalytic activity in the synthesis of 1,4-disubstituted 1H-1,2,3-triazoles via the three-component condensation reaction of various epoxide/alkyl or aryl halides, sodium azide, and phenylacetylene in a water medium. Furthermore, significant advantages such as stability, ease of preparation, low cost of the carbon resource, high regioselectivity, and good product yield have been achieved in the heterogeneous catalyst system attractive for large-scale in many industrial products. [ABSTRACT FROM AUTHOR]

Inspired by nature, this work presents a scalable method to produce opalescent 3D printing filaments with angle‐dependent structural color. Compared to conventional colorants, these colors provide a more vivid appearance, possess the ability to dynamically change in response to alterations of illumination, and may feature superior longevity as well as durability. Particle synthesis via emulsion polymerization in starved‐feed mode is utilized to achieve a tailored core–shell particle architecture. During processing, the thermoplastic polyacrylate‐based shells form a continuous matrix, wherein the pristine polystyrene cores self‐assemble. The colloidal ordered structure thus formed selectively scatters light according to Bragg's law. Adjusting particle size makes the perceived colors easily tunable over the visible spectrum. The filaments are printable on entry‐level printers with comparable settings to commercial filaments and state‐of‐the‐art print quality. With this work, sustainable 3D printing of iridescent objects with a novel and superior coloration strategy becomes possible for everyone – from hobbyists over customized goods and industrial prototypes to serial production. [ABSTRACT FROM AUTHOR]

Industrially, some parts, such as gears, are manufactured through additive manufacturing processes. These processes require high mechanical strength and high geometric accuracy. Therefore, combining geometric accuracy with mechanical performances may provide a full understanding of the industrial product. The authors proposed two indices in previous works; the first combines all dimensional deviations from the nominal values, while the second combines the dimensional deviation with the mechanical performances. This study aims to investigate the effects of printing process parameters on both indices. The results demonstrate the infill percentage, the print speed, the layer height, and the bed temperature exerted on the two proposed indices. The trend of the two indices versus the process parameter values is shown and quantified through relationships. These indices show significant correlations with process parameters, highlighting the need to consider individual and combined effects when optimizing 3D processes. Understanding the relationships and optimizing these indicators facilitates the identification of optimal process parameters, which leads to improved efficiency and consistency in additive manufacturing operations. [ABSTRACT FROM AUTHOR]

Organic compounds bearing one or more carbocycles in their molecular structure have a discernible presence in all major classes of organic products of industrial significance. However, sourcing carbocyclic compounds from exhaustible, anthropogenic carbon (e. g., petroleum) raises serious concerns about sustainability in the chemical industries. This review discusses recent advances in the renewable synthesis of carbocyclic compounds from biomass components following catalytic pathways. The mechanistic insights, process optimizations, green metrics, and alternative synthetic strategies of carbocyclic compounds have been detailed. Moreover, the renewable syntheses of carbocycles have been assessed against their existing synthetic routes from petroleum for better perspectives on their sustainability and technological preparedness. This work will assist the researchers in acquiring updated information on the sustainable synthesis of carbocyclic compounds from various biomass components, comprehending the research gaps, and developing superior synthetic processes for their commercial production. [ABSTRACT FROM AUTHOR]

To address the issues of sparse training data, complex entity structures, and uneven entity distribution in the industrial equipment failure domain, this study constructs an industrial equipment failure named entity recognition corpus. Due to the difficulty of character level named entity recognition models in representing the professional vocabulary information in the field of industrial equipment failure, this study proposes a character enhanced industrial equipment failure named entity recognition model to address this problem. In the embedding layer, professional vocabulary information is directly fused between the Transformer layers of ROBERT WWM (Robustly Optimized BERT Pretraining Approach with Whole Word Masking) to allocate word information to each of its constituent characters for enhanced semantics. The global semantic information is obtained through a BiLSTM (Bidirectional Long Short Term Memory), and the CRF (Conditional Random Field) is used to learn the dependency relationship between adjacent labels to obtain the optimal sentence level label sequence. Experimental results demonstrate that the proposed model has good performance on industrial equipment fault named entity recognition tasks, with an average F1 score of 92.403%. [ABSTRACT FROM AUTHOR]

Phthalic acid esters (PAEs) are synthetic diesters derived from o‐phthalic acid, commonly used as plasticizers. These compounds pose significant environmental and health risks due to their ability to leach into the environment and act as endocrine disruptors, carcinogens, and mutagens. Consequently, PAEs are now considered major emerging contaminants and priority pollutants. Microbial degradation, primarily by bacteria and fungi, offers a promising method for PAEs bioremediation. This article highlights the current state of microbial PAEs degradation, focusing on the major bottlenecks and associated challenges. These include the identification of novel and more efficient PAE hydrolases to address the complexity of PAE mixtures in the environment, understanding PAEs uptake mechanisms, characterizing novel o‐phthalate degradation pathways, and studying the regulatory network that controls the expression of PAE degradation genes. Future research directions include mitigating the impact of PAEs on health and ecosystems, developing biosensors for monitoring and measuring bioavailable PAEs concentrations, and valorizing these residues into other products of industrial interest, among others. [ABSTRACT FROM AUTHOR]

On account of their excellent properties, such as high surface area, non-toxicity, high porosity, and biocompatibility towards bodily fluids, calcium carbonate (CaCO3) nano particles have a good deal of industrial applications. Here, the present study reports a facile strategy combined with a ball milling process and a subsequent hydrothermal treatment to synthesize CaCO3 nano powders. Homogeneous CaCO3 precursors with an average size of 80 nm were prepared via a ball milling process, which subsequently transformed as well crystallized CaCO3 nano powders with an average size of 145 nm after the hydrothermal condition at 160 ℃ for 12 h. The as-obtained CaCO3 nano powders demonstrated good homogeneity with narrow size distribution and improved dispersibility, suggesting they are compatible in current industrial applications. [ABSTRACT FROM AUTHOR]

Sulfur-containing dynamic polymers had attracted significant attention due to their unique chemical structures with high reversibility. Utilizating sulfur, an inexpensive industrial waste product, to synthesize dynamic polysulfide polymers through reverse vulcanization has been a notable approach. However, this method required high temperatures and resulted in the release of unpleasant oders. In this study, we presented a robust method for the preparation of sulfur-rich polymers with dynamic polysulfide bonds from elemental sulfur and inexpensive epoxide monomers via a one-pot strategy at the mild room temperature. Different types of polysulfide molecules and polymers were synthesized by reacting various epoxide compounds with sulfur, along with the investigation of their structures and dynamic behaviors. It was noteworthy that the obatined polymers prepared from m-(2,3-epoxypropoxy)-N,N-bis(2,3-epoxypropyl)aniline and elemental sulfur exhibit multiple dynamic behaviors, including polysulfide metathesis and polysulfide-thiol exchange, enabling their rapid stress relaxation, self-healing, reprocessing and degradable properties of the cross-linked polymer. More importantly, the hydroxyl groups at the side chains from epoxide ring opening exhibited potential transesterification. This work provided a facile strategy for designing dynamic sulfur-rich polymers via a mild synthesis route. [ABSTRACT FROM AUTHOR]

The employment of Mazidagi phosphate ore for wet flue gas desulphurization was investigated. The ore was exposed to sulfation processes in a three-phase double-stirred reactor to determine the effects of certain parameters on SO2 absorption. The absorption rate significantly increased with inlet SO2 concentration, gas flow rate, stirring speeds, and solid concentration but not with temperature. The absorption rate increases as the mass transfer coefficients and interface area value increase with stirring speeds. These parameters have also a prominent impact on the absorption rate depending on the dissolution rate of CaCO3. Based on these, the most effective parameters on SO2 absorption are liquid-phase stirring speed and suspension concentration, and the process is diffusion-controlled in the liquid phase. In addition, optimum conditions were determined, and then the ore was exposed to sulfation processes via a semi-batch operation mode. A comparison between raw and sulfated ore was examined for the changes in physical structure and chemical composition. Although the ratios of fluorapatite and carbonate-fluorapatite remained nearly constant, the ratio of calcite decreased significantly. It was determined from these investigations that the calcite in the phosphate ore has been converted into the CaSO3∙½H2O compound. Consequently, both the calcite in the phosphate ore was utilized effectively for wet flue gas desulphurization, and a phosphate-rich industrial product was obtained. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection. [ABSTRACT FROM AUTHOR]

Object detection is a key technology to realize the blind inspection of industrial products. To improve the automation degree of building deep convolutional neural networks (CNNs) for object detection and further improve the detection accuracy, this paper proposes an improved neural architecture search method using exclusive-OR (XOR)-based channel feature fusion. First, an XOR-based channel fusion module is designed; it can fuse the feature mapping of different scales at the channel level in the case of multibranch access complementarily. Then, an improved cell pruning strategy is proposed to efficiently prune the connections between cells by setting the architecture parameters of the candidate operations to 0 s, which are in the alignment layers of the subsequent cells. The cell pruning strategy can directly search the multibranch CNN models and narrow the neural network architectures' gap between the search stage and the evaluation stage. The experimental results show that the proposed method takes approximately 0.75 GPU days to search the optimal neural network on a dataset including six classes for blind inspection of industrial products, and the mean average precision (mAP) is approximately 99.1% on a test dataset, which is higher than those of state-of-the-art methods, e.g., DenseNAS and CSPDarknet53. [ABSTRACT FROM AUTHOR]

As an intellectual property product that is highly farmer-friendly, geographical indication (GI) products have always garnered significant attention. In recent years, research on how GI products promote agricultural development has been increasing, yet the academic community remains divided on this issue. On one hand, some studies point out that GI products can drive agricultural development; on the other hand, other studies suggest that the impact of GI products is not significant or varies. Meta-analysis is a method that leverages statistical techniques to integrate the findings of multiple studies with a common research objective, addressing controversial issues and arriving at generalizable conclusions. Therefore, to more precisely uncover the intrinsic relationship between GI products and agricultural development and to delve deeper into the root causes of the aforementioned discrepancies, this study employed a meta-analytic approach. We extracted 478 correlation coefficients (r) as effect sizes from 82 empirical articles worldwide. Using these coefficients, we calculated the overall effect size and moderating effects of GI products on promoting agricultural development. Research indicates that GI products exert a positive influence on agricultural development. There is a low positive correlation between the two (r = 0.197). Further analysis reveals that various factors at the sample, data, literature, and methodology levels all impact the outcomes of GI products' promotion of agricultural development. Research has shown that, in pursuit of sustainable agricultural development goals, it is further recommended that governments should accord high priority to the cultivation and development of GI products. This is aimed at providing practical insights to facilitate the sustainable advancement of GI products and bolster agricultural competitiveness. [ABSTRACT FROM AUTHOR]

This study delves into the interplay between design strategies and their efficacy in harmonizing the functional and aesthetic aspects of industrial product design. It scrutinizes modern design tactics, delineating their features and benefits, and aims to forge a cohesive strategy that elevates both aesthetic appeal and functionality in industrial products. By examining literature on industrial design evolution, the merger of performance and aesthetics, and user experience, the research identifies five pivotal design strategies--Design Thinking, Agile Design, Lean Design, Six Sigma, and TRIZ theory--adopted by various industrial businesses. It utilizes qualitative methods to analyze these strategies' frameworks, pinpointing similarities and differences in their approach to design, alongside quantitative research through surveys with expert designers. The findings illuminate the distinct impacts of these strategies on industrial product development, offering insights into their potential to enhance both the utility and visual appeal of products. [ABSTRACT FROM AUTHOR]

Halide perovskites are reputed as highly promising photoelectronic materials for direct X‐ray detectors, but realizing large‐area flat‐panel imaging requires to address the compatibility issue of the electronic, surficial, and mechanical properties between the perovskite and the readout circuit board. Here, a low‐dimensional MA3Bi2I9 perovskite is chosen to achieve a good match in a balancing act between the two by exploiting an orientation control strategy for perovskite film growth. The most striking consequence of the orientation controlled growth is the excellent adhesion of the thick perovskite film to the electronic board in large area and effectively addresses the charge sharing effect, which has been notoriously difficult to achieve. The resulting detector, exhibits an X‐ray imaging area of 2.8 × 3.2 cm, with a spatial resolution of 4.0 lp mm⁻¹, the highest yet achieved for polycrystalline perovskite detectors based on TFT backplanes, and a sensitivity of 588 µCGyair−1 cm−2 while maintaining a dark current below 10 nA cm⁻2, this is also the highest value recorded to date for polycrystalline zero‐dimensional perovskite detectors. This device clearly revealing the intricate internal structures of both biological specimens and industrial products. This outcome demonstrates the potential of zero‐dimensional perovskites in X‐ray planar imaging and highlights the critical role of orientation control strategies. [ABSTRACT FROM AUTHOR]

In tangible irreversible human-machine interactions (HMIs), erroneous inputs resulting from users’ unfamiliarity, inattention, or ingrained habits can lead to unintended consequences. Although some design measures like buttons with protective shells or safety switches before critical actions have been implemented to minimize human errors in both everyday products and industrial interfaces, a cohesive guiding strategy on when, where, and how to prevent unintended consequences is lacking. Inspired by the confirmation dialog boxes commonly utilized in graphical user interfaces, this study proposes design strategies for effective tangible confirmation behaviors in HMIs. Initially, a workshop was conducted to gather design cases and explore potential design opportunities in future scenarios. By analysing the outcomes, we propose an evaluation method for assessing the necessity and complexity requirements of confirmation design and introduce a design library considering complexity levels graded by users’ time, effort, and cognitive load expenditure. Subsequently, a user behavior experiment was conducted to validate the feasibility and efficacy of the proposed strategies. Our strategies offer valuable references for designers, highlighting the significance of confirmation design in preventing human errors. Following these strategies, designers and engineers can integrate more Kansai engineering principles into HMIs to ensure user safety, protect property, and enhance interaction efficiency. [ABSTRACT FROM AUTHOR]

Digital advancements have reshaped the sales channels for industrial products like chemical fertilizers, allowing for the elimination of certain intermediaries. Utilizing survey data from 1374 rice‐growing smallholder farmers across counties in central China, this study explored the impact of disintermediation in chemical fertilizer sales channels on application rates among smallholder farmers. Benchmark regression analyses indicated that channel disintermediation significantly decreased chemical fertilizer application across various scenarios. Additionally, the alignment of contractual factors and technical services between chemical suppliers and farmers, facilitated by disintermediation, reduced chemical fertilizer application. Heterogeneity analysis revealed that the reduction in chemical fertilizer application due to channel disintermediation was more pronounced among farmers with higher digital literacy or lower production capacity. These findings highlighted the importance of encouraging chemical suppliers with direct‐to‐consumer channels to enhance their technical services and increase supervision over the quality of their products and services, thereby building trust among farmers and facilitating their adaptation to changes in the sales channel structure. [ABSTRACT FROM AUTHOR]

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method. [ABSTRACT FROM AUTHOR]

Form deviation generated during the milling profile process challenges the precision and functionality of industrial fixtures and product manufacturing across various sectors. Inspecting contour profile quality relies on commonly employed contact methods for measuring form deviation. However, the methods employed frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system ( R A M - S t a r l i t e T M ), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The R A M - S t a r l i t e T M vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. The results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, and valuable information to improve machining parameters, as well as quality classification. [ABSTRACT FROM AUTHOR]

AbstractLutein, zeaxanthin, and mesozeaxanthin, collectively termed as macular pigments, are key carotenoids integral to optimized central vision of the eye. Therefore, nutraceuticals and functional foods have been developed commercially using carotenoid rich flowers, such as marigold and calendula or single celled photosynthetic algae, such as the Dunaliella. Industrial formulation of such products enriched in macular pigments have often suffered from serious bottlenecks in stability, delivery, and bioavailability. The two chief factors largely responsible for decreasing the shelf-life have been solubility and oxidation of these pigments owing to their strong lipophilic nature and presence of conjugated double bonds. In this regard, oil-based formulations have often been found to be more suitable than powder-based formulations in terms of shelf life and targeted delivery. In some cases, addition of phenolic acids in the formulations have also augmented the product value by enhancing micellization. In this regard, a novel proprietary formulation of these pigments has been developed in our laboratory utilizing marigold extracts in a colloidal solution of extra virgin olive oil and canola oil fortified with antioxidants like thyme oil, tocopherol, and ascorbyl palmitate. This review article presents an updated insight into the stability and bioavailability of industrially manufactured macular carotenoids together with their safety and solubility issues. [ABSTRACT FROM AUTHOR]

Among the recent industrial products, one of the most significant developments affecting societies and people has been the advances in the automotive industry. Automobiles have made it possible redefine the relationship between the individuals and society, transforming spaces according to their own needs. Spaces have contributed to the creation of a network of social relationships where users can communicate with several people simultaneously in the relationship between automobiles and people. Therefore, while automobiles have become a part of private life, it has also provided individuals with the opportunity to enter the public sphere. In this study, young people who view owning a bird series car as a way to distinguish themselves from others in society are considered as a subcultural group. The main purpose of this study is to provide a sociological explanation for young people who see owning a symbolic vehicle as a way of gaining social acceptance. In this direction, the data in the study, which was conducted in accordance with the phenomenology research design within the qualitative research tradition, the data were obtained through interviews with a total of 33 people, two of whom were female and 31 of whom were male, between the ages of 18-35, who own Bird Series vehicles. Interviews were conducted between December 2022 and April 2023, using purposive and snowball sampling techniques. The collected data was analysed descriptively using MAXQDA 22 software. The analysis and interpretation of the data is based on Chris Jenks' classification of cultural, reflexive, linguistic, and visual turns to explain the difference between the approaches to the concept of subculture in the modern and postmodern periods. The differences, characteristics, transforming aspects and existence strategies of young people who use Bird Series vehicles through the ownership of a symbolic vehicle and their practices of separation from the dominant culture were analyzed based on Chris Jenks' quadruple classification. The research found that young people value the relationships built around a symbolic vehicle more, as Bird Series vehicles fulfil their need for a sense of belonging to a group. [ABSTRACT FROM AUTHOR]

There is a growing need for resilient and renewable materials to aid society in global sustainability. It is incumbent upon the agricultural and manufacturing industries to work together to achieve this vision. In particular, the hemp plant has been identified as an emerging industrial crop that will be pivotal in achieving the United Nations Sustainable Development Goals. However, this nascent industry has received an influx of research and development activity, resulting in various methods and practices globally, challenging the repeatability of results, research advancement, standards development, and sustainability assessment. A systematic literature review is conducted to identify and document (1) the various practices for harvesting and converting industrial hemp into materials and products and (2) existing hemp-derived products and those under development. Using the PRISMA methodology, 5295 articles were identified, and 109 articles were included for review. Unlike prior reviews focusing on specific hemp plant components, materials, or products, this study systematically evaluates the utilization pathways of the whole plant (stalk, flower, leaf, and seed) to traditional, industrial, and emerging products. Further, myriad opportunities for hemp material and product applications, sustainability performance assessment, and future research are discussed. This review will benefit future hemp research, advancing process technologies, developing novel products, establishing policies and standards, and assessing sustainability performance. [ABSTRACT FROM AUTHOR]

In this study, the volatile flavour compounds of baked potatoes from different cultivars were identified and analysed via headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) together with principal component analysis (PCA). Totally, 89 signal peaks were detected, and 64 compounds were identified and classified into 25 aldehydes, 14 alcohols, 11 ketones, 5 esters, 2 furans, 1 pyrazine and others. Based on the signal intensity of the identified compounds, topographic plots and fingerprints were established to investigate the differences caused by cultivars. The main compounds including aldehydes, alcohols, ketones, esters, furan and pyrazine varied with cultivars; they were more abundant in B5 and B6 cultivars. Moreover, PCA and fingerprint similarity analysis clearly showed that these samples are independent and distinguishable. Results also showed that the GC-IMS technique might be helpful and useful in the rapid detection of volatile compounds in cooked potatoes and provide support for the domestic and industrial products. The obtained information could serve for breeding projects of new varieties with specific flavour profiles which comply with consumer demand. [ABSTRACT FROM AUTHOR]

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system. [ABSTRACT FROM AUTHOR]

BACKGROUND: The steam processing characteristics of chicken are a key factor in the simplicity and versatility of steamed chicken dishes. The aim of this study was to investigate in depth the changes in tenderness and water retention of marinated chicken at different slow steaming endpoint temperatures, and to further explore the effect of the evolution of protein conformations on the water status. RESULTS: The results showed that chicken samples' shear force peaked at 80 °C and decreased rapidly at 90 °C. As the steaming endpoint temperature increased between 50 and 90 °C, T21, T22, moisture content and centrifugal loss decreased, but P21, P22 and myofibril water‐holding capacity showed regular changes. The electrophoretic bands and protein conformation changes showed that protein in marinated chicken underwent different degrees of denaturation, degradation and aggregation. And at 70 °C, with an increase of hydrophobic groups and crosslinking of disulfide bonds as well as an increase in the number of denatured sarcoplasmic proteins, the intermolecular network was enhanced, thus affecting the water retention. CONCLUSION: Water status of chicken meat heated at different steaming temperatures is closely related to the evolution of protein conformations. The present study serves as a robust theoretical foundation for enhancing the quality of steamed chicken products at an industrial scale. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Camellia drupifera is mainly used in forestry for its high-value industrial products; however, limited information is available on its transcriptome. This study aimed to construct a full-length transcriptome sequence based on the PacBio sequencing platform for various plant parts of C. drupifera, including flower buds, leaves, leaf buds, branches, the pericarp, and seed kernels. The transcriptomes were annotated with 23,207 genes, with 58 subgroups in the GO classification. The KEGG database revealed 10,407 genes involved in the metabolic pathway analysis, with 68,192 coding sequences, 3352 TF families, 48,541 SSRs, 1421 IncRNAs, and 2625 variable shears predicted. The transcriptomes of different parts were analyzed and compared. The majority of differentially expressed genes (DEGs) were found between the pericarp and seed kernels, followed by leaves and the pericarp with 5662 DEGs, and flower buds and leaf buds with 1616 DEGs. GO and KEGG enrichment analyses showed that KEGG differential genes were significant in microbial metabolism, carbon metabolism, and other functions. The data annotation and analysis of the full-length transcriptome and the comparative analysis between different plant parts provided a theoretical basis for studying gene function, metabolic pathway regulation, and gene expression analysis in KEGG. [ABSTRACT FROM AUTHOR]

The current object detection algorithm based on CNN makes it difficult to effectively capture the characteristics of subtle defects in online industrial product packaging bags. These defects are often visually similar to the texture or background of normal product packaging bags, and the model cannot effectively distinguish them. In order to deal with these challenges, this paper optimizes and improves the network structure based on YOLOv8 to achieve accurate identification of defects. First, in order to solve the long-tail distribution problem of data, a fuzzy search data enhancement algorithm is introduced to effectively increase the number of samples. Secondly, a joint network of FasterNet and SPD-Conv is proposed to replace the original backbone network of YOLOv8, which effectively reduces the computing load and improves the accuracy of defect identification. In addition, in order to further improve the performance of multiscale feature fusion, a weighted bidirectional feature pyramid network (BiFPN) is introduced, which effectively enhances the model's ability to detect defects at different scales through the fusion of deep information and shallow information. Finally, in order to reduce the sensitivity of the defect position deviation, the NWD loss function is used to optimize the positioning performance of the model better and reduce detection errors caused by position errors. Experimental results show that the FSNB_YOLOv8 model proposed in this paper can reach 98.8% mAP50 accuracy. This success not only verifies the effectiveness of the optimization and improvement of this article's model but also provides an efficient and accurate solution for surface defect detection of industrial product packaging bags on artificial assembly systems. [ABSTRACT FROM AUTHOR]

The drying procedure is an important consideration for the silicon powder processing. Herein, a comparative investigation of the drying efficiency, drying performance, and energy consumption of silicon powder after separation and purification of monocrystal silicon cutting slurry by using the hot air, far‐infrared and microwave drying treatments. The results show that microwave drying of 30 g silicon powder takes only 12 min, which is much shorter than hot air drying of 200 min and far infrared drying of 40 min. The water evaporation energy consumption of microwave drying reached 73.5 g/kW·h, which was 3.3 times than that of far infrared drying and 16.5 times than that of hot air drying. For 1 kg of silicon powder, the power consumption of microwave drying was 8 kW·h, while the far‐infrared drying was 26.7 kW·h, and the hot air drying was 133.4 kW·h, respectively. Therefore, effects of microwave power, drying temperature and drying time on the dehydration rate of silicon powder were investigated and optimized by response surface method, demonstrating that the microwave drying dehydration rate of 30 g silicon powder could reach 97.65%, and the power consumption could be reduced to 6.7 kW·h/kg under the conditions of microwave power of 1000 W, drying temperature of 89°C and drying time of 12 min. The present study shows that microwave drying of silicon powder is an energy‐saving and efficient process with good industrial application prospects. [ABSTRACT FROM AUTHOR]

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cicer arietinum or chickpea is an important and highly nutritious pulse, a source of complex carbohydrates, proteins, vitamins, and minerals, considered non-allergenic, and non-GMO crop. Processing technologies play an important role in modifying some chickpea properties and thus increasing its nutritional and health benefits. Herein is summarized and compared the available data on nutritional and functional aspects caused by thermal, nonthermal, and combinations of treatments for chickpea processing. The study focuses on describing the processing conditions necessary to change chickpea matrices aiming to enhance compound bioavailability, reduce anti-nutritional factors and modify functional characteristics for industrial application in product development. Thermal and nonthermal treatments can modify nutrient composition and bioavailability in chickpea matrices. Thermal treatments, moist or dry, prevent microbial spoilage, increase product palatability and increase protein quality. Nonthermal treatments aim to shorten the processing time and use less energy and water sources. Compared to thermal treatments, they usually preserve organoleptic attributes and bioactive compounds in chickpea matrices. Some treatment combinations can increase the efficacy of single treatments. Combined treatments increase antioxidant concentration, protein digestibility and available starch contents. Finally, despite differences among their effects, single and combined treatments can improve the nutritional and physicochemical properties of chickpea matrices. [ABSTRACT FROM AUTHOR]

Contamination of agricultural and industrial products by Aspergillus section Circumdati becomes a true problem, especially, because a lot of species in this section can yield ochratoxins and other toxins. In this study, morphological criteria and partial calmodulin gene were used to identify 34 strains belonging to Aspergillus section Circumdati isolated from Vitis vinifera and Calotropis procera plants. The population is characterised by A. insulicola, A. ochraceopetaliformis, A. ochraceus, and A. pseudoelegans. The polyketide synthase (pks) gene involved in ochratoxins (OTA) production was investigated by Aopks1, 2 and AoLc35–12L, R primers. Fifteen strains belonging to A. ochraceus were positive for pks gene, whilst A. insulicola, A. ochraceopetaliformis, and A. pseudoelegans were negative. All tested strains were able to produce ochratoxin A with different levels of 0.020–53 ppm except one isolate of A. ochraceus (IAEMAo6). The green synthesised ZnO-NPs have a significant inhibitory effect on OTA production by A. insulicola and A. pseudoelegans. [ABSTRACT FROM AUTHOR]

Polymer adhesives play an important role in many medical, consumer, and industrial products. Polymers of α-lipoic acid (αLA) have the potential to fulfill the need for versatile and environmentally friendly adhesives, but their performance is plagued by spontaneous depolymerization. We report a family of stabilized αLA polymer adhesives that can be tailored for a variety of medical or nonmedical uses and sustainably sourced and recycled in a closed-loop manner. Minor changes in monomer composition afforded a pressure-sensitive adhesive that functions well in dry and wet conditions, as well as a structural adhesive with strength equivalent to that of conventional epoxies. αLA surgical superglue successfully sealed murine amniotic sac ruptures, increasing fetal survival from 0 to 100%. [ABSTRACT FROM AUTHOR]

AbstractFailure analysis and fatigue life prediction are important steps in the design procedure of industrial products to assure the safety and reliability of their components. A model to calculate the heat generation by the hysteresis loss in natural rubber subjected to cyclic loads in a uniaxial direction was introduced and the fatigue life prediction model of the rubber was established based on the theory of continuum damage mechanics (CDM). Dynamic mechanical analysis (DMA) was used to measure the viscoelasticity of the rubber material. The heat generation in the rubber material was calculated using a subroutine with the programming software C++ and the temperature rise in the rubber material was analyzed by a thermal finite element analysis (FEA) method. Then static mechanical FEA models of a rubber mount were established and the strain contours of the rubber mounts at various loads were calculated. The total principal strain was used as the fatigue parameter to predict the fatigue life of the rubber mount. Finally, the fatigue lives of the rubber mounts at various loads were measured on a test rig to validate the accuracy of the prediction method. The test results indicated that the fatigue lives predicted considering the temperature rise agreed better with the test results compared with those not considering the temperature rise and we suggest our fatigue prediction method should be applicable to both rubber and other types of components. [ABSTRACT FROM AUTHOR]

Aim: This study investigates the content of Lactobacillus acidophilus, Levilactobacillus brevis and Lactiplantibacillus plantarum in traditional and industrially prepared cabbage and cucumber pickles. Material and Method: Pickle samples were categorized according to the industrial (n=20) and traditional (n=38) production methods. The chemical compositions, including salt contents and pH of the pickles, were evaluated. The salt content of the pickles produced by the traditional method was recorded based on the manufacturer’s declaration. Label information was assessed in industrially produced pickles. pH measurements were made using a desktop pH meter. Microbial load, including Lactobacillus acidophilus, Levilactobacillus brevis and Lactiplantibacillus plantarum counts of the pickles was carried out in a Real-time PCR device (RotorGene-Q, Germany) using the Diagen Real-time PCR Kit. Statistical analysis was performed using IBM Statistical Package for Social Sciences (SPSS) program version 22.0. Results: The pH values of the pickles produced by the traditional method had higher pH values than the industrial products (p <0.05). Conventional and industrial pickles had similar salt content except for the industrial cabbage pickles (3.16 g/100 g, p=0.002). Lactobacillus acidophilus and Levilactobacillus brevis contents of traditional cucumber pickles (4.25±0.88 and 5.55±1.37 log10 cfu/g, respectively) were found to be significantly higher than those of industrial cabbage pickles (3.33±0.55 and 1.53±2.11 log10 cfu/g, respectively, p <0.05). Conclusion: This study’s results, which found higher Lactobacillus spp. content in traditionally produced pickles than industrial ones, provide preliminary data for future studies investigating the bacterial community patterns and the proportions of predominant bacteria in pickles, which are fermented products consumed frequently in Türkiye. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFAS) have become internationally recognized over the past three decades as persistent organic pollutants used in the production of various consumer and industrial goods. Research efforts continue to gauge the risk that historically used, and newly produced, PFAS may cause to human health. Numerous studies report toxic effects of PFAS on the human liver as well as increased serum cholesterol levels in adults. A major concern with PFAS, also dubbed "forever chemicals," is that they accumulate in the liver and kidney and persist in serum. The mechanisms responsible for their disposition and excretion in humans are poorly understood. A better understanding of the interaction of PFAS with liver transporters, as it pertains to the disposition of PFAS and other xenobiotics, could provide mechanistic insight into human health effects and guide efforts toward risk assessment of compounds in development. This review summarizes the current state of the literature on the emerging relationships (eg, substrates, inhibitors, modulators of gene expression) between PFAS and specific hepatic transporters. The adaptive and toxicological responses of hepatocytes to PFAS that reveal linkages to pathologies and epidemiological findings are highlighted. The evidence suggests that our understanding of the molecular landscape of PFAS must improve to determine their impact on the expression and function of hepatocyte transporters that play a key role in PFAS or other xenobiotic disposition. From here, we can assess what role these changes may have in documented human health outcomes. [ABSTRACT FROM AUTHOR]

Microplastics have been recently detected in many environmental media and living organisms, yet their transfer and toxicity to humans are poorly known. Here, we review microplastic transfer in the food chain with focus on microplastic pollution sources, methods to analyze microplastics in food, health impact of food-related microplastic exposure, and remediation of microplastic pollution. Microplastic pollution sources include seafood, food additives, packaging materials, and agricultural and industrial products. Remediation techniques comprise the use of microbial enzymes and biofilms. Microplastic detection methods in food rely on separation and quantification by optical detection, scanning electron micrography, and Fourier-transform infrared spectroscopy. Human health impact following microplastic ingestion include cancers, organ and respiration damage, and reproductive impairments. Overall, microplastic toxicity is mainly due to their ability to enter the metabolism, adsorption into the circulatory system for translocation, and difficulty, if not impossibility, of excretion. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Finite Element Method (FEM) has emerged as a powerful tool for predicting the behavior of industrial products, including those with complex geometries or uncommon materials. Finite Element Analysis (FEA) is widely used to study structural vibration-related aspects such as stress, displacement, and velocity. Modal analysis, a standard technique for characterizing the vibrational behavior of structures, is essential for identifying resonance frequencies, optimizing component design, and assessing structural integrity. Finite Elements (FE) modal analysis enables engineers to evaluate numerically the modal parameters, whereas model order reduction (MOR) schemes are exploited to achieve a balance between computational efficiency and accuracy, enabling a more efficient solution for computing transient dynamic analysis. Assessing the accuracy and reliability of FE solutions is a crucial aspect of the design cycle, and model-updating procedures are commonly employed to maximize the correlation between measured and predicted dynamic behavior. This study investigated the accuracy and computational efficiency of linear, quadratic, and cubic hexahedral FE formulations for modal analysis and transient dynamic solutions. More specifically, the documented results demonstrate the profitable use of the eigenenergy norm obtained in eigen solutions as a valid predictor of the accuracy reported using either the time response assurance criterion (TRAC) or the frequency response assurance criterion (FRAC), measured in transient dynamic cases. Moreover, our results also highlight the superior computational efficiency of higher-order formulations for both the eigen and transient dynamic solutions. [ABSTRACT FROM AUTHOR]