Practical Applications Research Articles

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

Focus on senescence: Clinical significance and practical applications.

The older population is increasing worldwide, and life expectancy is continuously rising, predominantly thanks to medical and technological progress. Healthspan refers to the number of years an individual can live in good health. From a gerontological viewpoint, the mission is to extend the life spent in good health, promoting well-being and minimizing the impact of aging-related diseases to slow the aging process. Biologically, aging is a malleable process characterized by an intra- and inter-individual heterogeneous and dynamic balance between accumulating damage and repair mechanisms. Cellular senescence is a key component of this process, with senescent cells accumulating in different tissues and organs, leading to aging and age-related disease susceptibility over time. Removing senescent cells from the body or slowing down the burden rate has been proposed as an efficient way to reduce age-dependent deterioration. In animal models, senotherapeutic molecules can extend life expectancy and lifespan by either senolytic or senomorphic activity. Much research shows that dietary and physical activity-driven lifestyle interventions protect against senescence. This narrative review aims to summarize the current knowledge on targeting senescent cells to reduce the risk of age-related disease in animal models and their translational potential for humans. We focused on studies that have examined the potential role of senotherapeutics in slowing the aging process and modifying age-related disease burdens. The review concludes with a general discussion of the mechanisms underlying this unique trajectory and its implications for future research.

Underground Diagnosis Based on GPR and Learning in the Model Space.

Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or anomalies automatically. In this article, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model.

Adaptability and crack resistance effect of continuous fiber anti-crack composite sealing layer

Traditional anti-crack seals are greatly affected by temperature effects and lack adaptability, making them vulnerable under extreme conditions. Therefore, a novel composite sealing layer material for crack prevention was proposed. This material combines fibers with a fine anti-slip wear layer, which is placed on top of the crushed stone layer. Furthermore, the XFEM was utilized to analyze the adaptability and anti-crack effectiveness of the continuous fiber anti-crack composite sealing layer. The conducted experiments revealed that as the cracks in the base pavement widened from 1.0 mm to 2.0 mm, there was an observed increase of 11.4% in stress intensity factor. A variation in the stress intensity factor of 6.3% was observed within the specified range of crack width when the modulus of the continuous fiber anti-crack composite sealing layer was fixed at 300 kPa. These results are of vital importance in comprehending the performance of this material in terms of crack resistance adaptability and its influence on practical applications.

Oral administration of Bacillus coagulans TQ-35 alleviates allergic responses in OVA-sensitive BALB/c mice

Bacillus coagulans has been extensively studied so far, but there has been a lack of research on its usage in allergy. In this study, we designed to assess the effect of different concentrations of B. coagulans on food allergy in a BALB/c mouse model of ovalbumin (OVA)-induced food allergy and its effect on gut microbes. The assessment of symptoms, specific immunoglobulin E (IgE), T-cell differentiation, and related gene expression levels in sensitized mice by assay indicated that high doses of oral B. coagulans could alleviate allergic symptoms. Treatment with B. coagulans, in the high-dose group, significantly reduced IgE and IgG1 levels and modulated the balance of T helper type 1 cell (Th1) and Th2 and the expression of relevant genes in the spleen. 16S rRNA analysis showed that probiotics improved the structure of the microbiota, in particular by boosting the percentage of Clostridia, Bacteroides vulgatus and Enterococcus faecium, and by increasing the abundance of microbial species, thereby modulating the immune system. Therefore, this study can provide insights into the practical application of B. coagulans doses to alleviate OVA allergy.

Chattering Free Sliding-Mode Controller Design for Underactuated Tower Cranes With Uncertain Disturbance

Tower cranes are widely used in various industrial sites and require a high level of anti-disturbance performance from the crane controller due to the variability of the application environment. Although sliding mode control is a typical anti-disturbance control method, most sliding mode controllers suffer from output chattering and do not reflect well under severe external disturbances. To address these issues from a practical application perspective, a finite time sliding mode control method using the super-twisting algorithm (STA) is proposed for underactuated tower cranes operating in the presence of external disturbances. The proposed method includes a finite-time chattering-free sliding mode controller that eases the output torque chattering problem and a finite-time sliding mode observer based on STA to observe the uncertain disturbances. The observed values are then applied to the controller through feedforward compensation. Experimental results show that the proposed control method effectively weakens output torque chattering, compensates for disturbance quickly, and accurately completes control tasks.

Multi-stage generative adversarial networks for generating pavement crack images

The application of machine learning techniques in pavement health monitoring based on computer vision has greatly improved the accuracy and efficiency in the detection of pavement distress levels and categories. However, a persistent challenge in this field is the issue of sample imbalance, primarily arising from the scarcity of cracked pavement images, which hampers their effectiveness in road maintenance engineering. To address this issue and enhance the fast and stable generation of high-quality crack images for engineering purposes, this study proposes two frameworks based on Generative Adversarial Networks (GAN): Multi-Stage GAN-v1 and Multi-Stage GAN-v2. These frameworks break down the complex task of directly generating high-quality images into a series of incremental steps, gradually increasing the image resolution from initially generated low-precision images. Both versions, v1 and v2, consist of multiple sequentially connected generation units, with each unit utilizing the Wasserstein Generative Adversarial Network-Gradient Penalty (WGAN-GP). Furthermore, v2 has the additional capability of generating pavement crack images of specified types and simultaneously providing crack segmentation labels. This feature significantly enhances the practical applicability of the generated data in engineering contexts. In a comprehensive case study, the evaluation results clearly illustrate the superior image generation quality from the two proposed frameworks. Moreover, the results from ablation experiments, involving the training of nine state-of-the-art crack semantic segmentation and object detection networks using both generated images and real images, demonstrate the effective utility of these generated images for training pavement distress detection networks.

Detecting Line Segments in Motion-Blurred Images With Events.

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.

Green Synthesized Chitosan Nanoparticles for Controlling Multidrug-Resistant mecA- and blaZ-Positive Staphylococcus aureus and aadA1-Positive Escherichia coli.

Antimicrobial resistance has recently been considered an emerging catastrophe globally. The public health and environmental threats were aggravated by the injudicious use of antibiotics in animal farming, aquaculture, and croup fields, etc. Consequently, failure of antibiotic therapies is common because of the emergence of multidrug-resistant (MDR) bacteria in the environment. Thus, the reduction in antibiotic spillage in the environment could be an important step for overcoming this situation. Bear in mind, this research was focused on the green synthesis of chitosan nanoparticles (ChiNPs) using Citrus lemon (Assam lemon) extract as a cross-linker and application in controlling MDR bacteria to reduce the antibiotic spillage in that sector. For evaluating antibacterial activity, Staphylococcus aureus and Escherichia coli were isolated from environmental specimens, and their multidrug-resistant pattern were identified both phenotypically by disk diffusion and genotypically by detecting methicillin- (mecA), penicillin- (blaZ), and streptomycin (aadA1)-resistance encoding genes. The inhibitory zone's diameter was employed as a parameter for determining the antibacterial effect against MDR bacteria revealing 30 ± 0.4 mm, 34 ± 0.2 mm, and 36 ± 0.8 mm zones of inhibition against methicillin- (mecA) and penicillin (blaZ)-resistant S. aureus, and streptomycin (aadA1)-resistant E. coli, respectively. The minimum inhibitory concentration at 0.31 mg/mL and minimum bactericidal concentration at 0.62 mg/mL of yielded ChiNPs were used as the broad-spectrum application against MDR bacteria. Finally, the biocompatibility of ChiNPs was confirmed by showing a negligible decrease in BHK-21 cell viability at doses less than 2 MIC, suggesting their potential for future application in antibiotic-free farming practices.

НЕЙМИНГ: СУЩНОСТЬ, ОСОБЕННОСТИ И ПРАКТИКА ПРИМЕНЕНИЯ

The concept of naming as one of the most important stages of creating an effective brand is re-vealed. The author's classification of naming techniques, their characterization and examples of prac-tical application by foreign and domestic companies are presented

Simultaneous decomplexation of Pb-EDTA and elimination of free Pb ions by MoS2/H2O2: Mechanisms and applications

The critical challenge of effectively removing Pb-EDTA complexes and Pb(II) ions from wastewater is pivotal for environmental remediation. This research introduces a cutting-edge bulk-MoS2/H2O2 system designed for the simultaneous decomplexation of Pb-EDTA complexes and extraction of free Pb(II) ions, streamlining the process by eliminating the need for subsequent treatment stages. The system exhibits outstanding efficiency, achieving 98.1% decomplexation of Pb-EDTA and 98.6% removal of Pb. Its effectiveness is primarily due to the generation of reactive oxygen species, notably •OH and O2•- radicals, facilitated by bulk-MoS2 and H2O2. Key operational parameters such as reagent dosages, Pb(II): EDTA molar ratios, solution pH, and the presence of coexisting ions were meticulously evaluated to determine their impact on the system's performance. Through a suite of analytical techniques, the study confirmed the disruption of Pb–O and Pb–N bonds, further elucidating the decomplexation process. It also underscored the synergistic role of bulk-MoS2’s adsorption properties and the formation of PbMoO4-like precipitates in enhancing Pb elimination. Demonstrating the bulk-MoS2/H2O2 system as a robust, one-step solution that meets stringent Pb emission standards, this study provides in-depth insights into the removal mechanisms of Pb-EDTA, affirming its potential for broader application in wastewater treatment practices.

Exploring Practical Pedagogy in High School Biology Education: A Qualitative Study of Pre-Service Biology Teachers’ Experiences in Vietnam

<p style="text-align: justify;">This qualitative study examines the experiences of pre-service high school biology teachers in the Mekong Delta, Vietnam, focusing on the challenges and transformative learning experiences encountered during their school-based internship. It aims to explore the dichotomy between specialized biology knowledge and practical applicability in high school teaching and uncover the significance of fostering student engagement through innovative strategies and the role of mentor teachers in guiding pre-service teachers. Semi-structured interviews were used to collect data from pre-service biology teachers who completed a school-based internship in the Mekong Delta. Thematic analysis was employed to analyze the interview data. The study highlights the challenges of bridging the gap between theoretical knowledge and classroom practice, emphasizing the importance of innovative teaching strategies for student engagement. It recognizes the role of mentor teachers in guiding and enhancing the skills of pre-service teachers in creating effective learning environments. Findings suggest the need for curriculum reforms that balance specialized biology knowledge and practical pedagogical skills. The study emphasizes the importance of experiential learning through teaching apprenticeships to better prepare pre-service teachers for their professional journey.</p>

Measuring Interest: Development and Application of a Three-Dimensional Situational Interest Short Scale

<p>Situational interest is an important factor that has a great influence on learning success in both in-school and out-of-school learning situations. Although there has been extensive research on interest in its diverse forms for decades, an evaluated measurement instrument for situational interest that covers the three theoretically defined components of interest (emotional, cognitive, value-related) is still missing. Therefore, in this study, based on person-object theory of interest, a short scale was developed that can be used in a variety of learning programs independent of content or methods. In study 1, eight suitable items were selected and their structure was examined using exploratory methods. In study 2, the results of study 1 were verified using confirmatory factor analyses. Study 3 shows an example of a practical application of the newly developed scale in two different learning settings. The findings provide evidence that the scale developed here is a practical instrument to measure situational interest taking into account all its components. On the one hand, the scale can help teachers evaluate their educational programs; on the other hand, it can be used by researchers to empirically investigate the construct of interest. Thus, the scale makes an important contribution to research and practice.</p>

Enhanced Carbon-Carbon Coupling at Interfaces with Abrupt Coordination Number Changes.

Cu-catalyzed electrochemical CO2 reduction reaction (CO2RR) produces multi-carbon (C2+) chemicals with considerable selectivities and activities, yet required high overpotentials impede its practical application. Here, we design interfaces with abrupt coordination number (CN) changes that greatly reduce the applied potential for achieving high C2+ Faradaic efficiency (FE). Encouraged by the mechanistic finding that the coupling between *CO and *CO(H) is the most probable C-C bond formation path, we use Cu2O- and Cu-phthalocyanine-derived Cu (OD-Cu and PD-Cu) to build the interface. Using operando X-ray absorption spectroscopy (XAS), we find that the Cu CN of OD-Cu is ~11, favoring CO* adsorption, while the PD-Cu has a COH*-favorable CN of ~4. Operando Raman spectroscopy revealed that the interfaces with abrupt CN changes promote *OCCOH formation. As a result, the designed catalyst achieves a C2+ FE of 85±2 % at 220 mA cm-2 in a zero-gap CO2 electrolyzer. An improvement of C2+ FE by 3 times is confirmed at the low potential regime where the current density is 60-140 mA cm-2, compared to bare OD-Cu. We report a 45-h stable CO2RR operation at 220 mA cm-2, producing a C2+ product FE of ~80 %.

Principles and Design of Biphasic Self‐Stratifying Batteries Toward Next‐Generation Energy Storage

AbstractLarge‐scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self‐stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application.

Lowering Entropic Barriers in Triplex DNA Switches Facilitating Biomedical Applications at Physiological pH

AbstractTriplex DNA switches are attractive allosteric tools for engineering smart nanodevices, but their poor triplex‐forming capacity at physiological conditions limited the practical applications. To address this challenge, we proposed a low‐entropy barrier design to facilitate triplex formation by introducing a hairpin duplex linker into the triplex motif, and the resulting triplex switch was termed as CTNSds. Compared to the conventional clamp‐like triplex switch, CTNSds increased the triplex‐forming ratio from 30 % to 91 % at pH 7.4 and stabilized the triple‐helix structure in FBS and cell lysate. CTNSds was also less sensitive to free‐energy disturbances, such as lengthening linkers or mismatches in the triple‐helix stem. The CTNSds design was utilized to reversibly isolate CTCs from whole blood, achieving high capture efficiencies (>86 %) at pH 7.4 and release efficiencies (>80 %) at pH 8.0. Our approach broadens the potential applications of DNA switches‐based switchable nanodevices, showing great promise in biosensing and biomedicine.

Principles and Design of Biphasic Self-Stratifying Batteries Toward Next-Generation Energy Storage.

Large-scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self-stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application.

Lowering Entropic Barriers in Triplex DNA Switches Facilitating Biomedical Applications at Physiological pH.

Triplex DNA switches are attractive allosteric tools for engineering smart nanodevices, but their poor triplex-forming capacity at physiological conditions limited the practical applications. To address this challenge, we proposed a low-entropy barrier design to facilitate triplex formation by introducing a hairpin duplex linker into the triplex motif, and the resulting triplex switch was termed as CTNSds. Compared to the conventional clamp-like triplex switch, CTNSds increased the triplex-forming ratio from 30 % to 91 % at pH 7.4 and stabilized the triple-helix structure in FBS and cell lysate. CTNSds was also less sensitive to free-energy disturbances, such as lengthening linkers or mismatches in the triple-helix stem. The CTNSds design was utilized to reversibly isolate CTCs from whole blood, achieving high capture efficiencies (>86 %) at pH 7.4 and release efficiencies (>80 %) at pH 8.0. Our approach broadens the potential applications of DNA switches-based switchable nanodevices, showing great promise in biosensing and biomedicine.

A Self-Adaptive Inertia Hybrid Control of a Fully Constrained Cable-Driven Parallel Robot

A Cable-Driven Parallel Robot (CDPR) is a special type of parallel robot actuated by elastic cables instead of rigid links, which brings uncertainties of model nonlinearities and structural friction depending on the payload variation. A precise motion with minimum control effort against an unknown payload has been a challenging task. To conquer it, we propose an adaptive hybrid control approach for the fully-constrained CDPR with an unknown payload in this paper. A Cartesian space position control and a joint space cable force control are designed through inertia estimation to encounter the effect of the uncertain payload. And then, an artificial neural network (ANN) is employed to estimate the end-effector force in Cartesian space for precise force control and consecutive uncertainties compensation. The stability of the proposed controller is proven based on the Lyapunov method and the efficacy is verified by the experiments. As for feasibility results, the proposed adaptive hybrid control-based cable force estimation was evaluated on the Mini CDPR. Compared to the traditional control scheme, the tracking error was decreased approximately 56% and the utilized tension was significantly reduced for the given trajectory tracking test. Furthermore, the investigation of free movements of the helix trajectory and pick-and-place tasks showed that the proposed hybrid adaptive control can control the position and cable force altogether while being able to estimate the inertia of the end-effector. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The study was motivated by the control problem of adjusting cable tensions of a cable-driven parallel robot (CDPR) against varying payloads at the end-effector. Several advanced control approaches were proposed for but limited to CDPR model parameter estimation and did not consider varying payload. This paper suggests a practical methodology capable of both self-adaptation to unknown payload inertia and consequently improving tracking control performance for a CDPR. In this paper, we derived an adaptive control model and proved stability to ensure safety. In order to encounter unmodeled uncertainties such as pulley friction and cable characteristics, we utilized an artificial neural network (ANN) trained by experiments. The experimental results could confirm that the proposed scheme can estimate the payload inertia and generate minimized cable tension while maintaining good trajectory tracking performance. In future research, we will show more practical validation for a scale-up CDPR with heavy objects toward logistics or pick-and-place application in practice.

Diversity Management: Practical Application in a Healthcare Organization.

If you are not confused, you don't know what's going on. -Jack Welch In commenting on the business environment today (1994), Jack Welch could have been talking about healthcare organizations experiencing the double-barreled assault on two sweeping changes simultaneously. Not only are hospitals preparing for yet-to-be-determined changes in national healthcare delivery systems but they are doing so in the midst of shifting demographics in both the population and employee base. While many make the case for managing diversity, we would like to go one step further in offering a framework for developing an organization's ability to build an inclusive environment that gets the best from its staff and provides the best service to its customers.