Memoona Aslam, Nidhi Singh, Xiaowen Wang, Wenjin Li
YTHDC1 (YTH domain containing 1), a crucial reader protein of N6-methyladenosine (m6A) mRNA, plays a critical role in various cellular functions and is considered a promising target for therapeutic intervention in acute myeloid leukemia and other cancers. In this study, we identified orthosteric small-molecule ligands for YTHDC1. Using a molecular docking approach, we screened the eMolecules database and recognized 15 top-ranked ligands. Subsequently, molecular dynamics simulations and MM/PBSA analysis were used to assess the stability and binding free energy of these potential hit compounds in complex with YTHDC1. Notably, five compounds with IDs of ZINC82121447, ZINC02170552, ZINC65274016, ZINC10763862, and ZINC02412146 exhibited high binding affinities and favorable binding free energies. The results also showed that these compounds formed strong hydrogen bonds with residues SER378, ASN363, and ASN367 and interacted with the aromatic cage of the YTHDC1 reader protein through TRP377, TRP428, and hydrophobic residue LEU439. To assess their viability as lead compounds, we conducted absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies to reveal promising features for these identified small molecules, shedding light on their pharmacokinetic and safety profiles.
{"title":"Virtual Screening and Molecular Dynamics Simulation to Identify Inhibitors of the m6A-RNA Reader Protein YTHDC1","authors":"Memoona Aslam, Nidhi Singh, Xiaowen Wang, Wenjin Li","doi":"10.3390/app14188391","DOIUrl":"https://doi.org/10.3390/app14188391","url":null,"abstract":"YTHDC1 (YTH domain containing 1), a crucial reader protein of N6-methyladenosine (m6A) mRNA, plays a critical role in various cellular functions and is considered a promising target for therapeutic intervention in acute myeloid leukemia and other cancers. In this study, we identified orthosteric small-molecule ligands for YTHDC1. Using a molecular docking approach, we screened the eMolecules database and recognized 15 top-ranked ligands. Subsequently, molecular dynamics simulations and MM/PBSA analysis were used to assess the stability and binding free energy of these potential hit compounds in complex with YTHDC1. Notably, five compounds with IDs of ZINC82121447, ZINC02170552, ZINC65274016, ZINC10763862, and ZINC02412146 exhibited high binding affinities and favorable binding free energies. The results also showed that these compounds formed strong hydrogen bonds with residues SER378, ASN363, and ASN367 and interacted with the aromatic cage of the YTHDC1 reader protein through TRP377, TRP428, and hydrophobic residue LEU439. To assess their viability as lead compounds, we conducted absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies to reveal promising features for these identified small molecules, shedding light on their pharmacokinetic and safety profiles.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.
在生长发育阶段，茄子很容易受到病害的侵袭，从而影响作物产量和农民的经济收益。因此，及时有效地检测茄子病害至关重要。基于深度学习的物体检测算法可以自动提取茄子病害图像的特征。然而，在复杂的农田环境中捕获的茄子病害图像存在病害大小不一、遮挡、重叠和小目标检测等挑战，使得现有的深度学习模型难以达到令人满意的检测性能。为解决这一难题，本研究基于 You Only Look Once version 8 nano（YOLOv8n）提出了一种优化的茄子病害检测算法 YOLOv8-E。首先，我们在 C2f 模块中集成了可切换无绳卷积（SAConv），设计了 C2f_SAConv 模块，替代了 YOLOv8n 骨干网络中的部分 C2f 模块，使我们提出的算法能够更好地提取茄子病害特征。其次，为了便于在移动设备上部署检测模型，我们使用 SlimNeck 模块重构了 YOLOv8n 的 Neck 网络，使模型更加轻便。此外，为了解决遗漏小目标的问题，我们在 SlimNeck 中嵌入了大型可分离核关注（LSKA）模块，增强了模型对细粒度信息的关注。最后，我们结合了带辅助边界框的联合交集（Inner-IoU）和联合交集最小点距（MPDIoU），引入了 Inner-MPDIoU 损失，以加快模型的收敛速度，提高重叠和遮挡目标的检测精度。消融研究表明，与 YOLOv8n 相比，YOLOv8-E 的平均精度 (mAP) 和 F1 分数分别达到 79.4% 和 75.7%，分别提高了 5.5% 和 4.5%，同时还减少了模型大小和计算复杂度。此外，与其他主流算法相比，YOLOv8-E 实现了更高的检测性能。YOLOv8-E 在茄子病害检测中的实际应用潜力巨大。
{"title":"YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection","authors":"Yuxi Huang, Hong Zhao, Jie Wang","doi":"10.3390/app14188403","DOIUrl":"https://doi.org/10.3390/app14188403","url":null,"abstract":"During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.
关于通勤时间随时间变化的稳定性，已有许多理论和实证交通研究提出了质疑。通勤时间恒定假说认为，人们会调整出行时长、转换出行方式并对出行地点进行分类，从而使其平均通勤时间保持在一个恒定的预算范围内。采用总量分析的研究与采用分类分析的研究之间存在差异，数据收集方面的差异可能是导致文献中报告的结论各不相同的原因。本研究通过对波特兰地区的两次旅行调查进行了总量和分类分析。我们采用描述性分析和 t 检验来比较两年的总体通勤时间，并使用回归模型来探讨影响单次出行的分类通勤时间的因素，以研究在交通和土地使用系统发生重大变化后，通勤时间是否保持稳定。我们的研究表明，在研究期间，随着非驾车出行方式所占比例的变化，平均通勤时间和平均通勤距离都略有增加。由于非驾驶模式所占比例的增长比驾驶模式慢，再加上出行距离的增加，导致平均通勤时间略有增加。我们的分析还表明，公交乘客和司机的平均出行速度都有所提高，这与早先的研究相矛盾，因为早先的研究称公共交通投资加剧了波特兰的交通拥堵状况。
{"title":"Application of Historical Comprehensive Multimodal Transportation Data for Testing the Commuting Time Paradox: Evidence from the Portland, OR Region","authors":"Huajie Yang, Jiali Lin, Jiahao Shi, Xiaobo Ma","doi":"10.3390/app14188369","DOIUrl":"https://doi.org/10.3390/app14188369","url":null,"abstract":"There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.
{"title":"A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model","authors":"Piaofang Yu, Bo Lin","doi":"10.3390/app14188350","DOIUrl":"https://doi.org/10.3390/app14188350","url":null,"abstract":"Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that facilitate collaborative work on shared text documents across multiple users in near real time. It aims to dive into the intricacies of designing robust and effective document collaboration software focusing on understanding the requirements of such a system, the working principle of collaborative text editing, software architecture, technology stack selection, and tooling that can sustain such a system. To examine the pros and cons of the proposed system, the paper will detail how collaborative text editing software can benefit from such an architecture regarding availability, elasticity, and scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems and distributed systems. This research first examines the requirements of a real-time collaboration system and the necessary core features. Then, it analyzes the design, the application structure, and the system organization while also considering key architectural requirements as the necessity of scaling, the usage of microservices, cross-service communications, and client–server communication. For the technology stack of the implementation, this research considers the alternatives at each layer, from client to server. Once these decisions are made, it follows system development while examining possible improvements for the issues previously encountered. To validate the architecture, a testing strategy is developed, to examine the key capabilities of the system, such as resource consumption and throughput. The conclusions review the combination of modern and conventional application development principles needed to address the challenges of conflict-free document replication, decoupled and stateless event-driven architecture, idempotency, and data consistency. This paper not only showcases the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.
{"title":"Real-Time Document Collaboration—System Architecture and Design","authors":"Daniel Iovescu, Cătălin Tudose","doi":"10.3390/app14188356","DOIUrl":"https://doi.org/10.3390/app14188356","url":null,"abstract":"This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that facilitate collaborative work on shared text documents across multiple users in near real time. It aims to dive into the intricacies of designing robust and effective document collaboration software focusing on understanding the requirements of such a system, the working principle of collaborative text editing, software architecture, technology stack selection, and tooling that can sustain such a system. To examine the pros and cons of the proposed system, the paper will detail how collaborative text editing software can benefit from such an architecture regarding availability, elasticity, and scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems and distributed systems. This research first examines the requirements of a real-time collaboration system and the necessary core features. Then, it analyzes the design, the application structure, and the system organization while also considering key architectural requirements as the necessity of scaling, the usage of microservices, cross-service communications, and client–server communication. For the technology stack of the implementation, this research considers the alternatives at each layer, from client to server. Once these decisions are made, it follows system development while examining possible improvements for the issues previously encountered. To validate the architecture, a testing strategy is developed, to examine the key capabilities of the system, such as resource consumption and throughput. The conclusions review the combination of modern and conventional application development principles needed to address the challenges of conflict-free document replication, decoupled and stateless event-driven architecture, idempotency, and data consistency. This paper not only showcases the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Aurélio Bianchini, Mario Escobar, Maria Elisa Galarraga-Vinueza, Thalles Yurgen Balduino, Sergio Alexandre Gehrke
Background/Aim: The stability of peri-implant tissues is crucial for the long-term success of dental implant treatments. A new cervical implant design has been developed to address the challenges associated with peri-implant tissue stability, featuring a concave cervical portion to increase tissue volume in this area. The present study aimed to clinically evaluate the effectiveness of the new cervical implant design in maintaining peri-implant tissue stability. Materials and Methods: Five clinical cases involving completely edentulous patients were selected, in which 25 implants were installed. The marginal bone level around each implant was assessed at three different time points—T0: immediately after the prosthesis installation, T1: 6 months post installation, and T2: at the last control visit, up to 38 months later. Measurements were taken to analyze changes in marginal bone levels (MBLs) and the keratinized mucosa (KM) over time. Furthermore, the keratinized mucosa (KM) around the implants was evaluated. Results: The mean and standard deviation values of the marginal bone levels at each time point were as follows—T0: 0.59 ± 0.55 mm; T1: 1.41 ± 0.59 mm; T2: 1.76 ± 0.69 mm. Statistical analysis showed significant differences across the time points (ANOVA p < 0.0001). The overall mean KM values were 3.85 mm for T1 and T2, showing the stability of the peri-implant soft tissues at ≥1-year controls. Conclusion: Within the limitations of the present study, the results showed that the Collo implants presented measured MBL values increasing within the time range analyzed in each case but within the normal values cited in the literature for these types of rehabilitation treatments. However, the measured KM values presented, in all cases, an average above the values referenced in the literature as a minimum for maintaining the health of the peri-implant tissues.
{"title":"Peri-Implant Tissue Stability: A Series of Five Case Reports on an Innovative Implant Design","authors":"Marco Aurélio Bianchini, Mario Escobar, Maria Elisa Galarraga-Vinueza, Thalles Yurgen Balduino, Sergio Alexandre Gehrke","doi":"10.3390/app14188354","DOIUrl":"https://doi.org/10.3390/app14188354","url":null,"abstract":"Background/Aim: The stability of peri-implant tissues is crucial for the long-term success of dental implant treatments. A new cervical implant design has been developed to address the challenges associated with peri-implant tissue stability, featuring a concave cervical portion to increase tissue volume in this area. The present study aimed to clinically evaluate the effectiveness of the new cervical implant design in maintaining peri-implant tissue stability. Materials and Methods: Five clinical cases involving completely edentulous patients were selected, in which 25 implants were installed. The marginal bone level around each implant was assessed at three different time points—T0: immediately after the prosthesis installation, T1: 6 months post installation, and T2: at the last control visit, up to 38 months later. Measurements were taken to analyze changes in marginal bone levels (MBLs) and the keratinized mucosa (KM) over time. Furthermore, the keratinized mucosa (KM) around the implants was evaluated. Results: The mean and standard deviation values of the marginal bone levels at each time point were as follows—T0: 0.59 ± 0.55 mm; T1: 1.41 ± 0.59 mm; T2: 1.76 ± 0.69 mm. Statistical analysis showed significant differences across the time points (ANOVA p < 0.0001). The overall mean KM values were 3.85 mm for T1 and T2, showing the stability of the peri-implant soft tissues at ≥1-year controls. Conclusion: Within the limitations of the present study, the results showed that the Collo implants presented measured MBL values increasing within the time range analyzed in each case but within the normal values cited in the literature for these types of rehabilitation treatments. However, the measured KM values presented, in all cases, an average above the values referenced in the literature as a minimum for maintaining the health of the peri-implant tissues.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images.
生成食物图像的目的是将食物配料的文字转换成相应的图像，以实现颜色和形状调整的可视化、饮食指导和新菜肴的制作。它的应用范围非常广泛，包括食品推荐、食谱开发和健康管理。然而，现有的食品图像生成模型主要基于生成对抗网络（GANs），在保持图像和文本之间的语义一致性以及实现生成图像的视觉真实性方面面临挑战。这些局限性归因于稀疏成分嵌入的表征能力有限，以及基于 GAN 的食品图像生成模型缺乏多样性。为了缓解这一问题，本文提出了一种名为 MLA-Diff 的食品图像生成网络，该网络将食材特征和图像特征作为食材-图像对进行学习和整合，生成初始图像，然后通过注意力融合模块对图像细节进行细化。主要贡献如下(1) 通过将稀疏成分嵌入转化为紧凑嵌入和捕捉多尺度图像特征，构建了增强型 CLIP（对比语言-图像预训练）模块，为缓解语义一致性问题提供了有效的解决方案。(2) 通过嵌入预训练的扩散模型来生成具有多样性和真实性的初始图像，从而提出了记忆模块。(3) 提出了注意力融合模块，通过整合来自不同模态的特征来增强食材特征与图像特征之间的理解力。在迷你食品数据集上进行的大量实验证明，MLA-Diff 在语义一致性和视觉真实性方面具有优势，能生成高质量的食品图像。
{"title":"Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method","authors":"Jinlin Ma, Yuetong Wan, Ziping Ma","doi":"10.3390/app14188347","DOIUrl":"https://doi.org/10.3390/app14188347","url":null,"abstract":"Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.
{"title":"Study of Millimeter-Wave Fuze Echo Characteristics under Rainfall Conditions Using the Monte Carlo Method","authors":"Bing Yang, Zhe Guo, Kaiwei Wu, Zhonghua Huang","doi":"10.3390/app14188352","DOIUrl":"https://doi.org/10.3390/app14188352","url":null,"abstract":"Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duygu Akcan, Murat Yilmaz, Ulaş Güleç, Hüseyin Emre Ilgın
Advergames represent a novel product placement strategy that surpasses traditional advertising methods by fostering interaction between brands and their target audiences. This study investigates the unique engagement opportunities provided by video games, focusing mainly on the ‘flow experience’, an intensified state of immersion frequently encountered by players of computer games. Such immersive experiences have the potential to significantly influence a player’s perception, offering a new avenue for advertisements to impact and engage audiences effectively. The primary objective of this research was to examine the influence of advergames on players who are deeply immersed in the gaming experience, with a specific focus on the subsequent effects on brand recognition over time. The study involved 44 software developers, who were evenly divided into two groups for the experiment. Both groups were exposed to an identical gaming environment with the task of locating a designated product within the game. However, one group interacted with an enhanced version of the game, which included additional stimuli—such as dynamic music, an engaging narrative, time constraints, a competitive leaderboard, and immersive voice acting—to intensify the gaming experience. The experiment strategically placed various products within the game, and their detectability was assessed using eye-tracking technology. Following gameplay, participants completed questionnaires that measured their experience with flow state and brand recall. The data were analyzed using the Mann–Whitney U test and correlation analysis to facilitate comparisons. The findings indicated that the product associated with the primary task achieved the highest recall rate between both groups. Furthermore, eye-tracking technology identified the areas in the game that attracted the most attention, revealing a preference for mid- and high-level placements over lower-level ones.
广告游戏是一种新颖的产品植入策略，它通过促进品牌与其目标受众之间的互动，超越了传统的广告方法。本研究调查了电子游戏提供的独特参与机会，主要侧重于 "流动体验"，即电脑游戏玩家经常遇到的一种强化的沉浸状态。这种身临其境的体验有可能极大地影响玩家的感知，为广告有效地影响和吸引受众提供了新的途径。本研究的主要目的是考察广告游戏对深度沉浸于游戏体验的玩家的影响，特别关注随着时间的推移对品牌认知度的后续影响。这项研究涉及 44 名软件开发人员，他们被平均分成两组进行实验。两组人都置身于相同的游戏环境中，任务是在游戏中找到指定的产品。不过，其中一组与增强版游戏进行了互动，增强版游戏包括额外的刺激，如动态音乐、引人入胜的叙事、时间限制、竞争性排行榜和身临其境的语音表演，以强化游戏体验。实验在游戏中战略性地放置了各种产品，并使用眼动跟踪技术评估了这些产品的可探测性。游戏结束后，参与者填写了调查问卷，以测量他们对流动状态和品牌回忆的体验。数据分析采用了曼-惠特尼 U 检验和相关分析，以便于比较。结果表明，与主要任务相关的产品在两组中的回忆率最高。此外，眼动跟踪技术还确定了游戏中最吸引注意力的区域，显示出对中高级位置的偏好超过了对低级位置的偏好。
{"title":"Engagement and Brand Recall in Software Developers: An Eye-Tracking Study on Advergames","authors":"Duygu Akcan, Murat Yilmaz, Ulaş Güleç, Hüseyin Emre Ilgın","doi":"10.3390/app14188360","DOIUrl":"https://doi.org/10.3390/app14188360","url":null,"abstract":"Advergames represent a novel product placement strategy that surpasses traditional advertising methods by fostering interaction between brands and their target audiences. This study investigates the unique engagement opportunities provided by video games, focusing mainly on the ‘flow experience’, an intensified state of immersion frequently encountered by players of computer games. Such immersive experiences have the potential to significantly influence a player’s perception, offering a new avenue for advertisements to impact and engage audiences effectively. The primary objective of this research was to examine the influence of advergames on players who are deeply immersed in the gaming experience, with a specific focus on the subsequent effects on brand recognition over time. The study involved 44 software developers, who were evenly divided into two groups for the experiment. Both groups were exposed to an identical gaming environment with the task of locating a designated product within the game. However, one group interacted with an enhanced version of the game, which included additional stimuli—such as dynamic music, an engaging narrative, time constraints, a competitive leaderboard, and immersive voice acting—to intensify the gaming experience. The experiment strategically placed various products within the game, and their detectability was assessed using eye-tracking technology. Following gameplay, participants completed questionnaires that measured their experience with flow state and brand recall. The data were analyzed using the Mann–Whitney U test and correlation analysis to facilitate comparisons. The findings indicated that the product associated with the primary task achieved the highest recall rate between both groups. Furthermore, eye-tracking technology identified the areas in the game that attracted the most attention, revealing a preference for mid- and high-level placements over lower-level ones.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nazim Hasan, Embar Prasanna Kannan, Othman Hakami, Abdullah Ali Alamri, Judy Gopal, Manikandan Muthu
Antibiotic resistance is a major crisis that the modern world is confronting. This review highlights the abundance of different types of antibiotic resistance genes (ARGs) in two major reservoirs in the environment, namely hospital and municipal wastewater, which is an unforeseen threat to human lives across the globe. The review helps understand the current state of affairs and the whereabouts on the dissemination of ARGs in both these environments. The various traditional wastewater treatment methods, such as chlorination and UV treatment, and modern methods, such as electrochemical oxidation, are discussed, and the gaps in these technologies are highlighted. The need for the development of newer techniques for wastewater treatment with enhanced efficiency is urgently underscored. Nanomaterial applications for ARG removal were observed to be less explored. This has been discussed, and prospective nanomaterials and nanocomposites for these applications are proposed.
{"title":"Reviewing the Phenomenon of Antimicrobial Resistance in Hospital and Municipal Wastewaters: The Crisis, the Challenges and Mitigation Methods","authors":"Nazim Hasan, Embar Prasanna Kannan, Othman Hakami, Abdullah Ali Alamri, Judy Gopal, Manikandan Muthu","doi":"10.3390/app14188358","DOIUrl":"https://doi.org/10.3390/app14188358","url":null,"abstract":"Antibiotic resistance is a major crisis that the modern world is confronting. This review highlights the abundance of different types of antibiotic resistance genes (ARGs) in two major reservoirs in the environment, namely hospital and municipal wastewater, which is an unforeseen threat to human lives across the globe. The review helps understand the current state of affairs and the whereabouts on the dissemination of ARGs in both these environments. The various traditional wastewater treatment methods, such as chlorination and UV treatment, and modern methods, such as electrochemical oxidation, are discussed, and the gaps in these technologies are highlighted. The need for the development of newer techniques for wastewater treatment with enhanced efficiency is urgently underscored. Nanomaterial applications for ARG removal were observed to be less explored. This has been discussed, and prospective nanomaterials and nanocomposites for these applications are proposed.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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