广东蒙泰高新纤维股份有限公司是一家专业从事聚丙烯纤维研发、生产和销售的厂家。自成立以来，稳步发展，目前已建立丙纶FDY生产线70多条、POY生产线8条、DTY生产线2条、BCF生产线12条及其他生产研发设备若干，上年度各种规格丙纶长丝近3万吨，成为目前国内实际产量较大的丙纶长丝生产厂家。2020年，蒙泰成功登陆深交所创业板，证券简称：蒙泰高新，证券代码：300876。 公司拥有先进的生产、研发设备，完善的工艺流程，科学的管理框架，以及一支高素质的技术队伍。2010年，2016年被认定为广东高新技术企业，拥有9项发明专利。我们始终以创新求发展，以专业成大业，瞄准市场容量大、技术专业、附加值高的新产品。针对聚丙烯纤维需原液着色纺丝的特点，着力色母粒和功能母粒的源头开发，先后开发出各种颜色的抗老化、抗紫外线、抗菌、抗静电、温变、感光、接技、螯合的功能性纤维。同时不断加强技改研发能力，成为国内较早自主研发的熔体喂入技术的丙纶生产厂家。我们还积极与国内知名高校开展产学研合作，在“高强产业用聚丙烯纤维关键技术及设备研究”项目上，获得上海市政府颁发的“上海市科学技术进步二等奖”。2006年成功开发“高模低收缩丙纶工业长丝”并被认定为“2008年国家高新技术产品”，在过滤材料领域得到广泛的应用，申请国家发明专利两项，实用型专利一项，并为中国化纤工业协会起草制订了高强丙纶工业长丝的行业标准。与此同时我们还利用聚丙烯纤维的重量轻，保暖等特性开发出用于服装的丙纶超细旦纤维，并进一步在纤维截面结构上对纤维的功能性进行改进，成功开发出，高效保暖、导湿、超细旦环保纤维，于2012年注册蒙泰丝®MODERNS®商标，并批量地应用到佐丹奴（Giordano）等国际知名品牌的保暖内衣上，取得良好的经济效益和广泛的行业影响。由于公司紧跟行业发展形势，深入开展产品研发与创新工作，不断加深与上下游产业链企业间的紧密合作，2017年，中国化学纤维工业协会特授予我司“国家功能性聚丙烯纤维研发生产基地”荣誉称号。2018年,我司承办中国化纤行业盛宴“2018年丙纶分会年会暨丙纶行业高质量发展论坛”获得圆满成功。我司同期被当选为中国化学纤维工业协会丙纶分会会长单位。凭借着可靠的产品质量和良好的售后服务，我司产品畅销国内各个省份及出口到亚洲、欧美、中东、南美洲多个国家和地区。我们将抓住当前有利时机，以市场导向为中心，利用我们二十多年来的研发生产经验，为国内外客户提供更优质的产品，欢迎各界朋友莅临指导和业务洽谈。
Celiac disease (CD), globally affecting about 1
Amidst the buzz of technological advancement in education, our study unveils a more disconcerting narrative surrounding student chatbot interactions. Our investigation has found that students, primarily driven by intrinsic motivations like competence and relatedness, increasingly lean on chatbots. This dependence is not just a preference but borders on an alarming reliance, magnified exponentially by their individual risk perceptions. While celebrating AI's rapid integration in education is tempting, our results raise urgent red flags. Many hypotheses were supported, pointing toward a potential over-dependence on chatbots. Nevertheless, the unpredictable outcomes were most revealing, exposing the unpredictable terrain of AI's role in education. It is no longer a matter of if but how deep the rabbit hole of dependency goes. As we stand on the cusp of an educational revolution, caution is urgently needed. Before we wholly embrace chatbots as primary educators, it is imperative to understand the repercussions of replacing human touch with AI interactions. This study serves as a stark wake-up call, urging stakeholders to reconsider the unchecked integration of chatbots in learning environments. The future of education may very well be digital, but at what cost to human connection and autonomy?
The dramatic increase in IoT devices in a smart ecosystem like smart cities, transportation systems, and healthcare and industrial automation has greatly improved network connectivity and data-driven informed decisions. But this extraordinary level of connectivity generates important concerns associated with sensitive information and security risks. Therefore, this study proposes a novel framework for secure and sustainable IoT network and devices through a combination of a Hybrid Federated Learning Framework and GenAI. The proposed framework focuses on extending a secure learning platform for all different IoT devices through a Federated Learning Framework and utilizing GenAI capabilities for advanced information augmentation and customized anomaly detection. To improve the level of guaranteed privacy, this framework will utilize differential privacy techniques and a blockchain-assisted model validation process. Moreover, techniques for energy-efficient model optimization and edge intelligence in making decisions are considered to improve sustainability. The proposed work will examine and develop this novel hybrid model through intensive simulations and lab-based testing for its application in a building and energy management field. The impact will include a new federative generative architecture that offers enhanced cyber threat resilience, lower overhead costs of communication, and ensures user confidentiality of data. The end goal of this proposed project is to contribute positively towards advancing the state-of-the-art in sustainable AI for a secure and environment-conscious IoT.
Abstract The Industrial Internet of Things (IIoT), as a matter of fact, allows for operational efficiency through integrating real-time data from various resources; however, on the other hand, it also opens new frontiers for cybersecurity risks since the attack surface increases and the environment becomes resource constrained. Conventional intrusion detection systems in most scenarios do not adapt to the changing security requirements of the IIoT. In this research article, a basic lightweight intrusion detection framework that synthesizes Principal Component Analysis (PCA) for reducing dimensions with machine-learning-based ensembles, specifically Naïve Bayes and Random Forest classifiers. Having applied PCA for reducing the original features to 25 and 8 features, correspondingly, the information is well maintained (over 95% of the data variance), with substantially less computational complexity. The proposed system is evaluated on three benchmark datasets, CSE-CIC-IDS2018, CIC-IDS2017, and NSL-KDD, demonstrating robust performance with high detection accuracy, low mean squared error, and sub-millisecond inference latency. The results prove that the new framework maintains high detection performance, increases model generalizability and greatly decreases training and inference times compared with those of full-feature models. These results have justified the framework’s ability to balance accuracy with computational efficiency, offering a scalable and practical solution for real-time intrusion detection in industrial IIoT environments.
Environmental Performance Index, Operational Effectiveness, and Cost-effectiveness of Oman’s port infrastructure and industry were assessed in this study. In relation to sustainable logistics and supply chain metrics. Data was gathered from 407 logistics and supply chain experts in Oman’s main ports and associated industries. Using a quantitative survey-based methodology. To investigate the connections between important elements such as green supply chain practices and environmental management strategies, stakeholder collaboration, regulatory compliance, For technological innovation, the study used PLS-SEM. The results show that industrial environmental performance is greatly improved by adopting Green supply chain and environmental management techniques. Stakeholder Cooperation and environmental performance were found to be significantly influenced. According to regulatory compliance. Technological innovation has shown beneficial effects. However, stakeholder collaboration had little effect on environmental performance. The results offer guidance to policymakers and business executives regarding how to prioritize sustainable practices to enhance operational and environmental results in Oman’s port and logistics industry. The study recommends investing in advanced environmental technologies, strengthening regulatory oversight, promoting sustainable practices across supply chains, and reinforcing environmental management systems to improve environmental performance in Oman’s port facilities and logistics industry.
