食品包装材质对消费者绿色购买意愿影响的眼动研究

目的 探讨食品包装材质对消费者注意加工流程和绿色购买意愿影响的内在机制。方法 采用包装材质2(环保/普通)×食品类型2(实用品/享乐品)双因素实验设计,在货架购物场景下采用Tobii Glasses 2眼镜式眼动仪,采集被试者浏览货架不同食品时的眼动数据(平均注视时间、注视次数、热点图),并结合行为数据综合分析。结果 在平均注视时间上,包装材质与食品类型的交互作用显著,注视次数交互作用不显著;环保材质包装的食品获得更多关注,被试者偏好于选择环保包装的享乐食品。结论 包装材质与食品类型共同影响消费者的视觉感知效果与加工过程,环保包装对青年消费者的绿色购买意愿有正向促进作用。     

稻壳类生物沥青微观作用机理研究

为了探究稻壳类生物质油替代石油沥青的微观作用机理,以稻壳类生物质油和6种石油沥青为原材料,制备稻壳类生物沥青。采用针入度、软化点和延度指标评价稻壳类生物沥青的物理性能,通过傅里叶红外光谱实验(FTIR)和高温凝胶色谱实验(GPC)探究稻壳类生物沥青的微观作用机理。研究表明,稻壳类生物质油会使石油沥青的针入度和延度增大,同时使软化点略有降低;稻壳类生物质油与6种石油沥青的官能团相似,且生物沥青制备时未产生新的官能团;随着稻壳类生物质油的掺加,6种生物沥青中的小分子和中分子含量增加,大分子的含量变化不大,稻壳类生物质油小分子和中分子含量较多,生物沥青的数均分子量M_n和重均分子量M_w均降低。稻壳类生物质油的加入,使石油沥青的低温性能提高,流动性增大,高温性能稍有降低但并不会降低石油沥青的高温等级,稻壳类生物质油可以作为石油沥青的替代材料使用。     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

WiMA: Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks, they still experience degraded performance due to the number of inefficiencies. One of the main reasons is the received signal strength indicator (RSSI) association problem, in which the user remains connected to the access point (AP) unless the RSSI becomes too weak. In this paper, we propose a multi-criterion association (WiMA) scheme based on software defined networking (SDN) in Wi-Fi networks. An association solution based on multi-criterion such as AP load, RSSI, and channel occupancy is proposed to satisfy the quality of service (QoS). SDN having an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance. To implement WiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator. The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30% and enhances the throughput by 20%–50%, hence maintaining user fairness and accommodating more wireless devices and traffic load in the network, when compared to traditional client-driven (CD) approach and state of the art Wi-Balance approach.     

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.     

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains small subsets to these issues. First, this research introduces a deep neural network (DNN)-based GANomaly for semi-supervised learning. Second, this paper presents the proposed adaptive algorithm for the DNN-based GANomaly, which is validated with four subsets of the adaptive dataset. Finally, this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations, reconstruction error visualization, and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage, semi-supervised learning, and adaptive learning. Compared to other single-class classification techniques, the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13% and 8% of F1 scores and 4.17% and 11.51% for accuracy, respectively. Furthermore, experiments of the proposed adaptive learning reveal mostly improved results over the initial values. An analysis and monitoring system based on the combination of the three explainable methodologies is also described. Thus, the proposed method has the potential advantages to be applied in practical industry, and future research will explore handling unbalanced real-time datasets in various scenarios.     

Interdiffusion in the carbides of the Nb-C system

Interdiffusion coefficients in Nb₂C and NbC_1−x were measured using bulk diffusion couples in the temperature range from 1400 °C to 1700 °C. Marker experiments were used to show that carbon is the only component undergoing significant diffusion in both carbides. Carbon concentrations were measured by difference using electron probe microanalysis, and interdiffusion coefficients were taken from Boltzmann-Matano analyses of the resulting concentration profiles. This analysis clearly showed that, in NbC_1−x, interdiffusion coefficient varies with carbon concentration, and is expressed by

Thermodynamic properties of alumina-saturated iron-carbon-bismuth alloys

Using direct and indirect equilibration methods, the solubilities of iron in molten bismuth and those of bismuth in molten iron-carbon alloys have been measured over the temperature range 1473-1873 K. The Henrian behaviour of bismuth in molten ferrous alloys allows the calculation of its activity coefficient, which in turn facilitates modelling as a function of temperature and carbon content, using the unified interaction parameter formalism developed by Pelton and Bale. The modelling results generate the expression: . A model has also been developed expressing bismuth content in molten iron-carbon alloys as a function of temperature and composition: .     

Tennessee's failed managed care program for mental health and substance abuse services

In July 1996, Tennessee initiated a managed mental health and substance abuse program called TennCare Partners. This publicly funded "carve-out" experiment started chaotically and soon deteriorated into a crisis. Many patients did not receive care or lost continuity of care, and the traditional "safety net" mental health system nearly disintegrated. This qualitative case study sought to ascertain the impact of the TennCare Partners program. It points out that the program's difficulties stemmed directly from a flawed design that spread funds previously earmarked for severely mentally ill patients across the entire Medicaid population. States contemplating similar reforms should strive to protect vulnerable patients by risk-adjusting capitation payments and by focusing resources on care for severely mentally ill persons. States should also minimize program complexity and ensure the accountability of managed care networks for their patients' behavioral health care needs.