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The ambiguous trends in international trade in 2019 and the forecast for 2020 enhance the functional role of the customs bodies in every country. That is because the customs system largely determines the ease of conducting international trade, the security of international supply chains and economic development of the countries. Though many developed countries have been able to form progressive customs systems, there are still countries that are in the process of reforming customs administrations and require a unified conceptual approach to build their customs systems. Given this fact the goal of our study is to analyze current trends in the development of the international customs systems and on the basis of it to identify the main and support functions of customs administration. Based on the principle of the best practices, the countries with the best customs administrations according to WTO data, i.e. France, Germany, the Netherlands, Lithuania and Poland were selected for analysis. We analyzed the positions of these countries in the leading international rankings, the key quantitative indicators of their customs activity and the peculiarities of the organizational construction of the customs authorities by functional principle. As the result, based on the use of systematic, dynamic and topologically substantive approaches and results of research, we developed a unified conceptual structure of the customs administration. In particular, the main functions (i.e. control, security and fiscal) and support functions (i.e. regulatory, administration, communication, service, information and statistical subsystems, resource support subsystem and international cooperation) were proposed. The proposed structure is intended to be used by representatives of the customs authorities in different countries throughout the world.

Trade integration contributes substantially to economic development and poverty alleviation. In recent years much progress was made to liberalize the trade regime, but customs procedures are often still complex, costly and non-transparent. This situation leads to misallocation of resources. Customs Modernization Handbook provides an overview of the key elements of a successful customs modernization strategy and draws lessons from a number of successful customs reforms as well as from customs reform projects that have been undertaken by the World Bank. It describes a number of key import procedures, that have proved particularly troublesome for customs administrations and traders, and provides practical guidelines to enhance their efficiency. The Handbook also reviews the appropriate legal framework for customs operations as well as strategies to combat corruption.

The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss functions within the eXtreme Gradient Boosting (XGBoost) algorithm, which is a critical step in enhancing the algorithm’s performance for specific applications. Our research is motivated by the need for precision and efficiency in the industrial domain, where the implications of misclassification can be substantial. We focus on the drill-wear analysis of melamine-faced chipboard, a common material in furniture production, to demonstrate the impact of custom loss functions. The paper explores several variants of Weighted Softmax Loss Functions, including Edge Penalty and Adaptive Weighted Softmax Loss, to address the challenges of class imbalance and the heightened importance of accurately classifying edge classes. Our findings reveal that these custom loss functions significantly reduce critical errors in classification without compromising the overall accuracy of the model. This research not only contributes to the field of industrial machine learning by providing a nuanced approach to loss function customization but also underscores the importance of context-specific adaptations in machine learning algorithms. The results showcase the potential of tailored loss functions in balancing precision and efficiency, ensuring reliable and effective machine learning solutions in industrial settings.

Globalization of the world economy and increased international trade in economic development of countries seriously affect Customs Services and changes customs functions. Moreover, the measurement efficiency and effectiveness of Customs Services are determined based on an accurate identification of the customs functions to be performed and on the basis of the amount of dynamic analysis. The article shows that significant reduction of the customs fiscal function is identified in the period when Latvia joined the EU Customs Union. The reduction took place due to the country’s efforts to improve business environment and strengthen the competitiveness of enterprises, as well as to improve tax administration system.

This paper is organized as follows. In chapter two, Samson Bilangna and Marcellin Djeuwo from the Cameroon customs administration present the history and the outcomes of the performance measurement policy launched by their administra-tion: the General Directorate of Customs signed 'performance contracts' with the frontline customs officers in 2010 and with some importers in 2011. In chapter three, Jose-Maria Munoz, an anthropologist, offers a complementary view of the introduction of figures in the Cameroon tax administration. The fourth chapter ends the book's first part, which focuses on performance measurement. Xavier Pascual from the French customs administration describes the system implemented by his administration to measure the collective performance of customs units and bureaus. In chapter five, Anne-Marie Geourjon and Bertrand Laporte, who are both economists, and Ousmane Coundoul and Massene Gadiaga, who are from the Senegalese customs administration, present the use of data mining to select imports for inspection. This project is being developed in Senegal and embodies the concept of risk analysis. Sharing the same global aim to make controls more efficient, economists Gael Raballand and Guillermo Arenas from the World Bank and anthropologist Thomas Cantens from the World Customs Organization suggest, in chapter six, using mirror statistics to detect potentially fraudulent import flows. Mirror statistics calculate the gaps of foreign trade statistics between two trading partner countries. To conclude the second part on the integration of measurement in information systems, Soyoung Yang from the Korea Customs Service (KCS), in chapter eight, offers a case study on KCS's implementation of a single window system. With respect to risk analysis, the concept of single window is widespread in the trade and customs environments, but few concrete achievements have been presented and analyzed.

Boundary condition settings are key risk factors for the accuracy of noninvasive quantification of fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFR CT ). However, transient numerical simulation-based FFR CT often ignores the three-dimensional (3D) model of coronary artery and clinical statistics of hyperemia state set by boundary conditions, resulting in insufficient computational accuracy and high computational cost. Therefore, it is necessary to develop the custom function that combines the 3D model of the coronary artery and clinical statistics of hyperemia state for boundary condition setting, to accurately and quickly quantify FFR CT under steady-state numerical simulations. The 3D model of the coronary artery was reconstructed by patient computed tomography angiography (CTA), and coronary resting flow was determined from the volume and diameter of the 3D model. Then, we developed the custom function that took into account the interaction of stenotic resistance, microcirculation resistance, inlet aortic pressure, and clinical statistics of resting to hyperemia state due to the effect of adenosine on boundary condition settings, to accurately and rapidly identify coronary blood flow for quantification of FFR CT calculation (FFR U ). We tested the diagnostic accuracy of FFR U calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFR D )) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFR U calculation was greatly reduced from 1–4 h for transient numerical simulations to 5 min per simulation, which was 2-fold less than the FFR D method. According to the results of the Bland-Altman analysis, the consistency between FFR U and invasive FFR of 86 vessels was better than that of FFR D . The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFR D and FFR U at the lesion level were 0.62 (95% CI: 0.51–0.74), 0.67 (95% CI: 0.56–0.79), 0.85 (95% CI: 0.76–0.94), and 0.93 (95% CI: 0.87–0.98), respectively. At the patient level, the AUC was 0.61 (95% CI: 0.48–0.74) for CTA, 0.65 (95% CI: 0.53–0.77) for QCA, 0.83 (95% CI: 0.74–0.92) for FFR D , and 0.92 (95% CI: 0.89–0.96) for FFR U . The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFR CT calculation, and support its wide application as a diagnostic indicator in clinical practice.

This paper proposes a coupling analysis method combining self-defined mean air age and relative humidity field functions to study passenger compartment air conditioning cooling in summer environments with solar radiation. The impact of air supply velocity on cabin air distribution and thermal comfort is analyzed. A 3D cabin model including driver, seats, and dashboard was developed using CATIA. STAR-CCM + performed CFD simulations, coupled with TAITherm’s Berkeley human thermal regulation model incorporating physiological parameters. Simulations under 12 °C supply and 35 °C ambient show that increasing air velocity from 4 m/s to 10 m/s reduces driver nose-tip air age from 63.038 s to 27.318 s, showing a trend of high in the front row and low in the back row. Cabin relative humidity rises proportionally with air speed, with driver chest humidity increasing from 27.5% to 52.69%. The PMV thermal comfort index first increases and then decreases with the increase of airflow speed, reaching 0.35 at 8 m/s, which is within the comfortable range of −0.5 to 0.5. The research results indicate that appropriately increasing the air supply speed can enhance air exchange efficiency and thermal comfort. The main findings reveal that 8 m/s is the optimal speed threshold, shortening the time for fresh air to reach the driver’s nostrils by 46.1% compared to 4 m/s, while achieving compliant PMV values with humidity trade-offs managed. This method combines numerical simulations with human physiological response analysis, providing technical support for the development of HVAC systems.

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.

Clouds are one of the major limitations to crop monitoring using optical satellite images. Despite all efforts to provide decision-makers with high-quality agricultural statistics, there is still a lack of techniques to optimally process satellite image time series in the presence of clouds. In this regard, in this article it was proposed to add a Multi-Layer Perceptron loss function to the pix2pix conditional Generative Adversarial Network (cGAN) objective function. The aim was to enforce the generative model to learn how to deliver synthetic pixels whose values were proxies for the spectral response improving further crop type mapping. Furthermore, it was evaluated the generalization capacity of the generative models in producing pixels with plausible values for images not used in the training. To assess the performance of the proposed approach it was compared real images with synthetic images generated with the proposed approach as well as with the original pix2pix cGAN. The comparative analysis was performed through visual analysis, pixel values analysis, semantic segmentation and similarity metrics. In general, the proposed approach provided slightly better synthetic pixels than the original pix2pix cGAN, removing more noise than the original pix2pix algorithm as well as providing better crop type semantic segmentation; the semantic segmentation of the synthetic image generated with the proposed approach achieved an F1-score of 44.2%, while the real image achieved 44.7%. Regarding the generalization, the models trained utilizing different regions of the same image provided better pixels than models trained using other images in the time series. Besides this, the experiments also showed that the models trained using a pair of images selected every three months along the time series also provided acceptable results on images that do not have cloud-free areas.

Subjective visual examination by human dermatologists is associated with inter-observer variability and error. To address this problem, we present a method to accurately diagnose the 23 most common skin conditions, using an adaptive GrabCut approach with the EfficientNetB3 model, for accurate segmentation and classification, respectively. Using a custom loss function, this strategy is combined with data-level preprocessing employing algorithm-level approaches. The unbalanced Dermnet dataset, which includes 19,500 images of skin lesions representing the 23 most common skin conditions, was corrected by downsampling the major classes and enhancing the minor classes. The custom loss function and ADG significantly enhanced model accuracy by accurately segmenting regions of interest in the images, retaining the most relevant diagnostic information. The MSE, PSNR and Jacquard index support the best segmentation result with values 32.94, 70.23, 0.71 respectively. The results demonstrated very high accuracy, with top-5 and top-1 accuracy rates of 95% and 80.02%, respectively. The diagnoses of acne and nail fungi were exceptionally good, with precision rates exceeding 0.80 and 0.856, respectively. Our Dermnet-trained model is the most accurate of all state-of-the-art models published to date.