Single Window Research Articles

The importance of analysing a single common window of a concentric and eccentric dynamic muscle contraction: unveiling the components of EMG data and motor unit pool

This study proposes a method for analysing decomposed electromyographic signals to enhance the characterization of motor units (MU). It involves two steps: (i) clustering groups of motor units based on firing rate (FR), recruitment threshold, and MU action potential amplitude, and (ii) segmentation of data at the time of interest. Such method, capable of distinguishing different groups of MU, could help understanding muscle force production. FR of MU in Vastus Lateralis and Rectus Femoris during knee extension was investigated. The findings distinguish MU groups and reveals differences in: FR between both contractions types; clustered groups; and segmented MU data in both contraction types.

Transformation of Business Licensing through a Single Submission System on Public Service Efficiency: A Case Study of the Indonesia National Single Window Agency (LNSW)

The Indonesia National Single Window (LNSW) Agency developed a single submission system, namely the Indonesia National Single Window (SINSW) System, as a one-stop submission facility. Before the development of SINSW, the process of applying for business permits in Indonesia was complicated and time-consuming. This is because stakeholders have to process permits directly with various relevant ministries or institutions, causing data repetition and cost and time inefficiencies in the export, import and logistics processes. This research aims to analyze the transformation of LNSW into a one-stop business licensing and management agency through the implementation of SINSW and its impact on the efficiency of public services in exports, imports and logistics in Indonesia. This research also compares the implementation of the National Single Window (NSW) in Korea, Singapore, and Japan. The results of the research are recommendations for improving the efficiency of LNSW services in the future through the adoption of artificial intelligence (AI) via chatbots to handle complaints of slow responses for service users.

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.

Development of a locating method for optical windows in shock tube

A method was proposed in the present study for locating single and/or multiple optical windows in shock tubes. It involved establishing a diagnostic time calculation model by transforming challenging conventional conditions into equivalent special scenarios. The method strategically balanced experimental pressure and optical window locations. Pressure-time history experiments were conducted in a shock tube to verify the reliability of the proposed locating method. An example showcased the versatility of the locating method for determining the positions of single or multiple optical windows. Lastly, the proposed locating method provided evaluation for optimizing several existing shock tube setups.

CONCEPT AND CHARACTERISTICS OF CUSTOMS AUTHORITIES’ COMPETENCE IN THE CONTEXT OF INNOVATIVE PROCESSES IN UKRAINIAN CUSTOMS AFFAIRS

This article analyzes the concept and characteristics of customs authorities’ competencies in Ukraine within the context of ongoing innovations in customs practices. As Ukraine pursues its Euro-integration goals — aimed at modernizing economic infrastructure, overcoming technological lag, attracting foreign investments, and enhancing international competitiveness — customs authorities play a pivotal role in ensuring the effective functioning of foreign economic activities. The focus of the article is on the necessity to modernize the competencies of customs authorities in line with global standards and to implement advanced innovative technologies, as outlined in Ukraine’s Innovation Development Strategy up to 2030. It examines the impact and significance of innovations such as the ‘Single Window’ system, ‘Smart Customs’ initiatives, pre-arrival cargo information technologies radio-frequency identification (RFID), and blockchain for enhancing customs control, management, and interactions with external partners. A central issue discussed in the article is the development of new competencies required for the effective implementation of these innovations by customs authorities. Specifically, it addresses the need to cultivate digital, intellectual, partnership, and security skills. The article also explores the effects of economic and political instability, particularly the wartime conditions in Ukraine, on the implementation of innovations in customs operations. Key challenges are identified, such as difficulties in exporting high-tech goods, granting incentives for importing innovative equipment, and applying non-tariff regulatory measures, including certification, quotas, and licensing. Recommendations are provided for improving the organizational structures of customs authorities and updating the legal and regulatory framework to adapt to modern requirements and standards. The article advocates for the development of a new, intelligent customs system that can swiftly and effectively respond to changes by employing cutting-edge digital technologies and security methods. Emphasis is placed on the importance of enhancing the qualifications of customs professionals, which is essential for the successful implementation of innovations in customs control.

Customs competencies in ukraine: embracing innovation for enhanced efficiency

This article provides a comprehensive analysis of the concept and features of the competencies of Ukrainian customs authorities considering ongoing innovations in the customs sector. As Ukraine follows its Euro-integration path, aimed at modernizing its economic infrastructure, addressing technological gaps, attracting foreign investments, and enhancing its international competitiveness, the role of customs authorities becomes vital in facilitating effective foreign economic activities. The article underscores the critical need to update the competencies of customs authorities to meet global standards and incorporate advanced innovative technologies, as envisioned in Ukraine’s Innovation Development Strategy up to 2030. It explores the impact and relevance of innovations such as the ‘Smart Customs’ initiatives, ‘Single Window’ system, pre-arrival cargo information systems, radio-frequency identification, and blockchain technology in improving customs control, management, and interactions with external partners. A major focus of the article is on the new competencies required by customs authorities to successfully implement these innovations. It highlights the importance of developing digital, intellectual, partnership, and security skills. Furthermore, the article examines the effects of economic and political instability, particularly the ongoing conflict in Ukraine, on the innovation processes within customs operations. It identifies significant challenges such as the difficulties in exporting high-tech goods, offering incentives for the import of innovative equipment, and the application of non-tariff regulatory measures, including certification, quotas, and licensing. The article provides detailed recommendations for enhancing the organizational structures of customs authorities and calls for updating the legal and regulatory framework to align with current demands and standards. It advocates for the creation of a modern, intelligent customs system that can rapidly and effectively adapt to changes using state-of-the-art digital technologies and security methods. Emphasis is placed on the necessity of enhancing the qualifications of customs professionals, which is deemed essential for the successful integration of innovations in customs control.

Detecting market bubbles: A generalized LPPLS neural network model

To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Clinical effect on major trauma patients during simultaneous or overlapping presentations at an urban level I trauma center in Korea

Objective: Overcrowding in an emergency department (ED) or intensive care unit (ICU) of the trauma center (TC) is an important issue for timely acute health care of a critically injured patient. Accumulated scientific evidence has indicated the negative influence of overcrowding to the process and clinical outcome of trauma care. Method: The institutional trauma registry at an urban level I TC was retrospectively evaluated for 5 years (2018–2022). Major trauma was defined as an injury severity score (ISS) of >15. We determined simultaneous or overlapping presentations of major trauma (SOMT) in two or more patients with ISS of >15 who presented within a 4-h time window. When only two patients were included within a single time window, they were classified as SOMT-2, whereas when three or more patient clusters were identified in a single time window, they were classified as SOMT-3. The outcome measurement included process and clinical variables, such as trauma team activation (TTA) ratio, ED length of stay (LOS), time to blood product transfusion (TF), time to emergency surgery or intervention (ESI), ICU LOS, and mortality. Result: A total of 2,815 patients were included, of which 39.6% (N = 1,116) classified as SOMT.The SOMT group had lower TTA ratio than the non-SOMT group (69.4% vs. 73.4%, P = 0.022). The TTA ratio exhibited a decreasing trend in non-SOMT, SOMT-2, and SOMT-3 groups (P = 0.006). The time to TF was significantly delayed in the SOMT group (129 vs. 91 min, P < 0.001). Conclusion: SOMT regularly occurs and results in fewer trauma team activation and a delayed time to blood transfusion. The current intensive trauma care system should be strategically modified to improve critical trauma care and enhance disaster preparedness.

HADT: Image super-resolution restoration Using Hybrid Attention-Dense Connected Transformer Networks

Image super-resolution (SR) plays a vital role in vision tasks, in which Transformer-based methods outperform conventional convolutional neural networks. Existing work usually uses residual linking to improve the performance, but this type of linking provides limited information transfer within the block. Also, existing work usually restricts the self-attention computation to a single window to improve feature extraction. This means transformer-based networks can only use feature information within a limited spatial range. To handle the challenge, this paper proposes a novel Hybrid Attention-Dense Connected Transformer Network (HADT) to utilize the potential feature information better. HADT is constructed by stacking an attentional transformer block (ATB), which contains an Effective Dense Transformer Block (EDTB) and a Hybrid Attention Block (HAB). EDTB combines dense connectivity and swin-transformer to enhance feature transfer and improve model representation, and meanwhile, HAB is used for cross-window information interaction and joint modeling of features for better visualization. Based on the experiments, our method is effective on SR tasks with magnification factors of 2, 3, and 4. For example, using the Urban100 dataset in an experiment with an amplification factor of 4 our method has a PSNR value that is 0.15 dB higher than the previous method and reconstructs a more detailed texture.

Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was 7.2% higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached 98.8%. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated.

Shock-tube tests on conventional windows: Exploring retrofit concepts for enhanced blast protection

Ensuring blast protection for existing buildings, especially addressing the vulnerability of conventional windows, is a significant challenge. Such unprotected windows can shatter even with moderate blast loads, posing a substantial risk of injury to occupants. This article discusses experimental research on enhancing the blast protection of single casement windows with insulating glass units and frames made of unplasticized polyvinyl chloride (uPVC). A retrofit concept using anti-shatter films, metallic sash reinforcements, adhesive bonding of the glazing to the sash frame, and a burglary resistance fitting-system was developed and tested in an explosion-driven shock-tube. Moreover, novel patches made of glass fiber-reinforced polymer applied to the corners of the window frames have been tested and proven effective in providing additional strength to the window. The study concludes that the tested combination of retrofit measures can significantly reduce hazards from window fragments without compromising functionality or aesthetics.

Utrasound as a method for confirming the positioning of enteral catheters in critically ill patients.

To evaluate the agreement between the bedside ultrasound in a single epigastric window and the plain X-ray to confirm the positioning of the enteral catheter in critically ill patients. This was an observational, cross-sectional study conducted in two Intensive Care Units of a university hospital. The ultrasound exams were carried out immediately after the introduction of the enteral catheter, using only the epigastric window, with an injection of 5 ml of air associated with 5 ml of saline solution. In all cases, the plain radiography was taken to confirm the positioning of the enteral catheter and to define the beginning of nutritional therapy. This study included 83 patients, the positioning of the enteral catheter was confirmed by plain radiography in all cases and by ultrasound in 81 (97.6%) patients. The median duration of the ultrasound exam was 2 (2-3) minutes, while the time spent between the request for the X-ray and the release of the exam for a doctor's appointment was 225 (120-330) minutes. Bedside ultrasound proved to be an effective, quick, and safe method to confirm the position of the enteral catheter in critically ill patients.

Income generation, guideline, SU Income Generating Guidelines for Souphanouvong University

The purpose of this research is to analyze determinants of the guideline for income generation of Souphanouvong University, by distributing a questionnaire to 215 employees, using descriptive statistics and reference statistics, using a stepwise multiple regression model The results found that the guidelines to generate income from activities are based on the budget income provided by the state in combination with the creation of short-term courses, providing policies for students, improving teaching manuals, providing external academic services, expanding cooperation, transforming into a service and research center, renting space. On the one hand, to generate revenue in terms of management are creating incentives, putting in place the management rules that are oriented, having a person in charge, the revenue management task to have a single window, and providing a clear budget to be used in each period. Allow time for staff to provide services - generate income that is not too limited, each board should have a detailed plan to generate income, provide modern teaching and learning facilities, turn existing assets into capital, invest to improve the environment of the university more and more.

Інституціональний базис для регулювання ринку логістичних послуг в україні та її регіонах з боку держави

The transformation of the Ukrainian economy towards market relations has brought to light significant imbalances in the logistics sector. A lack of an adequate theoretical framework and a clear state policy in this area has led to several problems. These include insufficient information support of logistics processes, a limited number of large logistics operators, and a need for more research and development in logistics. These factors complicate the optimization of trade flows and reduce the competitiveness of Ukrainian enterprises, making it an urgent matter that requires immediate attention. This publication aims to provide a detailed study of the system architecture and the evolution of forms of state regulation in the logistics sector. The architecture of the state regulation system in logistics provides a structured and comprehensive approach to managing logistics processes at the state level. It includes several main components that interact to ensure the effective functioning of the logistics services market. It is necessary to implement modern approaches to the state regulation of logistics services to ensure the Ukrainian economy’s sustainable development and improve citizens’ quality of life. These approaches can potentially revolutionize the sector, bringing about significant improvements. In particular, the priority task is to create a single digital window platform that will combine all permitting procedures into a single electronic service. At the same time, the government should actively work on harmonizing Ukrainian customs legislation with European standards, which will significantly simplify customs formalities and speed up the movement of goods. Implementing these measures will help increase the competitiveness of Ukrainian businesses and attract foreign investment, paving the way for a brighter future for the Ukrainian logistics sector. It is crucial to ensure close cooperation between the state and business. Regular consultations, transparency of regulatory procedures, and flexibility of legislation are critical factors for attracting investment and stimulating innovation. Introducing special tax regimes, such as corporate tax rebates for companies investing in infrastructure development and support for start-ups and innovative companies, will help modernize the industry and increase efficiency. However, the most significant step is harmonizing Ukrainian legislation with European standards. This is not just a formality but a significant milestone that will facilitate Ukraine’s integration into the European Economic Area, opening new opportunities for the Ukrainian logistics sector and paving the way for its growth and development. Keywords: state regulation, institutionalism, institutional framework, logistics, logistics sector, logistics regulation.

SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection

Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, resulting in the object and its shadow being contiguous (referred to as the adjacent shadow in this paper). In this case, when the color of the object is similar to that of the shadow, existing methods struggle to achieve accurate detection. To address this problem, we present SwinShadow, a transformer-based architecture that fully utilizes the powerful shifted window mechanism for detecting adjacent shadows. The mechanism operates in two steps. Initially, it applies local self-attention within a single window, enabling the network to focus on local details. Subsequently, it shifts the attention windows to facilitate inter-window attention, enabling the capture of a broader range of adjacent information. These combined steps significantly improve the network's capacity to distinguish shadows from nearby objects. And the whole process can be divided into three parts: encoder, decoder, and feature integration. During encoding, we adopt Swin Transformer to acquire hierarchical features. Then during decoding, for shallow layers, we propose a deep supervision (DS) module to suppress the false positives and boost the representation capability of shadow features for subsequent processing, while for deep layers, we leverage a double attention (DA) module to integrate local and shifted window in one stage to achieve a larger receptive field and enhance the continuity of information. Ultimately, a new multi-level aggregation (MLA) mechanism is applied to fuse the decoded features for mask prediction. Extensive experiments on three shadow detection benchmark datasets, SBU, UCF, and ISTD, demonstrate that our network achieves good performance in terms of balance error rate (BER). The source code and results are now publicly available at https://github.com/harrytea/SwinShadow .

Tunable multifunctional terahertz metamaterial device based on metal-dielectric-vanadium dioxide

The Electromagnetically Induced Transparency (EIT) effect in terahertz (THz) metamaterial devices has garnered extensive attention owing to its distinctive advantages in the slow light devices and quantum communication. However, the application of EIT has been constrained by the limitation to a single transparent window and the fixed intensity of the electromagnetic response at the transmission peak after fabrication. To achieve a dual-frequency EIT effect and enable active tunability, this paper presents a tunable multifunction THz metamaterial device based on vanadium dioxide (VO2). The transition of VO2 from the insulator to the metal allows for the active control of EIT and multi-band absorption switching in the THz range. When VO2 is in its dielectric state, two EIT-like windows could be observed with a maximum value of 86.9 % and 71.7 % of the transmission amplitude, respectively. By adjusting the temperature of the VO2, a modulation depth of up to 99 % can be achieved, enabling active control of the EIT-like effect. In addition, active adjustment of group delay of up to 5.47 ps was demonstrated. When VO2 is in the metallic state, the device serves as a tunable multiband absorber, capable of achieving perfect absorption and modulation amplitude of up to 92 %. The results indicated that the presented devices hold significant value for research in the THz range, particularly for applications related to switching and slow light devices. Therefore, the tunable multifunctional THz metamaterial device is of great significance for THz technology.

FT-IR Microscopy: Sampling by Transmission

Fourier transform infrared (FT-IR) microscopy is a workhorse tool for a wide spectrum of applications. Obtaining high-quality data requires some skill in sample preparation, data collection, and data analysis. In this tutorial, we examine the techniques for preparing samples for transmission analysis using single windows, compression cells, and, finally, epoxy “pucks” and microtomes. The key factors for success will be emphasized.

Does the selection of the fracture model affect the calculation of the permeability of a rock mass?

The permeability of a rock mass is an indispensable and significant parameter for the construction of rock engineering. Since most fractures are buried in the rock mass, accurately assessing the permeability of the rock mass has posed a challenge to scholars in the field. The discrete fracture network (DFN) is recognized as an effective method for calculating the permeability of rock masses. However, the impact of the fracture model on permeability calculations is neglected during the construction of DFNs. This study uses the FracMan software to conduct a series of numerical simulation experiments, demonstrating that the selection of the fracture model significantly influences permeability calculations. Furthermore, we investigate the effects of constructing different circular DFNs based on trace information from various sampling windows. The results indicate that relying solely on a circular DFN derived from a single sampling window to calculate permeability is unreliable, and we reveal the fundamental laws of how DFNs constructed based on different fracture models influence the permeability assessment of rock masses. Therefore, integrating multi-source information from rock mass outcrops and boreholes, in conjunction with selecting an appropriate fracture model for constructing DFNs, is crucial for accurate permeability calculations of rock masses.

Wall Shear Stress Characteristics Of A Turbulent Boundary Layer Obtained With Multi-Aperture Defocusing Micro-PTV And High-Speed Stereo Profile-PIV

A multi-aperture 3d 3c micro PTV system is presented which relies on single window optical access for both high-speed tracer illumination and image recording. Similar to the “defocusing” concept described by C. Willert &amp; Gharib (1992), the wall distance of individual particles is obtained from the size of projected particle image triplets formed by a triplet of apertures on the entrance pupil of the microscope lens. The present article extends upon previously published material Klinner &amp; Willert (2022) by applying multi-aperture micro particle tracking velocimetry (MA-micro PTV) on a canonical turbulent boundary layer (TBL) to track the near-wall motion of tracer particles with the aim of estimating the unsteady wall-shear stress from particle tracks. The technique is validated with measurements of a TBL inside the closed test section of the 1 m wind tunnel of DLR in Göttingen (1mWK) at free- stream velocities of 5.2 to 20 m/s with corresponding shear Reynolds numbers between 560 and 1630. In the viscous sublayer, the probability density distributions of stream- and span-wise wall shear stress (WSS) components could be reliably captured down to probability densities of 10E−3. As far as we know, this has not been achieved in the past, particularly not for the span-wise component. For the stream-wise component the measured Reynolds-number dependency of the root mean squared fluctuations agrees to correlations by Örlü &amp; Schlatter (2011). Furthermore, the skewness of the stream-wise WSS agrees well to values from DNS. On the other hand, the span-wise WSS fluctuations consistently underestimate the predicted DNS values, for which it is assumed that the deviation can be explained by the rapid decrease of the span-wise velocity fluctuations with increasing wall distance. Wall-normal profiles of 3c velocity statistics were obtained by bin averaging of particle velocities. Up into the buffer layer, these profiles agree well with profiles from stereo PIV as well as from DNS and LES, indicating an accurate determination of both near-wall velocities and inner scaling.

The Prospects of AfCFTA in Promoting Industrialization and Economic Diversification in Africa

African Continental Free Trade Area (AfCFTA) functions as a boost for Africa's economic development by enhancing intra-African trade. This is because trade is an engine of growth and development. For the fact that most African nations that are located within the same region engage in similar and competitive production, the ensuing economic cooperation and trade exchanges not only have the promise of increasing trade but also stimulating other ancillary economic activities, creating new job opportunities, speeding up economic growth, promote the standard of living, and promote economic development. However, AfCFTA's record has not been very impressive. The main obstacles to intra- regional commerce among African countries include heavy reliance on trade in primary goods, high product and market concentration of exports, and inadequate regional manufacturing networks. This study therefore examines continental economic trends and Intra African Trade: Prospects for African Continental Free Trade Area. The study adopted qualitative methods of data collection and analysis and the comparative theory of integration. The study found that the low level of trade diversification implicated the capacity of the African Continental Free Trade Area (AfCFTA) to achieve improved intra-regional trade performances in Africa. African countries maintain as much as 77% export specialization, with higher external/world trade at 84.6 percent of their total import and export trade. The study recommended that Governments should invest in infrastructure development, including building roads, railways, ports, and airports, as well as improving access to electricity and broadband. Governments should adopt modern trade facilitation measures such as electronic customs systems, single window platforms, and risk management techniques and should align their technical regulations and norms with global best practices and standards, as those created by appropriate international standard-setting organisations like the International Organisation for Standardisation (ISO).