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With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.

Tax increment financing (TIF)--an economic (re)development tool originally designed for urban cities--has been available to rural communities for decades. This is the first study to focus solely on TIF in rural school districts, to examine TIF effects on school districts' property tax base and rates, and to conduct event-study estimations of TIF effects. The study finds that TIF has mostly positive effects on rural school districts' property tax base and mixed effects on property tax rates, and that TIF-induced increases in tax base come primarily from residential property and slightly from commercial property. The study's findings assert the importance of returned excess increment if rural school districts in Iowa and many other states are to benefit from TIF.

Logistics centres are currently performing a key function in the development of countries through their ability to regulate goods, markets, and transport. This is shown by the infrastructure, cost, goods flow, and quality of logistical services provided by these centres. Nevertheless, in developing nations or regions with antiquated logistics infrastructure, conventional logistics centres seem to struggle to manage the volume of commodities passing through them, resulting in persistent congestion and an unsteady flow of goods inside these facilities. This issue poses a challenge to the progress of any nation. The emergence of new technology offers a potential avenue to solve the problems inherent in traditional logistics centres. Most prominently, four technologies (the Internet of Things (IoT), Blockchain, Big Data and Cloud computing) are widely applied in traditional logistics centres. This work has conducted a thorough analysis and evaluation of these new technologies in relation to their respective functions and roles inside a logistics centre. Furthermore, this work proposes difficulties in applying new technologies to logistics centres related to issues such as science, energy, cost, or staff qualifications. Finally, future development directions, related to expanding policies in technological applications, or combining each country’s policies for the logistics industry, are carefully discussed.

The global supply chain has been growing strongly in recent years. This development brings many benefits to the economy, society, and human resources in each country but also causes a large number of concerns related to the environment since traditional logistics activities in the supply chain have been releasing a significant amount of emissions. For that reason, many solutions have been proposed to deal with these environmental pollution problems. Among these, three promising solutions are expected to completely solve environmental problems in every supply chain: (i) Application of blockchain in the supply chain, (ii) Use of renewable energy and alternative fuels, and (iii) Design of a closed supply chain. However, it seems to lack a comprehensive study of these solutions aiming to overcome the drawbacks of traditional logistics. Indeed, this work focuses on analyzing and evaluating the three above-mentioned solutions and the impacts of each solution on solving problems related to traditional logistics. More importantly, this work also identifies critical factors and challenges such as policies, laws, awareness, and risks that are found to be remarkable difficulties in the shifting progress of traditional logistics to green logistics. Finally, directions for developing and deploying green solutions to the logistics, supply chain, and shipping sectors toward decarbonization strategies and net-zero goals are discussed in detail.

INTRODUCTION In its most recent nationally representative survey, the National Center for Education Statistics reports that in 2013, the overall condition of over one fifth of schools with permanent buildings [...], This study is the first to empirically examine how school districts allocate resources in response to capital investment revenue from statewide penny sales taxes (called SAVE funds), and whether SAVE funds affect student outputs (i.e., educational achievement). We found evidence that school districts do not use SAVE funds to increase capital investment or to provide property tax relief. Rather, school districts are most likely to shift existing resources that would have been used for capital purposes toward non-capital purposes, including instruction and administration. Despite a small gain in the number of teachers and minimum teacher salaries, SAVE does not result in a significant increase in student outputs.

Recently, because of serious global challenges including the consumption of energy and climate change, there has been an increase in interest in the environmental effect of port operations and expansion. More interestingly, a strategic tendency in seaport advancement has been to manage the seaport system using a model which balances environmental volatility and economic development demands. An energy efficient management system is regarded as being vital for meeting the strict rules aimed at reducing the environmental pollution caused by port facility activities. Moreover, the enhanced supervision of port system operating methods and technical resolutions for energy utilisation also raise significant issues. In addition, low-carbon ports, as well as green port models, are becoming increasingly popular in seafaring nations. This study comprises a comprehensive assessment of operational methods, cutting-edge technologies for sustainable generation, storage, and transformation of energy, as well as systems of smart grid management, to develop a green seaport system, obtaining optimum operational efficiency and environmental protection. It is thought that using a holistic method and adaptive management, based on a framework of sustainable and green energy, could stimulate creative thinking, consensus building, and cooperation, as well as streamline the regulatory demands associated with port energy management. Although several aspects of sustainability and green energy could increase initial expenditure, they might result in significant life cycle savings due to decreased consumption of energy and output of emissions, as well as reduced operational and maintenance expenses.

This is the first study to examine the fiscal effects of the New York property tax levy limit, using variation from the degree of fiscal stringency across school districts and over time in its first five years of implementation. Based on a difference-in-differences estimator, coupled with an event study specification, we find that the tax limit has imposed a real cap on many school districts; that is, at-limit districts' total current expenditures per pupil are significantly lower than what they would have spent absent the limit. For those affected school districts, this expenditure gap does not come from spending on teacher salaries or fringe benefits but rather from other instructional salaries/expenses, central administration, transportation, interfund transfers, and undistributed spending. We also find heterogeneity in the constraining effects of the tax limit across different need-based groups of school districts.

Zinc oxide nanoparticles (ZnO NPs) were successfully synthesized by a green method using rosin and zinc chloride as salt precursors. The phase structure, morphology, and particle size of ZnO were determined by X-ray powder diffraction, field emission scanning electron microscopy, and high-resolution transmission electron microscopy. The fabricated ZnO NP samples are crystalline with a grain size of 30–100 nm. The ZnO NPs were used as catalysts for the photodegradation of methylene blue (MB) and methyl orange (MO) under visible and UV light. The results indicate that the prepared ZnO material excellently removed MB and MO (cinitial = 10 mg/L) with efficiencies of 100% and 82.78%, respectively, after 210 min under UV radiation with a ZnO NP dose of 2 g/L. The photocatalyst activity of the synthesized material was also tested under visible light radiation with the same conditions; however, it achieved lower efficiencies. In addition, ZnO NPs were also tested regarding their antibacterial activity, and the results showed that the prepared ZnO samples had the highest (i.e., 100%) antibacterial efficiency against E. coli.

An electrochemical method for the determination of tetracycline has been reported, employing cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques with an activated glassy carbon electrode serving as the working electrode. In the presence of a suitable surfactant, the peak current enhancement of tetracycline was achieved. The interference effects and their elimination of the electrochemical method were also studied in the presence of various ions. The current responses obtained under the optimum experimental conditions through the DPV technique of tetracycline increased linearly with concentrations in linear dynamic ranges of 1.0 - 10 µmol L-1 and 10 - 100 µmol L-1 with 0.31 µmol L-1 detection limits. Combined with solid-phase extraction (SPE), the proposed method was effectively used for the determination of tetracycline residues in honey, achieving recovery rates ranging from 81.8% to 91.8%. The results suggest that this method is applicable for the detection of TC in complex food matrices. [ABSTRACT FROM AUTHOR]

Nowadays, deep neural networks (DNNs) are helpful tools for mammogram classification in breast cancer screening. But, in Vietnam, there is a relatively small number of mammograms for training DNNs. Therefore, this study aims to apply transfer learning techniques to improve the performance of DNN models. In the first step, 10,418 breast cancer images from the Digital Database for Screening Mammography were used for training the CNN model ResNet 34. In the second step, we fine-tune this model on the Hanoi Medical University (HMU) database with 6,248 Vietnamese mammograms. The optimal model of ResNet 34 among these models achieves a macAUC of 0.766 in classifying breast cancer X-ray images into three Breast Imaging-Reporting and Data System (BI-RADS) categories, BI-RADS 045 ('incomplete and malignance'), BI-RADS 1 ('normal'), and BI-RADS 23 ('benign'), when tested on the test dataset. This result is higher than the result of the ResNet 50 model trained only on the X-ray dataset of 7,912 breast cancer images of the HMU dataset, which achieves a macAUC of 0.754. A comparison of the performance of the proposed model of ResNet 34 applying transfer learning with other works shows that our model's evaluation results are higher than those of the compared models. [ABSTRACT FROM AUTHOR]

This paper presents a novel formulation for static limit analysis of structures, for which the Airy stress function is approximated using stabilized Radial Point Interpolation Mesh-free method (RPIM). The stress field is determined as second-order derivatives of the Airy function, and the equilibrium equations are automatically satisfied a priori. The so-called Stabilized Conforming Nodal Integration (SCNI) is employed to ensure a present method is truly a mesh-free approach, meaning that all constraints in problems are only enforced at nodes. With the use of the Airy function, SCNI, and Second-Order Cone Programming (SOCP), the size of the resulting problem is kept to be minimum. Several benchmark problems having arbitrary geometries and boundary conditions are investigated. The obtained numerical solutions are compared with those available in other studies to perform the computational aspect of the proposed method.

Nowadays, breast cancer is one of the leading cancers in Vietnam, and it causes approximately 6000 deaths every year. The rate of breast cancer patients was calculated as 26.4/100000 persons in 2018. There are 21,555 new cases reported in 2020. However, these figures can be reduced with early detection and diagnosis of breast cancer disease in women through mammographic imaging. In many hospitals in Vietnam, there is a lack of experienced breast cancer radiologists. Therefore, it is helpful to develop an intelligent system to improve radiologists’ performance in breast cancer screening for Vietnamese patients. Our research aims to develop a convolutional neural network-based system for classifying breast cancer X-Ray images into three classes of BI-RADS categories as BI-RADS 1 (“normal”), BI-RADS 23 (“benign”) and BI-RADS 045 (“incomplete and malignance”). This classification system is developed based on the convolutional neural network with ResNet 50. The system is trained and tested on a breast cancer image dataset of Vietnamese patients containing 7912 images provided by Hanoi Medical University Hospital radiologists. The system accuracy uses the testing set achieved a macAUC (a macro average of the three AUCs) of 0.754. To validate our model, we performed a reader study with the breast cancer radiologists of the Hanoi Medical University Hospital, reading about 500 random images of the test set. We confirmed the efficacy of our model, which achieved performance comparable to a committee of two radiologists when presented with the same data. Additionally, the system takes only 6 seconds to interpret a breast cancer X-Ray image instead of 450 seconds interpreted by a Vietnamese radiologist. Therefore, our system can be considered as a “second radiologist,” which can improve radiologists’ performance in breast cancer screening for Vietnamese patients.

Allocation mechanisms are crucial to shaping the effectiveness of a country's school finance system. In Vietnam, local authorities make their own decisions about allocating operating resources for pretertiary education. Despite this high level of control, there is a lack of comprehensive studies on how Vietnamese provinces allocate resources for their schools. This study aims to fill this gap by examining official legal documents from Vietnamese provinces, focusing on the allocation of operating funds to P-12 schools. The findings reveal significant variations in resource allocation across operating purposes (salary vs. nonsalary), grade levels, methods of accounting for scale economies, and different student subgroups, including those who are socio-economically disadvantaged students, gifted students, and with disabilities. [ABSTRACT FROM AUTHOR]