National Ice Centre

Imports and exports are vital components of any nation’s economy. Each importer and exporter within a country is typically identified through a nationally assigned unique identifier. However, these identifiers are not standardized or recognized globally, leading to inconsistencies in international trade records. This challenge becomes even more pronounced for landlocked nations, whose import and export operations depend heavily on neighboring seafaring countries for access to global maritime trade routes. The lack of a globally interoperable identification system complicates the tracking, coordination, and verification of goods across borders, often resulting in delays, disputes, and inefficiencies in the supply chain. In such cases, a treaty is often signed between landlocked nations and their neighboring sea-linked countries to facilitate the transit of goods through ports and coastal routes. To engage in this process, importers and exporters from the landlocked country must register with the customs clearance system of the sea-sharing nation responsible for handling transit goods. However, this registration process is often time-consuming and susceptible to fraudulent activities, as illegitimate trade actors may register using forged documents. To overcome these challenges, we propose a Self-Sovereign Identity (SSI)-based framework for the secure registration and verification of importers and exporters involved in cross-border commodity movement. The proposed SSI-enabled system enhances trust, transparency, and efficiency by ensuring that every participant in the trade ecosystem possesses a verifiable digital identity recognized across jurisdictions. During the transshipment of export consignments from a landlocked country to a seafaring nation, an SSI issuer within the consignee country facilitates identity verification and credential issuance, thereby securing and streamlining inter-border trade operations. To implement, a prototype has been developed, and the execution time of the SSI issuer, holder, and verification has been analyzed. SSI implementation models and implementation challenges have also been discussed.

The Year of Polar Prediction (YOPP), an international research initiative organized by the World Meteorological Organization's (WMO) World Weather Research Program from 2013 to 2022, aimed to markedly enhance environmental prediction capabilities in the polar regions and beyond, particularly in the context of a rapidly changing climate. YOPP achieved this through a concerted effort in observation, modeling, verification, user engagement, and educational activities. This article offers a comprehensive overview of YOPP's key outcomes and impacts, using a dual approach that merges qualitative success stories with quantitative metrics. Scientifically, the focus is on the role of polar observations in improving prediction accuracy, enhanced understanding of processes to support model development, advancements in forecast verification, particularly in sea ice prediction, an improved understanding of the interconnections between polar and midlatitude regions, and effective user engagement. This paper also discusses how these scientific discoveries have been converted into practical applications, emphasizing the route from science to services. Additionally, it summarizes the education, communication, outreach, and coordination efforts employed to maximize YOPP's impact. Finally, the article provides a series of recommendations for future research, informed by the insights gained from YOPP's experiences and recent radical developments in technology. SIGNIFICANCE STATEMENT: The Year of Polar Prediction (YOPP) was a landmark initiative aimed at enhancing our ability to predict environmental changes in the polar regions, areas that are increasingly affected by climate change. By integrating global efforts in observation, modeling, and data analysis, YOPP has significantly contributed to improve the accuracy of weather and climate forecasts in these critical zones, and beyond. These advancements matter because they provide crucial insights into polar processes along with their remote impacts, enhance global prediction models, and inform stakeholders about predictive capabilities. The project's focus on user engagement and education ensures that these scientific achievements translate into practical benefits. The collaborative spirit of YOPP exemplifies how international scientific cooperation can address some of the most pressing environmental challenges of our time.

Exports and imports are the major activity of any country. At the time of bills of exports/ imports document filing, the importers/ exporters declare the item description and associated tariff head code. Every tariff head has its duty rate associated. The importers/ exporters may declare the item in a different tariff head to take the duty benefits. The assessment officials must match the item description with the item tariff head. However, due to the ever-changing tariff head, it is impossible to manually check and verify whether the item descriptions lie in the declared tariff head. Thus, gathering a comprehensive dataset consisting of globally available customs tariff heads and product description combinations is a difficult task. This paper utilizes blockchain technology to securely store and manages the training datasets and uses the collected datasets to train a Naïve Bayes classifier probabilistic machine learning algorithm. By combining blockchain technology for data integrity and transparency with the predictive power of the Naïve Bayes classifier, the proposed novel methodology offers a robust solution for preventing duty frauds and ensuring compliance with customs regulations.

We present a lattice determination of the leading-order hadronic vacuum polarization (HVP) contribution to the muon anomalous magnetic moment, $a_{\mu}^{\rm HVP}$, in the so-called short and intermediate time-distance windows, $a_{\mu}^{\rm SD}$ and $a_{\mu}^{\rm W}$, defined by the RBC/UKQCD Collaboration [1]. We employ gauge ensembles produced by the Extended Twisted Mass Collaboration (ETMC) with $N_f = 2 + 1 + 1$ flavors of Wilson-clover twisted-mass quarks with masses of all the dynamical quark flavors tuned close to their physical values. The simulations are carried out at three values of the lattice spacing equal to $\simeq 0.057, 0.068$ and $0.080$ fm with spatial lattice sizes up to $L \simeq 7.6$~fm. For the short distance window we obtain $a_\mu^{\rm SD}({\rm ETMC}) = 69.27\,(34) \cdot 10^{-10}$, which is consistent with the recent dispersive value of $a_\mu^{\rm SD}(e^+ e^-) = 68.4\,(5) \cdot 10^{-10}$ [2]. In the case of the intermediate window we get the value $a_\mu^{\rm W}({\rm ETMC}) = 236.3\,(1.3) \cdot 10^{-10}$, which is consistent with the result $a_\mu^{\rm W}({\rm BMW}) = 236.7\,(1.4) \cdot 10^{-10}$ [3] by the BMW collaboration as well as with the recent determination by the CLS/Mainz group of $a_\mu^{\rm W}({\rm CLS}) = 237.30\,(1.46) \cdot 10^{-10}$ [4]. However, it is larger than the dispersive result of $a_\mu^{\rm W}(e^+ e^-) = 229.4\,(1.4) \cdot 10^{-10}$ [2] by approximately $3.6$ standard deviations. The tension increases to approximately $4.5$ standard deviations if we average our ETMC result with those by BMW and CLS/Mainz. Our accurate lattice results in the short and intermediate windows point to a possible deviation of the $e^+ e^-$ cross section data with respect to Standard Model predictions in the low and intermediate energy regions, but not in the high energy region.

Quantum-enhanced computing methods are promising candidates to solve currently intractable problems. We consider here a variational quantum eigensolver (VQE), that delegates costly state preparations and measurements to quantum hardware, while classical optimization techniques guide the quantum hardware to create a desired target state. In this work, we propose a bosonic VQE using superconducting microwave cavities, overcoming the typical restriction of a small Hilbert space when the VQE is qubit based. The considered platform allows for strong nonlinearities between photon modes, which are highly customisable and can be tuned in situ, i.e. during running experiments. Our proposal hence allows for the realization of a wide range of bosonic ansatz states, and is therefore especially useful when simulating models involving degrees of freedom that cannot be simply mapped to qubits, such as gauge theories, that include components which require infinite-dimensional Hilbert spaces. We thus propose to experimentally apply this bosonic VQE to the U(1) Higgs model including a topological term, which in general introduces a sign problem in the model, making it intractable with conventional Monte Carlo methods.