Swapping Mechanism Research Articles

Application of Article 6-Linked Debt-for-Climate Swap for the Clean Energy Transition in Africa

This study presents an innovative financial model that integrates the debt-for-climate swap mechanism with Article 6 of the Paris Agreement, specifically designed to support Africa’s transition to clean energy. The model connects debt-for-climate swaps with the creation of internationally transferred mitigation outcomes (ITMOs), offering mutual benefits for both debtor and creditor nations. This approach aims to improve the debt sustainability of African countries while strengthening their climate resilience by combining Article 6 of the Paris Agreement with Official Development Assistance (ODA). Additionally, this model aligns with key Sustainable Development Goals (SDGs), including SDG 7 (Affordable and Clean Energy), SDG 13 (Climate Action), and SDG 17 (Partnerships for the Goals). Furthermore, the study proposes a restructuring of existing environmental safeguards by incorporating the “Do No Significant Harm” (DNSH) criteria and environmental contribution indicators to ensure alignment with the minimum safeguards mandated by Article 6 and international development standards. Through quantitative analysis, our findings indicate that the proposed debt-for-climate swap model could significantly contribute to Africa’s clean energy transition, address the region’s external debt challenges, and enhance climate resilience.

Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy

SummaryWireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

ISwap: A New Memory Page Swap Mechanism for Reducing Ineffective I/O Operations in Cloud Environments

This paper proposes iSwap, a new memory page swap mechanism that reduces the ineffective I/O swap operations and improves the QoS for applications with a high priority in the cloud environments. iSwap works in the OS kernel. iSwap accurately learns the reuse patterns for memory pages and makes the swap decisions accordingly to avoid ineffective operations. In the cases where memory pressure is high, iSwap compresses pages that belong to the latency-critical (LC) applications (or high-priority applications) and keeps them in main memory, avoiding I/O operations for these LC applications to ensure QoS; and iSwap evicts low-priority applications’ pages out of main memory. iSwap has a low overhead and works well for cloud applications with large memory footprints. We evaluate iSwap on Intel x86 and ARM platforms. The experimental results show that iSwap can significantly reduce ineffective swap operations (8.0% - 19.2%) and improve the QoS for LC applications (36.8% - 91.3%) in cases where memory pressure is high, compared with the latest LRU-based approach widely used in modern OSes.

Multi-View Gaze: Leveraging Multi-View Images to Disentangle Features for Accurate Gaze Estimation

Gaze estimation is a prominent field within artificial intelligence and machine learning, rapidly developing due to the practical uses and possibilities. However, this development brings challenges, such as inaccuracy with different facial features and external factors such as lighting or camera quality. In the past, research has led to a cross encoder, or a swapping mechanism of the disentangled data from an image. The proposed method takes this one step further and incorporates multi-view images to leverage this disentanglement. Multi-view images allow for more data pairs within images to be swapped, resulting in more maximized and fine-tuned accuracy in gaze detection. Another added detail was transfer learning, or the carry over of a pre-optimized encoder to make the training process much more efficient. This can be incorporated into the real world, for example, by using it to control a computer mouse without physical movement or to detect patterns to diagnose neurodevelopmental disorders such as Attention Deficit Hyperactivity Disorder (ADHD) which can be difficult to detect in young children otherwise. The results of this newly proposed method produced more accurate results than state-of-the-art mechanisms, only having an angular error of 7.4 when trained and tested within the EVE dataset.

Experiment on long-term forecasting of geomagnetic activity based on nonlocal correlations

The experiment was performed on the use of advanced macroscopic nonlocal correlations to forecast slow random fluctuations of the Dst index of geomagnetic activity. The global maximum correlation of Dst with the signal of the electrode detector reaches 0.97, which is sufficient for the forecast, and its time shift corresponds to the advance of the detector signal relative to Dst by 329 days. The large magnitude of the time shift is due to the slow diffusion mechanism of entanglement swapping between the detector and the source. At the same time, the position of the global maximum of the correlation function coincides with the position of the global minimum of the entropy independence function, which confirms its undistorted by possible nonlinearity of the relationship and determines the optimal lead time of the forecast. Long series of test forecasts Dst have been calculated using data from a nonlocal correlation detector with a fixed lead time using three methods: current regression, current impulse transient response and current neural network. The accuracy of the forecasts is sufficient for all practical purposes.

ИННОВАЦИОННЫЕ СТРАТЕГИИ ФИНАНСИРОВАНИЯ ГОЛУБОЙ ЭКОНОМИКИ: ОБМЕН ДОЛГА НА ПРИРОДУ

Environmental degradation and macroeconomic instability have a negative impact on the achievement of the Sustainable Development Goals of the blue economy strategy. Initiatives by the international community are aimed at developing innovative financial mechanisms aimed at jointly addressing these problems. Financial instruments aimed at the cancellation of all or part of sovereign debt in exchange for channelling financing into blue economy projects can improve environmental sustainability and reduce the public debt of developing countries and thus contribute to the stabilization of the international financial system. This research article is an attempt to identify and analyze the applicability of innovative mechanisms for sustainable financing of the blue economy, given its importance for achieving sustainable development goals. Debt-for-nature swapping is most effective in situations where alternative financing instruments are limited and/or when governments are interested in improving credit ratings. The financial instruments of sustainable financing, which underpin the debt-for-nature swap mechanism, are an effective tool for improving the country’s credit rating and at the same time preserving natural ecosystems. In order to identify projects that meet the criteria for participation in debt exchange programs, the author proposes a model for assessing the compliance of investment projects with the criteria of sustainable blue financing.

An Efficient Edge Swap Mechanism for Enhancement of Robustness in Scale-free Networks in Health Care Systems

An Efficient Edge Swap Mechanism for Enhancement of Robustness in Scale-free Networks in Health Care Systems

An efficient edge swap mechanism for enhancement of robustness in scale-free networks in healthcare systems

An efficient edge swap mechanism for enhancement of robustness in scale-free networks in healthcare systems

Elucidating the Mechanism of Multispecific Antibody Aggregation through Subunit Analysis.

Multispecific antibody constructs are quickly becoming more common constructs in biopharmaceuticals to improve specificity and efficacy. While the advent of this technology has led to improved therapeutics, its development has challenged the analytical tools through which these therapeutics are characterized. Moreover, new critical quality attributes, such as aggregation, have challenged the approaches to characterization even further. Herein, we describe a novel native subunit analysis using IdeS and IgdE analyzed by native size exclusion chromatography coupled with mass spectrometry to interrogate the mechanism of aggregation in a multispecific antibody. Digestion by IdeS and IdgE allows for the retention and detection of noncovalent interactions thereafter. Aggregation was localized to single-chain fragment variables (scFvs) wherein a domain swapping mechanism between VH1/VL2 and VH2/VL1 occurs.

TCX: Texture and channel swappings for domain generalization

Neural networks suffer performance degradation when the source and target data lie in a different distribution hampering direct deployment of the model to diverse target domains. To this end, domain generalization (DG) aims to generalize the model well to an unknown target domain by utilizing multiple source domains. This paper proposes two simple swapping mechanisms, texture and channel swapping (TCX), for DG. Texture swapping augments the source dataset by replacing textures of an image with other textures in source dataset to alleviate the texture bias problem in convolution neural networks (CNNs). Furthermore, channel swapping switches channels of input feature vectors of the classifier along with its labels to encourage the model to utilize more channels when classifying an image. Together, we expect the model to learn less domain-specific features but more generalized class-specific features resulting in better domain generalization performance. We demonstrate the effectiveness of our approach with state-of-the-art results on three domain generalization benchmarks.

Effect of mutations on the folding and stability of γD-crystallin protein

Interprotein interactions between the partially unfolded states of γD-crystallin (γD-crys) protein are known to cause cataracts. Therefore, understanding the unfolding pathways of native γD-crys is extremely crucial to delineate their aggregation mechanism. In this study, we have performed extensive all-atom Molecular Dynamics simulations with explicit solvent to understand the role of the critical residues that drive the stability of the motifs and domains of γD-crys in its wild type and mutant forms. Our findings show that while the individual motifs of wild type are not stable in the native form, the individual domains remain structurally stable at 425K. This enhanced stability of the domain was attributed to the hydrophobic interactions between the motifs. Single and double point mutations of the domains with negatively charged aspartic and glutamic acid amino acid residues (I3E, W42D, W42E, I3D/W42D, I3E/W42E, and L92D/W157D) decreases the structural stability, leading to unfolding of individual domains of γD-crys. We believe that our study sheds light on the weakest links of γD-crys, along with the role of interactions stabilizing the domains. Further, this study bolsters and provides a better understanding of the domain swapping mechanism of aggregation of γD-crys. Communicated by Ramaswamy H. Sarma

Human RNase 1 can extensively oligomerize through 3D domain swapping thanks to the crucial contribution of its C-terminus

Human ribonuclease (RNase) 1 and bovine RNase A are the proto-types of the secretory “pancreatic-type” (pt)-RNase super-family. RNase A can oligomerize through the 3D domain swapping (DS) mechanism upon acetic acid (HAc) lyophilisation, producing enzymatically active oligomeric conformers by swapping both N- and C-termini. Also some RNase 1 mutants were found to self-associate through 3D-DS, however forming only N-swapped dimers.Notably, enzymatically active dimers and larger oligomers of wt-RNase 1 were collected here, in higher amount than RNase A, from HAc lyophilisation. In particular, RNase 1 self-associates through the 3D-DS of its N-terminus and, at a higher extent, of the C-terminus. Since RNase 1 is four-residues longer than RNase A, we further analyzed its oligomerization tendency in a mutant lacking the last four residues. The C-terminus role has been investigated also in amphibian onconase (ONC®), a pt-RNase that can form only a N-swapped dimer, since its C-terminus, that is three-residues longer than RNase A, is locked by a disulfide bond. While ONC mutants designed to unlock or cut this constraint were almost unable to dimerize, the RNase 1 mutant self-associated at a higher extent than the wt, suggesting a specific role of the C-terminus in the oligomerization of different RNases.Overall, RNase 1 reaches here the highest ability, among pt-RNases, to extensively self-associate through 3D-DS, paving the way for new investigations on the structural and biological properties of its oligomers.

Revisiting VH1 phosphatase at the time of monkeypox: back to the spotlight.

Vaccinia virus is a poxvirus that has been successfully leveraged to develop vaccines for smallpox, which is caused by the closely related Variola virus. Smallpox has been declared as 'eradicated' by the WHO in 1980; however, it still poses a potential bioterrorism threat. More recently, the spreading of monkeypox (MPox) in non-endemic countries has further highlighted the importance of continuing the exploration for druggable targets for poxvirus infections. The vaccinia H1 (VH1) phosphatase is the first reported dual specificity phosphatase (DUSP) able to hydrolyze both phosphotyrosine and phosphoserine/phosphotheonine residues. VH1 is a 20 kDa protein that forms a stable dimer and can dephosphorylate both viral and cellular substrates to regulate the viral replication cycle and host immune response. VH1 dimers adopt a domain swap mechanism with the first 20 amino acids of each monomer involved in dense electrostatic interaction and salt bridge formations while hydrophobic interactions between the N-terminal and C-terminal helices further stabilize the dimer. VH1 appears to be an ideal candidate for discovery of novel anti-poxvirus agents because it is highly conserved within the poxviridae family and is a virulence factor, yet it displays significant divergence in sequence and dimerization mechanism from its human closest ortholog vaccinia H1-related (VHR) phosphatase, encoded by the DUSP3 gene. As the dimeric quaternary structure of VH1 is essential for its phosphatase activity, strategies leading to disruption of the dimer structure might aid in VH1 inhibitor development.

Distribution Path Segmentation Using Route Relocation and Savings Heuristics for Multi-Depot Vehicle Routing

This paper uses routing segmentation optimization for the planning of optimal distribution networks between urban depots and their respective customers. In this experiment, three steps are proposed in concession: search for the initial solution using local search properties, improve the solution using route relocation and perturb the solution using tabu search incorporating the savings heuristic. By applying multi-depot simultaneous deployment with ideal scheduling strategies and routing heuristics ensuring cost-optimal routing, the study presents an alternative to enhanced scheduling system optimization. Based on repopulation and sequential insertion algorithms, the initial solution is created, while route relocation and tabu swap mechanisms constitute the improvement strategy and perturbation. Test results comparing the proposed solution strategy to the previous genetic algorithm solution result in a better arrangement of route segregation aspects representing customer clusters. This strategy has proven to be more successful in optimizing route segregation than the original genetic algorithm solution. This demonstrates a significant improvement in route optimization.

A Reinforcement Learning Approach for Scheduling Problems with Improved Generalization through Order Swapping

The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy, but also for increasing the overall efficiency. Among the different job scheduling problems, the Job Shop Scheduling Problem (JSSP) is addressed in this work. JSSP falls into the category of NP-hard Combinatorial Optimization Problem (COP), in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as First-In, First-Out, Largest Processing Time First and metaheuristics such as taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using Deep Reinforcement Learning (DRL) to solve COPs has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the Proximal Policy Optimization (PPO) algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated a new method called Order Swapping Mechanism (OSM) in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.

Ultrafast Two-Electron Orbital Swap in Li Initiated by Attosecond Pulses

A universal mechanism of ultrafast 2-electron orbital swap is discovered through 2-photon sequential double ionization of Li. After a 1s electron in Li is ionized by absorbing an extreme ultraviolet photon, the other 2 bound electrons located on 2 different shells have either parallel or antiparallel spin orientations. In the latter case, these 2 electrons are in the superposition of the singlet and triplet states with different energies, forming a quantum beat and giving rise to the 2-electron orbital swap with a period of several hundred attoseconds. The orbital swap mechanism can be used to manipulate the spin polarization of photoelectron pairs by conceiving the attosecond-pump attosecond-probe strategy and thus serves as a knob to control spin-resolved multielectron ultrafast dynamics.

Sequential and Swap Mechanisms for Public Housing Allocation with Quotas and Neighbourhood-based Utilities

We consider the problem of allocating indivisible items to agents where both agents and items are partitioned into disjoint groups. Following previous works on public housing allocation, each item (or house) belongs to a block (or building) and each agent is assigned a type (e.g., ethnicity group). The allocation problem consists in assigning at most one item to each agent in a good way while respecting diversity constraints. Based on Schelling’s seminal work, we introduce a generic individual utility function where the welfare of an agent not only relies on her preferences over the items but also takes into account the fraction of agents of her own type in her own block. In this context, we investigate the issue of stability, understood here as the absence of mutually improving swaps, and we define the cost of requiring it. Then, we study the behaviour of two existing allocation mechanisms: an adaptation of the sequential mechanism used in Singapore and a distributed procedure based on mutually improving swaps of items. We first present the theoretical properties of these two allocation mechanisms, and we then compare their performances in practice through an experimental study.

Slow Evolution toward “Super-Aggregation” of the Oligomers Formed through the Swapping of RNase A N-Termini: A Wish for Amyloidosis?

Natively monomeric RNase A can oligomerize upon lyophilization from 40% acetic acid solutions or when it is heated at high concentrations in various solvents. In this way, it produces many dimeric or oligomeric conformers through the three-dimensional domain swapping (3D-DS) mechanism involving both RNase A N- or/and C-termini. Here, we found many of these oligomers evolving toward not negligible amounts of large derivatives after being stored for up to 15 months at 4 °C in phosphate buffer. We call these species super-aggregates (SAs). Notably, SAs do not originate from native RNase A monomer or from oligomers characterized by the exclusive presence of the C-terminus swapping of the enzyme subunits as well. Instead, the swapping of at least two subunits’ N-termini is mandatory to produce them. Through immunoblotting, SAs are confirmed to derive from RNase A even if they retain only low ribonucleolytic activity. Then, their interaction registered with Thioflavin-T (ThT), in addition to TEM analyses, indicate SAs are large and circular but not “amyloid-like” derivatives. This confirms that RNase A acts as an “auto-chaperone”, although it displays many amyloid-prone short segments, including the 16–22 loop included in its N-terminus. Therefore, we hypothesize the opening of RNase A N-terminus, and hence its oligomerization through 3D-DS, may represent a preliminary step favoring massive RNase A aggregation. Interestingly, this process is slow and requires low temperatures to limit the concomitant oligomers’ dissociation to the native monomer. These data and the hypothesis proposed are discussed in the light of protein aggregation in general, and of possible future applications to contrast amyloidosis.

A Dynamic Optimal Battery Swapping Mechanism for Electric Vehicles Using an LSTM-Based Rolling Horizon Approach

This paper proposes a new approach for optimal operation of an Electric Vehicle (EV) battery-swapping station (BSS) based on Rolling-Horizon optimization (RHO). The BSS has several swapping bays such that each can accommodate an EV for swapping single or multiple battery units. The proposed BSS model considers serving different types of EVs using a heterogeneous battery stock. The charging of the depleted batteries (DBs) is performed using continuously controlled variable chargers which makes it more flexible for providing grid services. While previous studies focused on day-ahead modeling of BSSs, our study considers BSS dynamic scheduling. The goal is to maximize the daily profit using an RHO mechanism to provide optimal swapping and charging/discharging processes. The problem is defined as mixed-integer nonlinear programming (MINLP), then it’s linearized into a mixed-integer linear problem (MILP) to reduce the computational complexity. To predict the EV’s swapping demand, a long short-term memory (LSTM) recurrent neural network is utilized as a time series forecasting engine. The proposed model is validated through a set of case studies comparing the LSTM-based RHO mechanism versus unscheduled operation and day-ahead scheduling. Simulation results demonstrate that the proposed dynamic scheduling mechanism increases the profit between 10% and 25.7% compared to the day-ahead scheduling. Furthermore, the number of EVs served using the proposed approach increases between 11% and 14% compared to the day-ahead model.

Supercurrent-induced long-range triplet correlations and controllable Josephson effect in superconductor/ferromagnet hybrids with extrinsic spin-orbit coupling

We predict that long-range triplet correlations (LRTC) in superconductor/ferromagnet (S/F) hybrids with extrinsic impurity spin-orbit coupling can be generated and manipulated by supercurrent flowing in the superconducting leads along the S/F interfaces. The LRTC appear via two basic mechanisms. The essence of the first one is the generation of triplets by the superconducting spin Hall effect. These pairs are long range in the ferromagnet under the appropriate mutual orientation of the condensate momentum and the ferromagnet magnetization. The second mechanism is based on the singlet-triplet conversion at the S/F interface followed by the rotation of the spin of the obtained short-range opposite-spin pairs via the spin current swapping mechanism. The structure of the supercurrent-induced LRTC is studied both for S/F bilayers and superconductor/ferromagnet/superconductor (S/F/S) Josephson junctions. We demonstrate that in S/F/S junctions, where the Josephson coupling is realized via the supercurrent-induced LRTC, the ground state phase can be switched between 0 and $\ensuremath{\pi}$ in a controllable manner. The switching is performed by reversing the supercurrent in one of the superconducting leads, thus realizing a physical principle of the $\text{0-}\ensuremath{\pi}$ shifter.