Advancing Open Science

Characterizing near-bottom ocean currents at deep-sea topographic features requires observations across multiple scales, from basin-wide circulation models to local in situ measurements. This study addresses this challenge at DeepInsight Hill, on Mohns Ridge, by combining eight months of in situ current measurements, global ocean model predictions, and novel ROV-based biological flow indicators. The flow regime shows a striking decoupling between tidal pressure and near-bottom currents. The semi-diurnal tide (M2) accounts for ~50% of tidal pressure amplitude yet contributes less than 0.5% of current kinetic energy, while the diurnal tide (K1) drives the largest tidal currents. Tidal energy accounts for only 8.8–9.4% of total current kinetic energy, consistent with other published current records along Mohns Ridge. A monthly comparison with the Copernicus global model shows that the model reproduces the seasonal modulation of the flow at the base of the hill but not the flow at the summit. During winter, sustained directional transport occurs simultaneously in opposite directions at the summit and base of DeepInsight Hill (S-SE and N-NW respectively), accounting for most of the effective water displacement over the record. Foliaceous sponges at the base of the hill show consistent W-SW orientations that do not match the long-term mean flow direction. Instead, these orientations align with episodic peak currents recorded during summer, suggesting that sponge morphology records specific high-energy flow events rather than time-averaged conditions, possibly amplified by peak seasonal nutrient supply. Bedforms observed at the same location eight months apart show no significant change, indicating that sediment-mobilizing currents are episodic and may not recur annually. These results highlight the value of combining physical and biological proxies for characterizing near-bottom flow regimes in topographically complex deep-sea settings.

Dissimilar alumina-to-copper joints are key enabling structures for hermetic ceramic–metal feedthroughs used in vacuum-electronic and X-ray-tube assemblies, yet the large mismatch in thermal expansion and the poor wettability of oxide ceramics by molten braze alloys make reliable joining difficult. In this work, the synergistic effect of zirconium added to a multicomponent Ag–Cu–Ti–Sn active filler metal was investigated by single-step induction brazing of Al₂O₃ to Cu. Three filler metals, Ag–Cu–Ti–Sn, Ag–Cu–Ti–Sn–3.5Zr and Ag–Cu–Ti–Sn–5.1Zr (wt.%), were produced by vacuum arc remelting, cold-rolled to foil and used to braze the joints for 15 min under a vacuum of 10⁻⁶ mbar at 840, 860 and 880 °C, respectively. The microstructure and interfacial reaction products were characterised by optical and scanning electron microscopy with energy-dispersive spectroscopy, while X-ray diffraction was performed on the filler metal foils to identify their crystalline phases. The mechanical response was assessed through shear strength, Vickers microhardness, and fractographic analysis. Every joint developed a brazing zone and a continuous reaction layer. With the Zr-free filler, the reaction layer comprised TiO and Cu₃Ti₃O, whereas both Zr-bearing fillers produced a TiO-plus-ZrO₂ reaction layer, with Ti reacting closer to the alumina interface than Zr, consistent with the more negative Gibbs free energy of TiO formation. The 3.5 wt.% Zr condition, which combined the thickest brazing zone with the thinnest reaction layer, gave the highest mean shear strength at 14 ± 2.5 MPa, about 56% above the Zr-free condition at 9 ± 1.5 MPa, while the 5.1 wt.% Zr condition, with a thicker ZrO₂ reaction layer, gave 12 ± 2.0 MPa. With three specimens per condition these differences approach but do not reach significance at the 5% level (one-way ANOVA, p = 0.062), and the ranking is supported by the accompanying microstructural and fractographic evidence. The reaction-layer microhardness increased monotonically with Zr content, from 146 HV (Zr-free) to 248 HV (3.5Zr) and 317 HV (5.1Zr). All joints failed in a quasi-brittle manner along the reaction layer and the adjacent alumina. Because Zr content, brazing temperature and foil thickness varied together, the results identify the 3.5 wt.% Zr condition as the best performing of those investigated rather than an intrinsic optimum Zr concentration, and they clarify the competing roles of reaction-layer chemistry and thickness in Zr-modified active filler metals.

The world has faced pandemics throughout its history. The most recent, COVID-19, took lives and disrupted healthcare and economies. COVID-19 vaccine programs were launched worldwide; however, the record time in which vaccines were developed raises questions about side effects and their severity. Also, as vaccines are delicate medical products, their cold supply chain must be continuously tracked and monitored to ensure their efficacy. Moreover, vaccine supply chain management is complex and requires trained staff to handle its different activities. As such, it is important to connect beneficiaries’ symptoms and registration systems with the vaccine supply chain to enable healthcare authorities to investigate whether observed side effects may be linked to cold chain handling conditions or production factors. Consequently, the need arises for a system that manages trained personnel certificate generation, which could also be used as a verification tool for employing the right experts for the right job. Therefore, our study developed a blockchain platform that integrates individuals’ registration and side effect data with vaccine cold supply chain information. The developed platform also includes a certification system for cold supply chain personnel training to support authenticated certificate generation and role assignment management. Blockchain was selected due to its features of security, decentralization, traceability, and trust. The key contributions of this work lie in providing a digital collaborative management and data tracing platform for vaccine side effects that are linked to beneficiaries’ registration and cold supply chain data to enable healthcare authorities to investigate whether any observed side effects may be linked to cold chain handling conditions or production factors while managing the issuance of certificates for trained cold supply chain personnel and controlling their employment in the correct chain(s). Our platform creates training certificate IDs without a central entity to regulate certificate generation and uses the created certificate information to control the employment of personnel with the appropriate training in the right chain to perform the right role. Our platform identified personnel employed in any chain where violations were recorded and side effects were subsequently reported, and presents their training and employment data, providing the interconnected data record necessary for accountability. The platform was tested successfully, producing the desired outcome of secure, traceable, and transparent vaccine data sharing, linking, and verification.

Osteosarcoma is an aggressive primary bone malignancy affecting both humans and dogs, and canine osteosarcoma represents a valuable comparative model for investigating novel therapeutic strategies. Targeting tumour metabolism may enhance the efficacy of existing anticancer treatments. This study investigated whether the mitochondrial uncoupler BAM15 modifies the response of canine D17 osteosarcoma cells to regorafenib. Cells were treated with BAM15 (20 µM) or regorafenib (REG; IC₂₅, 8 µM; IC₅₀, 16 µM) alone or pretreated with BAM15 before regorafenib exposure. Treatment effects were assessed using viability assays, Annexin V/propidium iodide staining, cell-cycle analysis, Seahorse XF bioenergetic profiling, quantitative PCR, and Western blotting. BAM15 alone did not significantly affect cell metabolic activity. Combined treatment with BAM15 and regorafenib produced a significantly greater reduction in cell metabolic activity than regorafenib alone at IC₅₀, whereas no significant difference between treatments was observed at IC₂₅. Combined treatment at IC₅₀ increased late apoptotic and necrotic populations and disrupted cell-cycle distribution. BAM15 reduced oxidative phosphorylation-derived ATP production without detectable glycolytic compensation but did not further suppress mitochondrial ATP production beyond regorafenib effect alone. Combined treatment also induced transcriptional changes in stress-adaptation-related genes, while protein analysis revealed reduced BAX and BAX/BCL-2 ratio. These exploratory findings provide preliminary evidence that mitochondrial uncoupling by BAM15 may modify the response to regorafenib and support further mechanistic investigation and validation in additional tumour and non-malignant canine models.

We assess two Albania–Greece corridor channels: visitor expenditure in Greece and Greece-origin finance potentially supporting Albanian capital formation. Bilateral visitor-receipt data complement immediate-partner direct investment liability transactions matched to nominal capital formation over 48 quarters (2014–2025), annual household transfers and two-direction travel receipts. The 2005–2025 framework retains thirteen outcomes and sensitivities for 68 allocated Albanian entry quarters. Greek contemporaneous visitor-receipt associations are positive, but bounds calculations and diagnostics do not establish cointegration. Across 44 forecast targets, lagged visitors do not demonstrate improved Greek receipt forecasts under full-window primary log-error measures. Albanian visitor-investment estimates include negative, allocation-sensitive results. Originally, a EUR 10 million increase in lagged direct investment liability transactions was associated with 0.861 nominal capital formation log-growth points (95 percent reference interval: −0.371 to 2.094). Four- and twelve-quarter sensitivities show sample-dependent signs. Twelve-lag joint tests depend on controls, whole-window sums remain imprecise, and residual serial correlation prevents reliable financial-transmission inference. Annual evidence establishes household resources and bidirectional spending, not investment use. This falsifiable two-channel assessment distinguishes recorded finance from real, additional investment. Neither a financing multiplier, investment predominance over consumption, stable long-run elasticity nor national-benefit ranking is established.

The Cap ‘n’ collar isoform C (CncC) transcription factor, the insect ortholog of mammalian nuclear factor erythroid 2-related factor 2 (Nrf2), serves as a central regulator of redox homeostasis and xenobiotic detoxification. Increasing evidence demonstrates that the functions of CncC extend beyond stress responses to include metamorphosis, immunity, reproduction, aging, and the development of metabolic insecticide resistance. In this review, we synthesize recent advances in insect CncC biology, with a focus on the conserved CncC/Keap1-ARE signaling pathway and its role in the detoxification of phytochemicals and insecticides. We further discuss insect-specific regulatory interactions between CncC and ecdysone signaling, as well as the gut microbiota, and compare insect CncC with mammalian Nrf2. From an applied perspective, we discuss CncC as a target for resistance management, including strategies involving RNAi and small-molecule inhibitors, its potential application in inducible bioreactors, and its value as a biomarker in environmental risk assessment. We conclude by identifying key open questions, including tissue-specific regulation, fitness costs of constitutive activation, and field relevance of CncC-mediated resistance, and highlight how addressing these gaps can support the development of sustainable pest control strategies.

Real-world driving tests reflect vehicle performance in everyday use, but extracting energy conversion data from extensive measurements remains challenging. The study compared a hybrid electric vehicle and a plug-in hybrid electric vehicle, with the latter operated in charge-sustaining mode, against three conventional vehicles in real-world driving tests. The moving average window method transformed second-by-second measurements into tens of thousands of quasi-trip samples representing diverse driving scenarios. Using the linear correlation between slope CO₂ emissions and slope height in the data window samples, a linear regression-based parameter identification method was proposed to infer the total braking energy recovery ratios of the hybrid vehicles, exploring a way to assess vehicle energy conversion characteristics on the roads. The tested hybrid vehicles exhibited lower urban CO₂ emissions and, under mountainous and aggressive driving conditions, greater advantages primarily attributable to braking energy recovery. However, under the tested conditions, the hybrid vehicles showed no significant advantage in engine thermal efficiency over the conventional counterpart equipped with a downsized turbocharged engine. Engine downsizing may contribute to efficient engine operation, offering some of the efficiency benefits comparable to those achieved through optimized engine–motor torque distribution in hybrid vehicles.

Reliable anomaly detection of sensor measurements is essential for monitoring complex systems in smart manufacturing environments. However, conventional approaches often require predefined statistical assumptions or explicit knowledge of physical measurement models. Therefore, a data-driven sparse reconstruction framework (DSRF) is proposed for anomaly detection without requiring explicit noise distribution or separate identification of a physical measurement model. The proposed framework decomposes observation deviations into a reconstructed normal measurement component, a sparse anomaly residual, and a remaining reconstruction residual. The DSRF is evaluated using current and vibration measurements from robot welding systems. Normal measurements are divided into training, validation, and independent test subsets, while labeled abnormal samples are reserved for final evaluation. Training data are used to learn an effective sensing matrix and determine anomaly thresholds. At the 99th-percentile threshold, DSRF achieves 100% detection with no false negatives and accuracies of 99.76% and 98.38% for the current and vibration datasets, respectively. Comparative experiments with PCA reconstruction and an autoencoder show similarly high classification performance while DSRF only provides a larger threshold-normalized separation and explicitly estimates sparse anomaly residuals. The anomaly residual also provides frequency feature-level information for interpreting prominent abnormal deviations. These results demonstrate the feasibility of DSRF for anomaly detection and residual-based interpretation without explicit noise assumptions or separately identified physical system models.