Abstract: The orbital drift of the Fengyun 4B (FY-4B) satellite from 133°E to 105°E in early 2024 significantly altered its viewing geometry over China, providing a unique opportunity to evaluate the impact of satellite positioning on retrieval accuracy. (1) Methods: This study systematically evaluates the performance of FY-4B surface downward shortwave radiation (DSSR) products before and after the drift, using ground radiation observation data from the China Meteorological Administration (CMA) as a reference, including 165 stations. (2) Results: The results demonstrate a substantial improvement in product accuracy post-drift. The correlation coefficient (R) increased from 0.93 to 0.95, while the root mean square error (RMSE) decreased by 11.8% (from 112.46 to 99.24 W/m²). The mean bias error (MBE) shifted from a negligible negative bias to a slight positive bias (2.68 W/m²), indicating reduced systematic deviation. Spatially, the "East-West" accuracy disparity was mitigated, attributed to the reduced viewing zenith angles (VZA) and minimized geometric distortions over western China. While performance over water bodies and urban areas is robust, challenges persist in complex terrains due to 3D topographic effects. (3) Conclusions: Ultimately, the validated high-fidelity radiative records position FY-4B as a reliable data source for solar energy resource assessment, while the demonstrated geometric benefits offer strategic guidance for the orbital deployment of future geostationary constellations.

Abstract: This work deals with the impact of surface acoustic treatment (holes and grooves) and primary material (plywood, MDF, solid wood panel) of acoustic panels on its fire characteristics. Fire characteristics were determined based on the cone calorimeter method, single-flame source test, and smoke generation assessment. In general, birch plywood demonstrated the highest values for heat release rate (HRR), maximum average rate of heat emission (MARHE), and effective heat of combustion (EHC), indicating its higher flammability compared to the other tested materials. MDF generally exhibited the lowest values for heat release rate (HRR) and maximum average rate of heat emission (MARHE), yet under certain perforated configurations, it generated the highest amount of smoke. Solid wood panels exhibited the lowest heat release rate (HRR) but developed the largest charred areas during the single-flame source test. Among the surface treatments, the 16/8 mm treatment resulted in the highest values of effective heat of combustion (EHC) and maximum average rate of heat emission (MARHE), while the 8/1.5–15T treatment exhibited the most rapid increase in heat release rate (HRR), attributed to the swift degradation of its thin surface layer and high void fraction. The presence of holes and grooves increased smoke production, which was most evident in MDF and plywood panels.

Abstract: Small hydro power plants (HPPs) play an important role in managing fluctuating energy requirements. This article presents a real-world case study where model predictive control (MPC) utilizing lightGBM-based machine learning (ML) forecasts of energy demand and water availability is employed to optimize the scheduling of a small HPP for peak shaving. A comparative analysis is conducted between the current non-predictive control strategy, which relies on operator decisions for peak shaving, and a fully automatic controller that optimally schedules the utilization of available water resources based on ML predictions. Preliminary results show that the MPC can outperform the operator’s decisions and that this has the potential of improving peak shaving capabilities of small HPPs, emphasizing the role of predictive control methodologies for exploiting energy storage resource in the management of the distribution grid. This approach offers a pragmatic solution that small utilities can adopt with minimal effort using their own data.

Abstract:

Efficient and low-cost electrocatalysts play a crucial role in hydrogen production through electrolysis of water. Molybdenum (Mo) carbide with a similar electronic structure to Pt was selected, both α-MoC_1−x and α-MoC_1−x/β-Mo₂C electrocatalysts were successfully fabricated for electrochemical hydrogen evolution. A continuous optimization of the hydrothermal and carbonization conditions was carried out for the preparation of α-MoC_1−x. The biphasic molybdenum carbide catalysts were further achieved via vanadium doping with a phase transition of molybdenum carbide from α to β, which increases the specific surface area of the electrocatalyst. It was found that the V-Mo_xC catalyst obtained at a Mo/V molar ratio of 100:5 exhibited the best hydrogen production performance, with a β to α phase ratio of 0.827. The overpotential of V-Mo_xC at η₁₀ decreased to 99 mV, and the Tafel slope reached 65.1 mV dec⁻¹, indicating a significant improvement in performance compared to undoped samples. Excellent stability was obtained of the as-prepared electrocatalyst for water splitting over 100 h at a current density of 10 mA cm⁻².

Abstract: The engine system requirements for different engine cycles significantly influence the design of the mixing head. A literature review of fuel-injection technology for hydro-gen and methane is presented. The literature review aimed to answer proposed questions specific to the liquid rocket engine fuel injector design. The current review methodology accounts for the engine system effect. Thus, a comprehensive literature review of the working principles of startup-staged combustion cycle engines based on original patents is provided. At the end of the review, the research gaps and suggestions for further work are summarised. At high mass flow rate and injection pressure in the supercritical regime (> 50 MPa), experience is limited to the staged combustion cycle developed in Russia and the US. It is necessary to consider a fluid-dynamic heat transfer coupling study for the multi-injection element design in the supercritical state. Cryogenic spray atomisation experiments need to be designed with research significance. It is still needed to study how the similarity of the spray flow field to the combustion performance affects a liquid rocket engine problem. Moreover, scaling stoichiometric mixing theory needs to be expanded to different injector types, such as tri-coaxial and pintle injectors, to validate the correlation between the nonreactive mixing length and flame length.

Abstract: This study provides a comprehensive overview of SARS-CoV-2 genomic surveillance in Central America and the Dominican Republic from February 2020 to January 2023, highlighting the collaborative efforts of the Pan American Health Organization (PAHO), and the Council of Ministers of Health of Central America (COMISCA). A total of 26, 595 sequences from the GISAID database were analyzed, correlating findings with key events reported by participating entities. The genomic analysis reveals significant co-circulation of variants, with notable lineage diversity observed throughout the pandemic. Variants of concern (VOC) like Alpha, Gamma, Delta and Omicron were identified alongside variants of interest (VOI) like Lambda and Mu. The emergence of recombinant lineages further illustrates the ongoing evolution of the virus and its spread across the region, underscoring the interconnectedness of Central America and the Dominican Republic. The collaborative model facilitated broader sequencing coverage, enabling more extensive surveillance than individual countries could achieve alone. Despite the successes of regional collaborations, challenges remain, particularly regarding sequencing capacity in countries impacted by socioeconomic inequalities. Addressing these gaps is essential to enhance public health responses to current and future pandemics.

Abstract: Over the past two decades, the world has experienced significant and relentless increase in environmental degradation, measured through carbon emissions (CO2). These emissions have been one of the persistent global concerns. South Africa boosts abundance of natural resources and some of the world’s most substantial mineral deposits endowment in the form of precious metals, diamonds and gold. The paper aims to examine impact of socio-economic and energy-related factors on environmental degradation from South African perspective. Using multivariate annual data spanning from 1991 to 2022, Autoregressive Distributed Lag Model (ADRL) was employed to determine both short-run and long-run impact of financial development (FD), renewable energy(RE), non-renewable energy (NRE), unemployment rate (UNE), economic growth (GDPPC), and population growth (PoPG) on CO2 emission. The results show that FD, RE, GDPPC, and PoPG promote environmental quality in the long run while NRE has opposite impact. The study thus calls for actions by relevant policymakers to stimulate economic growth and promote access to climate change finance, thereby encouraging investment in green energy technologies and consumption, to enhance and promote environmental quality in South Africa.

Abstract: In order to solve the problems of insufficient privacy protection and limited sharing of industrial Internet security situation data,a situation element extraction model integrating federated learning and deep learning was proposed. This model integrates deep residual networks, bidirectional long short-term memory networks, and Transformer architecture,which extract features from network security situation data from multiple dimensions such as local features, temporal characteristics, and global correlations, and establish a situation element extraction model. Under the federated learning architecture,each participant performs data processing and model updates locally,transmitting model parameters through security mechanisms to reduce unnecessary data sharing and flow. The experimental results show that this method further improves the situation element extraction performance while protecting data privacy.

Abstract: The frontlines of innate antiviral immunity center on type I interferons (IFN), which are expressed by nearly all cell types as a cellular alarm signal. IFNs drive the expression of IFN-stimulated genes (ISGs), which can both generate an intracellular antiviral state and regulate the IFN response itself. This key antiviral line of defense is con-served in all jawed vertebrates, including teleost fish. Since their identification nearly 70 years ago, many mammalian ISGs have been identified and characterized However, fish ISGs represent an exciting, largely unexplored avenue of antiviral effector research and present an opportunity to assess how IFN systems have been shaped by whole genome duplication events. This review summarizes advances in identification of bona fide teleost ISGs and examines studies in elucidating the antiviral mechanisms of con-served ISGs, including IFIT1, Mx, Nmi and IFP35, Viperin, TRIMs, and ISG15. Teleost-specific gene expansions and isoform divergence, particularly in the development of the fish novel TRIM family, will be considered under each relevant ISG. Under-standing teleost ISG biology promises not only to improve antiviral strategies in aquaculture but also to reveal novel antiviral principles with translational relevance for human health.

Abstract: In low-Earth-orbit (LEO) satellite networks, the requirement for intelligent parameter-adjustment strategies has become increasingly critical due to the presence of highly dynamic channel conditions, limited spectrum resources, and complex interference environments. In this paper, a method for optimizing LEO satellite communication links based on deep reinforcement learning (DRL) is proposed. Through the optimization of the transmit power, the modulation and coding scheme (MCS), the beamforming parameters, and the retransmission mechanisms, adaptive link control is achieved in dynamic operational scenarios. A multidimensional state space is constructed, within which the channel state information, the interference environment, and the historical performance metrics are integrated. The spatio-temporal characteristics of the channel are extracted by means of a hybrid neural architecture that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM) net-work. To effectively accommodate both continuous and discrete action spaces, a hybrid DRL framework that combines proximal policy optimization (PPO) with a deep Q-network (DQN) is employed, thereby enabling cross-layer optimization of the physical-layer and link-layer parameters. The results demonstrate that substantial improvements in throughput, bit error rate (BER), and transmit-power efficiency are achieved under severely time-varying channel conditions, which provides a new idea for resource management and dynamic-environment adaptation in satellite communication systems.

Abstract:

Melanin accumulation is the primary cause of skin hyperpigmentation, and most existing cosmetic agents address this process by inhibiting melanogenesis. In contrast, strategies that directly decolorize or degrade melanin remain largely unexplored. In this study, we report a novel biobased cosmetic ingredient derived from onion (Allium cepa)–associated endophytic fungi that exhibits direct melanin decolorization alongside skin-whitening and anti-aging activities. Endophytic fungi were isolated from onion tissues, and aqueous extracts were prepared to ensure cosmetic-grade compatibility. Preliminary screening demonstrated exceptional melanin-reducing capacity among the isolates, with a maximum reduction of 97.83%, highlighting their strong melanin degrading potential. A selected isolate, identified as Aspergillus brasiliensis (ACL05), was further investigated to elucidate the influence of sterilization methods on bioactivity. The autoclaved culture filtrate retained substantial melanin-reducing activity (62.85%), whereas ultrasonication-based cell inactivation resulted in significantly lower activity (32.54%), indicating that heat-stable extracellular metabolites are primarily responsible for melanin decolorization. A cosmetic essence formulated using the sterile ACL05 extract achieved a measurable melanin reduction of 15.39%, demonstrating formulation feasibility and functional efficacy. Beyond melanin decolorization, the ACL05 extract exhibited multifunctional anti-aging properties, including inhibitory activities against tyrosinase, collagenase, and elastase, as well as significant antioxidant capacity as determined by the DPPH assay. Collectively, these findings reveal, for the first time, the potential of onion-derived endophytic Aspergillus brasiliensis as a sustainable source of multifunctional cosmetic bioactives. This work introduces a new paradigm for skin-whitening based on direct melanin decolorization while simultaneously addressing skin aging, supporting the development of next-generation biobased cosmetic ingredients.

Abstract: Exposure to nature has been associated with benefits to human well-being, commonly evaluated using standardized psychological assessments and, more recently, neuroimaging modalities such as Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and functional Near-Infrared Spectroscopy (fNIRS). This systematic review and meta-analysis addresses the following questions: (1) How is the impact of nature on wellness studied using psychological and neuroimaging modalities and what does it reveal? (2) What are the challenges and opportunities for the deployment of wearable neuroimaging modalities to understand the impact of nature on brain health and well-being? A search on PubMed, IEEE Xplore, and ClinicalTrials.gov (March 2024) identified 33 studies combining neuroimaging and psychological assessments during exposure to real, virtual or imagined natural environments. Studies were analyzed by tasks, populations, neuroimaging modality, psychological assessment, and methodological quality. Most studies were conducted in Asia (N=23 or 70%). Healthy participants were the dominant target population (70%). 61% of the studies were conducted in natural settings, while 39% used visual imagery. EEG was the most common modality (82%). STAI (N=8), and POMS (N=8) were the most common psychological assessments. Only seven studies included clinical populations. A meta-analysis of nine studies with explicit experimental and control groups revealed a significant positive effect of nature exposure on psychological outcomes (Hedges’ g = 0.30, p = 0.0021), and a larger effect on neurophysiological outcomes (Hedges’ g = 0.43, p = 0.0004), both with moderate-to-high heterogeneity. Overall, exposure to nature was associated with reductions in negative emotions in clinical populations. In contrast, healthy populations showed a more balanced psychological response, with nature exposure being associated with both increases in positive emotions and reductions in negative emotions. Notably, 88% of the studies presented methodological weaknesses, highlighting key opportunities for future neuroengineering research on the neural and psychological effects of nature exposure.

Abstract:

Cadherin-8 (CDH8) is a type II cadherin that plays crucial roles in various aspects of neural development and disease. Although anti-CDH8 monoclonal antibodies (mAbs) are available for Western blotting and immunohistochemistry (IHC), anti-CDH8 mAbs suitable for flow cytometry have not been reported. In this study, we developed novel anti-human CDH8 mAbs (named Ca₈Mabs) using a flow cytometry-based high-throughput screening method. Among these, a clone called Ca₈Mab-4 (IgG₁, κ) specifically recognized CDH8-overexpressing Chinese hamster ovary-K1 (CHO/CDH8) cells, with no detectable cross-reactivity toward 21 other cadherins, including both type I and type II, by flow cytometry. Additionally, Ca₈Mab-4 detected endogenous CDH8 in the human esophageal squamous cell carcinoma cell line TE5. The dissociation constant (K_D) values for Ca₈Mab-4 binding to CHO/CDH8 and TE5 were estimated to be 3.8 × 10⁻⁹ M and 4.9 × 10⁻¹⁰ M, respectively. Furthermore, Ca₈Mab-4 was effective in Western blotting and IHC. Overall, these findings suggest that Ca₈Mab-4 is a versatile tool for basic research and holds potential for tumor diagnosis and therapy.

Abstract: New general-relativistic formulations to model an elementary charge are presented, based on an electromagnetic theory of gravity, where the gravity is equivalently expressed as a gradient function of an effective permittivity distribution of the empty space. The metric tensor elements of general relativity are directly related to the effective permittivity function of the empty space, using which the energy density associated with the electric field surrounding the charge is properly defined. The empty space, represented by the equivalent permittivity function, would be fundamentally non-linear, in which case the definition of energy density, as conventionally applied for a linear medium, needs to be corrected. Further, the definition of energy density itself is modified allowing both positive and negative values, such that the total energy remains unchanged while the local values are much stronger, resulting in much stronger local gravitational effects. Solutions for the metric-tensor elements and the resulting energy/mass of the charge particle are studied, based on the Einstein-Maxwell equations with the different new formulations of the energy density and of the associated full stress-energy tensor. The solutions are verified with Schwarzschild and Reissner-Nordstrom metrics, as well as for calculation of light deflection by a massive body, as validation of the general new formulations for the specific reference cases of conventional modeling. Stable solutions for energy/mass are successfully derived for a spherically symmetric, surface distribution of an elementary charge, with specific modified definitions of energy density. A stable solution of the charge with the lowest possible energy/mass is associated with a ''static'' electron without spin. Significance of the new results and formulations, specifically established for the electron, are recognized in relation to the fine-structure constant of quantum electrodynamics, and towards further application of the theory to model other elementary particles and general electrodynamic problems.

Abstract: The high energy consumption and spatiotemporal thermal asymmetry of data center cooling systems have become critical bottlenecks constraining their green and sustainable development. Traditional point-type temperature sensors suffer from insufficient spatial coverage, while conventional feedback control strategies exhibit delayed responses and limited adaptability under dynamic workloads. To address these challenges, this study proposes a real-time thermal symmetry management framework for data centers based on distributed fiber optic temperature sensing and model predictive control (MPC). The proposed system employs Brillouin scattering-based distributed sensing to continuously acquire high-density temperature measurements from thousands of points along a single optical fiber, enabling fine-grained perception of the three-dimensional thermal field. On this basis, a hybrid prediction model integrating thermodynamic physical equations with a TCN--BiGRU deep neural network is developed to achieve accurate and stable spatiotemporal temperature forecasting. Furthermore, a symmetry-aware MPC controller is designed with the dual objectives of minimizing cooling energy consumption and suppressing thermal field deviations, thereby restoring temperature uniformity through rolling-horizon optimization. Experimental validation in a production data center demonstrates that the distributed sensing system achieves a measurement deviation of 0.12~$^\circ$C, while the hybrid prediction model attains a root mean square error of 0.41~$^\circ$C, representing a 26.8\% improvement over baseline methods. The MPC-based control strategy reduces daily cooling energy consumption by 14.4\%, improves the power usage effectiveness (PUE) from 1.58 to 1.47, and significantly enhances both thermal symmetry and operational safety. These results provide an effective and practical solution for intelligent operation, energy-efficient control, and low-carbon transformation of next-generation green data centers.

Abstract: Artificial intelligence (AI) offers multiple advantages such as: improvement and accuracy of the diagnosis, decrease of the doctors’ workload, decrease of the hospitalization costs, becoming increasingly widespread, studied, and applied in medicine. The use of AI and the study of the specialty literature raise ethical and legal questions for which there is no unanimous answer yet. Medical liability (malpractice) for AI‑related errors and damages for the patient prompt legal reflections on this topic. The diagnostic algorithms of AI raise questions regarding the risks of using AI in the diagnosis and treatment of cancer (especially in rare cases), the information provided to the patient, all of these having moral and legal implications, as well as regarding the impact on the empathic doctor–patient relationship. Actually, the use of AI in the medical field has triggered a revolution in the doctor-patient relationship, but it has possible medico‑legal consequences as well. The current legal framework regulating medical liability when AI is applied is inadequate and requires urgent measures, because there is no specific and uniform legislation to regulate the liability of the various parties involved in applying AI, or that of the end-users. Consequently, greater attention should be paid to the risk of applying AI, to the necessity to regulate its safe use, and to maintain the safety standards of the patient by continuously adapting and updating the system.

Abstract: The Daily Move (TDMo) is a modified version of The Daily Mile, developed in previous research [1], that provides children greater choice in activities during participation. This study evaluated a teachers-led implementation of TDMo, aiming to assess its sustainability within primary schools. Teachers (N = 60) implemented TDMo with their classes for two 5-week blocks across two school semesters. Data were collected via questionnaires administered at the start and end of each block (Time 1 to Time 4), aligned with the RE-AIM framework’s effectiveness, adoption, implementation, and maintenance elements. Two teacher focus groups (n = 6) and one child focus group (aged 8-9 years; n = 6) were conducted at Time 4. TDMo was perceived to positively impact multiple health metrics across timepoints, including physical fitness (agreement decreased from 92-84%), movement proficiency (agreement increased from 84.6-96.2%), and attention and concentration (agreement decreased from 96.2-92.3%). Teachers reported all children responded positively to its adoption (100%). Children’s involvement in game selection increased significantly from Block One to Block Two (p = 0.01). The main implementation barriers were curriculum demands (agreement de-creased from 80-72%) and inclement weather (agreement increased from 50% to 53.8%). Most teachers intended to sustain their implementation of TDMo (96.2%). The diverse and novel design of TDMo offers potential holistic health benefits and supports long-term sustainability. The variety of physical activity appears to enhance children’s enjoyment and encourage teachers’ sustained implementation. Aligning government policies to formally incorporate movement breaks within the curriculum may further support sustainability by reducing curriculum-related pressures.

Abstract: Direct-current (DC) electrokinetics in microfluidic channels is inherently affected by Faradaic reactions at the electrode–electrolyte interfaces, which induce local changes in pH and conductivity and, consequently, alter particle behavior. In this work, we present a simple microfluidic T-junction device designed to mitigate these effects by continuously flushing the regions near the electrodes with fresh electrolyte, thereby preserving the physicochemical properties of the main channel. Using fluorescence imaging with a pH-sensitive dye and electrical resistance measurements, we demonstrate that electrolyte acidification caused by water electrolysis can be effectively suppressed when advection overcomes electromigration of H⁺ ions. Order-of-magnitude estimates based on ion transport reveal that this condition is achieved when the flow velocity exceeds the characteristic electromigration velocity. We further investigate the effect of Faradaic reactions on cross-stream particle migration in electrophoresis experiments by quantifying the separation between suspended particles and the channel walls. We find that the particle–wall separation is significantly larger when electrolyte modifications are suppressed, clearly demonstrating the influence of Faradaic reactions on this phenomenon. Our results show that minimizing electrolyte modifications leads to a significantly enhanced particle-wall separation, highlighting the strong influence of Faradaic reactions on electrokinetic outcomes. These findings emphasize the importance of controlling electrochemical effects in DC electrokinetics and provide a simple and robust strategy to improve the accuracy and reproducibility of microfluidic electrophoresis experiments.

Abstract: Three-dimensional medical image segmentation is critical for clinical applications, yet expert annotations are costly, driving the need for semi-supervised learning. Current semi-supervised methods struggle with robustly integrating diverse network architectures and managing pseudo-label quality, especially in complex three-dimensional scenarios. We propose Dynamic Multi-Expert Diffusion Segmentation (DMED-Seg), a novel framework for semi-supervised three-dimensional medical image segmentation. DMED-Seg leverages a Diffusion Expert for global contextual understanding and a Convolutional Expert for fine-grained local detail extraction. A key innovation is the Dynamic Fusion Module, a lightweight Transformer that adaptively integrates multi-scale features and predictions from both experts based on their confidence. Complementing this, Confidence-Aware Consistency Learning enhances pseudo-label quality for unlabeled data using DFM-derived confidence, while Inter-expert Feature Alignment fosters synergistic learning between experts through contrastive loss. Extensive experiments on multiple public three-dimensional medical datasets demonstrate DMED-Seg consistently achieves superior performance across various labeled data ratios, outperforming state-of-the-art methods. Ablation studies confirm the efficacy of each proposed component, highlighting DMED-Seg as a highly effective and practical solution for three-dimensional medical image segmentation.

Abstract: Rural architectural systems in the Mediterranean reflect a long-term entanglement between human agency, material conditions, and environmental constraints. This study uses this framework to explore architectural continuity in settlements near ancient Phoenix in Türkiye. It aims to understand how rural building practices like stone masonry, traditional carpentry, and spolia reuse have persisted from antiquity. The methodology combines UAV photogrammetry, GIS analysis, and oral histories to reveal spatial patterns and craft traditions across generations. Findings show structures are transmitted through technical knowledge, with stone and timber co evolving with local livelihoods. The Aegean's technical traditions share heritage with the Dodecanese islands of Symi and Tilos, supported by fieldwork and literature comparing masonry and craft techniques. The work emphasizes the need for conservation strategies that connect digital documentation with community experience to preserve this cross-border cultural landscape amid environmental threats and declining craftsmanship.