Abstract: This paper presents the Spike Processing Unit (SPU), a digital spiking neuron model based on a discrete-time second-order Infinite Impulse Response (IIR) filter. By constraining filter coefficients to powers of two, the SPU implements all internal operations via shift-and-add arithmetic on 6-bit signed integers, eliminating general-purpose multipliers. Unlike traditional models, computation in the SPU is fundamentally temporal; spike timing emerges from the interaction between input events and internal IIR dynamics rather than signal intensity accumulation. The model’s efficacy is evaluated through a temporal pattern discrimination task. Using Particle Swarm Optimization (PSO) within a hardware-constrained parameter space, a single SPU is optimized to emit pattern-specific spikes while remaining silent under stochastic noise. Results from cycle-accurate Python simulations and synthesizable VHDL implementations confirm that learned temporal dynamics are preserved in digital hardware. This work demonstrates that discrete-time IIR-based neurons enable reliable temporal spike processing under strict quantization and arithmetic constraints.

Abstract: Background: Sensor-based systems and virtual reality (VR) technologies provide new opportunities for the objective, technology-driven assessment and training of visuo-motor performance in applied contexts such as sport. Methods: This study examined the effects of an integrated visual training program combining stroboscopic stimula-tion, VR-based vergence exercises, and instrumented reaction-light tasks in adolescent handball players. Twenty-eight youth athletes completed two baseline assessments separated by six weeks, followed by a six-session training program integrated into reg-ular team practice. Sensor-derived outcome measures included dynamic accommoda-tive performance, simple and choice visual reaction times, peripheral-field response metrics, binocular alignment, stereoscopic depth perception, and basic oculomotor function. Results: Compared with both baseline measurements, the intervention pro-duced selective improvements in accommodative facility—particularly near–far fo-cusing speed—and in multiple reaction-time conditions involving manual and deci-sion-based responses. Specific peripheral-field locations showed increased response scores, whereas binocular alignment, AC/A ratio, near phoria, and stereoscopic acuity remained unchanged. Conclusions: These findings indicate that technology-supported visual training protocols incorporating sensor-based reaction systems and VR stimuli can be associated with measurable adaptations in dynamic visuomotor processing while preserving fundamental binocular vision parameters.

Abstract: Individuals with a lived experience are increasingly being included in the design of mental health services research. Establishing research priorities of service users is an opportunity to achieve equity in allocating resources so that policy makers and health research funders are made aware of the issues that matter most to people who are affected by research. The purpose of this study is to formulate a research agenda to improve the quality of care and service delivery for individuals with severe mental health conditions, informed by their lived experiences. This qualitative study was conducted in Australia and was informed by biographical research and Interpretive phenomenological analysis [IPA]. Twenty-one participants were interviewed for the study. The theme, ‘Access to care and early detection’ included eight categories. The theme, ‘Care and treatment’, included fifteen categories. The theme, Continuity of care’, included six categories. Research questions that were developed for each category formed the lived experience research agenda for mental health services research. Research topics identified in this study were categorized as insufficiently researched, emerging areas of interest or well researched with insufficient translation into practice. The research agenda developed in this study may inform local and national research strategies.

Abstract: This work does not propose a new cosmological model, nor does it claim to reproduce structure formation, recombination microphysics, or the CMB power spectrum. It instead examines limited, testable consequences of a previously established background level redshift framework in which frequency-independent redshift is described by collisionless Liouville evolution without requiring metric expansion. Treating an illustrative post-recombination relaxation form for the redshift kernel as a concrete hypothesis, this analysis shows that the parameterization predicts reduced cosmic assembly time at z ≳ 10 relative to standard ΛCDM. Observations of early massive galaxies by JWST therefore act as an empirical constraint on the relaxation timescale and initial kernel rate, bounding the allowed post-recombination dynamics rather than resolving timing tensions. The paper further proves that frequency- independent redshift operators preserve angular multipole structure, once present, under collisionless evolution, establishing that the existence of CMB anisotropies alone does not logically discriminate between expansion and non-expansion redshift mechanisms at the level of angular transport. The analysis concludes by clarifying the precise observational content of CMB background and anisotropy data, explicitly delineating which inferences are supported and which require additional dynamical assumptions.

Abstract: The allocation of European Union (EU) funds for security and defense is central to solidarity among Member States. Yet, as geopolitical instability and hybrid threats continue to grow in the EU, concerns have emerged about whether the current distribution of EU funding adequately reflects the varying degrees of exposure among states. Taking this problematic in consideration, this article draws on the findings from the UDebDS project, which examines how funds are currently allocated and whether the states facing the highest security risks are receiving commensurate support. To achieve our goals we used a multidisciplinary, data-driven approach. That approach included the development of a comprehensive dataset and a set of measurable risk indicators (MRIs), capable of capturing vulnerabilities and deficits. Based on the MRIs, our research proposed an alternative model to support a more balanced and responsive EU funding. By doing so, we expect to inform ongoing policy discussion around equity and the future of shared defense investments in the Union. Further steps should build on the refinement of our model through sensitivity testing and a simulation-based studies to examine how alternative allocation models can influence the EU defense posture.

Abstract:

The use of Virtual Reality as a replacement for, or augmentation to, traditional flight simulators has gained significant attention in both academia and industry. The use of new technology or techniques in the training of pilots, including in flight simulation, requires careful evaluation of the success of transferring required skills. In order that the efficacy of a Virtual Reality flight simulator could be evaluated, in terms of its capacity to transfer several basic flying skills, a quasi-transfer study has been completed. A quasi-experimental, separate-sample pretest-posttest design was employed, with the ability to perform straight and level flight, and turning, within a set pattern being assessed. Results indicate a significant improvement in flight performance between pretest and posttest, with a large positive effect size (g = 0.946). The findings indicate that Virtual Reality flight simulators are effective for the training of basic flight skills during the initial stages of pilot training. However, several observed limitations of the technology require further research.

Abstract:

Background/Objectives: Assessing the bioavailability of nutrients and bioactive compounds in vitro commonly relies on coupling standardized gastrointestinal digestion models with intestinal epithelial cell systems. However, digests produced using static digestion protocols such as INFOGEST often impair epithelial barrier integrity, limiting their direct application to intestinal models and reducing reproducibility across studies. Methods: This work systematically compared five commonly used digest conditioning strategies, including acidification, centrifugation, rapid freezing, and ultrafiltration using 10 kDa and 3 kDa molecular weight cut-off membranes, to identify the approach that best preserves intestinal epithelial viability and barrier function while enabling exposure at physiologically relevant concentrations. INFOGEST digests of yogurt were initially evaluated, followed by validation using biscuit and canned mackerel digests. Cell viability and monolayer integrity were assessed in differentiated Caco-2 cells using MTT assay and transepithelial electrical resistance (TEER) measurements. Results: Among the tested approaches, ultrafiltration using 3 kDa membranes consistently preserved epithelial viability and barrier integrity at a 1:10 dilution across all food matrices, whereas other conditioning methods failed to maintain TEER despite acceptable cell viability. At lower dilutions, food-dependent effects emerged, highlighting the importance of matrix-specific evaluation. Conclusions: These findings identify 3 kDa ultrafiltration as an effective and minimally invasive strategy to improve the compatibility of INFOGEST digests with intestinal cell models. By enabling reproducible exposure conditions that preserve epithelial integrity, this approach supports more reliable in vitro assessment of nutrient bioavailability and contributes to methodological standardization in nutrition research.

Abstract: In disaster scenarios where communication infrastructure is damaged, Unmanned Aerial Vehicle (UAV)-assisted wireless networks can provide temporary connectivity and hence the indispensable mobile edge computing functionality. However, limited resources on UAVs require prioritization of critical data in such scenarios. This research addresses re-liable transmission and task offloading by modeling user tasks as layered compositions, where the base layer is essential and enhancement layers are optional. TDMA-based prioritization is employed to ensure reliable decoding of high-priority layers of the computational tasks (i.e., intra-user priority) along with inter-user priority needed for urgent users like rescue teams. Under these reliability constraints, the work formulates a joint communication-computation optimization problem to allocate transmission power and UAV CPU cycles efficiently in order to minimize total weighted offloading latency. The original problem is non-convex and thus we leverage epigraph and perspective functions to recast the problem into convex. We also derive analytically, using KKT conditions, the optimal water-filling-like solutions for the reformulated problem. The numerical results show that, at a signal-to-noise ratio of 5 dB, the proposed algorithm achieves relative latency reductions vs the baseline algorithms (39.99% reduction vs Equal Allocation, 49.99% reduction vs Enhancement First, and 69.99% reduction vs No Priority) which reflect considerable latency reduction with priority-aware offloading.

Abstract: Ultrasonic vibration-assisted grinding (UVAIG) is a continuous-contact grinding process. In this process, the arc length of engagement for a single abrasive grain is longer compared to conventional grinding, which enhances the quality of the processed surface and improves processing efficiency. This study aims to establish a three-dimensional model of abrasive grains in space and to theoretically deduce the trajectory of abrasive grains during axial ultrasonic vibration-assisted internal grinding (UVAIG), as well as the resulting surface quality, measured as Ra. A three-dimensional simulation tool for ultrasonic vibration grinding micro-surfaces is developed using MATLAB. This tool enables the analysis of how various processing parameters affect workpiece surface morphology. Additionally, a predictive model is established for UVAIG simulations, allowing theoretical calculation of surface topography changes induced by different processing parameters, vibration settings, and abrasive grain models.

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.