Abstract: Schistosomiasis is a neglected tropical disease caused by parasitic trematodes of the genus Schistosoma, affecting millions of people globally. It poses a substantial public health challenge, particularly in low- and middle-income countries, most of which are in sub-Saharan Africa, where defaecating and urinating in or near freshwater bodies is prevalent. Despite decades of control efforts, including mass drug administration, reinfection by the parasites remains a common occurrence, with a vaccine being viewed as a crucial tool for sustainable control and ultimate elimination of the disease. Recent advancements in vaccine developments, particularly through the Vaccine Against Schistosomiasis in Africa initiative, therefore, offer hope of getting an anti-schistosomiasis vaccine soon. Several vaccine candidates, including Glutathione S-transferase, Sm-TSP-2, Sm14, and Sm-p80, are thus being explored currently, with Sm-p80, S. mansoni calcium-activated neutral protease, calpain, involved in tegmental maintenance, promising safety and immunogenicity results in Phase 1b trials conducted in a couple of African countries that pave the way for Phase 2a trials. These efforts, supported by global regulatory engagement and partnerships, aim to streamline the approval of the anti-schistosomiasis vaccine and ensure equitable access, but are not without challenges. This review, therefore, explores the current state of schistosomiasis vaccine development, highlighting key scientific, regulatory, and logistical gains made toward a viable and impactful vaccine solution against the disease.

Abstract: Background/Objectives: Post-COVID-19 pulmonary fibrosis (PCPF) and idiopathic pul-monary fibrosis (IPF) show clinical parallels, suggesting overlapping pathogenesis. This study investigated the dysregulation of key proteases, matrix metalloproteinases-2 and -9 (MMP-2/9), and associated inflammatory and endothelial markers in both conditions. Methods: We analyzed MMP-2 and MMP-9 gene expression in peripheral blood leuko-cytes and corresponding plasma protein levels in patients 6 and 12 months after SARS-CoV-2 infection, stratified by the presence (FB+) or absence (FB-) of post-COVID pulmonary fibrosis. These groups were compared to IPF patients and pre-pandemic healthy controls. Results: Results showed a significant, sustained increase in MMP-2/9 in post-COVID patients versus controls, which was most pronounced in the PCPF group and mirrored the dysregulation in IPF. This proteolytic shift corresponded to a distinct sys-temic profile: patients without fibrosis showed reduced levels of acute-phase cytokines (TNF-α, IL-6), whereas patients with fibrosis exhibited both elevated cytokines and in-creased markers of endothelial dysfunction (Endothelin-1, sICAM-1). Conclusions: The findings demonstrate that sustained MMP-2/9 overexpression is a hallmark of post-COVID fibrosis and is associated with a transition from systemic inflammation to chronic endothelial impairment. The convergence of this molecular profile in PCPF and IPF indicates shared pathophysiological pathways driving fibrosis. This positions MMP-2 and MMP-9 as promising biomarkers and potential therapeutic targets for mitigating progressive fibrotic lung disease.

Abstract: We develop a variational principle in which spacetime curvature emerges from the preservation of informational identity along dynamical trajectories. The approach is motivated by the Viscous Time Theory (VTT) framework, where finite informational latency replaces an assumed geometric background. Instead of postulating a metric structure a priori, informational geodesics are defined as the paths that minimize a latency functional representing the local cost of identity reorganization in viscous time. The second-order structure of this action induces a symmetric bilinear form that behaves as an emergent metric tensor. Classical geodesic motion and the Einstein field equation are recovered in the limit of uniform latency density, showing that General Relativity arises as a special case of the more general informational action. The framework predicts curvature-like effects in regimes with negligible mass–energy but strong identity constraints, including coherent condensed-matter phases and entangled quantum systems. These predictions outline a testable research program connecting differential geometry with informational dynamics.

Abstract: The paper focuses on materials characterization and «in vivo» biocompatibility tests of Ti6Al7Nb alloys microdoped by 0.3 wt. % of rareearth elements (REE) to use it as perspective materials to produce personalized medical implants. All Ti6Al7Nb0.3REE alloys (REE Y, Ce, La) were produced by electric arc melting method and characterized by scanning electron microscopy (SEM), optical microscopy (OM), energy-dispersive Xray spectroscopy analysis (EDX), helium pycnometer as well as reducing/oxidation melting methods. The measured true densities increased in the order: Ti−6Al−7Nb−0.3Y (4.4563 ± 0.1075 g/cm³) < Ti−6Al−7Nb−0.3Ce (4.7255 ± 0.2853 g/cm³) < Ti−6Al−7Nb−0.3La (4.8019 ± 0.0111 g/cm³). Diffraction analysis was performed to indicate phases composition and calculate crystallites sizes, crystal orientation and lattice parameters that confirmed REEmicrodoping due to increase of lattice volume. The single-phase Ti6Al7Nb0.3Y alloy had the finest αTi crystallites (22.32 nm), the larger αTi crystallites in the dualphase Ti6Al7Nb0.3Ce and Ti6Al7Nb0.3La (30.77 nm and 29.83 nm, respectively) suggest that the presence of the βTi phase. Hardness (H) and elastic modulus (E) were indicated by nanoindentation and increased in the order: Ti−6Al−7Nb−0.3La (4.01 GPa and 17.7 GPa respectively) < Ti−6Al−7Nb−0.3Y (4.39 GPa and 137 GPa respectively ) < Ti−6Al−7Nb−0.3Ce (4,67 GPa and 146 GPa respectively). In vivo tests showed that Ti6Al7Nb0.3La alloy had statistically significant increase of local inflammation at the one-week mark needed to further research and explanation as well, that could be indicator of toxicity in comparison with other studied alloys.

Abstract: The development of cross-border hydrogen energy value chains involves complex interactions between technological, regulatory, and logistical subsystems. Static assessment models often fail to capture the dynamic response of these coupled systems to external perturbations. This study addresses this gap by proposing the Dual Carbon Cooperation Index (DCCI), a data-driven framework designed to quantify the synergy efficiency of the China-Korea hydrogen ecosystem. We construct a dynamic state estimation model integrating three coupled dimensions—Technology Synergy, Regulatory Alignment, and Supply Chain Resilience—utilizing an adaptive weighting algorithm (Triple Dynamic Response). Based on multi-source heterogeneous data (2020–2024), the model employs Natural Language Processing (NLP) for vectorizing unstructured regulatory texts and incorporates an exogenous signal detection mechanism (GRI). Empirical results reveal that the ecosystem's composite synergy score recovered from 0.38 to 0.50, driven by robust supply chain resilience but constrained by high impedance in technological transfer protocols. Crucially, the novel dynamic weighting algorithm significantly reduces state estimation error during high-volatility periods compared to static linear models, as validated by bootstrapping analysis (1,000 resamples). The study provides a quantitative engineering tool for monitoring ecosystem coupling stability and proposes a technical roadmap for reducing system constraints through secure IP data architectures and synchronized standard protocols.

Abstract: Little is known about the effects of orthodontic loading on dental implants used for orthodontic anchorage in patients with Stage IV periodontitis. This retrospective case–control study included 58 dental implants in 24 patients with Stage IV periodontitis. The dental implants were used for both chewing function and orthodontic anchorages. The outcome measures included peri-implant marginal bone loss and peri-implantitis. Pair t test and Wilcoxon rank-sum test were used to analyze the impact of implants as orthodontic anchorage on MBL and peri-implantitis. No implants were lost during the 17-year follow-up. Patients with poor compliance significantly predicted the MBL. Poor oral hygiene, one-piece implants and implants with a KG < 2 mm were associated with a higher risk of peri-implantitis. Results from the present long-term study indicated dental implants could be successfully used as orthodontic anchorage in periodontal compromised patients.

Abstract:

Surface and subsurface acidity (pH < 4.4) limits nutrient availability and root exploration, whereas a pH range of 5.4–6.4 ensures the availability of most nutrients that are essential for crop productivity. To ameliorate acidity in the surface and subsurface layers and improve soil chemical fertility, different application methodologies (surface, incorporation by soil tillage, or subsurface) for calcium (Ca) compounds (limestone (LS), phosphogypsum (PG), and hydrated lime (HL)) were evaluated in an agropastoral system in an Arenic Hapludalf in Brazil during the 2017–2020 seasons. Two seasons after the last application of Ca compounds, the soil was sampled and analyzed to evaluate the long-term ability of these different application methodologies. In the 0.0–0.2 m layer, the correction of surface acidity via increased pH and base saturation (BS) and reduced total acidity was maintained, but the improvement in acidity in the 0.4–0.8 m layer previously observed after the incorporation of LS and subsurface application of HL in the 2017-2018 season was not. Moreover, the improvements in Ca²⁺ content and Ca²⁺/cation exchange capacity (CEC) after applying LS plus PG and Mg²⁺ content and Mg²⁺/CEC after applying HL plus PG were preserved in the surface layer. The positive effects of these amendments on sulfate-S (S-SO₄^2-) content throughout the soil profile (0.0–0.8 m) were not. Finally, Ca compound application had residual positive effects on P content in the 0.1–0.8 m layer and organic matter (OM) content in the 0.2–0.8 m layer.

Abstract: The petroleum industry faces intensifying challenges related to the depletion of easily accessible reservoirs and the growing energy demand, necessitating the adoption of ad-vanced chemical agents that can operate under extreme conditions. Cationic gemini sur-factants, characterized by their unique dimeric architecture consisting of two hydrophilic head groups and two hydrophobic tails, have emerged as superior alternatives to con-ventional monomeric surfactants due to their enhanced interfacial activity and physico-chemical resilience. This review provides a comprehensive analysis of the literature concerning the molecular structure, synthesis, and functional applications of cationic gemini surfactants across the entire oil value chain, from extraction to refining. The analysis reveals that gemini surfactants exhibit critical micelle concentrations signifi-cantly lower than their monomeric analogues and maintain stability in high-temperature and high-salinity environments. They demonstrate exceptional efficacy in enhanced oil recovery through ultra-low interfacial tension reduction and wettability alteration, while simultaneously serving as effective drag reducers, wax inhibitors, and dual-action bio-cidal corrosion inhibitors in transportation pipelines. Cationic gemini surfactants repre-sent a transformative class of multifunctional materials for the oil industry.

Abstract: We present Aṇubuddhi, a multi-agent AI system that designs and simulates quantum optics experiments from natural language prompts without requiring specialized programming knowledge. The system composes optical layouts by arranging components from a three-tier toolbox via semantic retrieval, then validates designs through physics simulation with convergent refinement. The architecture combines intent routing, knowledge-augmented generation, and dual-mode validation (QuTiP and FreeSim). We evaluated 13 experiments spanning fundamental optics (Hong-Ou-Mandel interference, Michelson/Mach-Zehnder interferometry, Bell states, delayed-choice quantum eraser), quantum information protocols (BB84 QKD, Franson interferometry, GHZ states, quantum teleportation, hyperentanglement), and advanced technologies (boson sampling, electromagnetically induced transparency, frequency conversion). The system achieves design-simulation alignment scores of 8--9/10, with simulations faithfully modeling intended physics. A critical finding distinguishes structural correctness from quantitative accuracy: high alignment confirms correct physics architecture, while numerical predictions require expert review. Free-form simulation outperformed constrained frameworks for 11/13 experiments, revealing that quantum optics diversity demands flexible mathematical representations. The system democratizes computational experiment design for research and pedagogy, producing strong initial designs users can iteratively refine through conversation.

Abstract: Since the beginning of modern computer history, the Turing machine has been a dominant architecture for most computational devices, which consists of three essential components: an infinite tape for input, a read/write head, and finite control. In this structure, what the head can read (i.e., bits) is the same as what it has written/outputted. This is actually different from the ways in which humans think or do thought/tool experiments. More precisely, what humans imagine/write on paper are images or texts, and they are not the abstract concepts that they represent in the human brain. This difference is neglected by the Turing machine, but it actually plays an important role in abstraction, analogy, and generalization, which are crucial in artificial intelligence. Compared with this architecture, the proposed architecture uses two different types of heads and tapes, one for traditional abstract bit inputs/outputs and the other for specific visual ones (more like a screen or a workspace with a camera observing it). The mapping rules among the abstract bits and the specific images/texts can be realized by neural networks like Convolutional Neural Networks, YOLO, Large Language Models, etc., with a high accuracy rate. Logical reasoning is thus performed through the transfer of mapping rules. As an example, this paper presents how the new computer architecture (what we call "Ren machine" for simplicity here) autonomously learns a distributive property/rule of multiplication in the specific domain and further uses the rule to generate a general method (mixed in both the abstract domain and the specific domain) to compute the multiplication of any positive integers based on images/texts. The machine's strong reasoning ability is also corroborated in proving a theorem in Plane Geometry. Moreover, a robotic architecture based on Ren machine is proposed to address the challenges faced by the Vision-Language-Action (VLA) models in unsound reasoning ability and high computational cost.

Abstract: It is shown that the off-shell renormalization schemes for subtraction of ultraviolet divergences in Quantum Field Theory produce zero for sums of perturbative corrections to physical quantities when all perturbation orders are taken into account. That is the off-shell renor malization schemes are in this sense unphysical. In this connection it is desirable to develop on-shell renormalization schemes for different quantum theories.

Abstract: We present new high-dispersion optical spectra of the planetary nebula NGC 2371 obtained with the Manchester Echelle Spectrometer at the OAN-SPM 2.1-m telescope, complemented with 3D morpho-kinematic modelling using ShapeX. The data reveal that the present-day morphology of NGC 2371 is the outcome of multiple episodic mass-loss events rather than a single outflow. Our best-fitting model simultaneously reproduces the direct images and the Position–Velocity (PV) diagrams, and consists of a barrel-shaped shell with younger polar caps, extended bipolar lobes, and a pair of misaligned low-excitation [N ii] knots interpreted as jet-like ejections. The derived kinematical ages of the main structures, spanning ≃1600 to ≃4400 yr, indicate successive episodes of mass loss with different geometries and timescales. The nearly perpendicular bipolar lobes, the absence of a pronounced waist, and the surface distortions of the large-scale structures cannot be explained solely by standard axisymmetric wind interactions. Instead, our results point to a combination of shaping agents, including a late thermal pulse that produced the H-deficient [WR] central star, binary-driven interactions, and episodic jet activity. NGC 2371 thus emerges as a highly unusual planetary nebula, possibly involving physical processes that remain poorly explored in current models of PN formation and evolution.

Abstract: The demand for ready-to-eat salads made from leafy vegetables such as wild rocket (Diplotaxis tenuifolia L.) continues to rise due to their convenience and high levels of bioactive compounds. However, both organically enriched substrates and sustainable packaging alternatives to conventional plastic films are required to reduce the envi-ronmental impact of wild rocket production. This study assessed the effects of three cultivation substrates as growing media and three biodegradable packaging materials (polylactic acid (PL), cellulose kraft (CK), and kraft-reinforced polylactic acid (PLK)) on the postharvest performance of wild rocket stored at 4 °C for 7 and 14 days. Plants were grown in coco peat (CP), coco peat supplemented with livestock compost (90:10; CP+LC), and coco peat combined with mushroom compost (50:50; CP+MC). Yield and key pre- and postharvest quality attributes, including nitrate accumulation, phenolic content, antioxidant capacity, colour, and weight loss, were evaluated. CP+LC pro-duced the highest harvest yield, whereas CP promoted greater phenolic content and antioxidant capacity. Among the packaging materials, PLK provided the most bal-anced internal atmosphere, effectively reducing dehydration and condensation while preserving superior sensory quality after 14 days. Overall, the integration of organic compost amendments, particularly CP+LC, with PLK bio-based packaging represents a promising and sustainable strategy to maintain postharvest quality and reduce the en-vironmental footprint of minimally processed wild rocket within short food supply chains.

Abstract: Melasma management is challenged by heterogeneity in patient presentation, particularly among individuals with darker skin tones. This study applied k-Means clustering to identify patient subgroups that could inform precision treatment approaches. We analysed clinical and demographic data from 150 South African women with melasma using k-Means clustering. The optimal number of clusters was determined using the Elbow Method and Bayesian Information Criterion (BIC), with t-SNE visualization for validation. The k-Means algorithm identified seven distinct patient clusters explaining 52.6% of data variability (R²=0.526), with model evaluation metrics including BIC=951.630 indicating optimal model fit and a Silhouette Score of 0.200 suggesting moderate cluster separation, while the Calinski-Harabasz index of 26.422 confirmed relatively well-defined clusters that were characterized by distinct profiles including "The Moderately Sun Exposed Young Women," "Elderly Women with Long-Term Melasma," and "Younger Women with Severe Melasma," with key differentiators being age distribution and menopausal status, melasma severity and duration patterns, sun exposure behaviours, and quality of life impact profiles that collectively define the unique clinical characteristics of each subgroup. This study demonstrates how machine learning can identify clinically relevant patient subgroups in melasma. Aligning interventions with the characteristics of specific clusters can potentially improve treatment efficacy.

Abstract: Rotating machinery is crucial element in mechanical equipment, and during serving cycle their failure is inevitable because of artificial and non-artificial reasons. Signal processing techniques are available to diagnose the failure. Due to the nonlinearity and simplicity in computation rules and the richness in theoretical system, mathematical morphology (MM) has received significant research attention in this area, and numerous papers had been published in academic journals, conference proceedings, etc. The review paper attempts to overview the morphological framework and to summarize these applications grouped as rolling element bearing and gear. Finally, the relevant discussions on MM are analyzed, and several potential prospects are suggested.

Abstract: The endocannabinoid system is expressed in brain centers involved in a wide variety of functions which makes it an ideal target for disease therapy and prevention. Unlike major excitatory and inhibitory neurotransmitters such as glutamate and GABA, en-dogenously produced cannabinoids have been shown to play a complimentary role as neuromodulators by acting as gain regulators of neural signals. The endocannabinoid system consists of cannabinoid receptors, CB1R and CB2R, and endogenously generat-ed lipid-based neurotransmitters, 2-AG (2-arachidonoylglycerol) and anandamide, the endocannabinoids. In the central nervous system, these signaling molecules are re-leased from postsynaptic cells in an on-demand manner. This retrograde transmission from post- to presynaptic neurons and the binding of endocannabinoids onto the pre-synaptic CB1 receptors modulates the magnitude of release of glutamate and GABA, either enhanced or inhibited, depending on the brain area under study. Research has focused on the role of the endocannabinoid system in the limbic system such as the hippocampus and amygdala. Research is increasing regarding the role that endocan-nabinoids play in other brain centers such as the olfactory system with particular em-phasis on the role of the endocannabinoid system in neural networks of the main ol-factory bulb. This review aims to bring together research within the overlap of the ol-factory system and the endocannabinoid system. By better understanding the unique neuromodulator and neurodevelopmental role of endocannabinoids in the brain, in-sight into understanding how to mitigate disease states that result from aberrant re-lease of glutamate and GABA such as stroke, epilepsy, and schizophrenia is expected to be gained.

Abstract: This paper presents the design and implementation of a 64-Multi-Phased Time-to-Digital Converter (TDC64) architecture on a low-cost Cyclone V FPGA. Operating at 500 MHz, the architecture successfully achieves a theoretical resolution of 31.25 picoseconds (ps). The modular design leverages a multi-phased counter methodology to significantly enhance temporal granularity. The performance was comprehensively characterized in two stages. Internal analysis, using the Signal Tap Logic Analyzer, confirmed the design's integrity, yielding a measurement result of 9660 for a 300 ns interval, representing a low deviation of approximate 0.62 %. Linearity tests conducted over a 20 ns span showed excellent performance with differential nonlinearity (DNL) ranging from +0.053 to -0.101 and integral nonlinearity (INL) between -0.192 and -0.218. External testing, utilizing a waveform generator and oscilloscope, revealed an uncompensated resolution of 47.43 ps (34 % deviation). Mitigating the noise, the compensated resolution for an interval of 300 ns result in 9,596, a resolution of 31.263 picoseconds, which represents 0.04%. A remarkable result close to the theoretically expected value. The mean values were employed to evaluate the linearity, yielding DNL of +0,0560 and -0,0129, as well as INL of +0,0484 and -0,2104. Representing high linearity and high resolution compared with a previous noisy entry signal. This work demonstrates that high-end timing performance is attainable on cost-effective FPGA platforms.

Abstract: Smartphones have become the backbone of the connected society, reshaping social interactions in a period of adolescence marked by a neuropsychology vulnerability that is sensitive to intensive technological mediation. This study analyzes the relationship between the problematic use of mobile phones and the social and assertiveness skills of adolescents. Through a cross-cutting design, the answers of 1864 adolescents aged between 11 and 21 years old from education centers located in Cordoba (Spain) were analyzed, through a questionnaire that collected sociodemographic variables, the MPPUSA scale, to measure the inadequate use of mobile phones, and the ADCA-1 to assess social skills and assertiveness. The results revealed inadequate levels of mobile phone use and low levels of social skills, with nomophobia and negative consequences as the main risk factors, with the cluster analysis confirming the latter as the main predictor of the level of social development. The findings point to a concerning situation, in which not only does the usage time, but also the quality, have an influence on the psychosocial development of this population group. The application of preventive and educational interventions that address literacy, management of emotions, and the promotion of face-to-face social skills are therefore necessary.

Abstract: The primary objective of the paper is to make a case that the evaluation of the expected returns in the Two-Envelope Paradox (TEP) is problematic due to the ill-defined framing of X and Y as random variables representing two identical envelopes, where one contains twice as much money as the other. In the traditional literature, when X is selected, Y is defined in terms of the amount of money x in X using the values y= x and y=2x with equal probability .5, and vice versa when Y is selected. The problem is that the event X=x stands for two distinct but unknown values representing the money in the two envelopes, say $θ and $2θ. This renders X and Y ill-defined random variables whose spurious probabilities are used to evaluate the traditional expected returns. The TEP is resolved by applying formal probability-theoretic reasoning to frame the two random variables in terms of the two unknown values {θ, 2θ}, giving rise to sound probability distributions, whose expected returns leave a player indifferent between keeping and switching the chosen envelope.

Abstract: We propose a generalization of the dissipative quantum field theory (DQFT) as developed by Celeghini, Rasetti, and Vitiello to describe the dynamic informational feedback underlying biological coherence. The new framework, termed the Quantum Blueprint Formalism (QBF), builds on the fact that in DQFT the conjugate field ψ̃ is an active dynamical partner of ψ, representing the time-reversed degrees of freedom that co-generate dissipation, irreversibility, and the selection of inequivalent vacuum states. Rather than functioning as a mere repository of past interactions, ψ̃ participates continuously in the system’s coherent evolution through SU(1,1) Bogoliubov mixing.QBF extends this structure by allowing the ψ–ψ̃ coupling to become explicitly state-dependent, thereby endowing the conjugate field with an informational role that reflects and influences the system’s ongoing coherence pattern. Correlation parameters Θ = {θₖ} quantify the instantaneous coherence relations between the two sectors and evolve in time according to a nonlinear stochastic differential equation derived from the dissipative field dynamics.This extended formalism provides quantitative links between informational coherence and physiological observables such as heart rate variability (HRV), EEG phase synchronization, water-domain ordering, and ultraweak photon emission. It thereby establishes a bridge between dissipative quantum physics, information theory, and experimental biophysics, offering a consistent mathematical and empirical basis for understanding life as an informationally guided, self-organizing process in which ψ and ψ̃ jointly sustain and regenerate coherence.