Abstract: Background: Substance Use Disorder (SUD) is characterized by recurrent craving episodes frequently associated with emotional dysregulation and altered reward processing. This study aimed to evaluate whether emotional states associated with craving episodes can be detected through automated facial emotion recognition during controlled emotional induction. Methods: Forty-one participants completed a 14-day ecological momentary assessment (EMA) monitoring anxiety and craving levels, followed by an emotional induction task using standardized stimuli from the EmoMadrid database and addiction-related images. Facial expressions were recorded and analyzed in real time using a computational facial emotion recognition model trained on the FER-2013 dataset. Results: Participants with SUD exhibited significantly reduced positive emotional valence and activation in response to positive stimuli compared with HC (p < 0.01). Item-level analyses revealed that most differences occurred in stimuli depicting social interactions. Positive emotions and energy were linked to less intense cravings and shorter substance use. People with SUD showed more fear and less disgust in their facial expressions than controls (p = 0.02). Conclusions: These results suggest that people with SUD have changes in how they process emotions, showing less response to positive things and unique facial expressions related to craving.

Abstract: Pathological complete response (pCR) after neoadjuvant therapy is an important indicator of treatment response and prognosis in breast cancer. Multi-modal breast MRI provides complementary information for pCR prediction, but existing methods often assume complete modality availability and do not fully exploit the complementary value of radiomics and deep features. To address these limitations, we propose a radiomics-guided multi-modal learning framework for pCR prediction from breast MRI under incomplete modality settings. The model employs a multi-branch 2.5D encoder to extract modality-specific features, a radiomics-guided gating module to enhance deep representations with handcrafted priors, and a masked fusion strategy to adaptively integrate available modalities while excluding missing ones. Experiments on the I-SPY1 Trial dataset show that the proposed method achieves promising performance and maintains robustness under incomplete modality settings. These results suggest that the proposed framework can effectively integrate multi-modal MRI and radiomics information for pCR prediction and shows potential under incomplete modality settings.

Abstract: This study presents a hybrid additive manufacturing approach to fabricate bioinspired stainless steel 316L-copper (SS316L-Cu) multimaterial structures using laser powder bed fusion (LPBF). The present study incorporates honeycomb lattice structures with varying wall thicknesses (0.25 mm, 0.5 mm, 0.75 mm, and 1.0 mm) to investigate the effect of geometric parameters on mechanical performance. Mechanical testing was conducted according to ISO 6892 standards, and the results revealed a strong dependence of tensile strength and ductility on lattice thickness. Copper (Cu) infiltration into SS316L lattice structures improved ductility by 30% compared to the monolithic SS316L lattice, with minimal compromise in tensile strength. To complement experimental results, molecular dynamics (MD) simulations were performed to study atomic-scale deformation and validate the trend of strength enhancement with increasing wall thickness. The findings demonstrate the potential of combining LPBF and liquid Cu infiltration to develop multifunctional, mechanically robust, and thermally conductive metallic composites. This approach provides valuable insight into structure–property relationships and supports the design of next-generation multifunctional composites for structural and thermal applications.

Abstract: The growing deployment of green hydrogen technologies is increasing pressure on freshwater resources, motivating the exploration of alternative water sources that do not compete with human consumption. In this work, the direct use of untreated produced water from the Shushufindi 78 oil well (Ecuador) as an electrolyte for the hydrogen evolution reaction (HER) was experimentally evaluated. A comprehensive physicochemical characterization (ICP-OES, anions, BTEX), combined with electrochemical techniques (cyclic voltammetry, Tafel analysis, electrochemical impedance spectroscopy) and gas chromatography, was performed to correlate electrolyte composition with electrochemical performance. Despite the high salinity (~52 ± 5 g·kg⁻¹) and complex matrix composition, hydrogen production was achieved without pretreatment. Absolute hydrogen quantification yielded 10.29 µmol after 4 h of electrolysis, corresponding to a Faradaic efficiency of 43.8% and an electrical efficiency of 54.1% under non-optimized conditions. Comparative gas chromatography experiments using different electrolyte compositions revealed that alkaline systems, particularly mixtures of produced water with KOH, enhance hydrogen production, as evidenced by increased chromatographic peak areas. Impedance analysis showed reduced ohmic losses with KOH addition, while mineral scaling (CaCO₃ and Mg (OH)₂) increased interfacial resistance and reduced catalytic activity. These results demonstrate the feasibility of using produced water as an electrolyte for hydrogen production, highlighting the critical role of electrolyte composition within a circular economy framework.

Abstract: Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs 0.630; RMSE = 0.224 vs 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, highlighting a structural source of error in carbon stock estimation in tropical dry forests.

Abstract: Background/Objectives Ki‑67 is widely used as an immunohistochemical marker of tumor proliferation in hormone receptor–positive (HR‑positive), HER2‑negative breast cancer, but its inter-pretation is limited by variability and uncertain concordance with genomic assays. The Oncotype DX® Recurrence Score (RS) is a validated multigene assay with established prognostic and predictive utility. This study evaluated the relationship between Ki‑67 and RS in clinical practice. Methods We retrospectively analyzed women in Greece with early‑stage estrogen receptor–positive, HER2‑negative breast cancer without distant metastasis (pM0) who under-went Oncotype DX testing between 2020 and 2023. Eligible patients were node nega-tive or postmenopausal with node positive disease. RS was categorized as low (0–25) or high (>25). Ki‑67 was assessed using binary (< 20% vs ≥20%) and three‑tier (≤5%, >5%–< 30%, ≥30%) classifications. Associations were analyzed using correlation, con-cordance, and nonparametric methods. Results Among 2,967 patients, the median RS was 16, with similar distributions across nodal subgroups. Ki‑67 and RS demonstrated a moderate positive correlation as continuous variables (r = 0.42, p <  0.001). After stratification, associations with RS were observed only in tumors with high Ki‑67 expression, whereas no correlation was detected in low or intermediate groups. RS distributions differed significantly across Ki‑67 strata. Overall concordance between Ki‑67‑based proliferation categories and RS‑based ge-nomic risk was 56.2%, with discordant cases in both directions. Conclusions Ki‑67 shows a moderate association with Oncotype DX RS, but substantial discordance indicates it should not substitute genomic testing in HR‑positive/HER2‑negative early breast cancer.

Abstract: Chirality is a cornerstone of biological systems and pharmaceutical activity, driving a crit-ical need for rapid and sensitive enantioselective analytical methods. Covalent organic frameworks (COFs) have emerged as versatile porous materials, and their chiral counter-parts, chiral COFs (CCOFs), uniquely combine high surface area, predesignable pores, and a confined chiral microenvironment, making them exceptional platforms for enantioselective fluorescence sensing. This review systematically summarizes recent advances in the construction and application of CCOFs for enantioselective fluorescence sensing. We first outline the primary synthetic strategies for CCOFs, including direct synthesis, post-synthetic modification, and chiral induction. Subsequently, based on the direction of fluorescence signal change upon analyte binding, we classify the sensing mechanisms in-to three categories: “turn-off” (quenching via static complexation or photoinduced electron transfer), “turn-on” (enhancement through rigidification or suppression of electron transfer), and ratiometric (self-calibrating dual-emission response). Representative examples for the detection of amino acids, amino alcohols, terpenes, and saccharides are highlighted for each mode. Special emphasis is placed on structure–property relationships, such as the synergistic roles of hydrogen bonding, π–π stacking, and framework confinement in amplifying enantioselectivity. Finally, we discuss current challenges and future perspectives, including the rational design of ratiometric sensors, integration into practical devices, and the convergence with machine learning to advance the field of smart chiral sensing.

Abstract: This study presents an integrated assessment of surface water and groundwater quality in the Tula River basin, Mexico, encompassing the Endhó Dam and its associated aquifer. Water quality index (WQI) analysis revealed severe contamination along the Tula River (WQI >300), driven primarily by untreated sewage discharges from Mexico City and inadequate regional sanitation infrastructure. Elevated concentrations of COD, BOD, and nutrients indicate significant organic loading and eutrophication risk across aquatic ecosystems. Near Tula City, heavy metals including arsenic, copper, and zinc were detected at levels posing direct risks to human health. Groundwater quality was com-paratively favorable, with 71% of sampled wells recording WQI < 100; however, arsenic concentrations exceeding permissible limits by more than twentyfold were identified in select wells, attributed to geological sources. Semi-volatile organic compounds (SVOCs) were detected in both hydrological compartments, confirming cross-compartment con-tamination and highlighting the need for contaminant transport and fate modelling. The inertial contamination trajectory of the aquifer indicates that point-source reduction alone is insufficient for remediation. Comprehensive sanitation strategies, including pre-discharge treatment of Mexico City effluents, alongside proactive long-term aquifer monitoring and remediation programs, are urgently required to safeguard water sup-plies, public health, and ecological integrity in the Tula Valley.

Abstract: Post-disaster emergency communication recovery is not merely a link-repair task but a high-level planning problem constrained by service priorities, inter-object dependencies, resource budgets, and time windows. Existing restoration optimization methods generally rely on fully structured inputs, whereas direct large language model (LLM) planning often produces fluent yet operationally infeasible plans due to missing prerequisites, stage-order conflicts, and budget violations. To address this challenge, we propose ICG-Restore, an intent-constrained, graph-enhanced LLM planning framework with rule-consistent minimal-edit repair. The framework compiles natural-language requests, structured network observations, and operational rules into a task-intent object, retrieves task-relevant local context from a heterogeneous scenario graph and a restoration knowledge graph, generates stage-wise restoration candidates, and repairs infeasible plans through bounded edits that preserve the original planning backbone. Feasible candidates are then evaluated and ranked by a safety-aware agent executor in an abstract restoration action space. Experiments on three topology scales, four restoration tasks, and five environmental evolution modes demonstrate that ICG-Restore consistently improves executability, critical-target coverage, and overall recovery quality. Compared with Direct-LLM, it improves CSR, WCTC@5, and CRS by 1.99%, 38.87%, and 24.56%, respectively.

Abstract: Geometric and topological methods play an increasingly important role in quantum information science and quantum computation. Beyond the conventional Hilbert space formalism, a variety of mathematical frameworks, including group representations, mapping class groups, modular tensor categories, and character varieties, have been proposed to describe quantum states and quantum gates in a structurally robust manner. This review surveys the development of topological and geometric approaches to quantum information, with particular emphasis on representations of fundamental groups into SL(2,C), their associated character varieties, and the algebraic surfaces arising from trace coordinates, such as Fricke and Cayley cubic surfaces. These structures provide a geometric encoding of quantum degrees of freedom and offer alternative perspectives on topological quantum computing beyond anyon-based models. We also examine connections with integrable systems and isomonodromic deformations, where Painlevé equations and monodromy data supply a dynamical viewpoint on quantum state evolution. A critical comparison is provided with other geometric and topological approaches to quantum information, including geometric quantum mechanics, information geometry, tensor network geometry, and category-theoretic formulations. By synthesising results from topology, algebraic geometry, and mathematical physics, this review aims to clarify the conceptual landscape of topological quantum information geometry and to identify open problems and emerging directions in the field.

Abstract: Microplastics are particles derived from polymer degradation, and their occurrence and abundance have been assessed across various environments and compartments. The method commonly used for their evaluation and quantification in sediments involves a marine salt solution for decantation. However, due to the high incidence of plastics in marine environments, this salt may already contain a considerable concentration of microplastics and must be carefully filtered to minimize interference during laboratory processing. To assess the importance of this procedure, sediment samples from an estuarine environment, in which the salt used for laboratory sorting was not filtered, were compared with samples from semiarid reservoirs, in which the salt underwent filtration before decantation. All other procedures were identical, performed by the same team under controlled airborne contamination conditions. The Mann–Whitney test applied to samples with and without NaCl filtration revealed a significantly lower incidence of microplastics in samples whose salt had been filtered. Based on these findings, a filtration protocol for NaCl used in sediment decantation was developed, emphasizing an accessible, low-cost product widely applied in natural environmental quality assessments. Only through the standardization of methodologies and sampling units will it be possible to compare environments in terms of actual anthropogenic impact, generating outcomes that provide scientific support for conservation actions and impact mitigation.

Abstract: Mars climatology is a growing interest domain for planetary research and for operational missions. In the last three decades, Martian General Circulation Models have been developed to support the interpretation of spacecraft and telescopic observations and for the advancement of theoretical understanding of the climate. They have been designed to represent key processes, such as dust cycle, seasonal CO2 condensation and interaction between boundary layer and surface. At the same time, new observations from orbiters and landers have enhanced the diagnostics, but several uncertainties in the parameterization, especially in dust representation and turbulent mixing, require further improvements. This review represents a synthesis of the state of the art of existing global and regional models, comparing numerical and physical approaches, identifying the main challenges for the next years, with particular attention to the needs of operational missions and machine learning techniques.

Abstract: The foundation of nuclear power plants is special as large-scale earth filling is often required. The properties of the backfill soil differ significantly from naturally deposited soils with regard to deformation and bearing capacity. For pile foundations, a thick backfill layer near the top may change the bearing mode around the pile. In this paper, parallel multi-pile tests were conducted to thoroughly investigate the vertical and horizontal bearing characteristics of long rock-socketed piles at backfill nuclear site. The results show that longer piles tend to have higher vertical bearing capacity, however, they do not necessarily exhibit smaller displacements when subjected to vertical load. When the applied vertical load increases, the effect of end resistance of piles becomes more pronounced, meanwhile, deeper soil layers exert a more significant impact on the axial forces within the pile body. Short piles with more part in backfill layer may endure a hoop tightening effect at the upper part of the pile, resulting in very little frictional resistance being provided by the lower soil. At the vertical load level preceding failure, the distribution of axial force and shaft resistance along the pile length will change. The typical mechanism of transition from static to dynamic friction between soil and piles that lead to shaft resistance is more apparent for longer piles but inhomogeneous soil like backfill layer will make the transition complex. When subjected to lateral loading, piles with better integrity show more pronounced elastic features, smaller maximum horizontal displacement and less residual horizontal displacement. The selection of the proportional coefficient for determining piles’ horizontal bearing capacity should correspond to the specific load and displacement. The results and in-depth analysis of the piles’ bearing capacity will provide intuitive experience for the analysis of pile foundations, thus offer valuable references for the design and construction in similar engineering.

Abstract: The study presented here is the first one in which surface disinfectants active against Candida auris were tested with the, state of the art, German/EU Technical Normative DIN/EN 17387. The results give a better understanding of the real efficacy of surface disinfectants for the use in medical health care facilities with respect to Candida auris. Therefore DIN/EN 17387 is an emphasized tool to get comparable robust surface disinfectants test results EU wide.

Abstract: The energy balance model (EBM) and its operational shorthand “calories-in, calories-out” (CICO) have dominated obesity research and clinical practice for nearly a century. While historically valuable, these frameworks rest on indirect mass-to-energy conversions and thermodynamic misconceptions that propagate measurement error and obscure physiological mechanisms. The recently published Perspective established the mass balance model (MBM) as a conceptually simpler, mathematically consistent, and biologically faithful alternative that tracks macronutrient mass directly – grams in, grams out – without intermediate energy-unit transformations. This companion manuscript delivers the missing methodological bridge. We present a complete, ready-to-implement toolkit: standardized protocols for precise macronutrient intake quantification, complete fecal and urinary excretion analysis, body composition assessment with stoichiometric corrections, and open-source computational pipelines for MBM-based data integration. Illustrative re-analyses of landmark trials (Hall et al., DIETFITS, CALERIE) demonstrate how MBM reframes long-standing controversies and yields quantitatively superior predictions of body composition change. By eliminating the two-step conversion pipeline that has plagued EBM analyses for decades, MBM reduces propagated uncertainty by an estimated 40–65 %, aligns research endpoints with atomic conservation laws, and opens new avenues for personalized nutrition, pharmacotherapy evaluation, and mechanistic discovery. The time for incremental refinement of EBM has passed. The field now possesses both the theoretical foundation and the practical instrumentation to adopt MBM as the new evidentiary standard in human metabolic research.

Abstract: This article provides general expressions for the phase velocity and the Doppler shift of the electromagnetic fields radiated from a uniformly moving Hertzian dipole measured by a uniformly moving observer. The results show that the phase velocity of the electromagnetic wave is always equal to when measured exactly in the direction pointing to the birthplace of the field. The expression for the Doppler effect is of the same form of the Newtonian type classical formula, which implies that it might be not proper to consider that the classical formula for the Doppler shift is the low speed approximation of the conventional relativistic formula.

Abstract: Industry 4.0 technologies offer substantial opportunities for sustainable business transformation, yet organisations consistently struggle to translate technological in-vestments into successful project outcomes. This study investigates the inner workings the "black box” of Industry 4.0 project implementation by examining how project management practices, team competencies, and decision-making processes interact. Using a mixed-methods case study of a leading industrial automation company, in-cluding a survey of project team members (n=50) and interviews with project managers (n=5), The identification of recursive feedback loop: competency gaps directly cause decision failures, and poor decision processes subsequently widen those competency gaps. Conversely, structured decision reviews and transparent communication trans-form routine choices into competency-building opportunities. An Integrated Imple-mentation Model (IIM) was proposed that explains these dynamics and demonstrates that sustainability outcomes, like resource efficiency, waste reduction, and circular economy practices emerge naturally when organisations manage processes, people, and decisions together. For practitioners, the core message is that every Industry 4.0 project should be treated as an opportunity to build long-term organisational learning capacity, not merely as a technology installation. This study provides both a theoretical framework for understanding implementation dynamics and actionable guidance for sustainable digital transformation.

Abstract: This study examined the mediating role of negative stress in the relationship between Psychological Capital (PsyCap) a higher-order construct comprising hope, self-efficacy, resilience, and optimism and psychological distress indicators among Ecuadorian uni-versity students. A cross-sectional survey was conducted with 1,732 students (55% women; M = 20.44, SD = 2.29), using validated self-report measures. Structural equa-tion modeling showed a good model fit (CFI = 0.947; TLI = 0.942; RMSEA = 0.055; SRMR = 0.040) Results indicated that PsyCap was negatively associated with negative stress (β = −0.261), which in turn showed strong positive effects on anxiety–depression symptoms (β = 0.782) and psychological inflexibility (β = 0.781). Direct effects of PsyCap on both outcomes were significant but comparatively small (β = −0.115 and β = −0.086, respec-tively), whereas indirect effects through stress were substantial and significant (β = −0.204), supporting a partial mediation model. The model explained 67.2% of the vari-ance in anxiety–depression and 65.2% in psychological inflexibility. These findings suggest that PsyCap operates primarily as a protective factor through its capacity to reduce negative stress, which subsequently influences downstream psy-chological outcomes. The results highlight the importance of stress-focused mecha-nisms in understanding how positive psychological resources impact mental health. From an applied perspective, the findings underscore the relevance of implementing strengths-based interventions in higher education that enhance PsyCap components while simultaneously targeting stress reduction. Such inter-ventions may contribute to decreasing psychological distress and improving students’ adaptive functioning and well-being. This study provides robust evidence from the Latin American context, advancing the understanding of transdiagnostic mechanisms linking positive resources and mental health in university populations.

Abstract: Poly(l-lactic acid) or poly(l-lactide) (PLLA) is an optically active polymer derived from renewable sources and fully biodegradable. It is known that PLLA assumes a left-handed helix in the solid state and also in solution it still keeps a certain degree of helical structure. Here we examine the Optical Rotatory Dispersion (ORD) behavior of two grades of PLLA (medium molecular weight and hexadecyl-terminated or a high molecular weight for 3D printing) in 13 different solvents and through the Moffitt-Yang equation of the ORD data. Furthermore, the ORD data of PLLA in additional 6 solvents were taken from literature and analyzed with the Moffitt-Yang approach. The results suggest that also in solution PLLA maintain the left-handed helix and the most structurizing and helicogenic solvents for PLLA are ethyl acetate, acetonitrile, and certain chlorinated solvents. The equilibrium association constant (K) and other thermodynamic parameters (ΔG°, ΔH° and ΔS°) between PLLA and polyphenylacetylene (PPA another helical polymer in the solid state and in solution) were determined in trichloromethane, dichloromethane and tetrahydrofuran. The K values found suggest a strong helix-helix interaction between the two polymers. The ORD analysis of the PLLA-PPA solutions show evidences of the extrinsic Cotton effect and confirming the chiral helicity induction between the two polymers with 1:1 complex formation.

Abstract: High consumption of ultra-processed foods (UPFs) contributes to the growing burden of non-communicable disease, yet many consumers struggle to recognise and interpret levels of processing. Digital tools using artificial intelligence (AI) offer potential to support nutrition literacy and UPF awareness. This study explored user perceptions, usability and cultural relevance of a Human Interference Scoring System (HISS)-based mobile application designed to classify foods and support reflection on food quality and dietary choices. A qualitative study was conducted in New Zealand, where participants used the HISS app for three days followed by semi-structured interviews. Thirty-one participants were recruited via social media and word of mouth, including adolescents (n=13), tertiary students (n=9), and Māori and Pacific health coaches (n=9). Transcripts were analysed using inductive thematic analysis. Three evaluative categories were identified: positive user experiences (intuitive interface, perceived AI accuracy, enhanced nutrition literacy, visual feedback, inclusivity of cultural foods); challenges (technical issues, database gaps, limited depth for advanced users); and suggested improvements (expanded food database, enhanced logging, culturally tailored education, optional advanced features). Participants reported increased awareness of UPF intake and reflection on food choices. The HISS app was perceived as usable, acceptable and relevant across diverse user groups, particularly for those with lower nutrition literacy. Addressing technical limitations and expanding functionality may enhance engagement and applicability. AI-enabled, culturally responsive food classification tools such as HISS show promise as scalable health promotion approaches to support UPF awareness and dietary reflection in community and clinical settings.