Abstract: This study examines whether renewable energy production is capable of covering energy costs in European Union family farms and identifies the determinants of the Energy Cost Coverage Ratio across different economic size classes. The analysis was based on Farm Sustainability Data Network (FSDN) data for 2014-2023 and combined descriptive statistics with panel data models. The results indicate that the Energy Cost Coverage Ratio remained relatively stable, fluctuating between 30.9% and 39.2%, despite a substantial increase in renewable energy production from €1594 to €2999 per farm. This limited improvement resulted from a simultaneous rise in energy costs, which increased from €5162 to €8465 per farm over the analysed period. Considerable differences in the Energy Cost Coverage Ratio were observed between economic size classes, while panel data models showed that its determinants vary across farm classes, with no single factor being significant for all classes. The findings suggest that renewable energy has strengthened the economic resilience of European farms, but its contribution remains insufficient to fully offset rising energy expenditures, highlighting the need for farm-size-specific policy support.

Abstract: Bioactive peptides are an important source of therapeutic molecules and molecular scaffolds involved in defense, signaling, and immune regulation. Despite the extraordinary diversity of Coleoptera, the structural landscape of beetle-derived bioactive peptides remains largely unexplored, limiting our understanding of their evolutionary diversity and biotechnological potential. Here, we performed a large-scale structural survey of predicted toxin-like peptide scaffolds across publicly available Coleoptera transcriptomes by integrating transcriptome mining, peptide maturation prediction, physicochemical characterization, AlphaFold 3 structural modeling, structural similarity analyses, and interpretable machine learning. We identified 291 candidate peptides, of which 155 contained canonical signal peptides and 273 produced mature peptides within the expected size range of known bioactive peptides. Structural analyses revealed that, despite extensive sequence diversity, many candidates converged toward a comparatively restricted repertoire of compact cysteine-rich architectures, suggesting that structural conservation exceeds primary sequence conservation during peptide diversification. Comparative structural analyses further identified recurrent protein architectures shared across multiple beetle lineages, while machine learning prioritization integrated structural and biochemical descriptors to identify high-confidence candidates for future functional characterization. These analyses establish the first structural atlas of predicted toxin-like peptides across Coleoptera and demonstrate that structure-guided transcriptome mining provides a powerful framework for uncovering evolutionarily conserved bioactive peptide scaffolds that would remain largely undetected using sequence-based approaches alone. Beyond expanding our understanding of peptide evolution in beetles, this resource is a foundation for future structural, functional, and biotechnological exploration of bioactive peptides in underexplored animal groups.

Abstract: The loss of a companion animal can provoke grief whose intensity rivals human bereavement, yet its biological basis remains largely uncharted. This structured narrative review synthesizes neurobiological, endocrinological, immunological, and psychosocial evidence into a mechanistic framework offered not as established fact but as a source of testable hypotheses. We trace the substrates of the human-animal bond across six interacting systems and propose how disruption of each upon an animal's death may drive grief. Three carry comparatively robust analogical support (oxytocinergic signaling, hypothalamic-pituitary-adrenal [HPA] axis regulation, and neuroinflammatory activation) and three are more exploratory (dopaminergic, serotonergic, and endocannabinoid pathways). Integrating extracted study-level data into transparent prioritization heuristics, we identify the HPA axis, assessed with its coupled oxytocinergic partner, as the most defensible first target for a direct biomarker study, a conclusion robust across three methodologically independent analyses. No direct pet-loss evidence yet supports any proposal, and this gap is the review's central motivation. As applied context, we address euthanasia-related guilt, disenfranchised grief under East Asian norms (a tentative "double disenfranchisement" hypothesis we want tested), assessment instruments, evidence-graded therapies, a Korean tool-development roadmap, and One Health research priorities.

Abstract: Hybrid renewable energy systems with photovoltaic generation, wind power, battery storage, and hydrogen pathways are becoming increasingly popular in off-grid and weak grid applications as they convert intermittent renewable energy sources into usable energy forms, thus decreasing dependency on fossil fuels. Traditional deterministic approaches to the system size may be unable to consider the risks associated with solar radiation, wind, ambient temperatures, and energy demand evolution due to climate-resource sensitivity analysis. In this research, HOMER Pro was used to apply least-cost sizing and resource availability sensitivity analysis for the sizing of hybrid photovoltaic–wind–battery-hydrogen system under four climate-resource sensitivity scenarios. Four different system layouts were compared based on the HOMER Pro optimization model using several evaluation criteria such as NPC, LCOE, renewable ratio, CO₂ emissions, unmet load ratio, and hydrogen energy production. Four different system layouts were compared based on the HOMER Pro optimization model using several evaluation criteria such as NPC, LCOE, renewable ratio, CO₂ emissions, unmet load ratio, and hydrogen energy production. The most appropriate design layout was identified as a PV-wind-battery-electrolyzer-hydrogen tank-fuel cell system with 900 kW PV, 350 kW wind power capacity, 1.85 MWh battery storage, 240 kW electrolyzer capacity, 520 kg hydrogen capacity and 180 kW fuel cell. At the base scenario, it produced the minimum LCOE equal to 0.246 USD/kWh, NPC of 4.38 million USD, renewable ratio of 98.7%, CO₂ emissions of 18 t/y and unmet load of 0.30%. In case of simultaneous pressure on resources due to climate, the LCOE rose to 0.253 USD/kWh while the unmet load did not exceed 0.43%. Thus, the inclusion of hydrogen allows one to ensure greater resilience compared to systems without hydrogen.

Abstract: Feature selection is one of the fundamental steps in producing simpler, more interpretable, and computationally efficient models while maintaining predictive power. In this study, rather than fixing the uncertainty relationship between the target variable and each candidate feature to a single entropy parameter, three volume-based indices are proposed that integrate the two-parameter structure of the Sharma-Mittal entropy over a defined parameter region. The indices, named Parameter-Integrated Conditional Sharma-Mittal Entropy, Parameter-Integrated Sharma-Mittal Entropy Information Gain, and Normalized Parameter-Integrated Sharma-Mittal Entropy Information Gain, represent, respectively, the conditional entropy volume, the information gain volume, and the form of this volume normalized relative to the total entropy volume of the target, respectively. Densities were estimated using Gaussian kernel density estimation; the alpha and beta parameters were numerically integrated over the range [0.05; 0.95] × [0.05; 0.95]. The methods were compared across six different regression datasets-Airfoil Self-Noise, AirQualityUCI, BodyFat, meteorology-based reference evapotranspiration, Concrete, and WineQualityWhite-using absolute Pearson correlation, absolute Spearman rank correlation, Shannon information gain, mutual information, and random forest variable importance. The comparison was conducted using raw importance scores, derived feature rankings, Spearman’s rho and Kendall’s tau rank correlations, bivariate rank scatter plots, and Mann–Whitney U test results. The results show that the proposed indices produced high or very high rank agreement with correlation- and information-based reference methods in the Airfoil Self-Noise, AirQualityUCI, BodyFat, and meteorological datasets. Specifically, the absolute Pearson correlation, absolute Spearman rank correlation, Shannon information gain, and mutual information, along with the average Spearman rho values across all datasets, were obtained as 0.884, 0.874, 0.883, and 0.903, respectively; the average agreement with random forest importance scores remained at 0.538. The findings reveal that the parameter-integrated Sharma–Mittal framework offers a density-based and discretization-independent filtering perspective for continuous data; however, they also highlight the need for additional sensitivity and out-of-sample prediction performance evaluations regarding absolute score scales, negative information-gain values, and the rank equivalence of the three proposed indices.

Abstract: Objective: To develop and evaluate EDBERT (Emergency Department Bidirectional Encoder Representations from Transformers), a BERT-based architecture built around a multi-input fusion network that enriches free-text triage notes with the additional context of presenting complaints and patient age to predict Emergency Department (ED) disposition, supporting early decision-making and reduced ED length of stay (LOS). Methods: A retrospective cohort of 570,143 ED presentations to the Royal Melbourne Hospital, Australia, was used. At the core of EDBERT, a fusion network integrates three complementary input sources—triage notes, presenting complaints, and patient age—through learnable weights, with the two free-text inputs encoded by customised 8-layer BERT encoder stacks pre-trained on triage text with Masked Language Modelling (MLM). The resulting architecture is also lightweight, containing 66 million parameters, substantially fewer than the 110 million of BERT-BASE. Eighty percent of the dataset was used for training and 20% for testing. Performance was benchmarked against BERT-Base configurations of varying depth. Results: EDBERT achieved an accuracy of 83.26%, a macro-averaged F1-score of 81.99%, and an area under the receiver operating characteristic curve of 0.91, outperforming all single-input BERT-Base variants, including a depth-matched 8-layer model (82.08%), indicating that the gain derives from the fusion of complementary inputs combined with domain-adaptive pre-training rather than from model capacity. Conclusion: A fusion-based BERT architecture that supplements the triage narrative with presenting complaint and age context predicts ED disposition more accurately than larger single-input BERT models, while requiring substantially less memory and computation, making it practical for deployment in hospitals with limited computing resources.

Abstract: Background: Glucagon-like peptide-1 (GLP-1) and dual GLP-1/glucose-dependent insulinotropic polypeptide (GIP) receptor agonists reduce cardiovascular events, but the underlying cardiac structural remodeling remains unclear. Objectives: This study evaluated longitudinal cardiac remodeling associated with semaglutide and tirzepatide in a real-world cohort and determined its weight-loss dependency. Methods: Using electronic health records from a federated network, we analyzed longitudinal echocardiograms of patients prescribed semaglutide or tirzepatide, stratified by 12-month weight loss into super-responders (>15%), moderate-responders (5%-15%), and minimal-responders (<5%). Additionally, GLP-1/GIP patients with >5% weight loss were propensity-matched with non-GLP-1/GIP weight-loss medication control patients on demographics and baseline body mass index. Results: Among 3,500 GLP-1/GIP patients (422 weight-loss super-responders, 1,426 moderate-responder, 1,652 minimal-responders), left ventricular (LV) mass decreased across all groups proportional to weight loss (super-responders: 198.4 ± 69.6 to 176.6 ± 66.0 g; moderate-responders: 210.7 ± 74.0 to 195.8 ± 68.1 g; minimal-responders: 216.1 ± 68.4 to 204.8 ± 67.4 g; p < 0.001). Left atrial volume and LV systolic and diastolic function did not change. Among weight-loss super-responders, semaglutide (n = 159) showed greater LV mass reduction than tirzepatide (n = 89) (Cohen’s d = -0.389 vs. -0.263), while tirzepatide showed small improvements in right ventricular function. Compared to matched non-GLP-1/GIP controls (n = 118/group), GLP-1/GIP patients exhibited significant LV mass reduction not observed in controls despite similar weight loss. Conclusions: Real-world findings demonstrate that incretin-based therapies are associated with reverse cardiac remodeling characterized by weight-loss-dependent and incretin-specific mechanisms, with potentially distinct structural targets among agents.

Abstract: Objectives: Exenatide is a glucagon-like peptide-1 receptor agonist (GLP-1 RA) that slows gastrointestinal (GI) motility. Whether exenatide produces clinically detectable GI motility slowing in real-world practice — and how this compares to other GLP-1 RAs — has not been systematically evaluated. We addressed this question using federated, electronic health record (EHR) data. Further, we present clinical data on vurolenatide, a long-acting variant of exenatide, in patients with short bowel syndrome (SBS), a condition defined by nutritional insufficiency where reducing GI motility could be beneficial to patient outcomes. Methods: We queried de-identified EHR data on the nSights platform to identify treatment episodes for exenatide, semaglutide, and tirzepatide among patients with documented GI hypermotility. Anti-constipation medication prescriptions, body weight, and HbA1c trajectories were compared between pre- and post-treatment periods. Given exenatide’s GI potency, we enrolled 9 SBS patients in a Ph 1b/2a study looking at vurolenatide, dosed subcutaneously every 2 weeks. Safety, tolerability, total urine output, and total stool output were the primary endpoints with the latter serving as a metric for GI motility. Results: We identified patients on exenatide (n=387), semaglutide (10,315), and tirzepatide (5,095) and found the prevalence of anti-constipation prescriptions increased significantly in the exenatide cohort over 12 months (11.09% to 18.62%; p=6.75×10⁻⁴). No significant change was detected with semaglutide (17.54% to 17.70%, p=0.734), and tirzepatide was associated with a decrease in use (18.72% to 14.84%, p<0.001). Exenatide did not impact body weight (-0.5%, p=0.299), compared to semaglutide (-3.95%, p<0.001) and tirzepatide (-8.44%, p<0.001), nor impact HbA1c (-0.02 %-points, p=0.926), compared to semaglutide (-0.26 %-points, p<0.001) and tirzepatide (-0.56 %-points, p<0.001). Consistent with exenatide’s potency at slowing GI motility, biweekly dosing of vurolenatide in human SBS patients showed immediate reduction in total stool output and in the number of bowel movements patients experienced. Conclusions: Exenatide is associated with potent slowing of GI motility in retrospective analysis of EHR data as well as in prospective treatment of SBS patients. This real-world signal proxies the mechanism underlying vurolenatide in SBS and provides a first-in-kind pharmacoepidemiological basis for GLP-1 RA–mediated modulation of GI motility.

Abstract: Canonical decompositions are among the most powerful organizing principles in mathematics. Prime factorization, Jordan canonical form, spectral decomposition, and Hodge decomposition demonstrate that complicated mathematical objects often admit unique descriptions in terms of elementary structural components. Surprisingly, despite the ubiquity of finite relational systems throughout mathematics, no general decomposition theory appears to exist for finite architectures themselves. Existing approaches typically begin with graphs, operators, Green functions, networks, or application-specific models rather than with architecture as the primitive mathematical object.In this paper we develop a decomposition framework for finite architectures generated by four primitive structural processes: transport, interaction, closure, and hierarchy. Within the free architectural algebra introduced here, every architecture possesses a unique canonical decomposition into these primitive generators. This decomposition induces canonical architectural coordinates and leads to a representation theorem showing that every positive multiplicative structural invariant is uniquely determined by its values on the primitive generators.The framework is formulated independently of any particular realization. Graph theory, Green-function methods, operator theory, and Green-Function Architecture Theory arise naturally as representations of the same underlying structural decomposition. The resulting theory separates structural decomposition from mathematical representation and provides a foundation for studying architectural invariants independently of their realization. The extension of the decomposition theory beyond the free architectural algebra and the determination of distinguished architectural basis constants are formulated as central problems for future research.

Abstract: Background: Pilonidal sinus disease (PSD) is a chronic inflammatory condition predominantly affecting young adults. Minimally invasive techniques such as pit-picking have gained increasing popularity; however, recurrence rates remain a significant concern. Microwave ablation (MWA) has recently emerged as a novel adjunctive modality that may improve treatment durability through enhanced destruction of residual sinus epithelium. Materials and Methods: This retrospective multicenter comparative cohort study included 403 consecutive patients treated for PSD between January 2022 and January 2024. Among these, 200 patients underwent pit-picking combined with MWA (MWA group) and 203 underwent pit-picking alone (PP group). Recurrence was defined as any clinical reappearance of pit, abscess, or discharging sinus confirmed at follow-up. Primary outcome was disease recurrence; secondary outcomes included operative time, postoperative pain (VAS), complications, SSI, wound healing time, pain-free sitting, and return to work. Multivariable logistic regression and Kaplan–Meier analysis were performed. Results: MWA group demonstrated superior postoperative outcomes: shorter pain-free sitting (2 vs 4 days), healing time (11 vs 16 days), and return to work (3 vs 5 days). Overall complication rates were lower (4.5% vs 11.3%; OR 0.37, 95% CI 0.16–0.84; p=0.018). Recurrence rates were significantly reduced (5.5% vs 13.8%; OR 0.36, 95% CI 0.17–0.75; p=0.006). MWA treatment remained an independent predictor of reduced recurrence on multivariable analysis (adjusted OR 0.35, 95% CI 0.16–0.76; p=0.007). Conclusion: Pit-picking combined with microwave ablation is a safe and effective minimally invasive treatment strategy for PSD, significantly improving postoperative recovery, reducing morbidity, and lowering recurrence rates. Further prospective randomized studies with longer follow-up are warranted.

Abstract: The increasing convergence of digital transformation, geospatial technologies and knowledge management has created new opportunities for libraries to redefine their strategic roles within knowledge societies. Alt-hough Geographic Information Systems (GIS) have been widely applied in domains such as urban planning, environmental management and public health, their integration into library knowledge management (KM) remains conceptually fragmented and theoretically underdeveloped. This study addresses this gap by pro-posing an interdisciplinary conceptual framework that explains how GIS capabilities can enhance knowledge management processes and facilitate the transformation of libraries into geospatial knowledge hubs. The study adopts a conceptual research design based on an interdisciplinary synthesis of literature drawn from Geographic Information Science, Library and Information Science, Knowledge Management and Digital Transformation studies. Using thematic analysis and conceptual modelling, the study develops a theoretically grounded framework that integrates the Knowledge-Based View, the SECI model of knowledge creation and Digital Transformation Theory. The framework conceptualizes GIS capabilities as strategic organizational resources that enhance knowledge creation, organization, sharing and application through the mediating role of knowledge management capabilities and the moderating influence of organ-izational readiness, ICT infrastructure, staff GIS competency, leadership support and data governance. The principal contribution of the study is the introduction of geospatial knowledge hubs as a novel conceptual construct that repositions libraries from traditional information repositories to intelligent, data-driven or-ganizations supporting digital scholarship, spatial intelligence, interdisciplinary collaboration, evi-dence-based decision-making and community engagement. The framework provides eight theoretically grounded propositions that establish a foundation for future empirical investigation. The study contributes to geoinformation management and Library and Information Science by advancing a comprehensive theo-retical model that bridges previously disconnected domains. It also offers practical and policy guidance for GIS-enabled library transformation while identifying priorities for future empirical validation across di-verse library environments.

Abstract: Parkinson’s disease (PD) involves progressive basal ganglia dysfunction, with hyper-excitability of striatal indirect-pathway D2 medium spiny neurons (D2-MSNs) linked to motor impairment. Neuromotor rehabilitation is an important therapy, but its efficacy varies across interventions. This thematic review integrates neurobiological mecha-nisms and clinical evidence across diverse rehabilitative strategies in PD, including voluntary and forced exercise, high-intensity interval training (HIIT), treadmill and re-sistance training, Tai Chi and dance, Lee Silverman Voice Treatment BIG (LSVT BIG), repetitive transcranial magnetic stimulation [rTMS], transcranial direct current stimu-lation [tDCS]), exoskeleton-assisted training, augmented reality-based interventions (AR), multimodal cueing, and dual-task paradigms. These approaches are unified by striatal indirect-pathway recalibration as the main mechanism underlying rehabilitation efficacy in PD. To formalize evidence integration, a Bayesian ranking framework is proposed, combining neurobiological plausibility with clinical evidence quality. Ap-plied to eleven interventions, this framework identifies three functional clusters: a high-recalibration cluster including forced exercise (posterior probability P ≈ 0.80) and LSVT BIG (P ≈ 0.62); an intermediate uncertainty cluster comprising rTMS, HIIT, tDCS, treadmill, resistance training, and Tai Chi/dance (0.40 ≤ P ≤ 0.56); and a low-recalibration or compensatory bypass cluster including AR, exoskeletons, and dual-task training (P ≤ 0.33). This framework highlights an epistemic distinction between interventions that may directly modulate basal ganglia circuitry and those that primarily recruit alterna-tive motor networks, including the lateral premotor cortex, parieto-premotor circuits, and cerebello-thalamo-cortical pathways, supporting a precision rehabilitation ap-proach in PD.

Abstract: Retail-bank credit decisioning still relies on binary good/bad classifiers, and published results on the field's three canonical public corpora, German Credit, Taiwan Default, and Lending Club, are largely incommensurable because they differ in preprocessing placement, resampling design, and leakage control. This study contributes a leakage-controlled ordinal benchmark: multinomial logistic regression, random forest, XGBoost, and a neural network are compared on a three-tier (Low/Medium/High) formulation of all three corpora under one identical, fully reproducible R protocol in which the split precedes all fitting, scaling and SMOTE are estimated inside cross-validation folds, and tier-defining columns are excluded from the predictors. Under this protocol the best model is corpus-dependent: logistic regression leads on the small, weakly correlated German corpus (High-tier AUC 0.738), while tree ensembles lead on the larger Taiwan and Lending Club corpora (accuracy 0.86 and 0.70). Re-admitting the excluded columns lifts the ensembles to a perfect 1.000 on Taiwan, quantifying how much apparent performance uncontrolled designs can manufacture and explaining the wide spread of published results. The study also documents a multi-class probability-decoding fault that silently drives a trained model to chance, and maps predicted tiers to graduated lending actions for deployment.

Abstract: Background: The interaction between sex, APOE ε4 status, and clinical progression in Alzheimer’s Disease (AD) remains a subject of debate. While females are often considered at higher risk for AD, the underlying structural neuroanatomical trajectories and how they are modulated by genotype are not fully elucidated. This study aims to evaluate how sex and the APOE ε4 genotype interact to influence longitudinal brain atrophy across three clinical groups. Methods: We analyzed longitudinal data from 2400 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), stratified by clinical group (i.e., cognitively normal, mild cognitive impairment, and AD), sex, and APOE ε4 carrier status. Using Type III Sum of Squares ANCOVA, we modeled the longitudinal variation in brain volume, controlling for baseline brain volume, and baseline severity of neurocognitive impairment and age at entry. Results: While main effects of sex and APOE genotype were not significant, the triple interaction (APOE * Sex * Clinical Group) was marginally significant (p = .051). Post-hoc analysis revealed a distinct pattern of structural dimorphism within the AD cohort among APOE ε4-negative individuals with females exhibiting significantly greater structural preservation compared to males (Mean difference = 11.32, p = .051). Among APOE ε4 carriers, atrophy trajectories for males and females were statistically indistinguishable (p = .922), suggesting that the ε4 allele exerts a dominant neurodegenerative influence that overrides sex-specific physiological differences. Conclusion: These findings highlight the importance of jointly considering biological sex and APOE ε4 status to improve the characterization of Alzheimer's disease heterogeneity and support precision medicine approaches.

Abstract: The removal of five neonicotinoid insecticides, acetamiprid (ACE), chlothianidin (CLO), imidacloprid (IMI), thiacloprid (THC), and thiamethoxam (THM), was explored using various commercial ultrafiltration (UF) and nanofiltration (NF) membranes. Several modification techniques have also been implemented for one UF membrane, including immersion in hot water, sodium hydroxide, and ethanol solutions, as well as polymerization with monomers such as polyethyleneimine (PEI) and trimesoyl chloride (TMC), to improve micropollutant retention while maintaining adequate permeability. The results show that only immersion in ethanol (60% solution or absolute ethanol) was a suitable immersion technique for improving membrane performance. The use of various reagents and conditions for the membrane surface modification via polymerization yielded the best results, with the sequential application of PEI+TMC+60°C followed by immersion in glycerol solution (GLY) being the most efficient. Once the optimal modification procedure was established, these membranes were tested with real water matrices (two secondary effluents from wastewater treatment plants (WWTP) and a surface water sample) in which the neonicotinoids were dissolved. The modified UF membrane showed improved retention levels compared with commercial UF and NF membranes, demonstrating greater efficiency in retaining neonicotinoids under real water conditions. Therefore, the proposed modification process is a promising alternative to commercial membranes for removing micropollutants from urban wastewater.

Abstract: Renewable energy sources are expanding rapidly, with wind energy representing one of the fastest-growing sectors. In parallel, digital twins are transforming structural monitoring and inspection by enabling near real-time structural assessment. However, the high computational cost associated with high-fidelity numerical models requires reduced-order or surrogate modelling strategies. Within this context, composite laminate thickness plays a critical role in wind turbine blade design and assessment. In this study, a computational fluid dynamics (CFD)-based numerical model developed in ANSYS was employed to predict von Mises stresses in a three-dimensional wind turbine blade geometry derived from the Carbo4Power project. The numerical results were validated against published literature, enabling identification of a thickness–stress relationship. This foundational analysis supports the development of reduced-order models suitable for digital twin applications in wind turbine blade structural assessment.

Abstract: The report presents the results of broadband dielectric spectroscopy (BDS) studies in bulk nanocolloids: E7 liquid crystalline (LC) mixture plus C₆₀ fullerene nanoparticles. BDS spectra for 260 temperatures from the isotropic liquid (I) phase at ~360K to the nematic (N) phase at the glass temperature T_g~220K was tested. The analysis focused on pretransitional critical-like features and complex glassy dynamics, highlighting their interplay and dominance. This is associated with pretransitional fluctuations, which impact the nematic phase even 90 K below the I-N transition. On approaching T_g, strong previtreous changes detected via dielectric constant and the loss curve maximum appear, starting at T_g+30K. The critical-like behavior on T→T_g appears explicitly also for the parameter describing the distribution of relaxation times. For the 3 main relaxation times in the long-range nematic phase, the optimal portrayal via the new Critical & Activated equation is evidenced. The derivative-based test of coupling/decoupling between translational and orientational processes reveals strong decoupling with the fractional exponent F< 1 below the I-N transition and related to F< 1 above T_g. Notable is the permanent ‘parallel’ orientation of LC molecules by C₆₀ fullerene nanoparticles, which can be significant for applications.

Abstract: Taste supports survival by helping animals identify useful nutrients and avoid harmful excess. Sodium is essential for extracellular fluid balance, osmotic regulation, nerve transmission, muscle contraction, and nutrient transport, but it is continually lost and must be replaced through diet. This review proposes the Sodium Priority Hypothesis: sodium is a primary nutritional target whose consumption is enabled by converging sensory mechanisms. Sodium chloride provides the dominant route to pure saltiness, while sodium-linked umami, bitterness suppression, and taste-mixture modulation may further support sodium acquisition and food acceptance. Potassium provides an important contrast because it is essential but has weaker salty quality, bitter or metallic side notes, and does not reproduce sodium’s bitterness-suppressing effect in model savory systems. By linking sodium detection, sodium-linked umami, bitterness suppression, and food-mixture modulation with appetite and potential pre-ingestive or post-ingestive regulation, the hypothesis extends the taste-nutrition interface to sodium acquisition. The hypothesis does not reject umami as a protein-related signal, but proposes that sodium-linked umami may have dual relevance for protein food recognition and sodium acquisition. This framework generates testable predictions and may inform sodium reduction, food reformulation, and dietary guidance.

Abstract: The Upper Rhine Graben (URG) has been a key area for subsurface energy activities for decades, with a focus on hydrocarbons during the mid- to late 1900s and a gradual shift toward energy transition applications such as geothermal energy and, potentially in the future, underground hydrogen storage (UHS). This study focuses on the northern URG, where legacy hydrocarbon fields and saline aquifers provide suitable subsurface structures and infrastructure for assessing the feasibility of UHS, although data coverage varies and remains limited in some areas. Some of these were converted into underground gas storage (UGS) in past and are still in use, providing operational experience. The aim is to use data from well-understood sites to forecast UHS scenarios and assess the suitability for locations with similar geological settings. Two UGS sites, Stockstadt and Hähnlein were used as analogue fields due to their comprehensive production and storage datasets to investigate their potential for future UHS. A structural model was developed using well and seismic data, followed by dynamic simulation to investigate flow behaviour under shallow reservoir conditions (depth of 300–500 m and temperature of ~29 °C) in a porous reservoir. The model was calibrated through history matching of both earlier production and UGS phases to capture aquifer dimensions and their role in pressure support and recovery, which is particularly important in this region due to the presence of an active aquifer. Based on this validated model, two hypothetical UHS scenarios were simulated using different working gas (WG) compositions: a low-H2 case (5% H2 and 95% CH4) and a pure-H2 case, while the remaining natural gas in the reservoir was considered as cushion gas (CG). The simulation results highlight the high mobility and low viscosity of hydrogen, as the pure-H2 case shows sharper production peaks, faster decline, and increased water production compared to the low-H2 case. In contrast, the CH4-rich WG provides a more stable flow behaviour and smoother production response. Over successive cycles, the hydrogen fraction in the produced gas increases, indicating the gradual establishment of the WG zone. Based on the analogue, the potential of the nearby old gas fields was assessed using map-based estimation with Monte Carlo simulation, which indicated promising conditions for UHS development.

Abstract: Rapid growth in artificial intelligence, machine learning, and high-performance computing has substantially increased data centre rack power densities, resulting in higher heat generation and more demanding cooling requirements. As water remains widely used in many cooling systems, understanding the relationship between cooling technologies and water consumption is essential for improving cooling efficiency and sustainability. This paper presents a survey of reported water usage effectiveness (WUE) across 83 data centre entries, providing a combined dataset that links WUE with and cooling categories. The reported data shows that 23 of these data centres exceed 0.4 L/kWh, which is a sustainability target specified by the Climate Neutral Data Centre Pact for new data centres. Dry facilities employing closed-loop liquid cooling require essentially no water, while evaporative systems typically report water usage effectiveness values up to 2.5 L/kWh. WUE is determined by heat rejected through evaporative cooling towers, which is driven by heat removed from servers and transferred to the facility cooling system. One-dimensional heat transfer models are developed to provide heat transfer coefficients for different cooling mechanisms widely used for cooling servers within data centres, including air cooling, single-phase immersion cooling, direct liquid cooling, and two-phase immersion cooling. While air cooling with a low heat transfer coefficient is widely used in small-scale data centres, direct liquid and two-phase immersion cooling provide heat transfer coefficient with three orders of magnitude higher and are increasingly deployed in high-density facilities, offering a viable route to near-zero water consumption in both new and retrofitted installations. An analysis for indicative WUE based on cooling mechanisms.