Abstract: Relation extraction is an important task for structuring information from unstructured text. However, Romanian language still lacks dedicated datasets and benchmarks for this task. To address this gap, we introduce RoRED, a Romanian relation extraction dataset built by combining two complementary data construction strategies: translating existing high-quality English resources and applying distant supervision to native Romanian Wikipedia data. We leverage a powerful open-source large language model to automatically translate English examples into Romanian. For the native subset, we align Romanian Wikipedia entities with Wikidata relations to obtain naturally occurring Romanian examples. To better reflect real-world relation extraction scenarios, we also introduce synthetic negative examples generated using existing Romanian named entity recognition models. Finally, we validate the dataset by fine-tuning and evaluating multiple baseline models. Our strongest model, LUKE-RoRED, achieves a macro-F1 score of 0.8744 on the RoRED test set, demonstrating that the dataset can support relation extraction for Romanian. Overall, RoRED provides a strong first native benchmark for Romanian relation extraction.

Abstract: This project checks methods in wind power forecasting by comparing Gregorian calendar based on seasonal alignments with the vedic lunisolar calendar parallely. Rather than using timestamps like most forecasting methods, this project seeks to determine whether periodic cycles based on nature’s cosmos could reveal correlational patterns of wind activity surges and enhance accuracy. This study exploits the SOLETE dataset from SYSLAB, Denmark, which consists of 15 months of power generation alongside weather data. The dataset underwent processing with the CleanTS tool (an R package) and it was transformed into Gregorian and Vedic time frameworks. Within both time frameworks, the forecast approaches a hybrid forecasting model integrating “Variational Mode Decomposition (VMD) with Gaussian Process Regression (GPR)” was designed and assessed [11][12 ]. The Vedic forecasting approach is slightly better as it gives RMSE of 2.5519 and MAE of 2.0763, while the Gregorian forecasting approach gives RMSE of 2.6123 and MAE of 2.1424. The MAE correlation analysis over months revealed differing patterns within the two forecasting approaches with vedic giving better correlation than gregorian. This suggests that the Vedic calendar forecasting approach is better than the gregorian calendar system, which is based on natural cycles and is lunisolar, it is more accurate in capturing the chaotic signal of wind patterns than the arbitrary gregorian forecasting approach. This project helps in research, questioning the standard time representation in forecasting models which uses the gregorian timestamps and gives idea that if we put natural cycles through alternative calendar systems will it enhance the accuracy of energy predictions, potentially updating grid integration and operational planning.

Abstract: Infective endocarditis (IE) is a severe pathology with recent changes in its epidemiological profile, characterised by older patients with more comorbidities. The objective of this study is to describe the clinical and microbiological characteristics, as well as potential relations with mortality, of patients with IE in a tertiary academic centre. Material and Methods: Descriptive, retrospective, and observational study of patients over 18 years of age with a confirmed diagnosis of IE, conducted between 2021 and 2023 at the Dr Hernán Henríquez Aravena Hospital in Temuco, Chile. Biodemographic variables, risk factors, microbiology, echocardiographic findings, and complications were analysed using descriptive statistics and logistic regression models. Results: 119 patients were included (average age 60 years; 65.5% male; 28.5% rural). The most frequent risk factors were arterial hypertension (55%) and diabetes mellitus (29%). 18% were on haemodialysis (HD). Microbiological isolation was achieved in 78.1% of cases, with Streptococcus gallolyticus the most frequent isolate (16.8%), followed by Staphylococcus aureus (15.1%) and coagulase-negative Staphylococcus (15.1%). Complications were present in 69% of cases, mainly emboli (43%) and septic shock (23%)—59.6% required surgery. Global mortality was 44.5%, with a decreasing annual trend (from 58% in 2021 to 33% in 2023). Independent predictors of mortality were chronic renal failure on HD (OR 5.76; p = 0.001), heart failure (OR 3.13; p = 0.025), and septic shock (OR 3.31; p = 0.016). Conclusions: IE in this centre presents an aggressive profile and a high burden of comorbidities. The prevalence of S. gallolyticus stands out, possibly associated with high regional rurality. Mortality remains high, although it is improving.

Abstract: Quantum-like models of cognition account for order effects, conjunction and disjunction fallacies, and contextuality in human decision data using the Hilbert-space formalism without claiming literal quantum processes in the brain. Two decades of theoretical development have produced a mature mathematical apparatus, but its empirical foundation rests almost entirely on human-subject paradigms that are subject to linguistic priming confounds, demand characteristics, and replication concerns. This paper proposes that engineered brain-organoid preparations on multielectrode arrays—specifically the Cortical Labs CL1 and DishBrain-class systems—constitute the first substrate on which the structural commitments of quantum-like cognition can be tested without these confounds. I specify four operational signatures (sequential-stimulation order effects, Contextuality-by-Default cyclic-system inequalities, response replicability under non-invasive measurement, and interference effects in combined stimulation), and characterize, for each, the formal observable, the discriminating prediction against classical adaptive-learning baselines, and the substrate-level constraints imposed by current commercial wetware. The paper is offered as a theoretical specification, not an experimental protocol, and is calibrated for falsifiability rather than confirmation: a positive result on any signature would constrain classical models of organoid learning without confirming quantum-like dynamics; a fully negative result would narrow—though not conclusively delimit—the empirical scope of the quantum-like cognition program, with one natural reading being that these signatures depend more strongly on linguistic, pragmatic, or task-structured features of human-subject paradigms than on generic neural substrate dynamics.

Abstract: Deutsch's influential argument holds that the exponential speedup of quantum algorithms such as Shor's is best explained by computation distributed across ontologically real parallel branches of the wavefunction. This paper interrogates that claim by asking what minimal ontological commitments are actually required to underwrite observed quantum computational advantages. Drawing on the framework of final-state constraints and informational pruning developed in prior work, we argue that Deutsch's computational argument depends on an unpruned Everettian ontology in which all branches persist as computational substrates. We show that pruned-histories interpretations—in which boundary conditions or decoherence-based selection mechanisms restrict the space of ontologically realized branches—can preserve the empirical predictions of quantum computation while denying the parallel-universes inference. The argument requires three positive commitments: a records-based criterion for ontological commitment, a thermodynamically graded boundary between unitary computation and outcome-stabilization, and a positive account of computational speedup grounded in global Hilbert-space structure and entanglement rather than in a population of parallel worlds. We situate this result within the ontological models framework and recent observer-dependence theorems—including Frauchiger-Renner, Bong et al., and Walleghem et al.—and engage directly with Hewitt-Horsman's functionalist defense of computational branch realism. We conclude that the Deutsch argument, while rhetorically powerful, is interpretation-laden rather than interpretation-neutral.

Abstract: Surface modification of zinc oxide nanoparticles (ZnO NPs) with organosilane capping agents represents an effective strategy to control their physicochemical and biological properties. In this work, we report for the first time the use of halogenosilanes, namely (3- chloropropyl)trimethoxysilane (CPTMS), (3-bromopropyl)trimethoxysilane (BPTMS) and (3-iodopropyl)trimethoxysilane (IPTMS), for the surface functionalization of ZnO NPs obtained by chemical precipitation. Structural and morphological characterization (PXRD, TEM, SEM-EDX and FTIR) confirmed successful surface modification and revealed a significant particle size reduction from ~31 nm for unmodified ZnO to ~8 nm for BPTMS-modified ZnO (ZnO_b). The biological evaluation showed that halogenosilane-modified ZnO NPs exhibit enhanced cytotoxic activity against prostate cancer cell lines (PC3 and 22Rv1), with ZnO_b displaying the highest activity, likely associated with improved cellular uptake and increased reactive oxygen species (ROS) generation. In contrast, antimicrobial assays revealed only moderate bactericidal effects against Escherichia coli and Staphylococcus aureus at relatively high concentrations (≥1250 µg mL⁻¹), while no significant activity was observed against Pseudomonas aeruginosa, Burkholderia contaminans or Candida spp. within the tested range. These findings suggest that halogenosilane functionalization modulates the biological profile of ZnO nanoparticles by enhancing anticancer effects while also influencing microbiocidal activity, highlighting the role of surface chemistry in tuning biological selectivity. The present study supports the concept that rational surface engineering of ZnO-based nanoplatforms can be exploited to favor tumor-targeted activity over broad-spectrum antimicrobial effects, providing new perspectives for the design of application-oriented nanomaterials.

Abstract: Background/Objectives: Search for the new drugs capable of suppressing the development of drug resistance in tumor cells is extremely important for clinical practice. Cell signaling pathway inhibitors that control cell proliferation and death can be used in the complex therapy of malignant tumors. Methods: Cell cycle assay by flow cytometry, In Vitro Cell Viability Assay Cells chemosensitivity was analyzed by direct cell counting after trypan blue staining using microscope. Results: In the present work, we have shown that the combined action of doxorubicin and XMU-MP-1, the inhibitor of the MST1/2 kinase in the Hippo signaling pathway, prevents the development of drug resistance in Namalwa cells and significantly slows it down in K562 cells. and restores the sensitivity of resistant K562 cells to doxorubicin. We have shown that the combined action of doxorubicin and XMU-MP-1, causes a significant decrease in cell division rate and leads to the death of hematological tumor cells the Burkitt's lymphoma Namalwa, and myeloma K562 cells compared to monotherapy. Cell cycle analysis has demonstrated that the combined action of XMU-MP-1 and doxorubicin results in a catastrophic disruption of the cell cycle, and a significant increase in the number of cells undergoing apoptosis containing fragmented DNA. Conclusions: Thus, XMU-MP-1 can potentially be used in combination with anthracy-clines for the treatment of hematological malignancies and, in particular, the drug-resistant forms of cancer.

Abstract: This research presents a novel approach for enhancing retinal fundus images to detect anomalies better and diagnose retinal diseases. The work is divided into two stages: image representation and enhancement. Fundus images are represented in a Clifford color space, a 3D color model based on the RGB system, where colors are stored as multivectors that preserve color information and luminance. A rotation operation is applied to correct the image's illumination by adjusting brightness and color deviations, with the rotation angle and axis being critical for accurate enhancement. The gray-level axis serves as the rotational plane and the rotational angle of with a grayscale bivector axis, determined via discrete entropy (DE), optimally corrects image illumination. Following this, the green channel is extracted and enhanced using the CLAHE technique before being recombined with the other channels, and the image is reverse-rotated to its original color space. The effectiveness of the proposed method is evaluated using PSNR, DE, and SSIM on the MESSIDOR and DRIVE datasets, showing superior image quality and information preservation compared to existing methods. This enhanced technique is particularly beneficial for retinal landmark and lesion detection, improving diagnostic accuracy in retinal imaging.

Abstract: Background: Categorical TNM staging evaluates anatomical mass in a vacuum, creating artificial prognostic "cliffs" and ignoring cellular kinetics. The Ghartey et al Frugal Scalar Model introduced a kinetic paradigm to oncology—1 / ([Mass] x [Velocity]) —demonstrating that multiplying mass by proliferative velocity significantly improves prognostication. However, its initial iterations remained mathematically constrained by ordinal stage approximations. This study aims to evaluate the Ghartey Scalar against baseline TNM staging and computationally validate the integration of continuous spatial mass to mitigate these categorical limitations. Methods: Clinical data and exact continuous tumor dimensions (mm) were extracted from the METABRIC breast cancer cohort (N = 1,300). Model discrimination and probabilistic calibration were evaluated strictly against 5-year Disease-Specific Survival (DSS, 240 events). Traditional TNM staging was compared against the Ghartey et al Frugal Scalar Model, as well as continuous 1-Dimensional (winsorized diameter) and 3-Dimensional (log-volume) spatial derivatives. Model stability was verified via nonparametric bootstrapping (1,000 resamples). Results: The continuous 1-D Scalar definitively outperformed traditional categorical TNM staging, achieving an AUROC of 0.7364 and a Brier score of 0.1358. To evaluate clinical triage utility within the cohort (N=1,300; 240 events), a "Kinetic Maximum" threshold for early-stage disease was established using the largest, fastest-growing tumor within the Stage I boundary (RSD=1.0, Diameter=2.0 cm, Velocity=3.5; TmdRxResCoef (%) cut off = 14.28%). At 5 years, this threshold demonstrated a Negative Predictive Value (NPV) of 89.3% and a Sensitivity of 75.8% for predicting disease-specific death. Furthermore, longitudinal analysis out to 10 years revealed a significant stage-migration effect, as the Positive Predictive Value (PPV) of the Red Zone increased from 32.8% to 46.0%, confirming that the scalar successfully flags intrinsic biologic momentum prior to delayed clinical failure. Conclusion: Substituting ordinal tumor stage with exact continuous spatial dimensions entirely eliminates the stage-cliff. The Ghartey Frugal Scalar provides an exceptionally safe exclusionary triage threshold (NPV ~90%) while isolating high-velocity phenotypes that require immediate systemic velocity-braking.

Abstract: Federated learning (FL) is an attractive learning paradigm for privacy-preserving edge intelligence because it allows distributed devices to train a shared model without moving raw data to a central server. This feature is especially relevant to 5G and emerging 6G networks, where ultra-low latency, dense connectivity, and edge-native computing are expected to support large-scale intelligent services. Nevertheless, practical FL deployment remains difficult in heterogeneous wireless environments because client devices differ in processing capability, battery budget, data volume, and channel quality. These differences create stragglers, increase round latency, and waste scarce communication resources when client participation is scheduled naively. This study develops a deployment-oriented framework for dynamic client selection and resource allocation in heterogeneous edge environments. We formulate each FL round as a latency-constrained optimization problem that jointly captures computation time, uplink transmission time, and minimum participation requirements. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that ranks clients using a weighted score combining computational capability, channel quality, and a fairness term, followed by a greedy radio-resource allocation procedure that prioritizes the largest marginal reduction in estimated completion time. Using the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces average round-completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. The results indicate that lightweight joint scheduling can substantially improve wall-clock efficiency for FL over heterogeneous 5G/6G edge networks.

Abstract: Although the goal of a “sustainable” urbanism has generated an impressive array of international frameworks and declarations, systemic progress remains elusive. A prior paper by the author identified "lock-in" as a central cause: the economic incentives, professional standards, codes, and institutional feedback structures that reinforce un-sustainable patterns of urban development despite stated commitments to reform. This paper advances that diagnosis by asking what sustains the lock-in itself, and what structural intervention can address it at the root. We argue that the answer lies in a fundamental deficit in the feedback architecture governing urban development — a systematic failure to account for two categories of capital on which human welfare de-pends: natural and resource capital, whose depletion standard metrics render invisible, and human and value-added capital, including the built public realm and the economies of place that markets systematically undersupply. Standard welfare-economic instru-ments, including Pigouvian taxes, address this at the level of price signals but cannot resolve it there, because multiple forms of goods, which we term “polycapital”, are structurally interrelated and resist single scalar remedies. The paper advances two complementary conclusions: first, that a generative modeling methodology — capable of encoding the interrelated, multi-scale character of polycapital structures — is a necessary precondition for adequate institutional response, and that pattern language methodology provides this capacity; and second, that transactional mechanisms going substantially beyond Pigouvian instruments — non-linear, asymptotic, and per-capita in structure — represent a necessary but largely open research frontier.

Abstract: Professional development for in-service teachers has long assumed synchronous participation as the pedagogical norm, positioning asynchronous formats as pragmatic concessions to logistical constraint. This Perspective Paper develops the notion that asynchronous-first design is not a compromise but an ethical imperative, grounded in recognition of the structural realities of teachers' professional and personal lives. Drawing on scholarship on interrupted academic trajectories, time poverty, equity in online learning, and the political economy of professional learning, the article advances three interrelated claims: that synchronous participation carries hidden costs disproportionately borne by practitioners navigating care responsibilities and precarious employment; that asynchronous formats enable forms of cognitive engagement that synchronous delivery constrains; and that access to professional learning should not be conditional on temporal availability. Implications for accreditation policy, micro-credential design, and the selective use of optional synchronous moments are discussed. The article repositions asynchronous-first design as evidence of institutional commitment to equity rather than accommodation of constraint.

Abstract: Triple-negative breast cancer (TNBC) lacks effective molecular targets, leading to poor prognosis. Previous computational methods to identify targets have suffered from low druggability, high complexity, and lack of robust validation. We propose a hybrid methodology combining Boolean network modeling with semidefinite programming (SDP) to analyze a TNBC cell line network. The resulting therapeutic pair underwent a multi-level validation framework, including Boolean simulations, statistical uncertainty quantification (bootstrap), sensitivity analysis, and independent verification by AlphaGenome v2, a deep learning model from Google DeepMind. Our analysis identified TK1 and VIM as a robust therapeutic pair. Dual inhibition achieved 99.03% similarity to the apoptotic state with a 95% confidence interval of [98.79%, 99.26%], and was statistically superior to alternative pairs (p < 0.001). The selection remained optimal across all tested model parameters, demonstrating high robustness. Importantly, the pair has full druggability because both targets have available specific inhibitors. AlphaGenome v2 validation in normal mammary tissue revealed that TK1 exhibits moderate expression while VIM shows low baseline expression. This differential pattern, combined with strong VIM upregulation in the mesenchymal-like TNBC phenotype, supports the synergistic mechanism of the dual-target strategy. Our methodology identified TK1-VIM as a high-confidence and druggable therapeutic pair for TNBC with strong biological plausibility. This work provides a clinically actionable strategy and establishes a new benchmark for computational rigor in drug target identification.

Abstract: Background: Interactions between the intestinal mycobiome and systemic metabolic regulation remain insufficiently characterised. While fungal colonisation is common in diabetes, its role in dynamic, event-driven dysglycaemia has not been defined. In particular, the impact of fungal morphological plasticity and biofilm formation under hyperglycaemic conditions remains unclear. Case Presentation: We report a 43-year-old female with a 25-year history of autoimmune insulin-dependent diabetes mellitus, characterised by refractory hyperglycaemia and chronic passage of high-volume, gelatinous, mucus-dominant intestinal material. Microbiological analysis confirmed Candida albicans with both yeast and hyphal forms. Episodes of evacuation were consistently preceded by extreme hyperglycaemia (>500 mg/dL) and followed by rapid declines to hypoglycaemic levels (<60 mg/dL). A 7-day observational log demonstrated reproducible glucose reductions ranging from 247 to >465 mg/dL per episode. Despite insulin therapy, glycaemic control remained unstable and was associated with systemic manifestations including pruritus, dehydration, and neuroenteric symptoms. Mechanistic Interpretation: We propose that chronic intestinal accumulation of a mucus-integrated fungal biofilm functions as a dynamic immunometabolic compartment. Hyperglycaemic conditions likely promote Candida albicans yeast-to-hypha transition via glucose-sensitive pathways, including cAMP–PKA and MAPK signalling, facilitating biofilm formation and persistence. Biofilm-associated β-glucans and mannans activate pattern recognition receptors such as Dectin-1 and Toll-like receptors, driving NF-κB and JNK-mediated inflammatory signalling. This results in inhibitory serine phosphorylation of IRS-1, impaired PI3K–AKT signalling, and functional insulin resistance. Accumulation of the biofilm amplifies this state, while threshold-triggered evacuation abruptly reduces inflammatory signalling, restores insulin sensitivity, and unmasks the pharmacodynamic effect of circulating insulin, resulting in rapid glucose decline.Conclusion: This case supports the proposal of a candidate syndrome, Candida-Associated Gut Biofilm–Driven Refractory Dysglycaemia Syndrome (CGB-RDS), characterised by reversible, compartment-driven insulin resistance and threshold-dependent metabolic switching. These findings highlight a previously unrecognised gut–mycobiome–metabolic axis and warrant further investigation into biofilm-mediated regulation of systemic glucose homeostasis.

Abstract: During the measurement process, load cells are susceptible to temperature variations, which can significantly degrade measurement accuracy. To address this problem, this paper presents a temperature compensation method based on an improved neural network. First, the mechanism of sensor temperature drift is analyzed from a thermodynamic perspective. Subsequently, an Improved Honey Badger Algorithm (IHBA) is developed to optimize the initial weights and biases of a Back-Propagation (BP) neural network, aiming to enhance global search capability and convergence stability. To validate the proposed method, a dedicated calibration experimental system was constructed, and temperature-dependent output data were collected over a range of 0 °C to 60 °C. Comparative experiments with conventional methods, including IMA-BP, PSO-BP, standard BP, and polynomial fitting, were conducted. In addition, an ablation study was performed to verify the effectiveness of the proposed improvements. The results demonstrate that the IHBA-BP model achieves superior compensation performance. The temperature drift coefficient and sensitivity temperature coefficient are reduced by 86.6% and 95.86%, respectively. The proposed method shows strong potential for improving measurement accuracy of load cells under varying temperature conditions and provides a practical solution for industrial sensor calibration applications.

Abstract: The connectivity paradox of contemporary platforms — unprecedented technical connectivity alongside rising loneliness, passivity, and erosion of deliberative public space — has been diagnosed as a problem of attention, design, or scale. We argue it is a symptom of a more fundamental shift: the architectural removal of the human social other from communicative circuits. This paper introduces directionality as a formal variable that captures the presence, absence, and configuration of the human other, and traces its variation from bidirectional social graphs through unidirectional interest graphs to Zero-Directionality, where the user interacts with a synthetic partner alone. Drawing on Luhmann’s social systems theory, Simmel’s analysis of dyadic and triadic forms, and the Latour–Verbeek tradition of technological mediation, we show that zero-directionality is a structural threshold rather than a point on a continuum. When the human other is removed, the Luhmannian third selection collapses, the Simmelian dyad faces a binary choice, and the social form bifurcates into two divergent trajectories. In the Inverted Loop (−1SC), the machine absorbs the structural position of the other, the user becomes operand in a self-referential circuit, and agency contracts from authorial to inhibitory. In the Triadic Mesh (3SC), AI mediates between humans rather than replacing them, preserving human connection while transforming its operation. We propose three diagnostic tests: Adaptation Loop, Agency Topology, Bounding Variable. These tests determine which regime a given system instantiates, and apply them across major consumer platforms. The framework reframes contemporary debates about AI and democracy, autonomy, and the right to the future tense as questions about which directionality regime a given AI-mediated environment instantiates — a question of design, not destiny.

Abstract: Background: E-waste is one of the fastest-growing waste streams globally, with India emerging as a major contributor. Despite existing regulatory frameworks, safe e-waste management remains suboptimal, particularly in vulnerable urban populations. This study aimed to assess knowledge, attitude, and practices (KAP) related to e-waste management and to examine the knowledge–practice gap in an urban slum of Delhi. Methods: A community-based cross-sectional study was conducted among 425 adults in an urban slum of Delhi using a stratified random sampling technique. Data were collected using a pretested, and semi-structured questionnaire assessing KAP domains. Multivariable linear regression, Spearman correlation, and mediation analyses were performed to identify determinants and pathways influencing practice. Results: Only 20.24% of participants demonstrated adequate knowledge, 43.29% had a positive attitude, and 11.29% reported good practices. Higher education was associated with better knowledge (p = 0.002), more positive attitudes (p = 0.001), and better practice scores ( p = 0.013). In hierarchical regression analysis, Knowledge emerged as a strong independent predictor of practice (β = 0.48, p < 0.001) and remained significant after further adjustment for attitude (β = 0.45, p < 0.001). Correlation analysis demonstrated significant positive associations between knowledge and attitude (ρ = 0.52), knowledge and practice (ρ = 0.38), and attitude and practice (ρ = 0.33) (all p < 0.001). Mediation analysis revealed that knowledge had both direct and indirect effects on practice through attitude, indicating partial mediation. Conclusion: The present study found that the levels of knowledge, attitude, and practices related to e-waste management remained low among residents of an urban slum in Delhi. While knowledge plays a central role in influencing behavior, the absence of accessible disposal systems and limited dissemination of policy information hinder translation into safe practices. Strengthening community-based awareness programs alongside improving and is essential for effective e-waste management.

Abstract: This paper explores how artificial intelligence (AI) transforms the foundations of strategic management theory. While traditional debates have centered on industry structure and resource-based perspectives, AI introduces a theoretical discontinuity that challenges assumptions about cognition, resources, and firm boundaries. We examine five influential streams: Behavioral Strategy, Microfoundations, Ecosystems and Platforms, Stakeholder Resource-Based View, and Strategy-as-Practice, to assess how AI reshapes their core premises. Our analysis reveals that AI creates hybrid cognitive architectures, embeds algorithmic actors into microfoundations, reconfigures ecosystems around foundation models, redistributes resource control to stakeholders, and alters strategizing practices through continuous, AI-augmented processes. The paper concludes with an agenda for empirical research, emphasizing multilevel analysis, algorithmic governance, and ethical considerations in an AI-infused strategic landscape.

Abstract: This article analyzes the impact of agricultural support measures on food import dependency for a 52-country sample from 1985 to 2017 using databases from the World Bank, the Center for Systemic Peace and the Groningen Center for Growth and Development. We apply a continuous treatment effect and control for endogeneity to describe the extent of food import dependency in response to domestic support for agriculture. Our results show strong evidence of heterogeneous impacts on aggregate food import dependency at different levels of political aid intensity. Estimates of dose-response functions confirm that countries providing moderate support to agriculture tend to do better in reducing their use of agri-food imports.

Abstract:

Okra (Abelmoschus esculentus) stems constitute an abundant lignocellulosic biomass with significant potential for sustainable composite reinforcement. In this study, okra fibers were extracted using biological retting, alkaline treatment (1-7.5 wt% NaOH), and combined extraction processes. The influence of extraction conditions on the physicochemical, mechanical, thermal, and structural properties of the fibers was investigated. FTIR analysis revealed the progressive removal of hemicellulose and lignin after alkaline treatment, while XRD results showed an increase in cellulose crystallinity. Optical microscopy observations revealed progressive fiber separation and cleaner surface morphology after alkaline treatment. Fiber density increased with NaOH concentration, whereas water absorption and moisture regain decreased due to the reduction of hydrophilic amorphous components. Mechanical properties, particularly tensile strength and Young’s modulus, improved under moderate treatment conditions but decreased under severe alkaline conditions because of partial cellulose degradation. The optimal treatment condition (1 wt% NaOH for 60 min) provided the best balance between delignification, structural preservation, and mechanical performance. These results demonstrate that okra fibers are promising lightweight reinforcements for sustainable bio-composite and technical textile applications.