Abstract: Urban mobility planning in smart cities requires sophisticated simulation tools, yet their complexity often creates a technical barrier for non-expert stakeholders. This paper presents a novel architecture that integrates generative artificial intelligence with digital twin technology to create an accessible and robust decision-support system. The framework employs a conversational AI agent based on Gemini 2.5 Flash Lite to interpret natural language intentions and translate them into validated simulation parameters. A critical safety layer, built using Pydantic, ensures that the agent’s stochastic outputs adhere to strict technical schemas and urban logic before execution. The underlying digital twin, developed with SimPy, NetworkX, and OSMnx, features a multi-source data integration strategy that includes demographic density (INE), tourism activity (ISTAC), and high-resolution traffic statistics (TomTom) to calibrate vehicle behavior. The architecture was validated through a Technology Readiness Level (TRL) 4 proof-of-concept in Las Palmas de Gran Canaria, simulating multimodal scenarios including buses, the future MetroGuagua (BRT), and pedestrian flows. Results demonstrate a 95.99% success rate in intent recognition and configuration mapping, with end-to-end execution times under 20 minutes for a 19-hour simulated day. This study demonstrates that LLM-driven orchestration, coupled with automated data pipelines and a decoupled microservice architecture, can democratize access to urban simulation, fostering more inclusive, agile, and evidence-based smart city governance.

Abstract: We propose a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the simultaneous bias correction of five hydroclimatic variables derived from the NASA POWER satellite product: minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (HR), solar radiation (Rad), and precipitation occurrence (Pbin). The GMFAMM extends the univariate functional framework by incorporating a shared latent Gaussian process Λ0i(t) that captures cross-variable thermodynamic dependence. A systematic experimental grid of more than 200 model configurations across four distributional families (Gaussian, Gamma, Poisson, Binomial), two effect structures (linear and smooth P-splines), and four nested covariate sets is evaluated on a strict chronological 70/30 hold-out – seven training years (2016–2022) and three hold-out years (2023–2025) – to identify the optimal marginal specification for each variable. The value of joint modelling is quantified through a two-stage cross-residual approximation to the GMFAMM shared latent process, which constitutes a conservative lower bound on the gains achievable by the full simultaneous model: out-of-sample RMSE is reduced by 53% for Tmin, 38% for Tmax, and 51% for relative humidity relative to the independent GAMM baseline. These gains are physically interpretable through the Clausius-Clapeyron thermodynamic coupling documented in the residual cross-correlation analysis. The trained model artefacts are deployed in ColClim, an open-access R Shiny web application that queries the NASA POWER API and the Open-Meteo forecast service for any user-selected location in Colombia, applies the GMFAMM correction pipeline, and delivers both historical bias-corrected time series and short-range (1–16 day) forecasts across the five variables.

Abstract: As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats.

Abstract: Forecasting crash severity is critical for emergency response, infrastructure spending & risk communication. Although machine learning has been widely applied to this problem, three shortcomings prevent its practical application: poorly calibrated probability scores, SHAP-based explanations whose faithfulness has not been verified, and models never tested in different regions. The proposed framework, termed SAE-XCrash (Safety-Aware and Explainable Crash Severity Prediction), considers all three using two public datasets - US-Accidents (7.0 million records, 2016-2023) and UK STATS19 (approximately 1,010,000 records, 2016-2022). Notably, the US-Accidents severity label refers to traffic disruption duration, not injury outcome, and results should be interpreted accordingly. Previously unknown label-schema drift led to a revised binary target with Severity 4 as only the positive class; strict temporal splits are used throughout. FIVE classifiers are compared. Post hoc isotonic Calibration reduces expected calibration. Error by 97.3 percent while maintaining negligible discrimination loss. A four-step SHAP audit confirms that explanations genuinely reflect model behavior: deletion-based per-budget faithfulness gaps exceed the 0.05 threshold at every feature budget (min gap=0.066, p<0.0001), though the aggregate trapezoidal AUC is borderline due to scale compression at AUPRC≈0.13, and insertion gaps are statistically significant at more than ten percent of features. Explanation stability holds under conservative noise levels but degrades at realistic perturbation magnitudes mainly in spatially sparse geohash cells. In a three-tier cross-dataset transfer experiment - zero-shot, recalibration and full retraining - spatial memorization is the major generalization barrier while temporal features transfer smoothly between jurisdictions.

Abstract: Objective: To synthesise the pathobiology of cortical spreading depolarization (CSD) and critically appraise current and emerging pharmacological strategies specifically targeting migraine aura prevention. Background: Migraine with aura affects 25–30% of patients, and the aura phenomenon remains a substantial unmet preventive need. Calcitonin gene-related peptide (CGRP) monoclonal antibodies do not readily cross the blood–brain barrier and frequently fail to suppress CSD, the neurophysiological substrate of aura.Methods: A literature search of PubMed, Embase, and the Cochrane Library (inception through January 2026) identified studies on CSD pathophysiology, preclinical CSD suppression, and clinical efficacy of candidate agents. Evidence quality was assessed with GRADE; risk of bias with Cochrane RoB 2 (RCTs) and ROBINS-I (observational); narrative synthesis followed SWiM. De novo quantitative estimations (post-hoc power analyses, sample-size projections, worst-case sensitivity analyses) were used as methodological tools, not as original empirical data. Results: CSD pathogenesis is organised into four phases: pre-CSD vulnerability, initiation, glial propagation, and neuro-inflammatory transduction. Lamotrigine and memantine target initiation and have the most advanced clinical evidence; both lack aura-specific RCTs. A 2024 network meta-analysis ranked memantine favourably (50% responder rate OR 5.58, 95% CI 2.41–12.92) but no contributing trial stratified by aura. An a priori sample-size calculation indicates 214 enrolled patients (170 evaluable; NNT≈4.9; n/(1−d) for 20% attrition) for a definitive aura-specific memantine RCT. Tonabersat—a Cx36/Cx43 gap-junction modulator—reduced aura attacks from 3.2 to 1.0 per 12 weeks in Phase 2; a worst-case intention-to-treat sensitivity analysis confirms that this signal survives even 16.6% unaccounted attrition. Spironolactone (pannexin-1 inhibition) and amiloride (ASIC1a) remain preclinical or pilot-stage. Tissue-selective KATP antagonists (Kir6.1/SUR2B) and the anti-PACAP-38 antibody Lu AG09222 represent the most promising pipeline agents. Conclusion: The therapeutic gap for migraine aura prevention reflects correctable methodological choices, not a lack of biological tractability. Four mechanism-based drug classes—NMDA-receptor antagonists, pannexin-1 inhibitors, gap-junction modulators, and KATP antagonists—offer entry points for aura-specific prevention. Adequately powered, aura-enriched RCTs with validated CSD biomarkers (DC-EEG co-registered against electrocorticography; neuron-derived extracellular vesicles) and pre-specified falsifiability thresholds are now the rate-limiting step. Seven testable methodological predictions are proposed.

Abstract: We present a systematic experimental investigation of the primary breakup of a planar liquid film subjected to high-speed co-flowing gas streams. A water film of thickness D_ℓ ≈ 150 μm is produced from a symmetric airfoil lip and sheared on both sides by compressed air. Interfacial dynamics were recorded with a high-speed camera and analyzed to extract transverse wavelengths, rupture modes, and their dependence on operating conditions. We find that the transverse wavelength λ_tra decreases strongly with increasing gas speed and that, for a given dynamic pressure ratio M = (ρ_gV²_g )/(ρ_ℓV²_ℓ ), different absolute combinations of V_g and V_l produce markedly different λ_tra. These observations indicate that gas-shear intensity and the gas flow instability modes (vortex shedding) control the breakup of the liquid film; the liquid inflow plays a secondary role under our conditions. The results provide experimental benchmarks for model validation and suggest routes to tune atomizer performance via gas-side control.

Abstract: Anemia management in chronic kidney disease patients is a significant challenge for modern healthcare professionals. Anemia in chronic kidney disease patients has multiple causes which include erythropoietin deficiency, abnormal iron metabolism, resistance to erythropoietin signaling, bone marrow suppression, blood loss, inflammation, nutrition deficiencies and oxidative stress. Vadadustat (a stabilizer of hypoxia-inducible factor (HIF) is indicated for the treatment of symptomatic anemia associated with chronic kidney disease (CKD) in adults on chronic maintenance dialysis. Evidence-based pharmaceutical care services are of great importance for chronic kidney disease patients because they provide safe, effective and economic care for patients. In the present article, we outline the most important pharmaceutical aspects that may affect the efficacy and safety of drug therapy with vadadustat and other HIF stabilizers. We conclude that evidence-based pharmaceutical care is one of the criteria that promotes management of vadadustat therapeutic efficacy and safety. Such an approach will contribute to improving patient adherence to treatment and, consequently, quality of life. Special attention is paid to structure-derived side effects of widely used HIF stabilizers, including their advantages and disadvantages. Based on all available safety and efficacy data for vadadustat, the overall risk-benefit profile remains positive for the approved indications for use.

Abstract: Whether the vibrational picture of elementary particles; now nearly half a century old; determines the spectrum we observe has remained without a settled answer. We close the question. At a fixed compactification of the heterotic string, the entire low-energy content follows from the geometry of the chosen Calabi–Yau manifold. Across compactifications, no internal rule singles out the one nature uses: the choice depends on input from outside the framework, of a kind we make explicit. Once that input is supplied, masses and mixings are computable; without it, no derivation is possible. Background. The picture of elementary particles as vibrational modes of a string is forty years old, and a steady catalogue of explicit heterotic constructions has shown that the observed gauge group and three-family chiral content can be reproduced. What no construction has settled is the converse question: does the resonance–particle correspondence, taken as a classification programme, determine the observed spectrum, or merely admit it? The literature has answered case by case; a framework-level resolution has been absent. Methods. We separate the framework into an unconditional mathematical layer (index theorem, Dolbeault cohomology, slope-polystability, Calabi–Yau metric existence, unobstructedness, BRST classification, anomaly cancellation) and a selection layer (choice of compactification datum). The achievable range of topological and cohomological selectors over the resulting landscape is computed by Kodaira–Spencer deformation theory and the special geometry of the complex-structure moduli space. Results. At fixed admissible datum the perturbative massless spectrum is fully determined by bundle cohomology and representation branching (positive direction). No topological or cohomological rule singles out the observed vacuum; the obstruction is a nontrivial class on a positive-dimensional moduli component (negative direction). A closure-completeness theorem unifies the two directions; universality, maximality, rigidity, stable-obstruction, categorical-impossibility, and quantitative-dimension theorems show the result holds for every framework whose predictive data is cohomological and is not improvable from within. A corrected audit lemma, with its converse, identifies the singleton condition any external selector must satisfy. Five residual closure theorems — selector completion, internal no-go, fixed-vacuum Yukawa computability, algebraic exotic lifting with post-stabilisation threshold, and ensemble finiteness — reduce the residual problems to one external axiom.

Abstract: Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates CYGNSS Level 3 soil moisture in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023. Extended Triple Collocation was applied to CYGNSS, SMAP Enhanced Level 3, and ERA5-Land anomalies. Quadruple Collocation then used ESA CCI ACTIVE as a fourth product to quantify CYGNSS-SMAP error dependence. The standard SMAP-inclusive configuration gives CYGNSS a correlation with unknown true soil moisture of r = 0.425, an error standard deviation of 0.036m³m⁻³, and a signal-to-noise ratio of −6.56 dB. Quadruple Collocation identifies a CYGNSS-SMAP cross-error correlation of 0.325 and reduces the SMAP-independent CYGNSS estimate to r = 0.386, indicating that SMAP-inclusive validation overstates retrieval skill. Performance is weakest under dry soils (r = 0.331), where drought detection is most important, and location-level ETC convergence fails during Harmattan conditions as anomaly variance collapses. Skill is higher over cropland (r = 0.447), shrubland or grassland (r = 0.455), and moderate precipitation conditions (r = 0.630), but lower over tree cover (r = 0.342). These findings show that uncorrected CYGNSS Level 3 soil moisture is not sufficient for standalone year-round drought monitoring in Guinea savanna agriculture. Its value is strongest in bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality.

Abstract: Smart cities are being advocated to solve the excessive urbanization. They are supposed to be more efficient, more connected and more sustainable. However, long-term sustainability cannot be achieved in a real situation unless we seal the loopholes that still prevail in our current efforts. The main issues are: integrating renewable-energy solutions to reduce carbon; applying the principles of the circular economy to consume fewer resources and produce less waste; and ensuring that every resident of urban areas is able to access available urban resources and services that are usually not evenly distributed. In this article, the sustainability challenges are gathered together and demonstrate how they can be addressed by use of technology, provided that the technology is supported by good governance and accepted standards. A city that strategically connects its smart-city strategies to the United Nations Sustainable Development Goals, as well as to the appropriate national, industry, and IEEE standards, has left its technology-centered viewpoint, where people, the environment, and long-term resilience are prioritized. Ultimately, to head in the right direction and create the sustainable urban futures, we need to combine the latest technology with equitable policies and plans that would respond to climate change. This is when smart cities will bring a sustainable advantage to the lives of people and the planet.

Abstract: We report the first successful synthesis of millimeter-sized single crystals of high-entropy perovskite manganites with the composition (La_0.25Pr_0.25Sm_0.25Gd_0.25)_1-xCa_xMnO₃ (x = 0.3, 0.4, 0.5). Single crystals exceeding 2 mm in size were grown via a flux method using a PbF₂/PbO/B₂O₃ system. The X-ray diffraction patterns exhibit only (0k0) series reflections, indicating strong preferred orientation and high-quality single-crystal character. Scanning electron microscopy reveals dense, grain-boundary-free surfaces. Energy-dispersive X-ray spectroscopy elemental mapping shows uniform distribution of all constituent elements without detectable segregation or secondary phases, confirming the formation of a true high-entropy solid solution in single-crystal form. To the best of our knowledge, based on the publicly searchable literature, this is the first report of bulk single-crystalline high-entropy perovskite oxide. This breakthrough provides a much-needed single crystal experimental platform for systematically investigating the intrinsic magnetotransport mechanisms of compositionally complex strongly correlated oxides, free from grain-boundary interference.

Abstract: A green and facile hydrothermal method for synthesizing of copper-doped hydroxyapatite (Cu-HA) nanowires was reported. In this research, oleic acid was completely replaced by food-grade peanut oil as both the solvent and template agent for the synthesis of hydroxyapatite nanowires (HAW). Results confirm that a uniform one-dimensional nanowire morphology Cu-doped HAW was successfully synthesized. In vitro cytotoxicity tests confirm that the material exhibits good biocompatibility and supports normal cell growth. This study presents a viable route for the green and multifunctional fabrication of HA nanowires.

Abstract: Moisture damage in buildings has conventionally been discussed mainly in relation to winter condensation in cold climates. In hot-humid buildings, however, deterioration develops under different boundary conditions, including persistently warm and humid outdoor air, frequent rainfall, air-conditioning operation, air leakage, and limited drying after wetting. Climate change is increasing atmospheric moisture loading and weakening nighttime recovery. These changes make hot-humid moisture risks more consequential not only in established hot-humid regions, but also in regions shifting toward more persistently humid climates. This review examines moisture damage in hot-humid buildings as a coupled problem linking climate change, building-envelope moisture response, risk assessment, microbial implications, and building adaptation. Representative scenarios include biological contamination on exterior surfaces, summer condensation and moisture accumulation within envelope assemblies, localized dampness at indoor surfaces and behind furniture, moisture stagnation in semi-enclosed spaces, and material deterioration or performance loss. These phenomena are interpreted not as isolated defects, but as manifestations of drying deficit. The review discusses climatic drivers, building-physics mechanisms, and major moisture and mold risk indices, including the Fungal Index (FI), the VTT Mold Index, isopleth-based approaches, Mold Resistance Design (MRD), and the Dose-Response Simple Isopleth for Mold (DR-SIM). It also highlights implications for envelope design, retrofit, ventilation, dehumidification, and operation. Overall, moisture damage in hot-humid buildings is best understood as the outcome of climate-driven drying deficit.

Abstract: Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false positive rates across modalities, and perform a cost-benefit analysis fitting contamination scenario with the most cost effective detection method. All datasets are publicly released (https://zenodo.org/records/19100554) along with an interactive web-map (https://staging.dmsamfvv1bhon.amplifyapp.com/) to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection.

Abstract: Background/Objectives: This study assessed the impact of different postoperative antithrombotic strategies on graft patency and overall survival after open posterior repair of popliteal artery aneurysms (PAAs). Methods: A retrospectively maintained dataset of consecutive PAAs electively treated in 40 centers between January 2010 and December 2023 was analyzed. The study included patients undergoing elective open posterior repair. Primary outcomes were primary patency and overall survival according to postoperative antiplatelet or anticoagulant therapy. Secondary outcomes included secondary patency and major adverse cardiovascular events (MACEs). Kaplan–Meier analysis, log-rank testing, and Cox regression models were applied. Results: Overall, 638 patients were included. The cohort was predominantly male (96%), with a median age of 70 years. At 30 days, one death (0.2%) and seven MACEs (1.1%) occurred. Postoperative therapy was not associated with 30-day primary patency or MACEs. At a median follow-up of 30 months, overall survival was 90.3%. Kaplan–Meier analysis showed comparable overall survival across antithrombotic regimens, with a significant difference only between patients receiving combined antiplatelet and anticoagulant therapy and those receiving other regimens. No differences emerged between vitamin K antagonists (VKA) and direct oral anticoagulants (DOAC), nor between single or dual antiplatelet therapy and anticoagulation. Multivariable Cox regression showed combined therapy was associated with poorer overall survival (HR 0.77). Primary and secondary patency were also similar among regimens except for combined therapy. Conclusions: Postoperative medical therapy was not associated with 30-day outcomes. During long-term follow-up, overall survival and graft patency were lower in patients receiving combined anticoagulant and antiplatelet therapy.

Abstract: Critical ventilation velocity is crucial for smoke control in tunnel fires, yet its behavior in tunnels with unconventional cross-sections remains inadequately quantified. This study numerically investigates the critical velocity in a full-scale, 1000-m-long semi-circular tunnel using Fire Dynamics Simulator (FDS). A systematic parametric analysis was conducted to evaluate the effects of fire heat release rate (HRR, 4-10 MW), cross-sectional geometry (semi-circular vs. three arched sections of equal area), and longitudinal slope (-1% to +2%). The critical velocity was determined using a successive approximation method, validated against multi-criteria safety thresholds including smoke back-layering length, upstream temperature, and visibility height. Results demonstrate that HRR is the dominant factor, with critical velocity increasing from 2.2 to 2.7 m/s. More importantly, cross-sectional shape exhibits a significant, non-monotonic influence; the streamlined semi-circular arch requires a lower critical velocity (2.2 m/s) compared to arched sections (2.4-2.6 m/s) of the same area, attributed to reduced flow resistance and a more coherent ceiling jet. Within the studied range, the effect of slope is minor compared to HRR and geometry, showing only a slight decrease in critical velocity for uphill gradients. These findings provide quantitative insights into optimizing ventilation design for semi-circular tunnels, highlighting that an aerodynamically favorable shape can reduce the required longitudinal airflow, thus balancing safety and energy efficiency.

Abstract: The challenge of radioresistance in radiotherapy is currently tackled by introducing new radiotherapy facilities with high-quality of ionizing beams, high-precision of radiation delivery to tumour, and optimized treatment plans. This strategy is further enhanced by the development of new therapeutic methods for suppressing radioresistance in tumours of cancer patients by combining radiotherapy with chemo-, immuno-, and targeted therapies tailored to patients’ molecular profiles. As a result of numerous preclinical and clinical trials of the combination therapy, the primary molecular mechanisms, driving an increase in radiosensitivity, and the key cellular signalling pathways responsible for radioresistance were identified. One of the established radioresistance mechanisms involves adaptation of cancer cells to an elevated levels of reactive oxygen species (ROS) by activating antioxidant systems (AOS) of cellular protection and survival. Since radiotherapy mainly relies on ROS production that damages DNA, causing cancer cell death, activation of the AOS can mitigate radiotherapy effectiveness. Therefore, suppressing the AOS and its associated adaptation mechanisms may increase tumour radiosensitivity and enhance treatment outcomes. In this review, we discuss the role of one of the key components of the cellular AOS which is under the control of the NRF2 transcription factor (nuclear erythroid factor 2) – a master regulator of cellular redox balance that protects cells from oxidative stress during radiotherapy by governing expression of a battery of antioxidant enzymes. We first outline the molecular mechanism of the redox-sensitive NRF2 AOS and its activation in response to the increased ROS levels following irradiation. We then evaluate experimental and clinical evidence regarding NRF2 activation in various cancer cells and tumours exposed to ionizing irradiation. Furthermore, we discuss results of numerous experimental and clinical investigations demonstrating that suppression of the NRF2 AOS enhances radiosensitivity of various cancers and improves radiotherapy outcomes. Collectively, these findings confirmed the potential of combining radiotherapy with targeted therapy aiming at the suppression of the NRF2 AOS. In this combination therapy NRF2 inhibitors act as radiosensitizers that promote overcoming radioresistance due to extra ROS accumulation and oxidative stress induction in cancer cells by inhibition of the NRF2-dependent antioxidant responses to radiotherapy.

Abstract: The global energy crisis drives the search for sustainable biomass resources. Microalgae, particularly Chlorella vulgaris, represent a promising third-generation feedstock for bi-ochar and biofuels. However, detailed kinetic schemes for its slow devolatilization are still scarce. This work compares the thermogravimetric behavior of commercially Chlorella vulgaris with data reported in the literature under identical experimental conditions and develops a multi-stage kinetic scheme using model-free methods and simultaneous global optimization. A complete set of kinetic parameter is provided in conjunction with a mass wights in order to close the reaction scheme. Biological composition of microalgae was experimentally determined resulting in 21.20, 59.30 and 19.50% for carbohydrates, protein and lipids. Thermogravimetric (TG/DTG) analyses were conducted with 5, 10 and 20 °C/min heating rates. Activation energy distribution was obtained through isoconversional model-free methods (Fried-man, FWO, KAS and Starink). A parallel multi-stage kinetic model was subsequently optimized globally against the experimental data to determine the complete kinetic tri-plet (E, A, n). TG/DTG profiles exhibited in general a good agreement with the literature refer-ence in the number and the temperature of features, peaks and shoulders, however different in intensity probably due to the different amount of biological components, carbohydrates, proteins and lipids. The multi-stage model achieved excellent fitting quality accounting for 5 reactions. Activation energies for the principal devolatilization stages ranged from 140 to 220 kJ/mol, while ln(A) values lay between 20 and 35 s⁻¹. The findings an results provided by this study is considered useful for the community con-tributing with discussion and a robust kinetic scheme suitable for example for slow pyrolysis process simulation.

Abstract: Autoregressive large language models (AR-LLMs) have achieved remarkable success, but their inherently sequential decoding process remains a fundamental bottleneck for efficient inference. Diffusion large language models (DLLMs), with bidirectional modeling and parallel token generation, offer a promising alternative to break this token-by-token limitation. Yet despite rapid progress, the practical inference efficiency of current DLLMs remains unclear. From a verification perspective, this survey establishes a systematic taxonomy of existing acceleration methods, benchmarks representative techniques under a unified experimental setting, and further evaluates strong strategy combinations to quantify the gap between mainstream DLLM inference methods and state-of-the-art AR baselines. Specially, the overall analysis highlights that the parallel decoding efficiency of DLLMs still remains a significant lag compared to the decoding efficiency of AR-LLMs under inference acceleration. We provide an in-depth experimental analysis about the underlying trade-offs among generation quality, latency, and system compatibility, and build up a standard evaluation bench open to the community. Remaining bottlenecks are also summarized, together with future directions for more practical and competitive DLLM inference. Code is available at \url{https://github.com/haoyun-jiang/DLLM-AccelEval}.

Abstract: Environmental Education (EE) and Education for Sustainable Development (ESD) play a crucial role in fostering environmentally responsible citizens and supporting the achievement of sustainability goals. This study aims to investigate primary school teachers’ knowledge, attitudes, and perceptions regarding EE/ESD, as well as the factors influencing their implementation in the educational process. A quantitative research design was employed using a structured questionnaire distributed to a sample of 500 teachers across Greece. Data were analyzed using descriptive statistics, content analysis, exploratory factor analysis, reliability testing, correlation analysis, and multiple regression analysis with the use of SPSS software. The results indicate that teachers generally demonstrate positive attitudes toward EE/ESD and recognize its importance in promoting environmental awareness, behavioral change, and students’ social development. Content analysis revealed that key environmental concerns identified by participants include pollution, climate change, and waste management, while EE/ESD is mainly associated with environmental practices and awareness. Factor analysis identified five core dimensions shaping teachers’ attitudes: (i) perceived value and impact, (ii) social and personal development outcomes, (iii) pedagogical design and evaluation understanding, (iv) institutional and structural barriers, and (v) practical implementation challenges. Significant correlations were found among these factors, particularly between perceived value and pedagogical understanding, as well as between institutional barriers and implementation challenges. Regression analysis showed that demographic and experiential variables have a modest but significant effect on perceived challenges, with age and participation in EE/ESD programs negatively associated with difficulties, while years of involvement increased awareness of implementation constraints. Overall, the findings highlight that although teachers possess a satisfactory level of awareness and positive attitudes toward EE/ESD, limited training, insufficient institutional support, and structural barriers hinder effective implementation. The study underscores the need for enhanced training opportunities, stronger policy support, and systematic integration of EE/ESD into school curricula to promote sustainable education practices.