This work presents an environmental approach to the development of novel porous carbon materials of controlled mesoporosity and ultrahigh surface area via KOH thermochemical method. The method involves direct single-step carbonization/activation of lignocellulosic sorghum stalks. The impact of different activation temperatures (500-800 degrees C) was investigated. The materials were characterized at the atomic scale level by XRD, Raman, ATR-FTIR, XPS techniques. Surface area and porosity were assessed by N2 gas adsorption. Results showed that the employed activation approach prevents the formation of graphitized carbon domains and help formation of the graphene-like layers, which confirmed by HR-TEM imaging. The produced materials were characterized as an oxygenated graphene-like structure with slit-shaped porosity between parallel graphene sheets. This was attributed to the interaction of KOH with the lignocellulosic precursor and the protective/ intercalation actions of KOH products (K2CO3, K2O and molten K) during the activation course. Thus, tailored materials exhibiting mesoporosity and ultrahigh surface area, SBET (up to 2324 m2/g) were formed at 800 degrees C. The materials were examined as adsorbents for methylene blue (MB) and exhibited fast and extraordinary adsorption capacity of 487.8 mg/g. The adsorption isotherms fit the Langmuir adsorption model, and the adsorption rates fit pseudo-second-order kinetic. Thermodynamic parameters (Delta H degrees, Delta S degrees and Delta G degrees) indicated the exothermic and spontaneous nature of the adsorption process. The novelty of the present efficient adsorbent materials can be correlated to their textural aspects (increase of surface area, external porosity), structural aspects (development of more electrostatic surface), and the unique material characteristics as oxygenated graphene-like sheets of slitshaped mesoporosity. The present work introduces novel biomass derived porous carbons for elimination of organic pollutants, which supports the current UN sustainable development goals.
Abstract Innovation and new technologies have become central pillars of economic development strategies globally, yet many modernizing economies, such as Bulgaria, struggle to design their own inidgenous policies, despite their access to advanced European Union models. With the help of the European Union and its Regional Innovation Strategies and Smart Specialization concepts, Bulgaria has strengthened elements of its innovation system, such as digital infrastructure, scientific output, startup formation, and access to risk financing, yet significant gaps remain in linking research, entrepreneurship, and scale-up support. This paper examines the application of the MIT Regional Entrepreneurship Acceleration Program framework to diagnose and interpret the interaction between innovation capacity and entrepreneurial capacity in Bulgaria’s innovation ecosystem, and propose possible solutions to decision-makers in the public and the private sector. Deriving from the literature on entrepreneurship and systems of innovation, the paper proposes an applied research approach aligned with MIT REAP’s framework. The methodology combines a multi-stakeholder co-creation, and a structured diagnostic framework and secondary data analysis. It is empirically grounded in the work of the Bulgarian team within the MIT REAP’s Cohort 12 (2025-2027), including the preparation of a preliminary ecosystem assessment, MIT-facilitated diagnostics session (November 2025), and the use of pre-defined indicator templates incorporating minimum, median, and maximum benchmark values. The results present a preliminary innovation and entrpepreneurial capacity assessment for Bulgaria, which suggests that innovation capacity is characterised by moderate yet uneven performance, with persistent fragmentation and weak commercialization linkages. Entrepreneurial capacity is limited by scale-up finance and demand-side deficits. The paper contributes to a growing body of applied research on regional innovation systems by operationalising a flagship MIT approach that links innovation and entrepreneurial capacities and by demonstrating a diagnostic approach that supports policy-relevant analysis geared towards modernising a European economy.
BACKGROUND:Teacher mental health is an important predictor of student outcomes and teacher workforce retention, and has been declining for some years, exacerbated by the COVID-19 pandemic. The various causes of this trend have been speculated to include a workforce that is younger and less experienced, as well as increasing work demands. METHODS:We evaluated the trends in teacher mental health between 2005 to 2022, using the 5-item Mental Health Inventory (MHI-5) from the annual Household Income and Labour Dynamics in Australia (HILDA) survey. We tested whether the trend was due to changes in non-work related factors (i.e., changes in workforce composition), or due to workplace risk factors (i.e., high job demands and low autonomy). RESULTS:Teacher mental health was stable to 2011 then declined from a median of 80 (IQR 68-88) to 76 (IQR 60-97) MHI-5. The decline was not explained by changes in the workforce composition. The prevalence of high job demands was stable over this period (53% to 55%) while low autonomy and control increased from 34 to 58%, especially after 2018. At the same time, the strength of the association of high job demands with poor mental health increased from 1.32 [95%CI -0.45 to 3.09] MHI-5 units to 4.91 [3.34 to 6.47] MHI-5 units. CONCLUSIONS:The decline in teacher's mental health was partly explained by an increasing sensitivity to job demands. Given the reported level of demands did not increase, addressing the reduction in job autonomy over time (which enables workers to cope with high demands) may improve policies to support teacher mental health and workforce retention.
This study is aimed at characterizing partial resistance (PR) to leaf rust (Puccinia triticina) in Egyptian wheat cultivars, assessing their performance compared to susceptible cultivars and the Moroccan check variety in both field and greenhouse conditions. Five cultivars, Giza 168, Sids 14, Sakha 94, Gemmeiza 10, and Shandweel 1, showed adequate and high PR levels, with significantly reduced final rust severity, lower Area Under the Disease Progress Curve (AUDPC) values, and slower disease progression rates compared to susceptible cultivars and the Moroccan cultivars. Under controlled greenhouse conditions, PR cultivars displayed distinct physiological and pathological divergence from susceptible counterparts, including suppressed fungal growth, diminished sporulation, reduced uredinium size, and delayed pustule eruption. These cultivars prolonged both the inoculation and latent periods of Puccinia triticina. The PR mechanism reduced significant grain yield losses by improving thousand-kernel weight, grain yield per plot, and chlorophyll retention. Molecular analysis confirmed the presence of the Lr34 gene, which provides durable adult plant resistance in all five PR cultivars. These cultivars are suggested for use in environments with high disease pressure, minimizing reliance on fungicides and serving as genetic resources for breeding programs focused on integrating leaf rust resistance genes. The study found a significant correlation between partial resistance to leaf rust and the presence of the Lr34 gene in the wheat cultivars. Varieties that carried Lr34 exhibited stronger partial resistance, demonstrating a reduced severity of the disease and slower disease progression.
Large language models (LLMs) promise process automation but entail reliability concerns. We investigate the capability of LLMs in the context of operations management, specifically demand forecasting of spare parts. For this, we develop a self-reflective simulation framework with an LLM producing automatic demand forecasts, using simulated and real-world data. This includes generating and debugging a Python script, verifying the results, and iteratively improving the outcomes. Our results demonstrate a high variability of responses, especially for stock-keeping units with irregular demand. Averaging over several repetitions can improve performance and result in forecasts that are comparable in accuracy to common benchmarks. Providing more detailed instructions in the prompt can improve forecasting accuracy. Training a decision tree to determine which method to choose for which kind of stock keeping unit results in lower forecasting errors compared to always using the same method or deciding on the method based on classifications from previous research.
