Two-step Model Research Articles

Analysis of the Wangping Brownfield Using a Two-Step Urban Brownfield Redevelopment Model

With societal progress, urban brownfields have become restrictive, and redevelopment studies have become an important part of urban renewal. In this work, we developed a two-step model for urban brownfield redevelopment, while considering the Wangping brownfield as the study area. Site suitability evaluation models for brownfield parks, agricultural picking gardens, and creative industrial centers were developed based on the elevation, slope, and surface runoff, and the evaluation results were categorized into five levels. The redevelopment plan was formulated based on these evaluation results. To study the effect of the plan, a transition matrix of land use was assessed using satellite images and the cellular automata (CA)–Markov model; based on the analysis, we predicted the land use situation of the Wangping brownfield, with respect to natural development, for 2030. A comparison of the redevelopment planning with the forecasted results revealed that the proportions of grassland, construction, and unused land decreased by 25.68, 3.12, and 2.38% and those of plowland and forest land increased by 6.61 and 24.57%. This confirms the advantages of redevelopment planning for restoring plowland and increasing biological carbon sinks. Notably, our two-step urban brownfield redevelopment model can enrich the current research on urban brownfields and guide similar urban renewal projects.

Analysis of flows in social media uncovers a new multi-step model of information spread

Abstract Since the advent of the internet, communication paradigms have continuously evolved, resulting in a present-day landscape where the dynamics of information dissemination have undergone a complete transformation compared to the past. In this study, we challenge the conventional two-step flow communication model, a long-standing paradigm in the field. Our approach introduces a more intricate multi-step and multi-actor model that effectively captures the complexities of modern information spread. We test our hypothesis by examining the spread of information on the Twitter platform. Our findings support the multi-step and multi-actor model hypothesis. In this framework, influencers (individuals with a significant presence in social media) emerge as new central figures and partially take on the role previously attributed to opinion leaders. However, this does not apply to opinion leaders who adapt and reaffirm their influential position on social media, here defined as opinion-leading influencers. Additionally, we note a substantial number of adopters directly accessing information sources, suggesting a potential decline in influence in both opinion leaders and influencers. Finally, we found distinctions in the diffusion patterns of left-/right-leaning groups, indicating variations in the underlying structure of information dissemination across different ideologies.

Biomedical Information Retrieval with Positive-Unlabeled Learning and Knowledge Graphs

The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedical database maintenance, explicit queries are missing. In this paper, we propose a two-step model for biomedical information retrieval in the case that only a small set of example documents is available without explicit queries. Initially, we extract keywords from the observed documents using large pre-trained language models and biomedical knowledge graphs. These keywords are then enriched with domain-specific entities. Information retrieval techniques can subsequently use the collected entities to rank the documents. Following this, we introduce an iterative Positive-Unlabeled learning method to classify all unlabeled documents. Experiments conducted on the PubMed dataset demonstrate that the proposed technique outperforms the state-of-the-art positive-unlabeled learning methods. The results underscore the effectiveness of integrating large language models and biomedical knowledge graphs in improving zero-shot information retrieval performance in the biomedical domain.

A Photometric Redshift Catalogue of Galaxies from the DESI Legacy Imaging Surveys DR10

The large-scale measurement of galaxy redshifts holds significant importance for cosmological research, as well as for understanding the formation and evolution of galaxies. This study utilizes a known sample obtained by cross-correlating the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys DR10 galaxy catalog with various galaxy catalogs from different spectroscopic surveys. The methods Easy and Accurate Photometric Redshifts from Yale (EAZY) and CatBoost are employed to estimate redshifts. In the case of EAZY, the known sample is used solely for testing, while CatBoost utilizes it for both training and testing purposes. The known sample is categorized into different subsamples based on various classification methods. Several CatBoost regression models are trained and optimized using these subsamples. By comparing the performance of different methods and models, it is observed that the two-step and two-part models outperform the one-step model, with further enhancements achieved through the combination of the two-step and two-part models. Based on the findings from all experiments, we propose a photometric redshift estimation workflow designed to facilitate the photometric redshift measurement of all galaxies within the DESI Legacy Imaging Surveys DR10. Consequently, a photometric redshift catalog has been released, comprising a total of 1,533,107,988 galaxies. Among these, 312,960,837 galaxies have reliable redshift estimates, determined using the CatBoost algorithm, with magnitude limits set at g > 24.0, r > 23.4, and z > 22.5. For galaxies with g, r, and z magnitudes exceeding these thresholds, the photometric redshifts estimated by EAZY can be employed as a reference.

A model predictive control strategy of global optimal dispatch for a combined solar and air source heat pump heating system

Due to low-carbon transformation of heating sources in northern China, the combination of the solar heating system (SHS) and the air source heat pump (ASHP) has attracted widespread attention due to their significant low-carbon and energy-saving characteristics. Nevertheless, different outdoor meteorological parameters simultaneously affect both the SHS and the ASHP. The heat supply of the SHS is primarily influenced by solar radiation intensity, and the efficiency of the ASHP is mainly constrained by outdoor temperature. The dual uncertainty and volatility of the output capacities of these two heat sources pose significant challenges to their coordinated control. Previous research has primarily focused on traditional rule-based control (RBC) and feedback control, prioritizing the utilization of solar energy while employing ASHP to compensate for heating deficiencies. However, these methods ignore the variations in ASHP efficiency due to fluctuations in outdoor temperature, leading to low whole-system operating efficiency and limiting the flexibility and responsiveness of the control. In this paper, a model predictive control strategy of global optimal dispatch for a combined solar and ASHP (SASHP) heating system is proposed, which focuses on the flexibility and adaptability of dual heat source dispatch under different external conditions. A Temporal Convolutional Network (TCN) was used to establish a solar radiation intensity and load prediction model. A solar heat production prediction was realized by combining the mechanism model based on solar radiation intensity prediction; a two-step room temperature prediction model was established by introducing new input parameters in the load prediction. This strategy achieves globally optimal dynamic planning of the heat supply and duration of the SHS and ASHP systems by considering solar radiation, outdoor temperature, room temperature, and energy consumption, ensuring the efficient and stable operation of the system. Compared to RBC, experimental results indicated that under conditions of sufficient solar energy, the heating proportion of the SHS increased by 31.1 %, the average COP of the ASHP improved by 8.7 %, the energy-saving rate was 14.6 %, and the room temperature control was also more effective; whole-season simulation results showed an average energy-saving rate of 8.35 %.

Modeling noisy learning in a dynamic oligopoly experiment

Estimating demand before production poses a significant challenge for many industries, including vaccine manufacturing, newspapers, and perishable foods. Both industry professionals and experimental subjects in laboratory settings often struggle to determine optimal production. This paper sheds light on the cognitive processes that explain individuals' inability to act optimally, using data from Nagel and Vriend (1999a,b). In their experiment, participants, acting as firms, set production levels without prior knowledge of the demand generated by own and competitors' advertising efforts. We first reexamine their two-step learning model, which involves directional learning for production levels and a hill-climbing algorithm for advertising, utilizing exogenous adjustment size distributions. We improve upon this model, inspired by the macroeconomic learning literature, with agents using constant gain learning for production decisions and hill climbing for advertising decisions with endogenous adjustments. We demonstrate that our model provides a better fit to the data than the original model by Nagel and Vriend (1999a) and yields superior out-of-sample forecasts, both for one-period-ahead predictions and simulated paths.

The effect of obligatory versus voluntary school food standard implementation on the fulfillment of the school food standard requirements in Germany

BackgroundIn high income countries there is increased focus on health- and sustainability aspects concerning school feeding. To ensure this in Germany, a school food standard (SFS) was established. Implementation of the SFS is mandatory only in some federal states and has remained voluntary in others. The aim of this first exploratory study is to analyze school lunch menus regarding their fulfillment of the SFS requirements and detect differences between obligatory and voluntary implementation of the SFS. MethodsAfter drawing a random sample of schools and reaching out for voluntary participation in this study, school menus of federal states with obligatory (OSFS) and voluntary (VSFS) SFS implementation were collected and analyzed. The SFS criteria are based on the conditions observed in the menu for a four-week mixed diet menu, an ovo-lacto-vegetarian dish, additional criteria for menu planning and menu criteria. In addition, we estimated binominal logistic regression models and two-step multiple regression models to control for the effects of geographical factors and socioeconomic status to the fulfillment of the SFS criteria. ResultsWe assessed 48 menus from OSFS federal states and 102 menus from VSFS federal states. OFSF menus met the requirements to a higher extent and exhibited a better dietary pattern. School food-standardized meals included significantly less breaded and deep-fried components (96 % vs. 79 %, p = 0.009), contained more whole grain products (83 % vs. 30 %, p < 0.001) and fewer meat products (60 % vs. 25 %, p < 0.001), more fruits (81 % vs. 63 %, p = 0.023) and more vegetables, legumes and salad (85 % vs. 61 %, p = 0.002). Menus with SFS obligation emphasized nutrient optimized menus significantly more frequently (42 % vs. 28 %, p < 0.001) and used unambiguous designations of meals more often (96 % vs. 82 %, p = 0.023). They contain more regional products (33 % vs. 13 %, p = 0.003), more organic products (71 % vs. 41 %, p < 0.001) and meat from animal friendly husbandry (21 % vs. 7 %, p = 0.012). The effects of the covariates were rarely significant. ConclusionSchool menus with obligatory use of the SFS met the requirements to a greater extent and contain healthier and sustainable food components more frequently. Nevertheless, results show that even in federal states with obligational use, the standard implementation is not fulfilled completely. Further research is needed to detect barriers in the daily implementation of the SFS.

Total cost analysis in purchasing education: A two-step open-ended teaching case approach

Total Cost Analysis, TCA, is an approach to handle all relevant costs in decisions concerning a choice between alternatives. Although TCA is considered important within Purchasing and SCM, there is little guidance on how to perform such analyses in a structured way. This is challenging to total cost analysts, but also to teachers wanting their students to learn TCA, especially since there is also a lack of guidance in literature on how to promote students' learning of TCA.Based on experiences from a teaching case on supplier selection used in master's level education for several years, this paper presents a two-step teaching case approach which can serve as inspiration for teaching and learning TCA in a way that promotes students to be critical and engaged learners. In the two-step procedure, students first define which cost factors are relevant for the specific situation and which data they need to perform the TCA. Second, provided with requested data, they perform the TCA and evaluate their solution's robustness.The proposed two-step model is novel, thereby adding to the case teaching literature. Moreover, the study adds to the scarce amount of research on purchasing education, as well as to the limited amount of literature on teaching and learning total cost analysis in general.

Are air travelers willing to pay for mandatory carbon emission policies? Evidence from China

The aviation industry is one of the fastest-growing sectors in terms of CO2 emissions and also one of the most challenging to decarbonize. China has become the world's second-largest aviation market. While a nationwide mandatory carbon reduction policy has yet to be introduced in China's aviation sector, it may be implemented in the coming years. The implementation of mandatory carbon reduction policies will inevitably raise questions about the distribution of additional costs. Therefore, air travelers' willingness to pay (WTP) and the factors influencing it are critical considerations for policymakers and airlines. However, there is a lack of research on the WTP of Chinese air travelers under mandatory carbon reduction policies. No studies have specifically focused on the regional differences in WTP among air travelers in China. Moreover, existing research on air travelers' WTP for carbon offsets rarely captures the impact of rational, emotional, and social interaction factors. Therefore, this study investigates the WTP of 3424 air travelers across 31 provincial-level administrative regions in China within the context of a carbon tax policy. Using the Heckman two-step model, the study analyzes the impact of rational, emotional, and social interactions on air travelers' WTP. The results indicate that, after removing biased samples, the average WTP among Chinese air travelers is 311.04 CNY ($44.14) per ton of CO2. The highest WTP is observed in the East China region at 341.20 CNY ($48.42) per ton of CO2, while the lowest is in the Northeast region at 263.19 CNY ($37.35) per ton of CO2. Epistemic performance expectancy, social performance expectancy, function performance expectancy, and anticipated pride positively influence WTP. In contrast, effort expectancy negatively impacts WTP. Additionally, social interaction can moderate the effects of certain rational and emotional factors on WTP. It is noteworthy that although anticipated guilt does not directly influence WTP, its positive impact on WTP can be moderated through social interactions. The results of this study can assist governments in formulating effective carbon emission reduction policies for the aviation industry. Moreover, these findings can provide valuable insights for airlines to adjust pricing strategies under mandatory carbon reduction policies, thus holding practical significance.

Structure of METTL3-METTL14 with an m6A nucleotide reveals insights into m6A conversion and sensing.

The nuclear METTL3-METTL14 transfers a methyl group from SAM to convert the N 6 of adenosine (A) in RNA to m 6 A and in ssDNA to 6mA. m 6 A marks are prevalent in eukaryotic mRNAs and lncRNAs and modulate their stability and fate in a context-dependent manner. The cytoplasmic METTL3 can act as a m 6 A reader. However, the precise mechanism during m6A writing, reading, or sensing is unclear. Here, we present a ~2.5 Å structure of the methyltransferase core of human METTL3-METTL14 in complex with the reaction product mimic, N 6 -methyladenosine monophosphate (m 6 A), representing a state post-catalysis but before the release of m 6 A. m 6 A occupies an evolutionarily conserved RNA-binding pocket ~16 Å away from the SAM pocket that also frequently mutates in cancer. We propose a two-step model of swiveling of target A upon conversion to m 6 A and sensing its methylation status by this pocket, enabling it to actuate enzymes' switch from writer to an m 6 A-sensor. Cancer-associated mutations show impaired RNA binding dynamics, de-stacking, and defective m 6 A writing and sensing.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Evaluating the influence of the initial high molecular weight level on monoclonal antibody particle formation kinetics using a short-term chemical stress study

Protein formulations may form proteinaceous particles that vary in size from nanometers to millimeters. Monitoring the kinetics of protein particle formation, e.g., through accelerated degradation studies, is an attempt to understand and assess the rate and progression of particle populations. Little is known about whether the initial level of high molecular weight (HMW) species, or initial HMW level (IHL), of a protein solution influences the propagation of protein particle formation, and thus affects the storage stability of proteins.In this study, we have established a method to generate protein solutions of different IHLs by thermal stress. We have evaluated a 16-week thermal stability study at 40 °C of two monoclonal antibodies (mAb-A and mAb-B) at different IHLs using size exclusion chromatography (SEC) and sub-visible particle analysis. We have performed an isothermal stress study with guanidinium hydrochloride (GuaHCl) at room temperature for 300-min to evaluate the formation of HMWs analysed by SEC. The application of the Finke-Watzky (F-W) two-step nucleation model allowed us to mathematically describe the kinetics of HMW formation and to extract kinetic parameters of this process.For mAb-A, the IHLs had a marginal influence on the loss of monomer rate; instead, mAb-A exhibited fragmentation at 40 °C, which was independent of the IHL. Nevertheless, above a threshold of ≥ 7 % IHL, existing trimers/tetramers undergo conversion into higher-order oligomers at 40 °C, which is not observed at lower IHLs. In contrast, mAb-B exhibited an increased HMW formation rate above a threshold of ≥ 4 % IHL, which was reflected in the monomer decay rates at 40 °C and the F-W kinetic parameters of the chemical stress study.This case study shows that the initial level of HMWs exerts a differential influence on the progression of HMW formation. In one instance, there is a discernible acceleration in the formation of HMWs with rising IHLs. Conversely, in another example, the IHL exerts only a slight influence on HMW formation. Moreover, the results of our short-term chemical stress study are in accordance with those of a classical storage stability study conducted at 40 °C, which evaluated different IHLs. The analysis of HMW formation kinetics will enhance our understanding of the protein particle formation process and facilitate the formulation development of biotherapeutics.

Numerical analysis on the fate and transport of TiO2 nanoparticles in fractured porous media: Addressing intrinsic heterogeneities in sorptive mass transfer

The aggregation and slow migration of nanoparticles in aqueous media have caused serious concerns about their fate and impacts in the subsurface environment. Anthropogenic release and distribution of TiO2 nanoparticles (TNP) have immense potential for surface adsorption, occlusion, impregnation, bioaccumulation, and phase partition into various environmental compartments, and the actual risks in their interactions are still unknown. In an attempt to realize the extent of source zone migration of TNP in a fracture-skin-matrix (F-S-M) medium, a numerical model is developed and analyzed for sensitivity of certain features of the flow field. In addition, the sorptive mass transfer is simulated under four characteristic scenarios with varying assumptions pertaining to the intrinsic heterogeneities. The simulation results highlight the non-selective regulatory role of the skin in providing temporary interfacial space for reversible adsorption between the fracture and the matrix as well as in retarding the desorption rate. A preferential detachment of TNP is observed to be favored by the enhanced properties of skin due to the similarity in diffusion and dispersion coefficients. Out of the four scenarios, the two-site model and two-step model simulated the dynamic pore-filling features of adsorption pertaining to the heterogeneities in TNP and F-S-M characteristics. The results demonstrate that the proposed numerical could fairly detect the transition between local equilibrium and dynamic adsorption-desorption cycles that would eventually determine the mass transfer limitations and the extent of elution of TNP along the flow.

Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms.

We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study. The best two-step model for predicting dementia incorporated Trail Making Test B (attention/executive function), Animal Fluency (verbal fluency), Mini-Mental State Examination (global cognition), and 10-word list recall (memory). The model's positive predictive value in ADNI was 85.8% and negative predictive value was 92.2% versus 62.5% and 95.6%, respectively, in BioFINDER-1. This two-step model accurately predicts individualized dementia risk. To our knowledge, this is the first algorithm for predicting all-cause dementia using a novel two-step model utilizing brief cognitive tests. Applying a validated model including the Trail Making Test B, Animal Fluency, MMSE, Alzheimer's Disease Assessment Scale delayed, and immediate recall can robustly and accurately categorize individuals into low, intermediate, or high risk of dementia progression and can facilitate clinical decision-making and personalized patient care. We created an app that is available for research and educational purposes at https://brainapps.shinyapps.io/PredictAllCauseDementia to provide an individualized risk score for dementia progression.

Numerical analysis of mixing performance in Y-junction mixers and its impact on yields from supercritical water hydrolysis

This study investigates the mixing behavior in a Y-junction mixer for supercritical water hydrolysis using large eddy simulation with a discrete phase model. Yield changes were simulated using a two-step reaction model with first-order kinetics, based on particles’ temporal temperature data. Effective mixing produced closely matched mass and particle flow temperature distributions, both exhibiting bell-shaped profiles near the mixed temperature. Although variations in flow rate within ±25 % and changes in the inlet temperatures of supercritical water from 350 °C to 430 °C and subcritical water from 100 °C to 170 °C did not significantly affect the overall mixing performance, they did alter the mixed temperature and, subsequently, yield changes. Additionally, backflow occurred when Richardson number for the subcritical inlet reached approximately 7. In effective mixing, simulated yields were approximately 15 % lower than the ideal theoretical yields, calculated using the reaction rate constant at the mixed temperature.

Reallocation of time after an exogenous reduction in mandatory travel: transport, work, and leisure in Chilean two-worker two-gender households

A two-step model framework is proposed and applied to analyze empirically the impact of a travel time reduction on labor supply in two-worker two-gender households, using a nationwide data set for time use collected in 2015 in Chile. First, a system of structural equations (SSE) is estimated to reveal which activities can be considered as committed, and to unveil the hierarchy of activities by gender undertaken by the household workers to help defining the labor supply model. Then a quadratic formulation including committed time for each individual and committed expenses for the family is applied to the time-use data to estimate a labor supply model, considering the findings with the SSE (hierarchy and committed activities). Results were obtained controlling for household size, region, and age, showing systematically that labor supply diminishes with the wage rates of either working member of the household (a forward falling shape), that committed expenses induces more work, and that diminishing mandatory travel time induces an increase in working hours that varies between 27 and 64 percent of the liberated time, in line with 2023 reports on the reallocation of liberated travel time due to remote working and confirming the theoretical findings by Jara-Diaz and Contreras (2024).

A Two-Step Dry Etching Model for Non-Uniform Etching Profile in Gate-All-Around Field-Effect Transistor Manufacturing.

The Gate-All-Around Field-Effect Transistor (GAAFET) is proposed as a successor to Fin Field-Effect Transistor (FinFET) technology to increase channel length and improve the device performance. The GAAFET features a complex multilayer structure, which complicates the manufacturing process. One of the most critical steps in GAAFET fabrication is the selective lateral etching of the SiGe layers, essential for forming the inner-spacer. Industry commonly encounters a non-uniform etching profile during this step. In this paper, a continuous two-step dry etching model is proposed to investigate the mechanism behind the formation of the non-uniform profiles. The model consists of four modules: anisotropic etching simulation, Ge atom diffusion simulation, Si/SiGe etch selectivity calculation and SiGe selective etching simulation. By calibrating and verifying this model with experimental data, the edge rounding and gradient etching rates along the sidewall surface are successfully simulated. Based on further examination of the influence of chamber pressure on the profile using this model, the inner-spacer shape is improved experimentally by appropriately reducing the chamber pressure. This work aims to provide valuable insights for etching process recipes in advanced GAAFETs manufacturing.

Inspiring Contributions: Fan Leaders’ Directives and Encouragement in the Chinese Online Celebrity Fan Community

Abstract In the neoliberal era, there is a new phenomenon where numerous fans follow fan leaders’ instructions to make tangible or intangible contributions to support celebrities, which has exacerbated economic polarization. Although past studies have explored fan empowerment in the Internet era, fan leaders’ promotion effect has remained unstudied. This study aimed to discover fan leaders’ role in guiding fans’ contributions in the Chinese fan community. By integrating a two-step flow model and participatory culture theory, it used qualitative content analysis to analyze 410 posts and 972 comments in the Chinese singer Song Yaxuan’s fan community from February 3 to 10, 2023. Through an inductive approach, four main themes were derived from results: (1) task delegation and empowerment; (2) user-friendly instructions and seamless communication; (3) highlighting a sense of crisis and putting pressure on fans; and (4) fostering a collective mindset. The findings revealed fan leaders use different techniques and the top-down structure in the community to encourage fans to give voluntary contributions of money or time to the celebrity’s development. Theoretically, this study showed internal operation logic in Chinese fan community. Practically, the study shed light on how to lessen the harmful consequences of neoliberalism’s features within the Chinese entertainment industry and realize healthy development.

Sulfur fixation performance of calcium-bearing carbonates in iron ore roasting process: Thermodynamics, phase transition, microstructure evolution, and kinetics

Roasting technology is increasingly used in the beneficiation of refractory iron ores. During the roasting process, the presence of pyrite in some iron ores leads to the formation of SO2, requiring effective sulfur fixation techniques. This study proposed an innovative in-situ sulfur fixation method by pre-oxidation roasting, in which sulfur was fixed during the oxidation roasting process before reduction roasting. The effects of the main iron mineral (hematite), primary gangue mineral (quartz), and common calcium-containing carbonate minerals (calcite and dolomite) on sulfur migration during pyrite roasting were investigated, with particular emphasis on the sulfur fixation capabilities of calcite and dolomite. The study included thermodynamic equilibrium analysis, phase transitions and microstructural evolution in different reaction systems. The results showed that calcite and dolomite effectively reduced SO2 emissions by forming CaSO4 and MgSO4, with calcite exhibiting a stronger sulfur fixation capacity for the same mass. Kinetic analysis indicated that pyrite pyrolysis followed a two-step random nucleation and growth model. Furthermore, the addition of calcite increased the apparent activation energy of SO2 formation and altered the reaction pathway, providing insight into the sulfur fixation mechanism of calcite from a kinetic perspective.

Determinants of livelihood diversification in an integrated agricultural and non-agricultural livelihood system in Ghana

The diversification of livelihoods by households has been widely acknowledged as a way to overcome food insecurity and poverty challenges in developing countries. Diversification of livelihoods helps spread the risk among multiple livelihood earning activities to provide households with a range of their food needs all year round. By examining the integrated livelihood systems of 405 rural farm households in the Upper East Region of Ghana, empirical evidence is provided in this study using the Sustainable Livelihoods Framework to advance arguments in the literature for the creation of sustainable strategies that improve diversified livelihood systems. The mean diversification indices estimated were 0.45 for agricultural diversification systems, 0.32 for non-agricultural diversification systems and 0.59 for integrated agricultural and non-agricultural diversification systems. With the use of the Cragg two-step regression model, we demonstrate that the decision to diversify and the extent of diversification of rural livelihoods are distinct decisions and are influenced by distinct sets of factors. Similarly, for the three categories of livelihood diversification studied, the effect of these factors also differed. The results emphasize the significance of access to good road network, credit and market information in encouraging rural farm households to diversify their livelihoods. In the short term, improving access to credit and market information through community-based initiatives can provide immediate support to rural households. Communities should also organize local markets and cooperatives to strengthen their economic resilience. While government and stakeholders should focus on long-term infrastructure projects, these community actions can complement such efforts and contribute to achieving global and regional goals targeting food insecurity and poverty eradication.