Current Issues: Is the workplace about to get better or worse for disabled people in the United Kingdom?

open access articleIn the United Kingdom, the new labour government has recently unveiled two new bills, the Employment Rights Bill and Equality Race and Disability Bill, that seem to strengthen the 2010 Equality Act. However, it is not clear how these bills will address the disability employment gap. The government’s policy to Make Work Pay has many good points like more transparency in terms of race, gender and disability pay gaps but it also raises questions about what devolvement to local authorities to get more disabled and chronically ill people into work will look like? This seems to target disabled and chronically ill people and does not think about how to create more enabling workplace environments following the social model

Role of Bioadditions in Sustainable Energy Production

This PhD research investigated the effects of yeast bioaddition on anaerobic digestion (AD) of lignin-rich agricultural waste, specifically rye and corn silage, with a focus on understanding the underlying mechanisms of yeast action on the lignocellulosic component of lignin, biogas production and microbial communities. While previous studies have explored bioadditives in lignocellulosic biomass degradation, the detailed mechanisms of these bioadditives, particularly yeast, in enhancing anaerobic digestion remain poorly understood. This study addresses key knowledge gaps by evaluating the impact of yeast addition on process parameters, microbial dynamics, and biogas production in batch and semi-continuous AD systems. The research was divided into four objectives. Firstly, the study assessed the effect of yeast on lignin-rich feedstock, hypothesizing that yeast enhances the degradation of lignin and improves biogas yields. Results revealed that yeast contains enzymes such as polyphenol oxidase (PPO) and phenol oxidizing enzymes (POE), which are able to react with G-units and S-units phenolic compounds following lignin degradation. These compounds, in particular H-units, can have inhibitory effect on methanogens. Some of these PPOs have also been reported to be able to demethylase these phenolics. This is the case in this work as yeast addition to rye, richer in Sunits, results in higher biogas production in the early stages of the reaction, linked to easily digestible methyl-groups. The result also confirms that not all the phenolic compounds following lignin degradation have an inhibitory effect. For example, syringic acid (S-unit) was metabolized and produced increased biogas yields compared to control and yeast addition significantly increased these yields. On the other hand, p-hydroxybenzoic acid, inhibited biogas production, with and without yeast addition. In addition, yeast can provide metals and micronutrients to the process for micronutrients-poor feedstocks such as food waste. This was not the case in this study as both rye and corn silage contained all the required micronutrients. The second objective involved the development of a novel method for full-length 16S rRNA archaeal extraction and sequencing using Oxford Nanopore® Technology (ONT). By combining newly designed primers targeting a broader range of archaeal groups with ONT’s long-read 14 chemistry kit, this method offers a novel alternative to traditional short-read technologies such as Illumina MiSeq. It provides greater taxonomic resolution, enabling comprehensive and real-time detection of methanogens and broader archaeal diversity in iv anaerobic digestion systems, thereby enhancing the detailed identification of critical microbial players for improved biogas production performance. For the third objective, the study explored the short-term and long-term effects of yeast bioadditions on microbial community composition and process performance in pilot-scale reactors digesting rye silage. Yeast addition was found to enhance soluble COD removal efficiency and mitigate ammonia inhibition. Despite comparable soluble COD levels in both the treated (DRY) and untreated (DR) reactors, the DRY reactor demonstrated more stable biogas production, suggesting that yeast bioaddition shifted the COD profile towards more readily biodegradable compounds, sustaining microbial activity. The DRY reactor also exhibited higher concentrations of hydrogenotrophic methanogens, suggesting that yeast supported the growth and activity of these critical microbes, even under elevated NH₃ concentrations. The final objective focused on long-term yeast effects under variable conditions. The study showed that yeast contributed to a more stable syntrophic acetate oxidation (SAO) process, which was linked to the increased presence of SAO bacteria such as Tepidanaerobacter acetatoxydans. This stabilization of the microbial community allowed the treated reactor to maintain high biogas yields, despite high ammonia and volatile fatty acid concentrations. In conclusion, yeast bioaddition positively influenced the degradation of lignocellulose in AD systems, improved microbial resilience, and enhanced biogas production. The findings suggest that yeast can play a critical role in optimizing AD processes, particularly in systems dealing with high-lignin feedstocks. Future research should focus on further understanding the molecular interactions between yeast and the very strong syntrophic association established between Tepidanaerobacter acetatoxydans and Methanoculleus bourgensis that ensured process stability in this study. Also, exploring the scalability of yeast bioadditives for industrial applications is equally recommended

Examining the Impact of Multilevel Courtyards in Hot-Dry and Humid Climates

open access articleUrbanisation has significantly transformed human settlements, presenting sustainability challenges, particularly in hot-dry and humid climates. The urban heat island effect and increased energy consumption exacerbate reliance on mechanical cooling and fossil fuels. As climate change escalates, developing sustainable architectural solutions that improve thermal performance and energy efficiency becomes crucial. This study examines the effects of various multilevel courtyard designs on building performance in Abuja, Nigeria, highlighting gaps in applying traditional principles to these models. A mixed-method approach, combining quantitative and qualitative techniques, assesses user perceptions, thermal performance, energy efficiency, and daylighting in multilevel courtyards. Findings indicate that optimised multilevel courtyard configurations yield a 2.15 °C reduction in temperature, enhancing indoor thermal comfort and improving natural ventilation. Users favour multilevel courtyard housing; however, challenges include inadequate daylighting on lower levels and the need for shading solutions. Compressed earth blocks exhibit better thermal performance, reducing peak temperatures by 1.19 °C compared to hollow concrete blocks. Guidelines for architects and urban planners are provided, as well as recommendations for future research on policy incentives to promote multilevel courtyard models

Integrating Citation Heterogeneity to Measure the Quality of Academic Journals

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Evaluating the quality of academic journals is important and complex. The journal impact factor (IF), which is the most widely used indicator to measure the quality of academic journals, assumes that all citations are homogeneous. The use of this indicator has been criticized widely due to its inherent limitations. In recent years, several sophisticated indicators have been proposed to allow the weighting of citations from different journals. However, the recursive computation process of these indicators requires a huge amount of data. This article proposes a new indicator with citation heterogeneity to measure journal quality, which is named the Citation Author Affiliation Index (CAAI). The CAAI is based on the assumption that citing paper authors’ institutions can be ranked and are considered a proxy to measure the quality of citations (in a statistical sense). It is shown that the CAAI is easy to use and interpret, time-efficient, and adaptable. The effectiveness of the CAAI is validated by using Web of Science citation data from journals in several research categories

A GRA-based heterogeneous multi-attribute group decision-making method with attribute interactions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the era of VUCA (Volatility, Uncertainty, Complexity, Ambiguity), multi-attribute group decision-making (MAGDM) problems face the challenges of heterogeneous uncertainty in decision information and complex interactions between attributes, which greatly affect the reliability of decision-making outcomes. To address these challenges, this paper proposes a novel heterogeneous MAGDM method based on grey relational analysis (GRA) that considers attribute interactions. First, the heterogeneous information is integrated, including crisp numbers, generalized grey numbers, intuitionistic fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets. Then, by incorporating the 2-additive Choquet integral into GRA, we establish a heterogeneous grey interactive relational model and explore its properties. Subsequently, a heterogeneous grey relational Mahalanobis-Taguchi System is designed to estimate the Shapley values of attributes. Additionally, a two-stage resolution mechanism, comprising a consensus reaching process followed by a grey relational multi-objective programming model, is devised to determine the interaction indices. Finally, the effectiveness of the proposed method is demonstrated through a case study from China’s aviation manufacturing industry, along with sensitivity analysis and comparison analyses

The value of expert judgments in Decision Support Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It is a challenge to improve a decision support system (DSS) based on expert judgments; the literature proposes to improve accuracy and performance by increasing the sophistication and complexity of the DSS, but at what cost? This study presents a model for encoding a DSS based on expert judgments and evaluating its efficiency, establishing a three-part analysis structure: information requirements (number of judgments), quality requirements (quality assurance mechanisms), and algorithmic complexity. With a focus on the cost of judgments, a systematic and quantitative coding of the performance and cost in each part of the DSS is established. A “break-even point” efficiency measure, defined as the maximum percentage of the optimal performance that can be paid per unit of resources, is proposed to ensure that the use of the DSS remains profitable. Counterintuitively, the results of a case study show that the efficiency of DSSs does not necessarily increase with respect to the informativeness level of DSSs. Overall, this study provides a new method for evaluating the efficiency of DSSs

A nonlinear mixed-frequency grey prediction model with two-stage lag parameter optimization and its application

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With the advancement of data science, the demand for methods capable of simultaneously processing and utilizing complex mixed-frequency data systems with uncertainty characteristics is increasing. To address this need, a novel nonlinear mixed-frequency grey prediction model with two-stage lag parameter optimization is proposed which integrates frequency-domain analysis and optimization algorithm. The proposed model innovatively incorporates the phase spectrum analysis method into the mixed-frequency modeling framework, determines a reasonable range for lag parameters using frequency-domain analysis, and enhances the characterization of system nonlinearity by introducing a power-driven term. The effectiveness and robustness of the proposed model are validated through both experiments on synthetic data and real-world case studies on electricity consumption. Comparative experiments against existing mixed-frequency grey prediction model, nonlinear grey prediction model, and mixed-frequency sampling regression model demonstrate that proposed model exhibits superior performance in key metrics, including mean absolute percentage error and standard deviation. This study provides a novel solution for modeling relationships among multi-frequency variables in complex systems

Smart Farming Solutions: A User-Friendly GUI for Maize Tassel Estimation Using YOLO With Dynamic and Fixed Labelling, Featuring Video Support

open access articleThe integration of Autonomous Aerial Vehicles (AAVs) has significantly advanced image processing and remote sensing, particularly in precision agriculture. These technologies enhance data collection and agricultural yield estimation, benefiting banks, insurance companies, and government agencies in decision-making for budget allocation and quality assessments. This study addresses the challenge of accurately quantifying corn production by developing an enhanced YOLO-v8-based deep learning model, incorporating dynamic and fixed labeling techniques, tested on 810 images and video data for real-time detection. The research utilized two primary datasets totaling 570 images. The evaluation process comprised four distinct tests: Test 1, conducted on Dataset 1 with 200 images, assessed seven attention mechanisms (SE, CBAM, GA, LKA, CA, SA, and TA) using deep learning metrics (Precision, Recall, mAP50, mAP50-95, F1-score) and statistical methods (Duncan’s test). Test 2 validated model performance on 370 images from external sources, where YOLO.SA achieved 97.48% accuracy, outperforming YOLO.LKA (95.13%). Test 3, comparing with the MTDC benchmark dataset, confirmed YOLO.SA’s accuracy at 95.93%, exceeding previous reports, while YOLO.LKA achieved 95.71%. Finally, Test 4, utilizing video-based evaluation via a developed GUI, demonstrated YOLO.SA’s superiority (95.77%) over YOLO.LKA (95.48%) and YOLO-v5 (95.72%), significantly outperforming the standard YOLO model (72.79%). This study advances computer vision in agriculture, offering a scalable, high-accuracy model for corn yield estimation, with broad applications in farming optimization, financial planning, and policy-making

A study of Natural Dyes to Create Color Palette for Creative Design

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. This study was supported by Thammasat University Research Fund, Contract No. TUFT67/2566.This study explored the application of natural dyes to develop a color palette for creative design, focusing on the traditional natural dyeing in Lampang Province, Thailand. Utilizing local natural resources such as Burma Padauk, Siamese Senna, Indian Almond Tree, Eucalyptus, and Lac, the research identified the properties and variations of colors achieved through the use of different mordants such as alum and rust. Color measurements were conducted using the CIE L*a*b* system, with results converted into RGB, CMYK, and HEX color systems for practical design applications. The findings emphasize the importance of preserving local wisdom in natural dyeing while addressing challenges in consistency and quality control. This research integrated traditional dyeing knowledge with modern design principles, providing a framework for developing sustainable and culturally enriched products. By establishing systematic dyeing processes and creating standardized color palettes, the study aims to enhance the global competitiveness of locally produced textiles, aligning with the goals of sustainable development and responsible consumption, promoting sustainable practices, economic growth, and cultural preservation

Machine Learning and Response Surface Methodology for Optimizing Olive Waste Compost in Sustainable Chickpea Production

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This study combines machine learning (ML) and response surface methodology (RSM) to optimize and predict the effects of compost made from olive mill waste cake residues (OMWC) on chickpea yield. Compost was applied to chickpeas irrigated with rainwater, and plant growth, phenology, and yield were monitored. Four modeling techniques RSM with Box-Behnken Design (RSM-BBD), artificial neural networks (ANN), support vector machines (SVM), and XGBoost, were employed to identify optimal compost application conditions. The RSM-BBD and ANN models showed superior predictive performance, with high coefficients of determination (R² = 0.9205 and 0.9718, respectively) and low root mean square error (RMSE = 8.0368 and 4.2833, respectively). In contrast, SVM and XGBoost showed lower accuracy. These results highlight the importance of selecting appropriate modeling approaches based on the problem and accuracy needs. This work advances understanding of crop yield prediction and supports sustainable agriculture through improved compost use, with clear practical implications for Moroccan chickpea production