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AI for Teacher Training, Personalised Support to Mitigate Crowded Classrooms, And Youth Upskilling: The Potential of AI in Kenya
Get PDFThis paper examines the transformative role of artificial intelligence (AI) in addressing the significant challenges faced by the Kenyan education sector, particularly in teacher training and youth employability. Kenya grapples with a severe shortage of educators and limited access to quality educational resources. As global economies increasingly rely on digital literacy and technical skills, equipping both teachers and learners with modern competencies has become critical. This paper draws on the implementation of Otermans Institute’s AI-driven educational platform, OIAI, as a scalable, accessible, and cost-effective solution.The research analyses qualitative and quantitative data from users, stakeholder feedback, and related academic literature to evaluate the effectiveness of AI teachers in enhancing educational outcomes. Key findings highlight high user engagement, substantial skill acquisition, and the feasibility of AI integration even in low-connectivity regions. The paper concludes by proposing policy recommendations and strategic frameworks to support nationwide AI adoption, ultimately contributing to the upskilling of Kenya’s teaching workforce and the empowerment of its youth for the digital economy
Compositional Chemical Property Analysis and Evaluation of Liquid Smoke Produced from Microwave-assisted Pyrolysis of Mixtures of Oil Palm Solid Waste
Get PDFMicrowave-assisted pyrolysis (MAP) was employed to valorise oil palm solid waste, namely empty fruit bunches (EFBs), kernel shells (KSs), and mesocarp fibres (MFs), into liquid smoke at 300 and 400 °C. Unlike conventional pyrolysis systems, which rely on slow, external heating and often yield broad, less selective chemical profiles; MAP offers rapid, volumetric heating and non-thermal effects that enhance product specificity and energy efficiency. This study investigates how MAP temperature and binary blending ratios (EFB-to-KS and EFB-to-MF) influence liquid smoke yield, chemical composition, and antioxidant capacity. Liquid smoke yields were significantly affected by temperature in EFB-KS mixtures, with higher yields at 400 °C, while EFB–MF mixtures showed yield stability across conditions. Gas chromatography–mass spectrometry (GC-MS) analysis revealed phenol as the dominant compound across all samples, with compound diversity and antioxidant activity varying by feedstock. KS-rich mixtures favoured catechol and cresol formation, MF-rich mixtures produced cyclopentenones and carboxylic acids, and EFB-rich mixtures yielded more carbonyl-containing compounds. Antioxidant capacities, measured via DPPH assay, were highest in KS-derived liquid smoke due to its catechol content, while EFB-rich samples exhibited lower activity. Principal component analysis (PCA) was applied to GC-MS data to elucidate the chemical transformation pathways, revealing distinct degradation routes for cellulose, hemicellulose, and lignin under MAP conditions. These routes were further supported by compound clustering in PCA loading plots, highlighting the influence of temperature and biomass composition on product speciation. This study demonstrates the innovative integration of MAP with oil palm waste valorisation, offering a sustainable alternative to wood-based pyrolysis. By tailoring feedstock ratios and operating temperatures, MAP enables the targeted production of high-quality liquid smoke with enhanced antioxidant functionality, contributing to environmentally friendly food preservation and agricultural applications.The authors would like to express their gratitude to Institut Teknologi Sepuluh Nopember for fully funding the research associated with this paper via the scheme of Dana Keilmuan ITS (1688/PKS/ITS/2023) organised by Direktorat Riset dan Pengabdian kepada Masyarakat
Borrowing, rephrasing, or inventing? How the African Commission and Court on Human and Peoples’ Rights have filled the gap on legitimate restrictions to freedom of expression
No full textThe African Charter on Human and Peoples’ Rights (ACHPR) does not contain a list of legitimate aims for the lawful restriction of freedom of expression. Article 9 ACHPR only provides a general formulation, leaving a wide margin to interpretation. Nevertheless, legitimate aims analysis is part and parcel of the case-law of the African Commission and Court on Human and Peoples’ Rights. This article investigates how the two African bodies identified and applied legitimate aims for the restrictions of freedom of expression, comparing it with the law and practice of the European and the Inter-American courts. By reviewing all the cases on freedom of expression decided to date, the article shows that the African Court and Commission have filled the gap of Article 9 ACHPR by either borrowing legitimate aims from international instruments, rephrasing existing language in African or international documents, or inventing completely new grounds
Making teaching an attractive profession: What are the challenges and opportunities for minority ethnic teachers in England?
No full textKey insights:
What is the main issue that the paper addresses?
There is a persistent concern about the underrepresentation of minority ethnic teachers in the teaching profession in England. This paper examines the issue of the recruitment and retention of minority ethnic teachers through the lens of ‘interest convergence’. It uses data from interviews with teachers of different ethnicities and career stages to investigate their experiences.
What are the main insights that the paper provides?
This paper shows that although participants generally did not face obstacles in securing classroom teaching roles, attributing this to teacher shortages and, in some cases, schools' diversity goals, the long-term success such as progression remains a challenge. This was particularly the case for Black teachers.Data Availability Statement:
Research data are not shared.This paper explores the challenges and opportunities surrounding the recruitment and retention of minority ethnic teachers in England. Drawing on interview data from 33 teachers and school leaders of diverse ethnic backgrounds, it investigates whether racialised barriers identified in earlier research have shifted in the current context of teacher shortages and workforce diversification efforts. The findings suggest that participants generally did not face obstacles in securing classroom teaching roles, attributing this to staff shortages and, in some cases, schools' diversity goals. However, systemic barriers to career progression persist, with experienced teachers—particularly Black teachers—reporting racism and discrimination more frequently than their other ethnicity colleagues. Opportunities for improvement were identified by the presence of school diversity, especially in leadership, which was a promising factor in supporting the retention of minority ethnic teachers. The paper argues for structural change to ensure that recruitment efforts are matched by meaningful pathways to progression.Economic and Social Research Council. Grant Number: ES/X00208X/1/
Preliminary evaluation of the correspondence between ion-acoustic signals and luminescence from the absorption of pulsed proton beams in a liquid scintillator
No full textFor eventual contribution to the subject of radiotherapy with heavy ions, our aim was to advance the field of ionacoustics by establishing the feasibility of using optical images of proton dose deposited in a liquid scintillator as a reference for thermoacoustic measurements in the same liquid. We made the first reported measurements of ultrasound speed and frequency dependent attenuation coefficient of a liquid scintillator, Ultima Gold XR®. We then directed pulsed beams of protons from a laser driven accelerator into a phantom filled with the scintillator while making simultaneous observations of the resulting luminescence and thermoacoustic waves. Preliminary comparisons were obtained between the two, as a function of proton energy and degree of collimation, laying a foundation for future work on 3D ionacoustic dosimetry
Recursive State Estimation for Nonlinear Cyber-Physical Systems under Random Access Protocol: A Token Bucket Strategy
Get PDFThis article investigates the recursive state estimation problem for a class of nonlinear cyber-physical systems (CPSs) operating under a token bucket strategy regulated by a random access protocol (RAP). Communication between sensor nodes and the remote estimator takes place over a shared network, where only one sensor node is permitted to access the network at each time instant to prevent data collisions. The transmission sequence of sensor nodes is governed by RAP scheduling, which is modeled as a sequence of independent and identically distributed variables representing the selected node granted network access. To efficiently manage limited communication resources, a token bucket strategy is employed. The measurement signal from the selected node is transmitted to the estimator only if a sufficient number of tokens are available in the bucket to meet the required token consumption. The objective is to design a state estimation algorithm that minimizes the estimation error covariance (EEC) by appropriately determining the estimator gain at each time step. The desired estimator gain is computed recursively by solving two Riccati-like difference equations. Finally, an illustrative example is presented to validate the effectiveness of the proposed estimation method.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61933007, 62273087 and U21A2019);
Shanghai Pujiang Program of China (Grant Number: 22PJ1400400);
Hainan Province Science and Technology Special Fund of China (Grant Number: ZDYF2022SHFZ105);
10.13039/501100000288-Royal Society of the U.K.;
Alexander von Humboldt Foundation of Germany
Identification of tau leptons using a convolutional neural network with domain adaptation
Get PDFData Availability Statement: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use, and open access policy.Code Availability Statement: The CMS core software is publicly available on GitHub (https://github.com/cms-sw/cmssw).A version of the article is available at arXiv:2511.05468v2 [hep-ex], https://arxiv.org/abs/2511.05468 . Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/TAU-24-001 (CMS Public Pages). Report number: CMS-TAU-24-001, CERN-EP-2025-233. Journal reference: JINST 20 (2025) P12032. Submission history: From: The CMS Collaboration: [v1] Fri, 7 Nov 2025 18:22:56 UTC (1,060 KB); [v2] Wed, 24 Dec 2025 15:03:51 UTC (1,095 KB).A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τh) from quark or gluon jets and electrons and muons that are misreconstructed as τh candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30−50% in the probability for quark and gluon jets to be misidentified as τh candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √s = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb^{−1}, respectively. Techniques to calibrate the performance of the τh identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.SCOAP3
“I Don’t Feel Like It’s Extra Work”: Gender, Parenting and Everyday Sustainability Labour
No full textIn this article we enlist the feminist lens of the everyday to theorise the way parenting and environmental sustainability intersect in the lives of parents committed to combating climate change. Based on interviews with 27 parents from 20 families in Iceland, we interrogate whether and how gender fractures parental sustainability labour - the work that goes into making family lives environmentally sustainable - in a country which is often described as the most gender equal in the world. While extant research is largely disjointed, our data reveals that everyday sustainability labour is undertaken disproportionately by mothers and that it encompasses overlapping yet distinct physical, cognitive, emotional and pedagogical dimensions. However, mothers reconciled their commitment to gender equality with their lived experiences of gendered division of sustainability labour by re-framing the latter as preference, hobby or enjoyable activity. We situate these gendered narratives of parental sustainability labour within broader discourses of neoliberal climate governance that seeks to shifts the responsibility of climate action to individual households and intensive parenting ideologies that individualises child-rearing, prompting parents to make informed choices for their children’s future. We conclude by reflecting on how gendered politics of child-rearing and neoliberal climate governance are shaping each other.The work was supported by Brunel University of London [Research Development Fund]; University of Iceland [University of Iceland Research Fund (No: 95928)]
Novel optimisation approach for community energy systems: Grid-connected capacity sizing with hydrogen storage and lifecycle
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe transition to high-renewable energy systems at the community level demands
optimisation frameworks that balance economic efficiency, operational flexibility, and
sustainability. While many existing studies focus on either static sizing or simplified dispatch
heuristics, they often fail to co-optimise key system parameters such as inverter capacity, grid
constraints, and hybrid storage integration under dynamic conditions. This thesis addresses
that gap by developing a deeply integrated optimisation architecture that unites long-horizon
sizing with short-horizon control, tailored for islanded and weak-grid energy communities.
The central objective is to design a techno-economically robust and energy sustainable hybrid
PV–battery–hydrogen system that minimises lifecycle cost and enhances renewable selfconsumption
while accounting for real-world constraints. To this end, a nested optimisation
approach is proposed, integrating a Genetic Algorithm (GA) for capacity sizing with a Mixed
Integer Linear Programming (MILP) framework that embeds a Model Predictive Control (MPC)
dispatch strategy. The GA generates candidate system layouts, each of which is validated via
the MILP model that co-optimises hourly dispatch under fixed tariff structures and inverter-grid
limits with AC/DC nodal representation. To capture operational uncertainty and improve
flexibility, a rolling-horizon MPC layer executes every 12 hours using a 24-hour forecast
window, incorporating flexible loads up to 8% of daily average demand, a level selected to
reflect realistic load-shifting potential based on typical non-critical applications such as water
pumps.
Results show that the framework achieves Net Present Cost (NPC) and Levelised Cost of
Energy (LCOE) reductions of 10% and 10.2%, respectively, compared to static or rule-based
baselines. Grid-related operational charges fall by 46% under MPC with load flexibility, and
self-consumption rises to 44.56%. A novel, extended Energy Return on Investment (EROI)
metric is introduced to capture full energy pathways, revealing battery storage as the dominant
contributor to lifecycle efficiency. To explore trade-offs between system size, energy return,
and cost, generalisation heatmaps of EROI and NPC are developed around the optimised
Formentera case study design from Chapter 4, which serves as the baseline (i.e. the
configuration with the lowest NPC). These heatmaps identify design “sweet spots” around 1.0–
1.1× the baseline capacity, where high EROI (>5.0) and low NPC (≤€610,000) are
simultaneously achieved. Beyond which oversizing leads to diminishing energy and cost
returns due to increased curtailment and underutilisation of grid infrastructure.
The proposed GA–MILP–MPC framework thus provides a replicable, scalable, and practical
tool for optimising community-scale energy systems. By tightly linking planning, operation, and
sustainability metrics, it enables planners to make data-driven decisions that are financially sound, operationally feasible, and environmentally justified. As distributed energy
infrastructures continue to evolve, such integrative methods will be crucial for shaping resilient
and sustainable energy futures
Social Informer: Pedestrian Trajectory Prediction by Informer With Adaptive Trajectory Probability Region Optimization
Get PDFPedestrian trajectory prediction is an important research area with significant applications in autonomous driving and intelligent surveillance. However, existing studies on pedestrian trajectory prediction often suffer from a noticeable discrepancy between predicted and actual trajectories, due to incomplete extraction of pedestrian trajectory features and the randomness of the pedestrian walking process. The key objective of this article is to address this issue by proposing a method that can reasonably simulate the randomness of pedestrian walking and comprehensively extract pedestrian trajectory features. To achieve this, a novel social informer model built upon the informer model is proposed in this article. The social informer utilizes a transformer encoder-based interaction module to comprehensively extract pedestrian trajectory features, which are input into the informer model for further processing. Additionally, an adaptive variance mechanism is proposed to determine the optimal variance and accurately simulate the random nature of pedestrian walking. Finally, the proposed model is evaluated in a comparative experiment on ETH and UCY datasets, with results demonstrating that the proposed model outperforms other models, exhibiting improved accuracy and performance.Jiangsu Provincial Scientific Research Center of Applied Mathematics (Grant Number: BK20233002);
10.13039/501100013088-Qinglan Project of Jiangsu Province of China;
10.13039/501100018556-Science and Technology Program of Suzhou (Grant Number: SYG202106);
Research Development Fund of XJTLU (Grant Number: RDF-20-01-18)
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