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Meru County Budget Review and Outlook Paper 2024
Get PDFThe 2024 Budget Review and OutJook Paper (CBROP) bas been developed against a backdrop of stable global and domestic economic outlook. Global growth is projected at 3.2 percent in 2024 and 3.3 percent in 2025 from 3.3 percent in 2023. The outlook reflects economic recovery in China, Euro areas and UK, despite a slowdown in activity in the USA and Japan. On the domestic front, the Kenyan economy is currently unwinding from layers of negative and persistent shocks that had a structural effect on economic activities. The shocks included: COVID-19 pandemic and its ensuing effects, conflict in Eastern Europe and the Middle East that led to global supply chain, disruptions and the adverse effects of climate change from the prolonged drought in 2021 to the floods in the first half of 2024. These shocks escalated the cost of essential household commodities including fuel prices, and led to a rapid depreciation of the Kenya Shilling exchange rate, pilling pressure on public debt. The Gross County Product which is a measure of county's contribution to Kenya's GDP was reported at 3.1 % in the Gross County Product 2023 report by KNBS. Meru County is among the 5 largest county economies. Meru County economy is expected to have a steady growth in the year 2024. This optimistic outlook is underpinned by ongoing initiatives such as the provision of subsidized farm inputs, establishing of agriculture value chain value-added products . Additionally, the government is actively supporting Small and Medium Enterprises (SMEs) by offering low-interest loans through the the micti finance Corporations, establishment of industrial and incubation centres among others. In the realm of water and sanitation, the government shall seek to revitalize water services and sanitation infrastructure, institutional development, external linkages, ensure financial sustainability, environmental conservation and provision of sustainability quality, affordable and adequate water for improved livelihoods
Wajir County Fiscal Sreategy Paper 2024
Get PDFThe County Fiscal Strategy Paper (CFSP) 2024, the second to be prepared under the leadership of HE FCPA Ahmed Abdullahi EGH administration focuses on key Medium Term policies and priorities in the Governors Manifesto and CIDP 2023/2027. These include; quality and affordable healthcare, food security, social protection, access to early childhood education, critical infrastructure development as well as building resilience to climate change shocks. Since its inception in 2022, the Government has initiated key programmes towards provision of quality health care across the county, provision of clean and safe water for domestic use, infrastructure expansion and rehabilitation as well as social programs for vulnerable groups. These include upgrading of Wajir referral hospital to level 5 which is ongoing, grants to people living with disability, bursary for bright and needy students, drilling of boreholes and excavation of water pans across the county, opening of access roads, construction of ECDE Classrooms and recruitment of health personnel
Special Paper No. 08 of 2024 on Effect of Cash Transfers on Food Expenditure, Dietary Diversity and Nutrition Status of Beneficiary Households
Get PDFCash transfers are among the most popular social protection instruments that can improve nutrition status by alleviating poverty and cushioning poor and vulnerable households from shocks and risks that affect their livelihoods. However, in Kenya, these programmes are predominantly not designed within a nutrition-sensitive approach, despite proposals to integrate nutrition interventions. This study examines the impact of cash transfers on key nutrition-related outcomes: household food expenditure, dietary diversity, and stunting among children under five years old. The study used data from the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) and applied linear regression models in the analysis of effect of cash transfers on food expenditure and dietary diversity and Probit models in analyzing the effect of cash transfers on child stunting status. The findings reveal that beneficiary households receiving cash transfers exhibit a 10.6 per cent lower expenditure on food and consume 0.5 fewer food groups compared to non-beneficiary households. This could be attributed to the unconditional nature of the transfers and their failure to keep pace with inflation, with the Ksh 2000 value of cash transfers per month adjusted for inflation standing at Ksh 874 in 2022. Interestingly, when cash transfers are adequate to meet the food poverty line, food expenditure and household dietary diversity scores increase significantly by 27.8 per cent and 0.22 units, respectively. In addition, receipt of cash transfers reduces the likelihood of stunting among children under five years by 2.6 per cent in beneficiary households, pointing to the need to leverage cash transfer programmes as effective tools not only for poverty reduction but also for improving nutrition outcomes
Modélisation de la distribution de fréquence au-delà d'un seuil en présence de sinistres tardifs
No full textInternational audienceIn reinsurance, Poisson and Negative binomial distributions are employed for modeling frequency. However, the incomplete data regarding reported incurred claims above a priority level presents challenges in estimation. This paper focuses on frequency estimation using Schnieper's framework for claim numbering. We demonstrate that Schnieper's model is consistent with a Poisson distribution for the total number of claims above a priority at each year of development, providing a robust basis for parameter estimation. Additionally, we explain how to build an alternative assumption based on a Negative binomial distribution, which yields similar results. The study includes a bootstrap procedure to manage uncertainty in parameter estimation and a case study comparing assumptions and evaluating the impact of the bootstrap approach.En réassurance, les distributions de Poisson et binomiale négative sont utilisées pour modéliser la fréquence des sinistres. Cependant, les données incomplètes concernant les sinistres déclarés au-delà d'un certain seuil de priorité posent des défis dans l'estimation. Cet article se concentre sur l'estimation de la fréquence en utilisant le cadre de Schnieper pour le décompte des sinistres. Nous démontrons que le modèle de Schnieper est compatible avec une distribution de Poisson pour le nombre total de sinistres au-delà d'un seuil de priorité à chaque année de développement, offrant ainsi une base solide pour l'estimation des paramètres. De plus, nous expliquons comment construire une hypothèse alternative basée sur une distribution binomiale négative, qui donne des résultats similaires. L'étude inclut une procédure de bootstrap pour gérer l'incertitude dans l'estimation des paramètres, ainsi qu'une étude de cas comparant les hypothèses et évaluant les conséquences de l'approche bootstrap
Analysis of the vertical variability and temporal frequency of the chlorophyll forcing field on temperature in the Mediterranean Sea and potential implications for regional climate projections
No full textInternational audienceThis work examines the impact of using a chlorophyll field to force the NEMOMED12 ocean circulation model in the absence of a biogeochemical model, focusing on key physical characteristics, primarily seawater temperature. Our analysis shows that a vertically homogeneous chlorophyll field causes heat accumulation below the Deep Chlorophyll Maximum, leading to a temperature increase over time. Extrapolating the 11-year simulation trend suggests a temperature bias of over +1°C in the intermediate layer after 100 years. To avoid such biases, we recommend using a reconstructed climatology of dimensionless vertical profiles provided by any biogeochemical model (here the Eco3M-MED model). Additionally, using the same chlorophyll field saved at different time frequencies introduces temperature differences between simulations that increase over time, especially in the intermediate layer. The simulation forced by daily chlorophyll is warmer in the surface layers due to the asymmetric impact of chlorophyll extremes on heat distribution
The short-term response of soil microbial communities to digestate application depends on the characteristics of the digestate and soil type
No full textInternational audienceAnaerobic digestion of organic waste is a key process to produce renewable energy and meet the growing demand for sustainable energy. The residues of anaerobic digestion - called digestates - can be used as soil amendments to improve crop yields. However, the effect of digestates on the soil biota, especially on microorganisms, needs to be better documented before a large scale use of digestates in agriculture. In addition, how the quality and composition of the digestate may affect soil microbial communities has not been properly addressed yet. We designed a microcosm experiment under controlled experimental conditions to compare effects (42 days) of four digestates produced from varying intakes (cattle manure and/or energy crop and/or food residues and/or slurry) on soil microbial communities; a control microcosm made of undigested cattle manure was also used. Each digestate was applied on three contrasting soils representing contrasted pedo-climatic conditions (especially soil type and climate). These three soils presented different prokaryotic and fungal communities structures. The effect of digestate inputs on the soil microbial biomass and diversity was assessed using molecular DNAbased tools (quantification of extracted soil DNA and high-throughput sequencing, respectively) in comparison to the untreated cattle manure control condition. Our results show that 42 days after digestate application, significant differences of soil microbial communities were observed according to the digestate characteristics; these differences were soil-dependent. Thus, in the silty clay loam soil, no effect of digestates was observed on soil microbial biomass or diversity (P > 0.05), as compared to the undigested cattle manure. In the two other soil types (loam and sandy loam), soil microbial biomass decreased (around -40 %, P < 0.001) when digestates having a low total organic carbon content (from 0.61 to 3.3 g.100 g(-1)) were applied. None of the digestates affected the soil prokaryotic diversity whatever the soil type (P > 0.05). Digestate application resulted in higher fungal diversity (around +35 %; P < 0.001) in soils with low C/N ratio (9.14 in average). The microbial community structure of coarse-textured soil appeared more impacted by organic inputs than fine-textured soils. To conclude, our results show that different soil types, harboring distinct microbial community structures, responded differently to different digestates application. This response was also digestate-dependent
Multipacting mitigation by atomic layer deposition: The case study of titanium nitride
No full textInternational audienceThis study investigates the use of atomic layer deposition (ALD) to mitigate multipacting phenomena inside superconducting radio frequency cavities used in particle accelerators while preserving high quality factors in the 1010 range. The unique ALD capability to control the film thickness down to the atomic level on arbitrary complex shape objects enables the fine-tuning of TiN film resistivity and total electron emission yield (TEEY) from coupons to devices. This level of control allows us to adequately choose a TiN film thickness that provides both high resistivity to prevent Ohmic losses and a low TEEY to mitigate multipacting for the application of interest. The methodology presented in this work can be scaled to other domains and devices subject to RF fields in vacuum and sensitive to multipacting or electron discharge processes with their own requirements in resistivities and TEEY values
A Study on Cell-Based Algorithms for Nearest-Neighbor Search between Hexagonal Clusters
No full textInternational audienceIn this paper, the general Nearest Neighbor (NN) search is mapped to the problem of finding NNs between clusters consisting of hexagonal cells. Namely, cells are clustered inside a Region of Interest (ROI) by using the hexagonal coordinate system. Two hexagonal ROI types with embedded hexagonal cells are selected and tessellated to form a tessellation ring. Three cell-based NN search algorithms are defined: Neighbor Cell-Based (NCB), Polygon Intersection-Based (PIB), and Euclidean Distance-Based (EDB). Algorithms are studied in conditions where accuracy is more important than computational cost, and their efficiency in predefined scenarios is examined. First, the NN clusters are extracted in the uniform hexagonal plane, after which the plane is distorted as ROIs in the tessellation ring are not unique. The study showed that acceptable results are obtained by using each algorithm, independently of whether the plane is distorted or not. However, NCB and EDB give approximate NN results, while PIB is the most efficient, outperforming both NCB and EDB in all measurement cases. It always provides the exact number of NNs, even in the critical border region between neighboring ROIs. Hence, it is concluded that the PIB algorithm is the most convenient to use in the general NN search between arbitrary-shaped hexagonal cluster
Tw-class Sub-2-cycle post-compression of multi-mJ energy Ti:sapphire laser pulses in a gas-filled multi-pass cell
No full textInternational audienceWe report on the nonlinear temporal post-compression of 7 mJ sub-40 fs pulses from a commercial kHz Ti:sapphire laser down to a record 3.8 fs duration (sub-1.5 optical cycle) in a compact single-stage gas-filled multi-pass cell (MPC), with 60% overall compression efficiency
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning
No full textInternational audienceTransfer learning has long been a key factor in the advancement of many fields including 2D image analysis. Unfortunately, its applicability in 3D data processing has been relatively limited. While several approaches for point cloud transfer learning have been proposed in recent literature, with contrastive learning gaining particular prominence, most existing methods in this domain have only been studied and evaluated in limited scenarios. Most importantly, there is currently a lack of principled understanding of both when and why point cloud transfer learning methods are applicable. Remarkably, even the applicability of standard supervised pre-training is poorly understood. In this work, we conduct the first in-depth quantitative and qualitative investigation of supervised and contrastive pre-training strategies and their utility in downstream 3D tasks. We demonstrate that layer-wise analysis of learned features provides significant insight into the downstream utility of trained networks. Informed by this analysis, we propose a simple geometric regularization strategy, which improves the transferability of supervised pre-training. Our work thus sheds light onto both the specific challenges of point cloud transfer learning, as well as strategies to overcome them