Around 14% of the global population does not have access to electricity. About 95% of those are living in rural Sub-Saharan Africa. Often in these regions, diesel generators are the only source of electricity. The operating cost of these diesel generators is high. However, solar and wind energy are available in most of African countries. This study presents the analysis of designing an off-grid hybrid system with a wind turbine, PV, diesel generator, and battery to power a hospital, school, and 200 household village in four locations across Somalia. The research investigated the availability of wind-solar resources in selected locations. Designing of the system and economic-technical calculations were performed using HOMER. The selection of the optimum design was based on the Cost of Electricity and Net Present Cost. The results show that for Kabaal and Ceel Buur, a WT-PV-DG-Battery is the optimal system as the wind resource in these regions is high. For Saakov and Baki, a PV-DG-Battery system proves to be optimum as the wind resource is limited here. The study also evaluated the control strategy and proved that combined dispatch was the most cost-effective for these locations. The study concluded that hybrid systems are more economical than diesel systems.

This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the Peak Demand Minimization problem and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.

An existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems which are accomplished to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

My innovative entropy measure, Ismail entropy, namely, (IE) is an ultimate generalization to numerous generalized entropies within the literature. The fractal dimension of IE is identified in this paper. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

A novel low-power MOS-only voltage reference is presented. The Enz-Krummenacher-Vittoz (EKV) model is adopted to provide a new perspective on the operating principle. The normalized charge density, introduced as a new variable, serves as an indicator when trimming the output temperature coefficient. The proposed voltage reference consists of a specific current generator and a 5-bit trimmable load. Thanks to the well-match between the current source stage and the output stage, the non-linear temperature dependence of carrier mobility is automatically canceled out. The circuit is designed in 55 nm COMS technology. The operating temperature ranges from -40 ∘C to 120 ∘C. The average temperature coefficient of the output voltage can be reduced to 21.7 ppm/∘C by trimming. The power consumption is only 23.2 nW with a supply voltage of 0.8 V. The line sensitivity and the power supply rejection ratio at 100 Hz are 0.011 %/V and -89 dB respectively.

Introduction: Nephrotic syndrome (NS) remains the most frequent mode of presentation of glo-merular diseases in children; in addition, the NS is primitive, i.e., idiopathic in 90% of cases with an average age of onset between 2 and 10 years. The objective of our study was to describe the char-acteristics and outcome of NS in children from 3 major hospitals from one of the poorest countries worldwide (Chad). Patients and Methods: Observational, cross-sectional, descriptive and multicenter study which took place over a period of 36 months (January 1,2019-December 31,2021) carried out in 3 hospitals in N’Djamena (Chad). All children aged 1-15 years presenting with NS were included. Results: Out of 16,776 children hospitalized or followed-up in outpatient clinics, 24 cases of NS were identified, giving a prevalence of 0.14%. The median age at presentation was 6.16(1-10) years. Nineteen children were male (sex ratio 3.8). There were 8 impure NS (33.3%). Edema was present in all patients, oliguria in 29.16%(n=7), and arterial hypertension in 20.83%(n=5) of cases. Mean proteinuria, albuminemia and total proteins were 2.86g/L, 19.13g/L and 30.41g/L respectively. Median serum creatinine was 87.3 µmol/L(75-1375µmol/L); three patients had acute renal failure on admission. Four patients had secondary NS. All patients (n=20) with idiopathic NS had received corticosteroid therapy; steroid-sensitivity was found in 90% of patients. Those who were not ster-oid-sensitive or relapsed underwent kidney biopsy (n=7); the most represented histological lesion was focal segmental glomerulosclerosis (FSGS; n=4), followed by minimal change disease (n=2), and membranoproliferative glomerulonephritis (n=1). The median length of hospitalization stay was 10.67 (5-27) days. None of the patients with idiopathic NS died. At last follow-up sixteen patients (80%) achieved long-term complete remission with normal renal function; however, 4 had subse-quent relapses. 16 patients. One patient with secondary NS died. Conclusion: In Chad childhood idiopathic nephrotic syndrome involves mostly young male; steroid sensitivity is as high as 95% and in the long-term 80% of the patients are in remission with normal renal function.

In this paper, a new multi parameter iterative algorithm is proposed to address the PageRank problem based on the multi-splitting iteration method described by Gu et al . The proposed method in each iteration needs to solve two linear subsystems by splitting the coefficient matrix, therefore, we consider inner and outer iteration to find the approximate solutions of these linear subsystems. It can be shown that the iterative sequence generated by the multi parameter iterative algorithm finally converges to the PageRank vector when the parameters satisfy the certain conditions. Numerical experiments show that the proposed algorithm has better convergence and numerical stability than the existing algorithms.

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. Spatial monitoring of fluke infection at the small -basin analysis scale is important because this can enable analysis at the level of the spatial factors involved and influencing infections. A geographic weighted regression model was developed to analyze the spatial characteristics of liver fluke infection, aiming to 1. analyze the spatial factors associated with human liver fluke infection according to sub-basin boundaries and 2. generate an alternative model for enhancing the effectiveness of preventive public health management to reduce the risk of liver fluke infection in humans. The number of infected persons was obtained from local authorities and converted into a percentage of infected people and generated as raster data with a heat map so that the data were continuous and defined as dependent variables. The independent set consisted of nine variables, both vector and raster data, that correlated the location with the village location of an infected person. The results showed that the variables X5stream, X7ndmi, and X9savi were statistically significantly correlated to the percentage of infected people, with the t-stat and p-value being (-2.068, 1.875, and -2.661) and (0.048, 0.034, and 0.021), respectively. The GWR model was able to increase accuracy more than the comparable models such as OLS, in all tests of the four alternative models, with an accuracy increase in R2 of 7.69% (0.576 to 0.624). This study confirms that the development of spatial models with GWR models can screen for factors associated with liver fluke infection at the level of small spatial units such as sub-basins.

Land resources have been under tremendous anthropogenic pressure with the consequence of their degradation. It is therefore necessary that the land resources must be managed effectively for sustainable development. Different from the developed countries, carbon inventories and data bank to monitor carbon sequestration potential of different ecosystems are unavailable in India. Micro-level studies are essential for sustainable land use management for a land scarce nation like India. To achieve the desirable goal of the present study, a total of 33 tree-based land uses were identified from forested and agricultural landscapes. Of these total land uses, five were in forest landscapes and rest in agricultural landscapes categorized into forest tree plantations (8 land uses), agroforestry (nine land uses), commercial crop plantations (six land uses) and fruit orchards (five land uses). A stratified random nested quadrate sampling method was adopted for vegetation analysis of the different land uses. The SOC, biomass and carbon accumulation in the tree-based land uses were significantly different from each other. Mixed forest soil had the highest amount of SOC, primary nutrients, standing biomass carbon, and ecosystem carbon. Positive correlations were observed between SOC, total standing biomass, litter production, and ecosystem carbon. The sequence of best tree based land uses in terms of total SOC (up to 60 cm depth), total plant biomass, total plant biomass carbon and ecosystem carbon was mixed species forest (126.67, 781.21, 390.61 and 517.27) > sole tree species stands in forest landscape (109.71, 192.56, 96.28 and 205.98) > tea plantations (103.19, 77.07, 38.54 and 141.74) > homegardens (90.34, 97.38, 48.69 and 139.02) > mixed plantation of Anthocephalus cadamba + Swietenia macrophylla (60.07, 111.86, 55.93 and 116.02) > Swietenia macrophylla based agroforestry (62.49, 83.82, 41.91 and 104.40) > mixed plantation of Tectona grandis + Milvus migrans (60.0, 85.97, 42.99 and 102.90). Similarly, the order of the major land uses was forest > commercial crop plantation > forest tree plantations > agroforestry > fruit orchards. The overall average ecosystem carbon accumulation in forests was 3.24 times more than the land uses in agricultural landscapes. The ecosystem carbon accumulation in the tree-based land uses in both forest and agricultural landscape was highly variable and was significantly different from each other. Land use conversion from forest to agriculture can reduce more than half of the carbon stock, but converting into homegardens, tree plantations or agroforestry enhanced carbon storage of the land use systems. The present findings can be used as baseline information for developing prediction models for probable effects of different land use, future intervention and sustainable management of land use systems.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters depth, that are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016-2018 period the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using high resolution SSS simulations, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity are observed in regions with large mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly considered. We find that 1) the sampling mismatch can explain most of the observed differences between Argo and CCI data, especially for monthly products and in dynamical regions (river plumes, fronts), 2) overall, the uncertainties are well estimated in CCI version 3, much better compared to CCI version 2. There are a few dynamical regions where discrepancies remain, and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

Global malaria epidemiology has changed in the last decade with a substantial increase in cases and death being recorded. Over 90% of global cases and deaths occur in Sub-Saharan Africa (SSA). Tanzania accounts for about 4% of all cases and deaths reported in recent years. It is believed that several factors contribute to the resurgence of malaria, parasite resistance to antimalarials and mosquito resistance to insecticides being at the top of the list. The presence of sub-microscopic infections poses a significant challenge to malaria rapid diagnostic tests (mRDT), particularly in low-endemic areas. Our cross-sectional surveys in Handeni and Moshi, Tanzania assessed the effect of low parasite density on mRDT. A significant difference (P˂0.001) in malaria prevalence by mRDT, light microscopy (LM) and nested polymerase chain reaction (PCR) was found among age groups. In comparison to all other groups, school-age children (5-15 years) had the highest prevalence of malaria. Based on the results of this study, mRDT may miss up to 6% of cases of malaria mainly due to low-density parasitaemia. Routinely used mRDT will likely miss the sub-microscopic parasitemia which will ultimately contribute to the continued spread of malaria and hinder efforts to control and eliminate it.

Human papillomavirus (HPV) self-sampling (Self-HPV) is a promising strategy to improve cervical cancer screening coverage in low-income countries. However, issues associated with women who prefer conventional HPV clinical-sampling over HPV self-sampling may affect screening participation. To address this issue, our study assessed factors associated with women’s preferences related to Self-HPV. This study was embedded in a large clinical trial recruiting women aged 30–49 years in a primary HPV-based study termed “3T-Approach” (for Test-Triage-Treatment), launched in 2018 at Dschang District Hospital, West Cameroon. Participants were invited to perform a Self-HPV. After the sampling and before receiving the results, participants completed a questionnaire about cervical cancer screening and their preferences and perceptions around Self-HPV. The median age of the 2201 participants was 40.6 (IQR 35–45) years. Most (1693 (76.9%)) preferred HPV self-sampling or had no preference for either method and 508 (23.1%) preferred clinician-sampling. Factors associated with an increased likelihood of reporting a clinician-sampling preference were tertiary educational level (14.4% CI: 12.8–16.1 vs 29.5% CI: 25.6–33.6) and being an employee with higher grade professional or managerial occupations (5.5% CI: 3.8–7.9 vs 2.6% CI: 2.3–2.8). The main reported reason for women preferring clinician-sampling was a lack of “self-expertise”. Most women (>99%) would agree to repeat HPV self-sampling and would recommend it to their relatives. HPV self-sampling in the cultural context of central Africa was well accepted by participants, but some participants would prefer to undergo clinician sampling. Health systems should support well-educated women to increase self-confidence in using HPV self-sampling.

Critical care of neonates involves substantial usage of antibiotics and exposure to multidrug resistant (MDR) nosocomial pathogens. These pathogens are often exposed to sub-MIC doses of antibiotics which might result in a range of physiological effects. Therefore, to understand the outcome of sub-inhibitory dosage of antibiotics on Staphylococcus populations, nasal swab specimens were collected from 34 neonates admitted to the Sick Newborn Care Unit between 2017-2018, a total of 41 non-repetitive isolates were included in this study. Staphylococcus haemolyticus was the prevalent species (58.54%) with high non-susceptibility to cefotaxime (CTX) (79.16%), gentamicin (87.50%), and meropenem (54.17%). Biofilm forming abilities of S. haemolyticus isolates in the presence of sub-optimal CTX (30μg/mL), the predominantly prescribed β-lactam antibiotic, were then determined by crystal violet assays and extracellular DNA (eDNA) quantitation. CTX was found to significantly enhance biofilm production among the non-susceptible isolates (p-valueWilcoxin test- 0.000008) with increase in eDNA levels (p-valueWilcoxin test- 0.000004). Additionally, no changes in non-susceptibility were observed among populations of two MDR isolates, JNM56C1 and JNM60C2 after >500 generations of growth in the absence of antibiotic selection in vitro. These findings demonstrate that sub-MIC concentration of CTX induces biofilm formation and short-term non-exposure to antibiotics does not alter non-susceptibility among S. haemolyticus isolates.

This paper examines the asymmetrical relationship between exchange rate and consumer prices in 40 Sub-Saharan African (SSA) countries from 1990q1 to 2017q4. The exchange rate pass-through (ERPT) to consumer prices is estimated for each country by using the nonlinear autoregressive distributed lags (NARDL) framework and dynamic panel estimators robust to cross-sectionally correlated errors. Firstly, our findings suggest an asymmetrical ERPT in the SSA region during the short-term, whereas there are mixed results across sub-regions in the long-term. Next, we find incomplete and significant ERPT to consumer prices in the entire SSA region which is higher during the depreciation of the local currency than after appreciations. Third, we find nonlinear ERPT with respect to the size of the exchange rate. The pass-through is higher during large exchange rate changes than after small changes. Finally, we find that the pass-through is greater in the countries with fixed exchange rate regime (CFA franc zone) having low inflationary environment than in the other SSA countries with flexible exchange rate regime and high inflation levels. As a result, policymakers should take into account these asymmetries and non-linearities to improve the credibility of monetary policy, strengthen trade liberalization and establish competitive market structures in the Sub-Saharan region.

The unsustainability of the services related to water, sanitation and hygiene in informal settlements in Sub-Saharan Africa services is deeply embedded in current societal and governance structures, cultures and practices; it is context-dependent and involves numerous actors with different interests. The field of sustainability transitions research addresses such persistent and large scale societal challenges, with transition management being one of its widely applied governance approach. By drawing on an analysis of the root causes of unsustainability and unreliability of WASH services in three case studies in Sub-Saharan Africa (Arusha-Tanzania, Dodowa-Ghana, Kampala-Uganda), we explore how a transition management approach can be designed to support a transition towards sustainable Water, Sanitation and Hygiene (WASH) in Sub-Saharan Africa. We distinguish the following contextual dimensions related to the unsustainability of WASH services: a) Multiplicity of WASH practices, structures and arrangements, b) Governance capacities for WASH services and maintenance, c) Landownership for sustainable access to WASH, d) Public participation in decision-making related to WASH, e) socio-economic structures governing access to WASH. These dimensions prompt the identification of conceptual and application challenges for transition management. Based on these challenges, recommendations were formulated for the design of a prescriptive transition management process that is not only functional but also emancipatory of character.

Abstract: Rice is one of the most important food crops in sub-Saharan Africa. Climate change, variability, and economic globalization threaten to disrupt rice value chains across the subcontinent, undermining their important role in economic development, food security, and poverty reduction. This paper maps existing research on the vulnerability of rice value chains, synthesizes the evidence and the risks posed by climate change and economic globalization, and discusses agriculture and rural development policies and their relevance for the vulnerability of rice value chains in sub-Saharan Africa. Important avenues for future research are identified. These include the impacts of multiple, simultaneous pressures on rice value chains, the effects of climate change and variability on parts of the value chain other than production, and the forms and extent to which different development policies hinder or enhance the resilience of rice value chains in the face of climatic and other pressures.

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using Computational Fluid Dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to develop a guideline for verifying CFD models for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification processes of the CFD models were conducted using 2D and 3D iterative simulations of a 2-hectare greenhouse model, using the improved Grid Independence Test (GIT) and wall Y+ approaches. Subsequently, the aerodynamic characteristics were analyzed in a 3D greenhouse model to access its performance when the wind direction was 90° in summer season. The findings revealed that a grid resolution of 0.8 meters and a first layer height of 0.04 meters were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. These results will serve as design standards for large-scale greenhouses.

Modern power system integrates more and more new energy and use a large number of power electronic equipment. This makes it face more challenges in online optimization and real-time control. Deep reinforcement learning(DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. In this paper, we explore DRL based online combination optimization method of grid section for large complex power system. In our method, to improve the convergence speed of the model, we propose to discretize the output action of the unit and simplify the action space. We also design a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain the stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, we propose to use the annealing optimization algorithm to make the granularity of the unit output finer. We verify our method on IEEE 118-bus system. The experimental results show that our model has fast convergence speed and better performance, and can obtain stable solutions.

The rapid growth of Earth's global geospatial data necessitates an efficient system for organizing the data, facilitating data fusion from diverse sources, and promoting interoperability. Mapping the spheroidal surface of the planet presents significant challenges, as it involves balancing distortion and splitting the surface into multiple partitions. The distortion decreases as the number of partitions increases, but at the same time the complexity of data processing increases, since each partition represents a separate data set and is defined in its own local coordinate system. In this paper, we propose the dual orthogonal equidistant cylindrical projection method to mitigate distortion and reduce the number of partitions. Additionally, we use the rotation of projection cylinders to effectively minimize average angular and areal distortions of the Earth’s landmass and reduce the interruption of continental plates caused by partition edges. By incorporating auxiliary latitudes and proposing an approximate authalic latitude, we further enhance the mapping of the ellipsoid onto the sphere, simplifying calculations. Experimental results demonstrate a substantial reduction in distortion and interruption of continental plates. With only two partitions, an average landmass angular distortion of less than 3.56 degrees and an average normalized surface distortion of less than 1.07 were achieved.

Offshore grids can have a key role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling. This paper quantifies the socio-economic value of offshore grids and identifies their major drivers, performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050. The increasing wake loss with the size of hub-connected wind farms is integrated in the modelling. We find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. Therefore, it can be strongly stated that offshore grid infrastructure development should not be discussed as a separate political topic, but seen in connection to sector coupling.

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants, information on the semantics may also be included in the desired representation. Multi-layer grid maps allow to include all this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as LIDAR and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity it is desired to add vision based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline including estimates for eight semantic classes that is designed for straightforward fusion with range sensor data. Unlike in other publication our representation explicitly models uncertainties in the evdiential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping resulsts are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the Kitti odometry benchmark and demonstrating the advantages compared to other semantic grid mapping approaches.

This document introduces a new cryptosystem mixing two improvement standards generally used for text encryption, in order to give birth a new color image encryption algorithm capable of dealing with known attacks. Firstly, two substitution matrixes attached to a strong replacement function will be generated for advanced Vigenere technique application. At the end of this first round, the output vector is subdivided into size blocks according to the used chaotic map, for acting a single enhanced Hill circuit insured by a large inversible matrix. A detailed description of such a large involutive matrix constructed using Kronecker products will be given. accompanied by a dynamic translation vector to eliminate any linearity. A solid chaining is established between the encrypted block and the next clear block to avoid any differential attack. Simulations carried out on a large volume of images of different sizes and formats ensure that our approach is not exposed to any known attacks.

This paper presents preliminary operational performance results of a pilot grid-connected photovoltaic (PV) system designed and installed on the rooftop of the Ministry of Petroleum, Energy and Mining headquarter in Nouakchott (latitude of 18.1°N and the longitude of 16.0°W), Mauritania. The aim is for the government to demonstrate the relevance of using solar energy and to encourage the uptake of solar PV technology for commercial and residential building applications in Mauritania.. In this study, the grid-connected PV system has a peak power of 48 kW and the performance monitoring was carried out during one year, with a system that allow to measure DC power, inverter and system conversion efficiency, energy generated by the PV arrays, solar radiation in the inclination plane of panels, ambient temperature and module temperature. During this period, the PV plant was found to supply 65,668 kWh to the grid. The final yield ranged from 3.91 to 5.09 kWh/kWp/day. The performance ratio was found to vary from 69.69% to 89.35% and the annual capacity factor was found to be 19%. Finally, performance parameters were compared with other PV plants installed in the same region of Northern Africa. The outcome of this work is deemed important in assisting accurate PV system design and decision-making.

The differential gear train and speed regulating motor constitute the variable ratio transmission for grid-connected wind turbine with differential speed regulation. The synchronous generator in the system can accessing the power grid without frequency converter. The transmission can realize the mode of variable speed constant frequency that the wind rotor speed is varying and the generator rotor speed is constant. The power control method is studied under the different wind speed which is lower or higher than rated wind speed with using the relational expression of utilization rate of wind energy Cp, pitch angle β and the tip speed ratio λ. The SIMULINK software is used to build the 1500 kW wind turbine model with differential speed regulation. Some different wind speed is made as input. The feasibility of power control method for grid-connected wind turbine with differential speed regulation is verified by the comparison between the simulation results and the theoretical value of the key parameters.

Data mining plays a critical role in the sustainable decision making. The k-prototypes algorithm is one of the best-known algorithm for clustering both numeric and categorical data. Despite this, however, clustering a large number of spatial object with mixed numeric and categorical attributes is still inefficient due to its high time complexity. In this paper, we propose an efficient grid-based k-prototypes algorithms, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can remove unnecessary distance calculation. The second proposed algorithm as extensions of the first proposed algorithm utilizes spatial dependence that spatial data tend to be more similar as objects are closer. Each cell has a bitmap index which stores categorical values of all objects in the same cell for each attribute. This bitmap index can improve the performance in case that a categorical data is skewed. Our evaluation experiments showed that proposed algorithms can achieve better performance than the existing pruning technique in the k-prototypes algorithm.

The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable had a larger impact on the downscaling results and (2) what was the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the Chinese mainland. To solve this queries, 41 3-month long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements; nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. Moreover, the optimum nudging scheme varied with simulated regions and layers. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable.

ABSTRACT Ethiopia is a developing country, where majority of the population lives in rural areas without access to electricity. 83% of the total population of the country use traditional biomass energy as a basic source of energy. In contrast, the country is endowed with sufficient renewable energy resources which can be used as a standalone electric energy supply system for electrifying remote areas of the country. These resources are mainly micro hydropower and wind which can be used individually or the best combination of one another. The application of hybrid renewable energy system has become an important alternative solution for rural electrification program. The Modeling and control of a hybrid PV-Wind-Hydro DG system is also addressed. Dynamic models for the major system components, namely, wind energy conversion system, PV energy conversion system, hydro, inverter, and overall fuzzy logic controller units are developed. Then, a simulation model for the proposed hybrid power system has been developed using MATLAB /Simulink environment. This is done by creating subsystem sets of the major dynamic component models and then assembling into a single aggregate model. The overall power management strategy for coordinating and/or controlling the different energy sources is also presented in the thesis work. Generally there are 800 households with total electric demand of 71.6KW.To satisfy this demand 52%, 35% and 13% is to be contributed from wind/hydro/solar respectively. To use the power economically fuzzy logic controller is used. The controller monitors the demand and the available sources and then switches to the appropriate power supply according to the written rules. Simulations have been carried out to verify the system dynamic performance using a practical load profile and weather data. The result shows that the overall power management strategy is effective and the load demand is balanced. To complete this work, a grid extension from the closest substation has been compared with hybrid system. Cost of the grid extension is estimated based on the data obtained from EEP office. This is done in order to compare the cost of the designed hybrid power system against the cost of grid extension. The result shows that breakeven grid extension distance to be 23.9km which indicates that grid extension is preferable.

Ultra-short 230 fs laser pulses of 515 nm wavelength were tightly focused into 700 nm focal spots and utilised in opening ~ 400 nm nano-holes in a Cr etch mask that was tens-of-nm thick. Nano-holes ablated at slightly above the threshold of ablation irradiance became nano-disks and nano-rings at slightly lower pulse energies. Subtle sub-1 nJ pulse energy control was harnessed to pattern large surface areas with controlled nano-alloying of Si and Cr. This technique is extendable to vacuum-free large area patterning of nanolayers by alloying them at distinct locations with sub-diffraction resolution. Such metal masks with nano-hole opening can be used for formation of random patterns of nano-needles with sub-100 nm separation when applied to dry etching of Si.

This paper investigates how agroecology in Africa is researched for two purposes. First, we present evidence of links between agroecology and food and nutrition security in sub-Saharan Africa. Second, we investigate which pathways of influence are dominant in the existing research and which pathways are under-represented. To achieve these objectives we anchor our analysis within feminist economics, thereby making use of the concepts of social reproduction and agency in our analysis of the literature. By employing a systematic literature review of empirical studies from African countries, starting from 1996 to 2020, we consolidate evidence that agroecology has contributed to food and nutrition security by acting toward sustainability at the farm level. However, our review shows in a second step that social and household dimensions of agroecology at the level of households and territories are not well documented in research linking agroecology to food security and nutrition in sub-Saharan Africa. Given that sustainable production practices such as agroecological practices are not mutually exclusive from the social activities of farmers and cultural contexts in which farmers are embedded, it is important to consider social and ecological processes concomitantly when assessing the value of Agroecology programs.

Abstract: Diabetes mellitus (DM) is associated with severe COVID-19 infection and complications. This study assessed COVID-19 vaccine acceptance and hesitancy in diabetes and explored reasons for nonvaccinating. This was a web-based cross-sectional survey using a mixed-method approach conducted between March-May 2021 corresponding to most SSA countries' early vaccine rollout period. Participants were those aged ≥18 years with self-reported DM in 11 Sub-Saharan African (SSA) countries. Responses to comments on reasons for vaccine hesitancy and facilitators for vaccine uptake were analyzed. Of the 73 participants with DM, 65.8% were males older than 35 years (86.3%), had post-secondary education (90%), and a significant proportion was from South Africa (39.7%), Nigeria (28.8%) and Ghana (13.7%). 64.4% had COVID-19 symptoms, 46.6% were tested for COVID-19, of which 19.2% tested positive. Few participants (6.8%) had taken the COVID-19 vaccination, 65.8% were willing to take the vaccine, while 26.0% either refused or hesitated to take the vaccine. The main reasons identified for not taking the vaccine were: advice from religious leaders, concerns about the vaccine safety, its effects, and efficacy, mistrust of the pharmaceutical companies, the conspiracy theories around the vaccines, the process of production, and the personal belief of the participants. However, participants stated they would take the vaccine if given more education about it, receive positive feedback from those vaccinated, are rewarded for taking the vaccine or if vaccination becomes a condition for travel and employment. The findings of this study showed that uptake of the COVID-19 vaccine was very low in this high-risk group. It is imperative that efforts to increase the uptake of vaccines, such as the provision of education and relevant information, are made.

Satellite-based broad-scale (i.e., global and continental) human settlement data offer foundational information for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology, and demographic modeling. While many human settlement products report exceptional detection accuracies above 85%, there is a substantial blind spot in that product validation is typically centered on large urban areas rather than rural, small-scale settlements that are home to 3.4 billion people. In this study, we make use of a data-rich collection of 30 refugee settlements in Uganda to produce a targeted assessment of small-scale settlement detection by four broad-scale human settlement products: Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), World Settlement Footprint (WSF), High Resolution Settlement Layer (HRSL), and Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). We measured each product’s areal coverage within refugee settlements, assessed product detection accuracies in comparison to an independent dataset of 317,416 refugee settlement building footprints, and examined agreement between products. For refugee settlements established before 2016, the human settlement products had a low median F1-Score (F1) of 0.24, a high median false alarm rate of 0.59, and tended to only agree at locations of highest building density. Individually, WSF entirely overlooked 8 of the 30 study refugee settlements (median F1=0.17); GHS-BUILT-S2 underestimated the building footprint area by a median 50% (F1=0.15); GRID3 overestimated the building footprint area by a median 280% (F1=0.38); and HRSL underestimated the median area by 7% (F1=0.34). All products suffer from low detection accuracy and high false alarm rates, but GRID3 and HRSL, based on 0.5 meter resolution imagery, offer better detection accuracy than GHS-BUILT S2 and WSF, which are based on 10-30 meter resolution imagery. These results show that human settlement products have far to go in providing an accurate depiction of small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validation of broad-scale human settlement detection.

Microfluidic devices which integrate both rapid mixing and liquid jetting for sample delivery are an emerging solution for studying molecular dynamics via X-ray diffraction. Here we use finite element modelling to investigate the efficiency and time-resolution achievable using microfluidic mixers within the parameter range required for producing stable liquid jets. Three-dimensional simulations, validated by experimental data, are used to determine the velocity and concentration distribution within these devices. The results show that by adopting a serpentine geometry, it is possible to induce chaotic mixing, which effectively reduces the time required to achieve a homogeneous mixture for sample delivery. Further, we investigate the effect of flow rate and the mixer microchannel size on the mixing efficiency and minimum time required for complete mixing of the two solutions whilst maintaining a stable jet. In general, we find that the smaller the cross-sectional area of the mixer microchannel, the shorter the time needed to achieve homogeneous mixing for a given flow rate. The results of these simulations will form the basis for optimised designs enabling the study of molecular dynamics occurring on millisecond timescales using integrated mix-and-inject microfluidic devices.

Lead exposure is associated with poor cognitive development in children. Very few studies in sub-Saharan Africa (SSA) have studied blood lead levels (BLLs) and non-gasoline sources of exposure in children. Data from a birth cohort in Benin (2011-2013) suggested that 58% of one-year-old children had BLLs > 50 ug/L. We aimed to investigate the prevalence of elevated BLLs (>50 µg/L and >100 µg /L) among 425 of these children at six-years-of-age in 2016-18 and to compare BLLs between age one-year and six-years and study sources of lead at six years. BLLs were analyzed by inductively coupled plasma mass spectrometry. Multiple linear regression and quantile regressions were used to study potential sources of lead. The prevalence of BLLs >50 µg/L in children was 59.5% [Geometric Mean (GM) 56.4 µg/L, 95% CI: 54.1 - 58.7] at six years of age compared to 54.8% [GM 56.5 µg/L, 95% CI: 53.4-59.6] at one year of age. The prevalence of children with BLLs >100 µg/L decreased from 14.4% at one year of age to 8.2% at six years of age. After adjustment for all other covariates, consumption of peanut more than once per month was significantly associated with a 22.0% (95% CI: 4.6, 42.5) increment in BLLs at six years compared with no consumption. Consumption of bushmeat killed by lead bullets at six years was associated with an increase in the higher percentiles of BLLs (P75) compared with the absence of this source. Other potential sources of lead associated with BLLs with marginal significance were consumption of rice, paternal occupational exposure, and the presence of activity with the potential use of lead. This prospective cohort confirms the persistently high prevalence of elevated BLLs in children residing in a rural region in the south of Benin as well as the presence of multiple and continuous sources of lead. These results highlight the need for prevention programs to reduce and eliminate lead exposure in children.

Sustainable development goals (SDGs) are a global agenda consisting of 17 goals which are to be achieved in 2030 by all member states. SDGs are more holistic goals i.e. these goals are closely interrelated and they affect the progress of one another. Sub-Saharan Africa countries are, once more lagging behind in the implementations of SDGs despite the efforts by governments, non-government organisations and international agencies. Rwanda, South Africa and Zambia where the three Sub-Saharan Africa countries on which the study focused. The three countries in this study were chosen on the basis that they cater to the general overview of African countries performance on SDGs. To conduct this study, a desk research method was adopted and secondary data was utilised. An in-depth analysis was done on the on three subs Saharan African countries i.e. Rwanda, South Africa, Zambia. Those goals where serious attention is needed are goals 1-9, 16 and 17. Most Sub-Saharan African countries performed better on goals 11, 12 and 15. It was concluded that the achievement of Sustainable development goals remains a mere dream for Sub Saharan Africa unless serious interventions are made.

The importance of ICTs for dissemination of information to farmers has been verified by extension practitioners, information and communication scholars, policy makers and development agencies. Information related to new seed varieties, production technologies, livestock breeds, vaccines, including weed and pest control, as well as relevant market information is constantly required by smallholder farmers. The identification of specific attributes among smallholder farmers which contributes to their adoption of a proposed ICT-based information source provides an important tool for developing interventions which address the information needs of farmers. Using a literature survey methodology, pertinent studies related to adoption of ICTs, farmers’ information source usage and relevant frameworks were identified, including applicable theories and models in technology adoption and information behaviour. In the proposed framework, the socio-economic characteristics of smallholder farmers were posited as key variables influencing smallholder farmers, within a farming system, to adopt ICT-based information sources. The framework contributes to discern the prospects of adopting ICT-based information sources by individual farmers within a farming system, and may also envisage other related welfare outcomes and market participation pathways among smallholder farmers. The review also addresses the paucity of conceptual discourse, while contributing to a growing pool of research on ICT in African agriculture.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

In this study, the unsteady Reynolds-averaged Navier-Stokes (URANS) equations are employed in conjunction with the Menter Shear Stress Transport (SST)-Scale-Adaptive Simulation (SAS) turbulence model in compressible flow, unstructured mesh and complex geometry. Whereas, other scale-resolving approaches in space and time, such as Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES), supply more comprehensive information about the turbulent energy spectra of the fluctuating component of the flow; however, previously mentioned avenues imply computationally intensive situations, usually performed over structured meshes and relatively simply geometries. In contrast, the SAS approach is designed according to “physically” prescribed length scales of the flow. More precisely, it operates by locally comparing the length scale of the modelled turbulence to the von Karman length scale (which is prescribed according to local fluid velocity derivatives and depends on the grid point distribution in the computational domain). This length scale ratio allows the flow to dynamically adjust the local eddy viscosity in order to better capture the large scale motions (LSM) in unsteady regions of URANS simulations. While SAS may be constrained to model only low flow frequencies or waivenumbers (i.e., LSM), its versatility and computational low-cost make it attractive to obtain a quick first insight of the flow physics; particularly, in those situations dominated by strong flow unsteadiness. The selected numerical application is the well-known M219 three-dimensional rectangular acoustic cavity from the literature at two different freestream Mach numbers, M∞ (0.85 and 1.35) and a length-to-depth ratio of 5:1. Thus, we consider the “deep configuration" in experiments by Henshaw. The SST-SAS model has demonstrated a satisfactory compromise between simplicity, accuracy and flow physics description.

Developing a low-cost wireless energy meter with power quality measurement for smart grid applications represents a significant advancement in efficient and accurate electrical energy monitoring.In increasingly complex and interconnected electrical systems, this device is essential in various applications, such as smart grids, enabling real-time energy usage monitoring. In such a context, smart meters expand the possibilities for sustainable and efficient energy use and promote better utilization of energy sources, especially non-renewable ones. According to the 2020 International Energy Agency (IEA) research, non-renewable energy sources represent 65% of the global total. The smart meter developed in this work is built based on the ESP32 microcontroller and easily accessible components, using a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of the smart meter development was to enhance the software and simplify the hardware. Unlike traditional meters that perform electrical parameter calculations using complex hardware circuits, this project performed the calculations directly on the microcontroller. This approach reduced the complexity of the hardware, simplifying the meter’s design. With the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters were achieved without the need for additional circuits. This software-driven approach with simplified hardware brought benefits such as reduced production costs, lower energy consumption, and increased meter development and updates flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the transmission of collected data to remote monitoring systems for later analysis. The developed smart meter offers cost, energy efficiency, and flexibility benefits. It represents an innovative, efficient, and versatile solution for smart grid applications.

The optimization of the energy grid is a critical task for ensuring a sustainable and efficient energy future. Deep machine learning techniques have the potential to improve energy grid optimization by predicting energy demands and supplies, optimizing energy production and distribution, and detecting and preventing fraud. However, there are also several challenges associated with the use of deep machine learning in energy grid optimization. These include the lack of standardized datasets and data quality issues, interpretability and explain-ability of machine learning models, ethical and social implications of using machine learning, and integration with existing energy infrastructure and regulatory frameworks. Having a clear understanding that continued research and developments in deep learning applications to energy field are crucial for achieving a sustainable and efficient energy future. This paper therefore reviewed existing literature for challenges and opportunities associated with deep machine learning in energy grid optimization; and highlights the importance of continued research and development in the field. The paper found out that opportunities for future research in deep machine learning for energy grid optimization include advancements in machine learning algorithms and techniques, development of new datasets and data collection methods, integration of machine learning with other emerging technologies. It also established needs for collaborative research and public-private partnerships.

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

The article deals with the current state of energy consumption and CO2 emissions in the urban area of building clusters. There are many requirements, such as those set by the EU (FIT 55, decarbonisation in 2050, full electromobility in 2035, or mandatory annual reductions in energy consumption and CO2 production) or societal requirements, such as stable energy supply and its optimisation while significantly reducing CO2 emissions. This inspired us to design an energy model of a building (EMB) with smart grid implementation in a decentralized sustainable energy system. Simulation model studies were carried out to show the possibility of their application in the process of fully satisfying energy needs in terms of reducing their environmental impact. A decentralized photovoltaic system (microgrid) connected to a distribution grid. The main objective is to propose an original methodology for the design of smart "Nearly Zero Energy Buildings" (NZEB) and a subsequent solution for energy sustainability. This has led us to use HOMER, PV*SOL, Monte Carlo and DesignBuilder software which were chosen from the range of options that were and are available. Subsequently, a synthesis of the selected software solutions was carried out and a new model - the "Energy Model of a Building" (EMB) - was proposed in the process of integration with the energy performance of buildings, as an original optimization basis for designing smart buildings and smart areas, and even cities. The proposed EMB has been verified and evaluated within the experiment.

Under the goal of carbon peak and carbon neutrality, the carbon emission reduction of the automobile industry has attracted more and more attention in recent years. Electric vehicle has the dual attributes of power load and energy storage unit. With the increase of the number of electric vehicles, reducing carbon emissions through the collaborative interaction between electric vehicle and power network will become an important way to control carbon emissions in the automotive field. In this study, an optimization model of emission reduction benefits based on integrated development of electric vehicle and power grid is proposed, which explores the best technical way of synergy between power grid and electric vehicle, achieves the best carbon reduction effect and provides a model basis for large-scale demonstration application. Numerical simulations based on the real case in Beijing are conducted to validate the effectiveness of the proposed method.

The normal and Poisson distribution assumptions in the normal-Poisson mixed effects regression model are often too restrictive for many real count data. Several works have independently relaxed the Poisson conditional distribution assumption for counts or the normal distribution assumption for random effects. This work couples some recent advances in these two regards to develop a skew t–discrete gamma regression model in which the count outcomes have full dispersion flexibility and random effets can be skewed and heavy tailed. Inference in the model is achieved by maximum likelihood using pseudo-adaptive Gaussian quadature. The use of the proposal is demonstrated on a popular owl sibling negotiation data. It appears that, for this example, the proposed approach outperforms models based on normal random effects and the Poisson or negative binomial count distribution.

Modernist planners were given the opportunity to apply the idea of ​​the Modern Movement in the implementation of cities transforming after World War II, which was not possible before in long-term evolving structures. Usually, however, it was impossible to change everything: some urban and even architectural elements were necessary to be preserved. As a result, the elements of the ancient landscapes of these cities - historical objects and spaces - necessary for the continuity of evidence of history and identity have been preserved, sometimes through reconstruction. Finally, in some of these cities, both old and new elements contribute to the contemporary urban landscape. The aim of the research is to examine two key categories of urban structure in this perspective: 1. landmarks 2. urban network nodes. For this purpose: 1. the changes in the number and rank of the space with which the landmarks exhibition was carried out were compared; 2. the changes in the rank of nodes in the urban structure functioning both in the pre-war and in the present structure, as well as changes in their distribution and concentration areas were analyzed. The numbers of individual elements were balanced and the changes in the structure of their arrangement were analyzed with the use of a polygon grid. The analyzes were based on the case of a medium-scale European city (Białystok). Research shows that as a result of post-war reconstruction, the number of elements of the urban grid decreased, especially those of lower rank, the rank of others decreased.

The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.

The growing number of the accessed energy-efficient frequency conversion air conditioners is likely to generate a large number of harmonics on the power grid. The following shortage in the reactive power of peak load may trigger voltage collapse. Hence, this conflicts with people’s expectations for a cozy environment. Concerning the problems mentioned above, an active management scheme is put forward to balance the electricity use and the normal operation of air conditioning systems. To be specific, schemes to suppress the low voltage ride through (LVRT) and harmonic are designed firstly. Then to deaden the adverse effects caused by nonlinear group load running on the grid, and to prevent the unexpected accidents engendered from grid malfunction, the dynamic sensing information obtained by an online monitor is analyzed, which can be seen as an actively supervise mechanism. The combined application of active and passive filtering technology is studied as well. Thirdly, the new energy storage is accessed reliably to cope with peak-cutting or grid breaking emergencies, and the fuzzy control algorithm is researched. Finally, system feasibility is verified by functional modules co-operation simulation, and active management target is achieved under scientific and reasonable state-of-charge (SOC) management strategy.

This paper proposes a novel micro-grid structure, which can operate fully-autonomously with inherent seamless switching. It can operate independently in both grid-connected and islanded mode as a self-governed entity without relying on the utility grid. An AC/DC/AC converter is employed as the interface between the micro-grid and the utility grid, which enables the two entities to have different voltages in grid-connected mode. Seamless switching between operation modes can be achieved naturally. The micro-grid is regulated to exchange predefined amount of power with the utility grid in grid-connected mode. This will benefit the power dispatching algorithm of the power system. The predefined power is estimated based on power forecasting of local renewable generations and loads with consideration of the Sate of Charge (SOC) of the battery, and is updated and broadcasted every certain period. A small scale AC micro-grid with a rotating generator, battery storage and solar arrays etc. is built for investigation. Matlab/Simulink results are provided to validate the robustness and flexibility of proposed micro-grid and its operation strategy.

Rural electrification in remote areas of developing countries has several challenges which hinders energy access to the population. For instance the extension of the national grid to provide electricity in these areas is largely not viable. The Kenyan government has put a target to achieve universal energy access by the year 2020. In order to realize this objective, focus is being shifted to establishing off-grid power stations in rural areas.Among rural areas to be electrified, Habaswein is a settlement in Kenya's North Eastern region without connection to the National Power Grid where Kenya Power installed a stand alone hybrid mini-grid.Based on field observations, power generation data analysis, evaluation of the potential energy resource and simulations, this research intends to evaluate the performance of the Habaswein mini-grid and optimize the existing hybrid generation system to enhance its reliability and reduce the operation costs.The result will be a suggestion of how Kenyan rural areas could be sustainably electrified by using renewable energy based off-grid power stations. It will contribute to bridge the research gap currently existing on that area, and it will be a vital tool to researchers, implementers and the policy makers in energy sector.

Background: Several studies recently confirmed the emergence of resistance to antimalarial drugs in sub-Saharan Africa. Multiple first-line treatment (MFT) is one of the measures envisaged to respond to the emergence and spread of this resistance. The aim of this study was to identify perceived advantages and disadvantages of several MFT deployment strategies and to better understand potential implementation drivers and barriers. Methods: A qualitative survey was conducted in seven sub-Saharan countries amongst key opinion-leaders, national decision-makers, and end users. A total of 200 individual interviews were conducted and findings were analyzed following a thematic inductive approach. Results: From a policy perspective, the new MFT intervention would require endorsement at the global, national, and regional levels to ensure its inclusion in guidelines. Funding of the MFT intervention could be a bottleneck due to costs associated with additional training of healthcare workers, adaptation of drug delivery mechanisms, and higher costs of drugs. Concerning the MFT deployment strategies, a slight preference for the segmentation strategy was expressed over the rotation and geographic approaches, due to the perception that a segmentation approach is already in place at country level. Conclusions: The findings highlighted the need for a collective approach to MFT deployment through the engagement of stakeholders at all levels of malaria management.

Background: Waterborne diseases pose a significant global public health threat, compelling enhanced comprehensive surveillance. This study investigates the current infectious disease and antimicrobial resistance (AMR) surveillance systems, including wastewater and environmental surveillance (WES), in three sub-Saharan African countries: Tanzania, Burkina Faso, and the Democratic Republic of the Congo (DRC). The three countries have their specificities regarding wastewater networks and logistic systems. So, emphasizing the need to strengthen existing disease surveillance, the paper advocates for incorporating WES systems specifically designed for countries' context to monitor waterborne and re-emerging pathogens, as well as AMR.Methods: National workshops were conducted to assess the current clinical and environmental surveillance systems and identify priority pathogens for new environmental monitoring. Data were collected through surveys from experts in academia, research, policy, and healthcare. Results: Prioritized pathogens for WES include waterborne (poliovirus, Salmonella Typhi, Vibrio cholerae), respiratory (influenza A&B, SARS-CoV-2), other (Measles and Rubella, Mycobacterium tuberculosis). Recommended AMR pathogens include drug-resistant Mycobacterium tuberculosis, Salmonella spp., methicillin-resistant Staphylococcus aureus, and extended-spectrum beta-lactamase and carbapenemase-producing E. coli. Tanzania, Burkina Faso, and the DRC employ DHSI2-based centralized electronic systems for clinical data collection, complemented by Excel and paper-based registries. The WES approach is commonly employed for monitoring poliovirus and rarely for AMR and other pathogens. Discussion and conclusions: WES is a valuable tool for early detection of locally circulating human-derived pathogens, aiding in outbreak detection, data-driven epidemic response, and prevention. The availability of WES results underscores the importance of effective sanitation for safeguarding human, animal, and environmental health. WES is a pivotal tool for integrated risk management, preventing waterborne outbreaks, protecting drinking water sources, and ultimately gaining various UN Sustainable Development Goals. The study highlights the importance of customized WES systems in line with each country's context, emphasizing localized approaches for effective monitoring of waterborne pathogens and AMR.

Food and nutrition insecurity, affecting 30% of the global population in 2020, poses a significant challenge to achieving Sustainable Development Goal (SDG) 2 - Zero Hunger. Urbanization, particularly in sub-Saharan African cities e.g. Lagos, Nigeria, exacerbates these issues, impacting resources and contributing to informal settlements. There is emphasis on innovative solutions in urban farming, with Recirculating Aquaculture Systems (RAS) emerging as one of the promising approach due to its efficiency in space and capital utilization. However, maintaining optimal conditions in RAS, crucial for SDG 2 and 11, requires robust water quality monitoring. This study explores the availability of digital tools for water quality monitoring in small-scale urban RAS, evaluating handheld devices and IoT sensors' reliability through t-test statistical method. The results aim to guide practitioners in selecting effective monitoring tools, contributing valuable insights for sustainable aquaculture in urban areas, particularly in sub-Saharan Africa, where access to affordable digital solutions is pivotal for success and can attract youth to agri-food technologies.

Agroecology is a holistic approach to farming that emphasizes the use of local resources and ecological processes to increase productivity, reduce environmental impact, and enhance resilience. Despite its potential benefits, the adoption of agroecological practices in Sub-Saharan Africa has been slow. This paper examines the opportunities for promoting agroecology in the region, including the growing demand for organic and sustainable food, the availability of local knowledge and resources, and the potential for agroecology to improve rural livelihoods and support climate change adaptation. The premise is that the promotion of agroecology faces several challenges. which include, inadequate policy and institutional support, lack of access to credit and markets, limited extension services, and weak land tenure systems. The paper draws on case studies from across Sub-Saharan Africa to illustrate the opportunities and challenges of promoting agroecology in the region. These case studies highlight the diversity of agroecological practices and the importance of context-specific approaches. Overall, the paper maintains that agroecology has the potential to transform agriculture in Sub-Saharan Africa, but that realizing this potential will require concerted efforts from governments, civil society, and the private sector.

The larvicidal potential of olive mill waste water (OMWW) extract against mosquito larvae was evaluated. We exposed second instar Culex laticinctus (Edwards) and Aedes albopictus (Skuse) larvae to increasing OMWW concentrations. In addition, we tested the effects of long term exposure to a low OMWW concentration on larval development. The two mosquito species responded differently to OMWW extracts. Larval Cx. laticinctus showed higher vulnerability to increasing OMWW concentrations in comparison to Ae. albopictus. By contrast, Cx. laticinctus exposed to a low OMWW concentration until pupation did not exhibit survival differences, but rather sex-specific development responses. These trends were not observed in Ae. albopictus whose survival was significantly reduced following long exposer to OMWW extraction. These results showed that OMWW, which is known as agricultural byproduct waste, may be reused as a biopesticide.

The urban population in developing countries, especially sub-Saharan Africa, is rapidly increas-ing. As towns and cities grow, so does the demand for fish protein. While flow-through aqua-culture can provide fresh, healthy and nutritious fish protein, it is plagued by extensive land re-quirement as well as effluent discharge, thus, unsuitable for city regions. Alternatively, small-scale Recirculating Aquaculture System (RAS) could improve food and nutritional security (FNS), livelihoods as well as reduce environmental degradation in urban areas despite land and water constraints. The question however remains - what are the key technical, business and managerial issues, surrounding small-scale RAS in urban farming? This study reviews the RAS prototype of the Sustainable Aquaponics for Nutritional and Food Security in Urban Sub-Saharan Africa (SANFU) II project based on mass balance and stock density, relevant for fish survival and/or availability as well as net cash-flow analyses. The results suggest that small-scale RAS are technically and financially viable only with family labor having proper aquaculture monitoring and management skills. Furthermore, access to adequate equipment and inputs as well as electricity for the recirculating system are crucial. Urban innovation actors will adopt RAS if operations are profitable given that family labor is employed.

The energy transition requires integration of different energy carriers, including electricity, heat, and transport sectors. Energy modeling methods and tools are essential to provide a clear insight into the energy transition. However, the methodologies often overlook the details of small-scale energy systems. The study states an innovative approach to facilitate sub-national energy systems with 100% renewable penetration and sectoral integration. An optimization model, OSeEM-SN, is developed under the Oemof framework. The model is validated using the case study of Schleswig-Holstein. The study assumes three scenarios representing 25%, 50%, and 100% of the total available biomass potentials. OSeEM-SN reaches feasible solutions without additional offshore wind investment, indicating that they can be reserved for supplying other states’ energy demand. The annual investment cost varies between 1.02 bn – 1.44 bn €/yr for the three scenarios. The electricity generation decreases by 17%, indicating that with high biomass-based combined heat and power plants, the curtailment from other renewable plants can be decreased. Ground source heat pumps dominate the heat mix; however, their installation decreases by 28% as the biomass penetrates fully into the energy mix. The validation confirms OSeEM-SN as a beneficial tool to examine different scenarios for sub-national energy systems.

SARS-CoV-2 is mutating and creating divergent variants across the world. An in-depth investigation of the amino acid substitution in the genomic signature of SARS-CoV-2 proteins is highly essential for understanding its host adaptation and infection biology. A total of 9587 SARS-CoV-2 structural protein sequences collected from 49 different countries are used to characterize protein-wise variants, substitution pattern (type and location), and major substitution changes. The majority of the substitutions are distinct, occurred mostly in a particular location, and leads to a change in amino acid's biochemical properties. In terms of mutational changes, Envelope (E) and Membrane (M) proteins are relatively stable than Nucleocapsid (N) and Spike (S) proteins. Several co-occurrence substitutions are observed, particularly in S and N proteins. Substitution specific to active sub-domains reveals that Heptapeptide Repeat, Fusion peptides, Transmembrane in S protein, and N-terminal and C-terminal domains in N protein are remarkably mutated, and also found few deleterious mutations in these domains.

Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range, and greatly reduce the time complexity. The experimental results on the two real data sets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm and pBmH algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large scale social network.

Land use and land cover change (LULCC) is a critical factor for enhancing the soil erosion risk and land degradation process in the Wabi Shebelle Basin. Up-to-date spatial and statistical data on basin-wide erosion rates can provide an important basis for planning and conservation of soil and water ecosystems. The objectives of this study were to examine the magnitude of LULCC and consequent changes in the spatial extent of soil erosion risk, and identify priority areas for Soil and Water Conservation (SWC) in the Erer Sub-Basin, Wabi Shebelle Basin, Ethiopia. The soil loss rates were estimated using an empirical prediction model of the Revised Universal Soil Loss Equation (RUSLE) outlined in the ArcGIS environment. The estimated total annual actual soil loss at the sub-basin level was 1.01 million tons in 2000 and 1.52 million tons in 2018 with a mean erosion rate of 75.85 t ha^–1 y^–1 and 107.07 t ha^–1 y^–1, respectively. The most extensive soil loss rates were estimated in croplands and bare land cover, with a mean soil loss rate of 37.60 t ha^–1 y^–1 and 15.78 t ha⁻¹ y⁻¹, respectively. The soil erosion risk has increased by 18.28% of the total area, and decreased by 15.93%, showing that the overall soil erosion situation is worsening in the study area. We determined SWC priority areas using the Multi-Criteria Decision Rule (MCDR) approach, indicates that the top three levels identified for intense SWC account for about 2.50%, 2.38%, and 2.14%, respectively. These priority levels are typically situated along the steep slopes in Babile, Fedis, Fik, Gursum, Gola Oda, Haramaya, Jarso, and Kombolcha districts that need emergency SWC measures.

Imperceptible vibratory noise stimulation has shown to be an effective means of improving stability for both whole body postural control and simple motor control tasks. While the physiological mechanism affording this improvement is uncertain, it is suspected that sensory noise stimulation may elicit a stochastic resonance-like effect within the somatosensory system. A stochastic resonance effect describes the phenomenon in which noise added to a non-linear system improves signal detection rather than degrading it. One hallmark of stochastic resonance is the existence of an optimal noise level which elicits the best system performance. There is disagreement in the literature regarding the presence of an optimal stimulation level for motor stability in humans. The goals of this study were to: 1) determine optimal stimulation level as a function of an individual’s sub-sensory threshold level, and 2) to determine whether performance of a force stability task was significantly better when subjects received stimulation at this identified optimal level compared to other sub-sensory threshold stimulation levels. Eighteen (18) participants completed an isometric finger flexion task with visual feedback while receiving noise stimulation scaled to varying percentages of their individual sub-sensory threshold level. Performance for this force stabilization task was quantified as the root-mean-square (RMS) error between the target force and the actual generated force values. Despite controlling all other signal properties and varying only amplitude, optimal noise stimulation values still varied widely across participants (10-100% sub-sensory threshold level). Statistical modeling revealed a significant improvement in task performance with optimal noise stimulation compared to other sub-sensory stimulation levels (p ≤ 0.019) with estimated marginal mean differences in force errors ranging from 0.13 to 0.23 N. Moderate significant Spearman correlations (r_s = 0.49 and r_s = 0.56, respectively) were found between finger flexion maximal voluntary contraction (MVC) and sub-sensory threshold level and MVC and optimal stimulation level. A strong, significant Spearman correlation (r_s = 0.65) was observed between sub-sensory threshold level and optimal stimulation level. Although these correlations do not provide a means to predict optimal stimulation level as a function of these other measures, optimal stimulation level appears to increase with sub-sensory threshold and MVC.

The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.

As the global transition towards renewable energy accelerates, the integration of grid-scale energy storage systems has become increasingly critical for stabilizing energy supply and enhancing grid reliability. Among various storage technologies, lithium-ion batteries stand out for their high energy density, scalability, and relatively low environmental impact. This study investigates the performance and longevity of lithium-ion batteries in grid-scale applications, focusing on the impact of operational parameters such as cycling frequency, depth of discharge, temperature fluctuations, and charging/discharging rates. Employing a mixed-methods approach, we combined quantitative analysis of performance metrics (cycle life, capacity fade, efficiency) with qualitative assessments from case studies of existing grid-scale projects. Our findings reveal that while lithium-ion batteries exhibit robust performance in grid-scale storage, their longevity and efficiency are significantly influenced by operational conditions. Specifically, temperature management and optimized cycling strategies were identified as key factors in mitigating degradation and extending battery life. This research provides a comprehensive understanding of lithium-ion battery behavior in grid-scale environments, offering valuable insights for the design, optimization, and management of energy storage systems. By addressing the challenges of battery performance and longevity, our study contributes to the development of more sustainable and reliable grid storage solutions, facilitating the broader adoption of renewable energy sources.

Energy management systems (EMS) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of a MILP-based EMS developed and implemented in MATLAB using Yalmip software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year considering real electricity selling and purchase price. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. The insights and strategies developed through this research contribute to the overall goals of improving energy efficiency and reducing the environmental footprint of future energy districts.

Solid-state transformer (SST) is a promising technology for smart grids due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The SST is designed in tandem with three stages, i.e., the input, isolation, and output stages. As three converter stages are used to design the SST to make a more reliable device in practice, it is highly prone to instabilities. Moreover, the key objective of SST design is to implement a modern power distribution system to make it more intelligent and reliable. The stability issue can be even more complicated by the presence of distributed energy resources in the distribution system. Thus, the stability of SST must be measured prior to /during the design, and to determine, a state-space model is used, which is developed in this paper. Each stage of SST is modeled with its controller, and the system's stability is measured through the controllability and observability test. Further, the conversion from state-space to transfer function model is done, and the stability of SST is shown using frequency and time-domain diagrams. With these, the different plots, namely, the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot, are used to ascertain the relative and absolute stability. Finally, the SST Simulink model is tested and validated through hardwate-in-the-loop simulation, i.e., OPALRT, to show its effectiveness and applicability.

Smart grid capabilities have grown significantly in recent years. The smart grid provides advanced real-time handling of faults, advanced automatic control for efficient electricity transmission, monitoring and collection of the electrical system's capacity, and communication for information sharing. Unfortunately, its exposure to public networks makes it increasingly vulnerable to privacy breaches, vulnerabilities, and cyber-attacks. Cyber security threats and vulnerabilities in smart grid networks have become a primary concern that needs to be addressed before deploying a smart grid. Furthermore, the wide range of protocols increases the attack surface of a smart grid. This study focuses on the vulnerability of Modbus, which is regarded as one of the most prevalent protocols in smart grid communication networks. This paper presents preliminary findings of analyzing cyber-attacks against the Modbus protocol using a virtual testbed to investigate its effects on the smart grid network protocol. The concept incorporates an emulated Modbus/TCP network environment built from open-source software components that imitate fundamental industrial control features of the smart grid. Finally, we analyze the cycle of a cyber-attack leading through Reconnaissance to a DoS attack on the Modbus/TCP protocol and propose improvements to the test bed for protocol attack detection and mitigation.

The present study describes the development and application of a computer model of the national electricity system for the Caribbean dual-island nation of Antigua and Barbuda to investigate the cost-optimal mix of solar photovoltaics (PV), wind, and in the most novel contribution, concentrating solar power (CSP). These technologies, together with battery and hydrogen energy storage, can enable the aim of achieving 100% renewable electricity and zero carbon emissions. The motivation for this study was that while most nations in the Caribbean rely largely on diesel fuel or heavy fuel oil for grid electricity generation, many countries have renewable resources beyond wind and solar energy. Antigua and Barbuda generates 93% of its electricity from diesel-fueled generators and has set targets of becoming a net-zero nation by 2040 and having 86% renewable energy generation in the electricity sector by 2030, but the nation has no hydroelectric or geothermal resources. The modeled, optimal mix of renewable energy technologies presented here was found for Antigua and Barbuda by assessing the levelized cost of electricity (LCOE) for systems comprising various combinations of energy technologies and storage. Other factors were also considered, such as land use and job creation. It was found that 100% renewable electricity systems are viable and significantly less costly than current power systems, and that there is no single defined pathway towards a 100% renewable energy grid, but several options are available.

Electric vehicles (EVs) will have a greater need for the amount of electricity needed to charge them as their popularity grows. It is anticipated that in order to accomplish this objective, it will be essential to implement a variety of solutions for grid transportation that are designed to complement one another and to make significant changes to the transmission infrastructure. It is possible to reduce the amount of energy that is lost on the power network through strategic planning and control, which may include economic models and methods to engage and reward users. This would eliminate the need for grid upgrades. Charging electric vehicles can also assist alleviate problems with transmission systems that are caused by the allocation of electric vehicles (EVs) using bidirectional charging method. The most significant problems that can occur with a transmission network are power loss and unstable voltage. Adding EV units to the transmission network is typically an effective method for resolving these challenges. As a result, EVs need to have the appropriate arrangement and dimensions. This research establishes where and how many electric vehicles (EVs) should be in a radial transmission network both before and after the adjustment is made. An artificially intelligent (AI) approach, known as a hybrid genetic algorithm particle swarm optimization (HGAIPSO), is used both before and after the radial network modification to find the optimal EV location and size. When electric vehicles are coordinated in an active transmission network, power losses are decreased, voltage profiles are raised, and system stability is increased. These benefits can be attributed to the greater use of electric vehicles. The simulation found that incorporating EVs into the testing system resulted in a considerable decrease in the quantity of power that was wasted. The minimal bus voltage of the system also undergoes similar kinds of enhancements. According to the findings of the comparative study, the proposed method mitigates both the voltage fluctuations and the power losses that occur in the transmission system. For type 1, type 2, and type 3 EV allocations, the IEEE-30 bus test system reduced real power loss by 40.70%, 36.24%, and 42.94%, respectively. IEEE-30 bus voltage reaches 1.01 pu.

From the standpoint of Neoliberal Institutionalism, this study explores Malaysia's participation in activities that make energy the topic and/or object of foreign policies, whether through bilateral or multilateral engagements. This research, which aims to evaluate the relevance of energy in Malaysia's economy and diplomacy and to explain Malaysia's reason for its involvement in these cooperations, is necessary because of Malaysia's growing involvement in the global energy market. Information for this study was gathered from a variety of government sources, interviews, published statistical data, and previous studies. The study demonstrates how vital energy is to Malaysia's economy. The Lao PDR-Thailand-Malaysia-Singapore Power Integration Project (LTMS-PIP) initiative, in which Malaysia is participating, is evidence of the viability of multilateral power commerce in the area. The Peninsula Gas Utilisation (PGU) pipeline in Peninsular Malaysia and the Four-Fuel Diversification Policy 1981 (4FDP 1981), which increases natural gas utilisation, are related to Malaysia's participation in the Malaysia-Thailand Joint Development Area (MT-JDA). Malaysia would want to think about streamlining its energy administration.

ABSTRACT Background Ubiquitination plays an important role in protein post-translational processes and has been found to be involved in a number of regulatory functions including proteasome degradation, DNA repair, transcription, signal transduction, endocytosis, and sorting. As the identification of ubiquitination site is critical to furthering our understanding of the mechanism of ubiquitination, various experimental and machine learning methods have been used to conduct this task. It has been an important but challenging task to improve the accuracy of ubiquitination site prediction. In this research, we explore the possibility of improving the prediction performance of machine learning by incorporating grid search in the training process. Method We developed grid search procedures for each of six widely used machine learning methods including NB, LR, DT, SVM, LASSO, and KNN, and applied them to ubiquitination site prediction using the six PCP datasets that were previously developed. For each of the ML methods, we developed a set of values for each of the tunable hyperparameters available to the method. These sets of values then can be combined to form a large grid of hyperparameter settings, and each of these settings is used in the grid search. We integrated 5-fold cross-validation in grid search to train and test ML models and applied an additional independent validation procedure by conducting a pre-training 80-20 sample split. We evaluated the performance of the six methods by comparing them side by side for each of the six datasets. We also compared the grid search results with the results that were previously published without doing grid search. To optimize the prediction performance, we trained 1.1 million ML models in total through grid search. Results We compared the overall prediction performance of these six methods, as well as their prediction performance when working with balanced vs. imbalanced data, and large-scale vs. small-scale data. From the perspective of dataset, we find that the overall performance of every PCP dataset has been significantly improved in this study compared to the previous study, with the percentage increase of the average AUC of all six datasets ranging from 7.9% (PCP-4) up to 17.0% (PCP-1). From the perspective of method, we find that three out of four methods significantly benefit from grid search comparing to their previously published non-grid search results, with the maximum AUC improvement as high as 47% (LASSO on PCP-5), 43.3% (NB on PCP-1), and 33.7% (SVM on PCP-6). SVM overall ranks number one, followed by KNN as the number two performer based on their average AUCs on all datasets. But these two also ranked the top two (KNN 76 days and SVM 15 days) in terms of the total running time that they need to do grid search. We also find that SVM, KNN, and DT tend to handle small-scale and imbalanced datasets better, while LR, and LASSO are doing well with large-scale and balanced datasets. NB is more sensitive to data imbalance while less sensitive to the size of a dataset. Conclusions Our results show that using grid search has improved the performance of ubiquitination prediction significantly. We find that the performance of a method is closely related to its hyperparameter setting and the type of data it handles. Even though SVM is on average an outperformer, none of the methods can provide the best performance for all datasets. When sufficient computing resources are well accessible, grid search is an effective way to identify both a top performing model for a machine learning method and a suitable machine learning method for a particular dataset.

Because of the significant enabling role smart meters can play in securing the transition towards sustainable energy distribution, the paper provides insights to support smart meters implementation projects. Energy utilities must propose adequate solutions to manage grid-upgrading projects and, in the meantime, increase efficiency levels. Based on empirical data analysis the paper provides insights aimed at maximize probability of success of smart meters projects. Results show common patterns of variables that may support project undertakers, policymakers and scholar when it comes to analyze projects to predict to maximize opportunities. For smart meters projects to succeed, regulatory stability is essential as long-period investments grids produce benefits for energy utilities, and for society.

In this paper, the overall performance of Cu-(In,Ga)-Se₂ (CIGSe) solar cells was improved in the ultraviolet (UV)–visible wavelength region by two-segment process. In which, adjusting DC sputtering powers (20–40 W) for absorbers (segment I), and metal-alloy compositions (CdS, ZnO/CdS, ZnMgO/CdS and ZnMgO) for buffers (segment II) were explored and characterized. Upon choosing the optimal-30-W CIGSe-absorber (with 0.95-CGI ratio) sputtered by the segment-I process, the Cu-rich film can distinctly boost grain growth, thus reducing the trap state density. After segment-II process as the toxic-CdS alternative, the optimal Zn_0.9Mg_0.1O-alloy buffer can reach the best conversion efficiency (η = 8.70%) was attained for the environmental protection. Meanwhile, and the overall internal/external quantum efficiencies (IQE/EQE) were improved by 13.15%, respectively for the 2.48–3.62-eV bandgap (short wavelength) range. The developed photovoltaic (PV) module (with 9 optimum-CIGSe cells) exhibits acceptable stability with a variance within ±5% over the 60-day experiment. This discovery in PV-device research contributes to a new scientific understanding of renewable energy. Furthermore, this study undeniably enhances the progress of practical applications for PV-modules in alignment with sustainable development goals. It also actively supports the development of eco-friendly communities.

Background: One challenge in implant dentistry is to reduce marginal bone resorption to avoid long-term esthetic and biological complications. Sub-gingival implant placement has been suggested as a suitable strategy. Methods: 38 healthy patients were enrolled to receive bone level (BL – Control group) or 2 mm sub-crestal (SC - Test group) conical connection, platform-switched implants. An immediate tissue level abutment was used in the Test group by following a One Time Abutment (OTA) restorative procedure. Marginal Bone Modification (MBM) was calcu-latied on standardized radiographs at surgery (T0), loading (T1), and 6 months (T2) and 12 months after loading (T3), and cathegorised in Bone Loss (BL) if the resorption occurred below the implant neck and Bone Remodeling (BR) if above. Pocket probing depth (PPD), Bleeding on probing (BoP) and Plaque Index (PI) were also collected. Results: The mean MBM in the test group after 12 months was 0.61mm, compared to 0.52mm in the control group. However, no BL was observed in the test group, as the MBM completely occurred above the implant neck as BR, whilst in the control group MBM corresponded in total to BL. The average PPD was 2.38mm in the test group and 3.04mm in the control group after 12 months, BoP 50% and 43%, PI 33% and 19.44% respectively. Conclusions: Sub-crestal conical connection, platform-switched implants could be a suitable clinical choice to avoid BL with exposure of the treated implants surfaces.

Sarocladium and Fusarium species are commonly identified as causal agents of rice sheath rot disease worldwide. However, limited knowledge exists about their genetic, pathogenic, and toxigenic diversity in sub-Saharan African (SSA) countries, where an increasing incidence of this disease has been observed. This study aimed at identifying, characterizing, as well as assessing the genetic, pathogenic, and toxigenic diversity of the pathogens associated with rice sheath rot disease in Mali, Nigeria, and Rwanda. In this study seventy fungal isolates were obtained from rice plants displaying disease symptoms on research and farmer’s fields in Mali, Nigeria, and Rwanda. Thus, an extensive comparative analysis was conducted to assess their genetic, pathogenic, and toxigenic diversity. The Fusarium spp. were characterized using the translation elongation factor (EF-1α) region, while a concatenation of Internal Transcribed Spacer (ITS) and Actin-encoding regions were used to resolve Sarocladium species. Phylogenetic analysis revealed four Fusarium species complexes. The dominant complex in Nigeria was the Fusarium incarnatum-equiseti species complex (FIESC), comprising F. hainanense, F. sulawesiense, F. pernambucatum, and F. tanahbumbuense, while F. incarnatum was found in Rwanda. The Fusarium fujikuroi species complex (FFSC) was predominant in Rwanda and Mali, with species such as F. andiyazi, F. madaense, and F. casha in Rwanda, and F. annulatum and F. nygamai in Mali. F. marum was found in Nigeria. Furthermore, Fusarium oxysporum species complex (FOSC) members, F. callistephi and F. triseptatum, were found in Rwanda and Mali, respectively. Two isolates of F. acasiae-mearnsii, belonging to the Fusarium sambucinum species complex (FSAMSC) were obtained in Rwanda. Isolates of Sarocladium which were previously classified in three phylogenetic groups were resolved into three species which are: attenuatum, oryzae and sparsum. S. attenuatum was dominant in Rwanda, while S. oryzae and S. sparsum were found in Nigeria. Also, the susceptibility of FARO44, a rice cultivar released by Africa Rice Centre (AfricaRice) was tested against isolates from the four Fusarium species complexes and the three Sarocladium species. All isolates evaluated could induce typical sheath rot symptoms albeit with varying disease development levels. In addition, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to determine variation in the in vitro mycotoxins of the Fusarium species. Regional differences were observed in the in vitro mycotoxins profiling. Out of the forty-six isolates tested, nineteen were able to produce one to four mycotoxins. Notably, very high zearalenone (ZEN) production was specific to the two F. hainanense isolates from Ibadan-Nigeria, while Fusarium nygamai isolates from Mali produced high amounts of fumonisins. To the best of our knowledge, it seems this study is the first to elucidate genetic, pathogenic, and toxigenic diversity of Fusarium species associated with the rice sheath rot disease complex in selected countries in SSA.

The main target of this study was to evaluate the impact of future climate change on the available surface water resources in the Dhidhessa Sub-basin, Abbay Basin, Ethiopia. For the prediction, high-resolution Regional Climate Models (RCMs) from multiple climate models with data from Representative Concentration Pathways (RCPs) prepared by the CCAFS were used. The predictions of future discharge (stream flow) were based on climate scenarios data of baseline period of 1991 to 2020 and for the future with two time windows, 2044 (2030–2059) and 2084 (2070–2099), on a monthly time step after bias correction was conducted to both precipitation and temperature in the future climate under each RCP scenario. After sensitivity analysis, calibration (1994–2011), and validation (2012–2020) of the model for the Dhidhessa Sub-basin with the SUFI-2 program in the SWAT-CUP model, the SWAT model was used to determine water balance and stream flow from the SWAT model system. The SWAT model performed well in predicting stream flow in the Dhidhessa Sub-basins, with a coefficient of determination (R2) ranging from 0.8 to 0.94 and a Nash Sutcliffe value (NSE) ranging from 0.76 to 0.89. The percent decrease in mean annual stream flow from 2044 and 2084 were 10 %, in 2044, and 6.3% in 2084 respectively. The seasonal result under short-term 2044 of stream flow present change significantly decreased in spring, winter, and autumn with 20.2%, 67.4%, and 67.4% respectively. While summer season increased by 43.1%, under short-term 204. In long-term 2084 percent change declined except summer seasons, with 14.7%, 58.1%, and 3.3%, change in spring, winter, and autumn, respectively, and summer, with 51.1% increased and With RCP4.5 (1.64%) and RCP8.5 (2.1%) changed. In the future Dhidhessa stream flow increases and decreases from the baseline era. The decreasing stream flow in 2044 and 2084 will negatively affect agricultural production. This study revealed that any effect on this river resulting in a drop in flow will have a direct impact on ongoing water resource development and socio-economic development of the area.

In this contribution, we present a high-speed multiplex grating spectrometer based on a spectral coding approach that is founded on principles of compressive sensing. The spectrometer employs a single-pixel InGaAs detector to measure the signals encoded by an amplitude spatial light modulator (digital micromirror device, DMD). This approach leads to a speed advantage and multiplex sensitivity advantage atypical for standard dispersive systems. Exploiting the 18.2 kHz pattern rate of the DMD, we demonstrate 4.2 ms acquisition times for full spectra with a bandwidth of 450 nm (5250 cm-1 – 4300 cm-1; 1.9 µm – 2.33 µm). Due to the programmability of the DMD, spectral regions of interest can be chosen freely, thus reducing acquisition times further, down to the sub-millisecond regime. The adjustable resolving power of the system accessed by means of computer simulations is discussed, quantified for different measurement modes, and verified by comparison with a state-of-the-art Fourier-transform infrared spectrometer. We show measurements of characteristic polymer absorption bands in different operation regimes of the spectrometer. The theoretical multiplex advantage of 8 was experimentally verified by comparison of the noise behavior of the spectral coding approach and a standard line-scan approach.

Urban cities in sub-Saharan Africa (SSA) are faced with ambient air pollution. This is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM_2.5). Going through most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM_2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24-hour concentration measured across all sites was 38 µg/m³ with the highest PM_2.5 period average concentration of 91 µg/m³ measured in Kampala, Uganda and lowest concentrations of 15 µg/m³ measured in Faraja, The Gambia. Kampala-Uganda and Nnewi-Nigeria recorded the longest periods with concentrations>250µg/m³. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM_2.5 concentrations in SSA countries. Such data are crucially needed to raise public-, societal and policymaker awareness about air pollution across SSA.

The coronavirus disease 2019 (COVID-19) pandemic is the defining global health and socioeconomic crisis of our time and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that climatic features, specifically the temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics (e.g., SARS, MERS); however, the influence of climatic parameters on COVID-19 remains extremely controversial. The goal of this study is to quantify the existing relationship between several daily climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, wind direction and intensity, and evaporation), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic. The daily deaths parameter was not considered because it is not reliable due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy area from March 1, 2020, to April 20, 2020. This region in Italy exhibited the largest number of official deaths in the world per million inhabitants, with a value of approximately 1700 per million on june 30, 2020. Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas. The Mann-Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least in the spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20%. Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania, the most weakly affected by the pandemic. The temperatures at these stations were approximately 1.5°C lower than that in the cities in Lombardy of Bergamo and Brescia, again confirming that a significant relationship between the increase in temperature and decrease in virology from COVID-19 was not evident, at least in the Italian peninsula.

Aflatoxins continue to raise health concerns as unavoidable and widespread natural contaminants of foods and feeds with serious impact on health, agricultural and livestock productivity, and food safety. They are secondary metabolites produced by Aspergillus species distributed on three main sections of the genus (section Flavi, section Ochraceorosei, and section Nidulantes). Aflatoxin-producing species, mainly A. flavus and A. parasiticus thrive under hot and humid conditions in the field or during storage, which are met in tropical and sub-tropical regions. Poor economic status of a country exacerbates the risk and the extent of crop contamination due to faulty storage conditions that are usually suitable for mold growth and mycotoxin production; temperature of 22 to 29°C and water activity of 0.90 to 0.99. This situation paralleled the prevalence of high liver cancer and the occasional acute aflatoxicosis episodes that have been associated with these regions. Few of the presently known aflatoxins (>18) have been sufficiently studied for their incidence, health-risk, and mechanisms of toxicity to allow effective intervention and control means that would significantly and sustainably reduce their incidence and adverse effects on health and economy. Among these, aflatoxin B1 (AFB1) has by far been the most studied; and yet, many aspects of the range and mechanisms of the diseases it causes remain to be elucidated. Its mutagenicity, tumorigenicity, and carcinogenicity, which are the best known still suffer from many limitations regarding the relative contribution of the oxidative stress and the reactive epoxide derivative (Aflatoxin-exo 8,9-epoxide) in the induction of the diseases, as well as its metabolic and synthesis pathways. Additionally, despite the well-established additive effects for carcinogenicity between AFB1 and other risk factors, e.g., hepatitis viruses B and C, and the algal hepatotoxic microcystins, the mechanisms of this synergy remain unclear. A review of publications on the incidence and concentrations of aflatoxins in selected foods and feeds from countries whose crops are classically known for their highest contamination with aflatoxins, reveals that despite the intensive efforts made to reduce such an incidence, there has been no clear tendency, with the possible exception of South Africa, towards sustained improvements. The levels and incidence are essentially influenced by the rainfall and temperature during the cultivation year or two successive years with alternating dry and wet seasons. This review aimed to update the main aspects of aflatoxin production, occurrence and incidence in selected countries, and associated adverse health effects. In addition to AFB1 which was the main focus of the review, other aflatoxins were addressed whenever relevant data were available.

Microgrid is one of the important ways to accommodate distributed PV generation. However, the optimal planning scheme of microgrids is closely related to solar resources, load characteristics, and the key components of microgrid systems. This paper uses HOMER software to conduct collaborative planning for grid-connected PV-storage microgrids with electric vehicles in multiple scenarios. Specifically, we construct a multi-scenario capacity optimization model for PV generation, energy storage, and converters, considering both the cleanliness and economic performance of microgrids. The performance is quantified using indices such as net present cost, levelized cost of electricity, and carbon dioxide emission under multiple scenarios. Finally, we conduct extensive case studies on a business park in Wuhan, China, to compare and discuss the planning performance under multiple scenarios, as well as sensitivity analysis with specific cases.

The article describes the methodology of the experimental analysis of operational and cost-effective assessment using geographic information system (GIS) technologies, as opposed to the assessment of the tourism potential of the nature of the Fergana Valley in Uzbekistan using long term and costly classical approaches. Based on the ArcGIS (10.8) grid index approach of 20 natural geographical indicators of the valley, such as geographical location, relief, climate, hydrographic and biological, the level of tourism comfort was determined and the location of 6 tourist-recreation zones were defined. The level of accuracy of the tourist-recreational zones all situated utilizing GIS technologies was confirmed by field-expedition approaches.

The North Sea Offshore Grid concept has been envisioned as a promising alternative to: 1) ease the integration of offshore wind and onshore energy systems, and 2) increase the cross-border capacity between the North Sea region countries at low cost. In this paper we explore the techno-economic benefits of the North Sea Offshore Grid using two case studies: a power-based offshore grid, where only investments in power assets are allowed (i.e. offshore wind, HVDC/HVAC interconnectors); and a power-and-hydrogen offshore grid, where investments in offshore hydrogen assets are also permitted (i.e. offshore electrolysers, new hydrogen pipelines and retrofitted natural gas pipelines). We compare these scenario results with a business as usual scenario, in which offshore wind is connected radially to the shore and no offshore grid is deployed. All scenarios are run with the IESA-NS model, without any specific technology ban and under open optimization. This paper also presents a novel methodology, combining Geographic Information Systems and Energy System Models, to cluster offshore spatial data and define meaningful offshore regions and offshore hub locations. This novel methodology is applied to the North Sea region to define nine offshore clusters taking into account offshore spatial claims, and identifying suitable areas for single-use and multi-use of space for renewable energy purposes. The scenario results show that the deployment of an offshore grid provides relevant cost savings, ranging from 1% to 4.1% of relative cost decrease (2.3 bn € to 8.7 bn €) in the power-based, and ranging from 2.8% to 7% of relative cost decrease (6 bn € to 14.9 bn €) in the power-and-hydrogen based. In the most extreme scenario (H2) an offshore grid permits to integrate 283 GW of HVDC connected offshore wind and 196 GW of HVDC meshed interconnectors. Even in the most conservative scenario (P1) the offshore grid integrates 59 GW of HVDC connected offshore wind capacity and 92 GW of HVDC meshed interconnectors. When allowed, the deployment of offshore electrolysis is considerable, ranging from 61 GW to 96 GW, with capacity factors of around 30%. Finally, we also find that, when imported hydrogen is available at 2 €/kg (including production and transport costs), large investments in an offshore grid are not optimal anymore. In contrast, at import costs over 4 €/kg imported hydrogen is not competitive.

As a consequence of the increasing share of renewable energies and sector coupling technologies, new approaches are needed for the study, planning, and control of modern energy systems. Such new structures may add extra stress to the electric grid, as is the case with heat pumps and electrical vehicles. Therefore, the optimal performance of the system must be estimated considering the constraints imposed by the different sectors. In this research, a dispatch optimization method with an iterative grid constraint generation, decoupled from the linear unit commitment problem, is employed. From the considered scenarios, it was found that in a typical German neighborhood with 150 households, PV penetration of ∼5kWp per household can lead to curtailment of ∼60MWh per year due to line loading. Furthermore, the proposed method eliminates grid violations due to the addition of new sectors reducing the curtailment up to 60%. With the optimization of the heat pump operation, an increase of 7% of the self-consumption was achieved with similar results for the combination of battery systems and electrical vehicles. In conclusion, a safer and optimal operation of a complex energy system is fulfilled. Safer control strategies and more accurate plant sizing could be derived from this work.

Laser reduced graphene oxide (LRGO) on a PET substrate was prepared in one-step to obtain the LRGO grid electrode for sensitive carbaryl determination. The grid form results in a grid distribution of different electrochemically active zones affecting the electroactive substance diffusion towards to the electrode surface and increasing the electrochemical sensitivity for carbaryl determination. Carbaryl is electrochemically irreversibly oxidized by the secondary amine moiety of the molecule with the loss of one proton and one electron in the pH range from 5 to 7 by LSV on the LRGO grid electrode with a scan rate of 300 mV/s. Some interference of the juice matrix molecules did not significantly affect the LSV oxidation current of carbaryl on the LRGO grid electrode after adsorptive accumulation without applied potential. The LRGO grid electrode can be used for LSV determination of carbaryl in fruit juices in the concentration range from 0.25 to 128 mg/L with LOD of 0.1 mg/L by. The fabrication of the LRGO grid electrode opens up possibilities for further inexpensive monitoring of carbaryl in other fruit juices and fruits.

An indisputable fact about our planet is that its atmospheric temperature has risen dramatically during the past century. Combustion of fossil fuels and their subsequent greenhouse gas emissions are thought to be the main contributors to recent changes within the Earth’s ecosystem. The transportation sector and electricity generating power plants are each responsible for approximately one-third of these emissions. Shifting towards a cleaner and renewable resources to generate electricity is believed to omit a big portion of polluting substances. Improvements in vehicles’ fuel efficiency and the introduction of alternative fuels besides strategic plans to control travel demand are among the most promising approaches to alleviate emissions from the transportation sector. Recent technology advancements, however, drew much attention to the production and manufacturing of alternative fuel vehicles, electric vehicles in particular. Since these vehicles use electricity as part of or all their powertrain, assessing the amount of emissions they produce is closely tied to the cleanliness of the electricity source. In order for a valid comparison to be made between internal combustion and electric vehicles, hence, a life cycle assessment procedure needs to be followed from production stages to terminal life of vehicles. Involvement of numerous affecting factors during the lifetime of a vehicle on one hand, and the ambiguity in the exact source of electricity used to charge electric vehicles on the other hand bring about more complexities. The latter case is more commonly known as the marginal grid problem, which deals with how a combination of sources used to generate electricity can influence the life cycle emissions. There are also other concerns regarding the growth in fuel-efficient and electric vehicles. Transportation planners argue that new developments in the vehicle industry may attract more people to owning and driving cars. This phenomenon which is better known as a rebound effect not only will result in increased traffic congestion, but it can also outpace the environmental benefits from utilizing electric vehicles. Moreover, since fuel taxes comprise the majority of Highway Trust Funds, alternative ways to compensate for state and federal revenues should be devised. This paper is an attempt to review the existing literature to better elaborate on the role of the transportation sector in controlling climate change threats. More specifically, issues around the use of electric vehicles and how they can contribute to more environmentally friendly communities are discussed.

A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

Energy storage systems will play a key role for individual users in the future smart grid. They serve two purposes: (i) handling the intermittent nature of renewable energy resources for a more reliable and efficient system, and (ii) preventing the impact of blackouts on users and allowing for more independence from the grid, while saving money through load-shifting. In this paper we investigate the latter scenario by looking at a neighbourhood of 25 households whose demand is satisfied by one utility company. Assuming the users possess lithium-ion batteries, we answer the question of how each household can make the best use of their individual storage system given a real-time pricing policy. To this end, each user is modelled as a player of a non-cooperative scheduling game. The novelty of the game lies in the advanced battery model, which incorporates charging and discharging characteristics of lithium-ion batteries. The action set for each player comprises day-ahead schedules of their respective battery usage. We analyse different user behavior and are able to obtain a realistic and applicable understanding of the potential of these systems. As a result, we show the correlation between the efficiency of the battery and the outcome of the game.

The evolving multiphase induction generators (MPIG) with more than three phases are receiving prominence in high power generation systems. This paper aims at the development of a comprehensive model of the wind turbine driven seven-phase induction generator (7PIG) along with necessary the power electronic converters and controller for grid interface. The dynamic model of the system is developed in Maltlab/Simulink. Synchronous reference frame phase-locked loop (SRFPLL) system is incorporated for grid synchronization. The modeling aspects are detailed and the system response is observed for various wind velocities. The effectiveness of seven phase induction generator is demonstrated with the fault tolerant capability and high output power with reduced phase current when compared to conventional 3-phase wind generation scheme. The response of the PLL is analyzed and the results are presented.

Intestinal epithelial cell activities during homeostasis and regeneration are well described, but their potential interactions with stromal cells remain unresolved. Exploring the functions of these heterogeneous intestinal mesenchymal stromal cells (iMSCs) remains challenging due to the lack of specific markers for most functionally homogenous subpopulations. In recent years, however, novel clustering techniques such as single-cell RNA sequencing (scRNA-seq), fluorescence-activated cell sorting (FACS), confocal microscope, and computational remodeling of intestinal anatomy have helped identify and characterize some specific iMSC subsets. These methods help researchers learn more about the localization and functions of iMSC populations during intestinal morphogenic and homeostatic conditions. Furthermore, it is imperative to understand their cellular pathways for activation and how they interact with surrounding cellular components, particularly during intestinal epithelial regeneration that follows mucosal injury. This review provides insights into the spatial distribution and functions of some identified iMSC subtypes in intestinal morphogenesis, homeostasis, and regeneration. We reviewed related signaling mechanisms implicated during epithelial and subepithelial stromal cell crosstalk. Future research should focus on elucidating the molecular intermediates of these regulatory pathways to open a new frontier for potential therapeutic targets that can alleviate intestinal mucosa-related injuries.

This study leverages the relationships between the Great Plains low-level jet (GP-LLJ) and the circumglobal teleconnection (CGT) to enhance the 30-day rainfall forecast skill in the Northern Great Plains (NGP). The assessment of 30-day simulated precipitation using the Climate Forecast System (CFS) is contrasted with the North American Regional Reanalysis, searching for sources of precipitation predictability associated with extended wet and drought events. We analyze the 30-day sources of precipitation predictability using (1) the characterization of dominant statistical modes of variability of 900-mb winds associated with the GP-LLJ, (2) the large-scale atmospheric patterns based on 200-mb geopotential height (HGT), and (3) the use of GP-LLJ and CGT conditional probability distributions using a continuous correlation threshold approach to identify when and where the forecast of NGP precipitation occurs. Two factors contributing to the predictability of precipitation in the NGP are documented. We found that multi-scale geospatial interactions occur at the daily and sub-seasonal time windows. The CFS reforecast suggests that the ability to forecast sub-seasonal precipitation increases in response to the enhanced simulation of the GP-LLJ and CGT. Finally, the multi-dimensional analysis of covariance reveals that high-precipitation forecasting skill is associated with a better prognostic of GP-LLJ and CGT.

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with complex carcinogenesis. Although there has been significant progress in the treatment of HCC over the past decades, drug resistance to chemotherapy remains a major obstacle in its successful management. In this study we were able to reduce chemoresistance in cisplatin-resistant HepG2 cells by either silencing the expression of transglutaminase type 2 (TG2) using siRNA or by pre-treatment of cells with the TG2 enzyme inhibitor cystamine. Further analysis revealed that, whereas the full-length TG2 isoform (TG2-L) was almost completely cytoplasmic in its distribution, the majority of the short TG2 isoform (TG2-S) was membrane-associated in both parental and chemoresistant HepG2 cells. Following induction of cisplatin toxicity in non-chemoresistant parental cells, TG2-S together with cisplatin quickly relocated to the cytosolic fraction. Conversely, no cytosolic relocalisation of TG2-S or nuclear accumulation cisplatin was observed following identical treatment of chemoresistant cells, where TG2-S remained predominantly membrane-associated. This suggests that deficient subcellular relocalisation of TG2-S from membranous structures into the cytoplasm may limit the apoptic response to cisplatin toxicity in chemoresistant cells. Structural analysis of TG2 revealed the presence of binding motifs for interaction of TG-S with the membrane scaffold protein LC3/LC3 homologue that could contribute to a novel mechanism of chemotherapeutic resistance in HepG2 cells.

With the ongoing COVID pandemic, the emergence of a novel omicron variant in November 2021 has chaos the world. Despite mass vaccination, this omicron has spread rapidly raising concerns around the globe. The Omicron variant has a vast array of mutations as compared to another variant of concern with overall 50 mutations where 30 mutations are present in its spike protein. This mutation has led to immune escape and more transmissibility compared to other variants, including Delta. A cluster of mutations (H655Y, N679K, and P681H) present at the omicron spike protein could aid in transmission. Currently, no virus-specific data are available to predict the efficacy of anti-viral and mAbs drugs. However, two monoclonal antibody drugs: Sotrovimab and Evusheld are authorized for emergency use in COVID patients. This virus is not fading away soon. The easiest solution and less expensive measure to fight against this pandemic are following COVID appropriate protocols.There is need to strengthen the level of research for development of potential vaccines and anti-viral drugs. It is also important to monitor and expand genomic surveillance to keep track of the emergence of new variants thus avoiding the spread of new diseases worldwide. This article highlights the emergence of omicron and vast number of mutation in its protein. In addition, recent advancement in drugs approved by FDA to treat COVID patients has been listed and focused in this paper.

Despite the ongoing land administration reforms being implemented across sub-Saharan Africa (SSA), including Ghana as viable pathway to achieve tenure security and greater efficiency in land administration, the subject of land dispute resolution has received relatively less attention. Whereas customary tenure institutions play a central role in land administration (controlling ~80% of all land in Ghana), they remain at the fringes of the formal land dispute adjudicatory process. Recognizing the pivotal role traditional institutions as development agents and potential vehicles for promoting good land governance, recent discourse on land tenure have geared towards mainstreaming traditional land disputes institutions into the architecture of formal judicial process via alternative dispute resolution pathways. Yet little is known at least empirically as to the operations of traditional dispute resolution institutions in the contemporary context. This study therefore explores the importance of traditional dispute resolution institutions in the management of land-related disputes in southcentral and western Ghana. Drawing on data collated from 380 farming households operating 746 plots. The results show that contrary to the conventional thinking that traditional institutions are anachronistic and not fit for purpose, they remain strong and preferred forum for land dispute resolution (proving resilient and adaptable) given the changing socio-economic and tenurial conditions. Yet these forums have differing implications for different actors within the customary spheres accessing them. The results highlight practical ways for incorporating traditional dispute resolution in the overall land governance setup in Ghana and elsewhere in sub-Saharan Africa. This has implications for redesigning context-specific and appropriate land-use policy interventions that address local land dispute resolution.

This study investigated the speciation, transformation and availability of P during indigenous vegetable production by employing a combination of chemical and spectroscopic techniques. The study focused upon sites in two ecozones of SSA, the Dry Savanna (lna, Benin Republic) and Rainforest (Ilesha, Nigeria). Both sites were cultivated with two indigenous vegetable species; local amaranth (Amaranthus cruentus (AV)) and African eggplant (Solanum macrocarpon (SM)). The soils were treated with 5 t/ha poultry manure and urea fertilizer at the rate of 0, 20, 40, 60 and 80 kg N/ha. Soil samples were collected before planting and after harvest. Phosphorus K-edge X-ray absorption near-edge structure (XANES) spectroscopy was used to determine P speciation in these soils. Quantitative analysis showed that adsorbed and organic P were the two dominant P species in the manure amended Dry Savanna (DS) soils before planting and after harvest in soils cultivated with both AV and SM, with the addition of urea (40 kg N/ha) causing an increase in the organic P form in Dry Savanna soils cultivated with AV. Soils of the Rainforest (RF) cultivated with AV initially had large amounts of apatite P in the manure amended soils prior to planting which was transformed to adsorbed and organic P after harvest. Urea addition to the Rainforest soils shifted the dominant P species from organic P to adsorbed and apatite P, which is likely to limit P availability. Soils cultivated with SM had similar proportions of both organic and adsorbed P forms, with 40 kg N/ha addition slightly increased the proportion of adsorbed P.

In the present work we study the properties of geodesic sub-$ (\alpha,b,s) $-preinvex functions on Hadamard manifolds and establish some basic properties in both general and differential cases. Further, we study sufficient conditions of optimality and proved some new inequalities under geodesic sub-($ \alpha,b,s $)-preinvexity.

Transportation electrification has been identified as a crucial shift to be made for achieving energy security and emission reduction targets. Electric Vehicles (EVs) play a significant role in achieving these targets; therefore, there is a growing trend to develop new EV technologies and associated infrastructure. Along with these developments, EVs' Quality of Service (QoS) aspects should be enhanced to increase the number of EVs on the road. Various QoS factors are defined to assess the QoS of EVs, and one of the two aims of this paper is to provide a comprehensive review of these factors. The other aim is to review recent developments of associated communication technologies which are essential for exchanging information between EVs and charging stations. This paper serves as a comprehensive review and useful reference for researchers working on QoS aspects of EVs and associated communication systems aiming to enhance the QoS.

The smart grid system, an improvised form of the traditional grid, is considered a critical cyber-physical system. Due to its complex combination of smart and legacy technologies, this technology is characterized by a wide range of features such as full duplex communication, advanced metering infrastructure, remote monitoring, and control. However, despite this system’s substantial environmental and socio-economic impact on modern life, it poses severe security vulnerabilities due to its integration and interconnected and interdependent cyber-physical components. Based on the rapid development of cyber-physical systems (CPS), academics and industries have investigated relevant techniques to enhance and strengthen the security measures of the SG system. The intrusion detection system is a countermeasure, a powerful safeguard against various cyber-attacks and threats. The contribution of the IDSs to the SG paradigm is examined in this study. A comprehensive review has been presented in this study, highlighting different techniques, basic ideas, and analyses. Besides discussing the contemporary pertinent research studies, the shortcomings of the existing IDS for the SG system are also identified with a recommendation for future research direction.

Visual data on the geographic distribution of carbon storage helps policy maker to formulate countermeasures on global warming. However, Taiwan, as an island showing diversity in climate and topography, had lacked valid visual data in distribution of forest carbon storage between the last to forest surveys (1993-2015). This study established a model to achieve an estimation that capable to demonstrate the distribution of forest carbon storage. This model uses land use, stand morphology, carbon conversion coefficient databases accordingly for 51 types of major forest in Taiwan. An estimation on 2006 was conducted and shows an overall carbon storage of 165.65 Mt C, with forest carbon storage per unit area of 71.56 t C ha–1, where natural forests and plantations respectively contributed of 114.15 Mt C (68.9%) and 51.50 Mt C (31.1%). By assuming no change in land use type, the carbon sequestration from 2006 to 2007 by the 51 forest types was estimated to be 5.21 Mt C yr–1 using historical tree growth and mortality rates. The result reflects the reality of the land use status and event of coverage shifting with time by combining to the two forest surveys in Taiwan.

This paper provides an optimization strategy for flexible operation at the system level, so as to guide the real-time flexible ramping of the power grid. Firstly, the concept of regional power grid flexibility was clarified, and the ramping factor is proposed as a flexibility metric. On this basis, taking the output priority of each node as the objective function and considering constraints such as line flow, power balance, and system flexibility, a mathematical model for regional power grid transmitting flexible power externally is proposed. Compare with the traditional models, this model focuses more on flexibility rather than economy. The case analysis based on the improved IEEE 30-Bus System verifies the feasibility and effectiveness of the proposed strategy and its advantages over the traditional models.

The construction industry has witnessed the increasing use of digital tools and smart solutions, particularly, in the realm of buildings energy automation within the concept of smart cities. While to truly realize the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, appreciated as the critical unit of urban development. To support this interplay of smart solutions between buildings and neighbourhoods, this article aims to collect and review all smart solutions presented in existing scientific articles, technical literature, and realized European projects. These solutions are classified under two main sections of buildings and neighbourhoods, which are investigated through five domains: buildings energy-related uses, renewable energy sources, water, waste, and open space management. Showcasing the quantitative outcomes demonstrates the potential benefits of implementing smart solutions from buildings to neighbourhoods. Moreover, this research concludes that the true enhancement of energy conservation goes beyond the buildings’ energy component and can be genuinely achieved by integrating intelligent elements of the neighbourhood, due to their strong interdependencies. Future research is recommended to focus on assessing the effectiveness of these solutions towards resource conservation.

The use of smart grids has enabled a number of planning methods to be developed to optimize energy costs, Peak to Average Ratios (PARs), and consumer satisfaction for load management in industrial, commercial, and domestic sectors. From a technical point of view, achieving optimal outcomes requires Demand Side Management (DSM). In smart grids, utility companies and electric users communicate two-way using digital technology to make a sustainable and economic system. This paper proposes a novel framework within which an Energy Management Controller (EMC) keeps track of each appliance, its operational time, and the costs associated with them. Customers of smart grids are motivated to shift their Off-Peak Hours (OPH) from Peak Hours by presenting incentives in OPH. The metering devices would also save customers costs by preventing load shifting between high- and low-cost periods. In addition, the study proposes the bacterial foraging algorithm and grasshopper optimization algorithm for lessening power price and PAR without compromising user comfort (UC) through appliance planning. The simulation results on a practical test system advocate the high effectiveness and reliable performance of the proposed model.