Recent studies releaved that the high production of reactive oxidative species due to exposure of fine or ultrafine particles are implicated in many chronical respiratory disorders. However, the poor standard of clinical data in sub-Saharan countries makes very difficult the assessment of knowledge of air pollution health impact in urban cities. Objective: the aim of this study was to evaluate distribution of respiratory disorders associated to exposure of air fine and ultrafine particles through changes of some oxidative stress biomarkers among motorbike drivers from two cities of Cameroon Methods: A cross-sectional survey using a standardized questionnaire was conducted in 2019 on 191 motorcycle drivers (MD) working in Douala and Dschang. Then, the activity of superoxide dismutase (SOD) and the level of malondialdehyde (MDA) were measured by colorimetric methods. The data of participants after clustered in Excel Microsoft were analysed and statistically compared using Spss 2.0 software. Results: The motorbike drivers recruited to both cities were aged from 21 to 40, with mean age of 29.93 (± 0.82). The distribution of respiratory disorders as running nose, cold, dry cough, chest discomfort, and breathlessness, was significantly increased among MD in Douala. According to results of biological assays, SOD and MDA, were significantly more increased among MD recruited in Douala, compared to those of Dschang. The change of these oxidative stress markers was significantly correlated with mobilization of neutrophils and Monocytes showing initiation and evolution of subjacent inflammatory reactions, and seemed to be significantly influenced by place of habitation of MD. Conclusion: Through this study it is confirmed the evidence of initiation and evolution of oxidative stress with long-term exposure to air fine or ultrafine particles among active people in Urban cities. Further studies should be conducted with more analysis to provide evidence cellular damage and dysfunction related to chronically exposure to fine air PM among active people in metropolitan of sub-Saharan Africa context.

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.

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.

A detection system, modeled in a graph, uses "detectors" on a subset of vertices to uniquely identify an "intruder" at any vertex. We consider two types of detection systems: open-locating-dominating (OLD) sets and identifying codes (ICs). An OLD set gives each vertex a unique, non-empty open neighborhood of detectors, while an IC provides a unique, non-empty closed neighborhood of detectors. We explore their fault-tolerant variants: redundant OLD (RED:OLD) sets and redundant ICs (RED:ICs), which ensure that removing/disabling at most one detector guarantees the properties of OLD sets and ICs, respectively. This paper focuses on constructing optimal RED:OLD sets and RED:ICs on the infinite king's grid, and presents the proof for the bounds on their minimum densities; [3/10, 1/3] for RED:OLD sets and [3/11, 1/3] for RED:ICs.

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

This paper proposes a game-theoretic framework for quantifying effects of strategic bidding behavior of load aggregators (LAs) on wholesale electricity market equilibrium. The independent system operator (ISO) employs double-sided auction design to settle transactions in the wholesale electricity market. Generating companies (GENCOs) and LAs submit their bids to the ISO in the economic model of supply function equilibrium. Under the smart grid paradigm, a LA participates in the electricity market on behalf of end-consumers to minimize their total payment for purchasing electricity. In this paper, bi-level programming (BLP) method is employed to determine optimal bidding strategy of self-interested market participants. In the upper level, the profit of intended market participant is maximized and in the lower level, the ISO clears the market via a bid-based security constrained economic dispatch (SCED). Also, competition of GENCOs and LAs is modeled as a non-cooperative game. To examine the effects of strategic bidding behavior of LAs on market equilibrium, the proposed model is applied to the IEEE 9-bus and the IEEE 30-bus test systems. According to numerical results, to allow the LAs to behave strategically along with the GENCOs makes the former better off and the latter worse off, while the net effect of this on total social welfare turns out to be case-contingent.

Frequency, amplitude and phase information of the grid voltage are of great importance in constructing a robust controller structure for grid connected inverter systems. This paper presents a simple and robust approach for the instantaneous estimation of positive and negative sequence voltage components under distorted voltage conditions. A second order generalized integrator (SOGI) is used to filter the distorted voltage and to generate orthogonal voltage components for each of the three phases. These filtered and orthogonal components are used for instantaneous calculation of symmetrical components. The implemented method is frequency adaptive; the method is demonstrated and compared to a conventional phase locked loop (PLL) technique with both MATLAB/Simulink simulations and experiments utilizing the dSPACE ds1103 digital controller.

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.

In a context of increasingly necessary energy transition, precise modelling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSO) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modelling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow the estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

One of the ramifications of the COVID-19 pandemic is that it has lent urgency to ongoing discussions on mental well-being. While standard techniques are available to diagnose mental health disorders such as depression, anxiety, and stress, ambiguity persists regarding the emotional aspect of well-being. Emotional well-being (EWB) is a recently developed concept that seeks to understand the contribution of emotions to one’s well-being. Interactive approaches for such investigations are recommended to understand people’s contextual experiences. In this study, we use a qualitative approach to examine the psychological impact of the built environment on its residents to formulate the desiderata for an ideal place based on their perceptions. We use personal construct theory and the associated qualitative repertory grid technique. We recruited 15 students from the United Arab Emirates University and obtained information on their perceptions of three built environments they experienced. The findings revealed information on the students’ emotional constructs that were associated with a set of design-related factors, and the way the ideal hostel should be characterized in response to these associations. These findings enrich our knowledge of EWB within built environments and can inform their future design by considering the emotional aspect of the well-being of residents.

Integrating numerous renewable resources has been extensively researched and studied to create a dependable energy system. This study investigated the best techno-economical hybrid energy system configuration while using hydrogen energy to keep the system's energy balanced. Additionally, this study aims to determine the best hybrid system configuration for the location under investigation. A novel technique of DBM (Deterministic Balanced Method) was introduced to determine the total annual energy output for the project's lifetime. This approach is used to contrast the outcomes of other HOMER software tools. Then, using HOMER, it is possible to confirm the sizing optimization results for the suggested method by comparing the power ratings and yearly energy output. A detailed comparison between our proposed method and HOMER Pro has been conducted for validation and verification. Our method has been rigorously tested in various hybrid off-grid energy systems, ensuring its applicability and reliability across different scenarios and configurations. The results of our study indicate a high degree of similarity between the proposed approach and HOMER pro for computing annual energy power for the studied energy systems. Specifically, our analysis revealed that the difference between the approaches was within a range of 5%, indicating a high level of consistency and reliability in our method. These findings suggest that our approach can be confidently applied to determine the appropriate size of hybrid energy systems accurately. Overall, our study provides valuable insights into the development of effective sizing methodologies for hybrid energy systems. After considering the amount of solar radiation and wind speed gathered by global weather data platforms like NASA and METEONORM, this paper has also included a real case study of the Cairo International Airport's hybrid renewable energy production systems. To guarantee the viability of this study, the system design was founded on an actual load that was researched and simulated. A cluster of distributed networks was finally proposed to use the excess energy and enable energy conversion processes to be used in industry and transportation applications.

Two-wheeler vehicles are the most significant mode of transportation for Kenyans in both rural and urban regions thereby contributing to local air pollution, and greenhouse gas emissions (GHG). The transition to electric two-wheeler vehicles can make a significant contribution to reducing GHG and improving the socio-economic lives of people living in rural Kenya. Re-newable energy systems can considerably contribute to the charging of electric two-wheeled vehicles, thus leading to the reduction of carbon emissions and the expansion of renewable energy penetration in rural Kenya. Therefore, this paper focuses on integrating and modelling electric two-wheeled vehicles (e-bikes) into an off-grid photovoltaic Water-Energy Hub located in the Lake Victoria Region of Western Kenya using the Conventional and Renewable Energy Opti-mization (CARNOT) Toolbox in MATLAB / Simulink. Electricity demand data obtained from the Water-Energy Hub was investigated and analysed. Potential solar energy surplus was identified and electric two-wheeler vehicles were integrated based on the surplus. A field measurement investigation on the energy consumption of the electric two-wheeler vehicles based on the rider’s driving behaviour was also carried. The annual electricity demand of 27,267 kWh, photovoltaic (PV) electricity production of 37,785 kWh with an electricity deficit of 370 kWh were obtained from the simulation results. To reduce the electricity deficit, a load optimisation algorithm was de-veloped to optimally integrate the electric 2-wheeler vehicle into the Water-Energy Hub. It was found that using the load optimisation algorithm, the annual electricity deficit was reduced to 1 kWh and the annual electricity demand was increased by 11% (30,767 kWh) which is enough to charge 4 additional electric two-wheeler batteries daily.

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future cost of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.

Environmental assessment is a concept that has been designed to facilitate the present generation to meet their needs without compromising the ability of future generations to meet their own needs as well. Thus, this concept has drawn significant attention from various scholars, researchers and industrial practitioners around the world over the past three decades. Life Cycle Environmental Assessment (LCEA) is a widely metric used to assess the potential ecological impacts, which can be caused by electricity generating supply systems or by other systems than power production plants. However, the current LCEA model is biased and ineffective. Because, its omits factors that are increasingly contributing to the ecological degradation. This study has identified the omitted factors through a critical analysis of a set of previous journal articles conducted in the energy sector. In light of this, this study has developed a novel LCEA framework addressing those blind spots. The framework developed in this study is holistic in nature including all the life cycle stages of a power supply system such as Extraction of the Raw Material (ERM), Transport of Raw Material (TRM), Conversion of Raw into Electricity (CRE), and Transmission and Distribution of Electricity (TDE) to the end users. The novel developed LCEA model has been tested and applied to nine power generation plants such as coal, gas, nuclear, biomass, geothermal, hydro, solar thermal, wind onshore and wind offshore. The results have demonstrated that of conventional technologies including coal, gas, and nuclear, coal energy generating source has got the highest life cycle greenhouse gas Grid Emission Factor (GEF) of 2866 kg CO2e/MWh, followed by gas with 728 kg CO2e/MWh, and nuclear has got the least GEF of 35 kg CO2e/MWh. Whereas of renewable energy sources biomass has got the highest GEF of 1508 kg CO2e/MWh, followed by solar thermal with 46.6 kg CO2e/MWh, hydro 39 kg CO2e/MWh, wind offshore 25.25 kg CO2e/MWh, wind onshore 10.1 kg CO2e/MWh, and geothermal closes the ranking with 6.23 kg CO2e/MWh.

In this paper, we succeed to discover the new exact wave solutions to the truncated M-fractional complex three coupled Maccari’s system by utilizing the Sardar sub-equation scheme. The obtained solutions are in the form of trigonometric and hyperbolic forms. These solutions have many applications in nonlinear optics, fiber optics, deep water-waves, plasma physics, mathematical physics, fluid mechanics, hydrodynamics and engineering where the propagation of nonlinear waves is important. Achieved solutions are verified with the use of Mathematica software. Some of the achieved solutions are also described graphically by 2-dimensional, 3-dimensional and contour plots with the help of Maple software. The gained solutions are helpful for the further development of concerned model. Finally, this technique is simple, fruitful and reliable to handle the nonlinear FPDEs.

In this paper, we prove that the sub convexlikeness introduced by V. Jeyakumar [1], and the subconvexlikeness defined in V. Jeyakumar [2] are equivalent in loccallly convex topological spaces. And then, we work with set-valued vector optimization problems and obtain some vector saddle-point theorems and vector Lagrangian theorems.

The essential oils (EOs) obtained from aromatic plants are rich in natural compounds with interesting biological effects. The aim of this study was to evaluate the chemical composition of EOs of Mentha pulegium (MP-EO) and Mentha suaveolens (MS-EO) collected from Morocco, and their antioxidant properties and antibacterial activity against Salmonella enterica and Listeria monocytogenes. The EOs were extracted by hydro-distillation, while the chemical compositions were determined by GC-MS. The antioxidant activity was evaluated by DPPH and FRAP assay. Antibacterial activity was tested with disc diffusion assay; determination of minimum inhibitory concentration, minimum bactericidal concentration and the evaluation of sub-lethally injured cell were also performed. The results of chemical composition showed the presence of compounds not still reported in EOs obtained from these plants. MS-EO was characterized by the best antioxidant and antibacterial activity vs S. enterica and L. monocytogenes respect to MP-EO. The EOs tested in this study were rich in compounds with interesting activities and they could be applied in the medical fields, as well as in food industries as natural preservatives against tested food borne pathogens.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

The main objective of this study was to compare the removal efficiency of nutrients using Lactuca sativa, Medicago sativa and Phragmites australis in subsurface flow constructed wetlands with horizontal flow. In order to test water quality, fabricated reactors designed and the plants cultivated in the soil while their root were inside the wastewater. A long time study carried out from spring till end of autumn (9 months) in order to evaluate the difference in removal rate based on the seasonal changes. The highest removal rate was during summer which followed by spring and autumn. Thus, the effect of plants on the removal efficiency of organic matter (COD, BOD), TSS and nutrient (P and TN) appeared to be dependent on the seasonal growth. Phragmites australis the most sensitive species in order the removal of nutrient from wastewater.

Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) and better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. To handle gridding artifacts and extract both coarse and fine features from the images, this research suggests using a dilated parallel deep convolutional neural network (PDCNN) architecture that preserves a wide receptive field. To reduce complexity, initially, input images are resized and then grayscale transformed. Data augmentation has since been used to expand the number of datasets. Dilated PDCNN makes use of the lower computational overhead and contributes to the reduction of gridding artifacts. By contrasting various dilation rates, the global path uses a low dilation rate (2,1,1), while the local path uses a high dilation rate (4,2,1) for decremental even numbers to tackle gridding artifacts and extract both coarse and fine features from the two parallel paths. Using three different types of MRI datasets, the suggested dilated PDCNN with the average ensemble method performs better. The accuracy provided by the Multiclass Kaggle dataset-III, Figshare dataset-II, and Binary tumor identification dataset-I is 98.35%, 98.13%, and 98.67%, respectively. In comparison to state-of-the-art techniques, the suggested structure improves results by extracting both fine and coarse features, making it efficient.

The power generation structure and load characteristics in different provinces are quite different in China, and the distribution of peak regulating resources and demands is extremely unbalanced. Due to many factors such as low power marketization degree, shortage of peak regulating resources and block of transmission channels, the consumption of new energy is facing greater pressure. In order to improve the mutual aids in regional power grid, this paper proposes a two-layer optimization dispatching model considering the mutual aids of peak regulation resources between each province, and determines the optimal units startup mode and power output in each provinces, thus obtaining all the power supply output arrangements and the technical and economic indicators. Finally, take the actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the new energy utilization rate, which verified the effectiveness and feasibility of the proposed model and methods in this paper.

Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

The need for a relatively quick solution to the problem of providing highways with roadside service facilities necessitates the development of a series of appropriate standard projects. To increase the efficiency of these series, it is advisable to carry out the interconnection of space-planning solutions based on a particular module. Taking into account the variety of planning and landscape characteristics of the sites for the placement of objects of the mainline service, it seems advisable to choose as a module not a square or rectangular, but a triangular configuration, which allows in most cases to harmoniously block the modules. The proposed roof module in the form of a "regular" triangle facing the tetrahedron has a structural basis in the form of a single-tier rod spatial plate. The principal space-planning solutions of all four dozen objects from the approved nomenclature of the mainline service performed in the process of analyzing the possibilities show the real possibility of solving the development tasks on the basis of this system. The use of the proposed modular system makes it possible to successfully solve a number of tasks to reduce the harmful impact on the environment and effectively use renewable energy sources.

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

This work presents a control scheme to control the grid-connected single-phase photovoltaic (PV) system. The considered system has four 250W solar panels, a non-inverting buck-boost DC-DC converter, and DC-AC inverter with LCL filter. The control system aims to track and operate at the maximum power point (MPP) of PV panels, regulate the voltage of DC link, and supply the grid with a unity power factor. To well achieve these goals, the proposed control system consists of three parts, that are MPP tracking controller module with a fuzzy-based modified incremental conductance (INC) algorithm, a DC-link voltage regulator with a hybrid fuzzy proportional-integral (PI) controller, and a Current Controller module using the linear quadratic regulator (LQR) for grid-connected power. Based on fuzzy control and LQR, this work introduces a full control solution for grid-connected single-phase PV systems. The key novelty of this research is to analyze and prove that the newly proposed method is more successful in numerous aspects by comparing and evaluating the previous and present control methods. The designed control system settles quickly, which is critical for output stability. In addition, as compared to backstepping approach used in our past study, the LQR technique is more resistant to sudden changes and disturbances. Furthermore, backstepping method produces the larger overshoot, which has a detrimental impact on efficiency. Simulation findings under various weather conditions were compared to theoretical ones to indicate that the system can deal with variations in weather parameters.

The work addresses the problem of Power Quality (PQ) metrics (or indexes) suitable for DC grids, encompassing Low and Medium Voltage applications, including electric transports, all-electric ships and aircrafts, electric vehicles, distributed generation and microgrids, modern data centers, etc. The two main pillars on which such PQ indexes are discussed and built are: i) the physical justification, so the electric phenomena affecting DC grids and components (PV panels, fuel cells, capacitors, batteries, etc.), causing e.g. stress of materials, ageing, distortion, grid instability; ii) the existing standardization framework, pointing out desirable coverage and extension, similarity with AC grids standards, but also inconsistencies. The first point is made more clear and usable by a graphical overview of the discussed phenomena. On this basis PQ is interpreted beyond the usual low-frequency range, including thus supraharmonics and common-mode disturbance, and filling the gap with the Electromagnetic Compatibility domain. However, phenomena typical of EMC and electrical safety (such as various types of overvoltages and fast transients) are excluded. Suitable PQ indexes are then reviewed, suggesting integrations and modifications, to cover the relevant phenomena and technological progress, and to better follow the normative exigencies.

Maize is an important staple food crop in sub-Saharan Africa (SSA). However, the average grain yield on farmers’ fields falls considerably short of the demand of the region’s population. In addition, the normal endosperm maize widely consumed in SSA is deficient in vitamin A. Drought, a consequence of climate change, is a major constraint to maize production, resulting in seasonal low grain yield in farmers’ fields. The objectives of this study were to assess the performance of newly developed extra-early maturing provitamin A hybrids under managed drought and well-watered conditions, compare the outcomes of multiple trait base-index and multi-trait genotype-ideotype distance index selection procedures, and identify drought-tolerant hybrids with stable performance across contrasting environments for commercialization in SSA. One hundred and ninety provitamin A hybrids and six checks were evaluated under managed drought and well-watered conditions at Ikenne for two seasons between 2021 and 2023. A 14*14-lattice design was used for the field evaluations under both research conditions. Drought stress was achieved by the complete withdrawal of irrigation water 25 days after planting. Results revealed significant differences among the hybrids under drought and well-watered conditions. Grain yield, ears per plant, and plant aspect under managed drought were correlated to the same traits under well-watered conditions suggesting that the expression of these traits is governed by common genetic factors. Twenty-nine hybrids were identified as top-performing drought-tolerant hybrids by the multiple trait base index and multiple-trait genotype–ideotype distance index. Of the selected outstanding 29 hybrids, 34% were derived from crosses involving the tester TZEEIOR 197, demonstrating the outstanding genetic potential of this inbred line. Further analysis of the 29 selected hybrids revealed TZEEIOR 509 × TZEEIOR 197 as the hybrid that combined the most drought-tolerant adaptive traits. However, the hybrids TZEEIOR 526 × TZEEIOR 97, TZEEIOR 384 × TZEEIOR 30, TZEEIOR 515 × TZEEIOR 249, TZEEIOR 510 × TZEEIOR 197, TZEEIOR 479 × TZEEIOR 197, and TZEEIOR 458 × TZEEIOR 197 were identified as the most stable hybrids across drought and well-watered conditions. These hybrids should be extensively tested in multi-location trials for deployment and commercialization in SSA.

This paper presents a design and performance analysis of a 10-element 5G massive MIMO antenna array for sub-6GHz mobile handsets, specifically for LTE bands 42 (3400-3600 MHz), 43 (3600-3800 MHz), 48/49 (3550-3700 MHz) applications. The proposed antenna array consists of 10 closely spaced linearly polarized inverted-F antennas with a compact size of 20 × 9 mm2 of a single element. The proposed antenna array provides high gain, high efficiency, and low correlation between the antenna elements, which results in improved channel capacity, increased data rate, and enhanced signal quality. The performance of the antenna array is evaluated in terms of the radiation pattern, gain, efficiency, and correlation coefficient. The simulation and measured results show that the proposed antenna array achieves an approximate peak gain of 3.1 dBi at the resonance frequency, a total efficiency of 65 %, and a low correlation coefficient of 0.06 between the antenna elements. Therefore, the proposed 5G massive MIMO antenna array is a promising candidate for sub-6 GHz mobile handsets, particularly for LTE bands 42/43/48/49 applications.

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

Mental health and emotional responses to the effects of COVID-19 lockdown in sub-Saharan Africa (SSA) are of serious public health concern and may negatively affect the mental health status of people. Hence, this study assessed the prevalence of mental health symptoms as well as emotional reactions among sub-Saharan Africans (SSAs) and associated factors among SSAs during the COVID-19 lockdown period. This was a web-based cross-sectional, a study on mental health and emotional features from 2005 respondents in seven SSA countries. This study was conducted between April 27 and May 17, 2020 corresponding to the lockdown period in most SSA countries. Respondents aged 18 years and above and the self-reported symptoms were feeling anxious, being worried, angry, bored and frustrated. These were the main outcomes and were treated as dichotomous variables. Univariate and multivariate logistic regression analyses were used to identify the factors associated with these symptoms. We found that over half (52.2%) of the participants reported any of the mental health symptoms in SSA and the prevalence of feeling bored was 70.5% followed by feeling anxious (59.1%), being worried (57.5%), frustrated (51.5%) and angry (22.3%) during the COVID-19 pandemic. Multivariate analysis revealed that males, those aged >28 years, Central and Southern Africans, those who were not married, the unemployed, those living with more than six persons in a household, had higher odds of mental health and emotional symptoms. Similarly, people who perceived low risk of contracting the infection, and those who thought the pandemic would not continue after the lockdown had higher odds of mental health and emotional symptoms. Health care workers had lower odds for feeling angry than non-healthcare workers. During COVID-19 lockdown periods in SSA, about one in two participants reported mental health and emotional symptoms. Public health measures can be effectively used to identify target groups for prevention and treatment of mental health and emotional symptoms. Such interventions should be an integral component of SSA governments’ response and recovery strategies of any future pandemic.

On 9 March 2020, two cases of COVID-19 were reported in Burkina Faso. As of 10 April 2020, a total number of 484 cases (404 cases in the Kadiogo province) were reported nationwide. Real-time forecasts of COVID-19 are important to inform decision-making in the country. Here, we propose an approach that tests the performance of four models (Exponential Growth model, the Generalized Growth model (GGM), the Generalized Logistic Growth, and Richards Growth model) to select the model that best fit data and to generate short-term forecasting (5-, 10-, and 15-day forecasts from 11 to 25 April 2020) in Kadiogo, the epicenter of the outbreak. Using daily number of confirmed COVID-19 cases, the results suggests that GGM performed the best out of the 4 models. Overall, our GGM predictions suggested an average total number of cumulative cases of 514 (95% CI, 464–559), 629 (95% CI, 559–691), and 750 (95% CI, 661–840) between 11 to 15 April, 16 to 20 April, and 20 to 25 April 2020, respectively. COVID-19 in this province was best approximated by sub exponential growth rather than exponential or logistic growth. Current data suggest that COVID-19 cases would continue to increase over the next 15-days.

Titanium micro-scale topography results in excellent osteoconductivity and bone-implant integration. However, the biological effects of sub-micron topography are unknown. We compared osteoblastic phenotypes and in vivo bone and implant integration abilities between titanium surfaces with micro- (1–5 µm) and sub-micro-scale (0.1–0.5 µm) topographies and machined titanium. Average roughness was 12.5 ± 0.65 nm, 123 ± 6.15 nm, and 24 ± 1.2 nm for machined, micro-rough, and sub-micro-rough surfaces, respectively. The micro-rough surface showed the fewest cells attaching during the initial stage and the lowest proliferation. Calcium deposition and expression of osteoblastic genes were highest on the sub-micro-rough surface and lowest on the machined surface. Bone-to-implant integration was strongest for the micro-rough surface, consistent with it having the greatest ability to retain cells in vitro. Thus, the biological effects of titanium surfaces are not necessarily proportional to the degree of roughness in osteoblastic cultures or in vivo. Sub-micro-rough titanium ameliorates the disadvantage of micro-rough titanium by restoring cell attachment and proliferation and enhances the rate of osteoblastic differentiation over that of micro-rough titanium; however, bone integration and the ability to retain cells are compromised due to its lower interfacial mechanical locking compared to that of micro-rough titanium.

By imposing the condition of non-vanishing expectation values for the amplitude and phase of field operators, pure quantum states are identified composed of two consecutive number states. These pure states also deliver noise-free radiation modes restricting the “half-photon noise” to the expectation value of the lowest level of dynamic and coherent number states. As a result, instantaneous phase-sensitive amplification of photons is easily controlled and direct evaluation of time or distance - varying wavefront distributions of photons and phases can be carried out for sub-Poissonian distributions of photons without the need for quasi-probabilities.

Technology is in the way to reaching in its climax but the basic understanding of science in many phenomena is still awaited. Scientific research reveals strong analogy between electron and photon. Atoms that execute suitable electronic transitions, on absorbing heat energy at shunt level, excite their electrons. De-excitation of an electron under the gravitational force of its nucleus, where inertia is involved, results depicting energy in the shape like Gaussian distribution. The wavelength of photon remains in inter-state electron’s gap where the source of generating energy in wave-like fashion is due to electronic transitions under confined electron-dynamics; energy configures under electron’s trajectory in the excitation period is due to inertia-levitation-inertia behaviours while energy configures under electron’s trajectory in the de-excitation period is due to inertia-gravitation-inertia behaviours. Silicon atom is a model system of it. Uninterrupted confined inter-state electron-dynamics results into configure energy in a wave-like fashion that can travel immeasurable length and on interruption from the point of generation, it becomes a photon. Such photons increase wavelength on decreasing energy while travelling through inherently built gap of splitted inert gas atoms where they give light (glow) on reaching wavelength in the visible range. Here, I discuss that heat energy is due to merged photons, photons characteristic current are due to photons having wavelength in inter-state electron’s gap and light is due to photons, following the wavelength in the visible range. Force of repulsion or attraction in certain materials engages the phenomenon of levitism or gravitism where inertia is exempted. All structural motifs and dynamics are subjected to characteristic photons as long as atoms are under neutral behavior of field force. A structural design delivers straight-forward application on dealing photons at different wavelengths. Here, materials science explores matter at electronic level while absorbing heat energy and generating photon energy. Thus, devise science to describe.

Power quality (PQ) plays an important role in ensuring the smooth operation of power systems. PQ disturbances may cause significant consequences, impacting the reliability and efficiency of power networks. This paper presents a comprehensive review of PQ within smart grids and the integration of renewable energy sources. Methods of detecting, classifying, optimizing, and mitigating PQ events are reviewed with providing a brief theoretical explanation for each technique. Related work done in the past two decades, was conducted, presenting a clear view of the development of research in a chronological order serving as a guide for future investigations in this field of study.

This paper is concerned with developing an efficient numerical algorithm for fast implementation of the sparse grid method for computing the $d$-dimensional integral of a given function. The new algorithm, called the MDI-SG ({\em multilevel dimension iteration sparse grid}) method, implements the sparse grid method based on a dimension iteration/reduction procedure, it does not need to store the integration points, nor does it compute the function values independently at each integration point, instead, it reuses the computation for function evaluations as much as possible by performing the function evaluations at all integration points in a cluster and iteratively along coordinate directions. It is shown numerically that the computational complexity (in terms of CPU time) of the proposed MDI-SG method is of polynomial order $O(Nd^3 )$ or better, compared to the exponential order $O(N(\log N)^{d-1})$ for the standard sparse grid method, where $N$ denotes the maximum number of integration points in each coordinate direction. As a result, the proposed MDI-SG method effectively circumvents the curse of dimensionality suffered by the standard sparse grid method for high-dimensional numerical integration.

Life in the modern century is heavily reliant on an enormous amount of electricity consumption as technology has become the most integral part of daily life. In this context, smart grid systems play a pivotal role to maintain the uninterrupted power supply which needs to be monitored in a timely fashion to keep track of the electric consumers’ usage pattern. The smart meter is the one of smart applications of the smart grid that collects huge amounts of consumer load data on a daily basis which has become a focus for various researchers and analyzers to study load characterization. In this paper, an approach has been proposed to recognize the energy consumption patterns among diverse types of consumers ranging from residential to industrial levels. This approach is worth considering not only for load pattern recognition but also for involving customers in different events such as demand response or peak shaving. In such a way, this analytical mechanism certainly assists in reducing power wastage and saving costs. The proposed methodology is based on a two-fold clustering algorithm with the use of state-of-the-art technology, machine learning. The primary goal is to classify electric customers' data collected from smart meters. Then, analyzing the classified results with an aim to predict power consumption patterns for the customers in the future and making the right energy policy that will benefit both the grid operator and consumers as well.

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

The article introduces a new solution approach to grid-connected single-stage three-phase PV inverter whereby a dc-link voltage can successfully achieve the desired performance. As a single-stage, the variable output of perturb and observe maximum power point tracking (P&O based-MPPT) is fully utilized as dc-link voltage reference in inverter control scheme. As a result, it is challenging for the dc-link voltage to preserve energy balance by tracking its variable dc-link voltage reference under unpredictable environmental changes. To overcome this challenge, an adaptive reference proportional-integral (ARPI) controller, self-tuning, is designed and implemented to compel the dc-link voltage tracking its voltage reference to be equal as closely as possible; hence, the capability of the dc-link under abnormal events substantially guarantees the best energy balance and transient performance enhancement. To verify the validity of the method, simulation results for multiple events scenarios on 250 KW single-stage grid-connected PV systems show the effectiveness of the ARPI controller compared with PI controller.

In this paper we describe a nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions. Given $N$ independent observations, convexity theory shows that the NPML estimator is discrete with at most $N$ support points. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. The probability of the support points is found by a Primal-Dual Interior-Point method; the location of the support points is found by an Adaptive Grid method. Our method is able to handle high-dimensional and complex multivariate mixture models.An important application is discussed for the problem of population pharmacokinetics and a non-trivial example is treated.Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics.

This paper presents the experimental implementations of the mathematical models and algorithms developed in Part I. Two experiments are carried out. The first experiment aims at the determinations of the correction coefficients of the mathematical model. The dot grid target is measured and the measurement data are processed by our developed and validated algorithms introduced in Part I. The values of the coefficients are indicated and analysed. Uncertainties are evaluated with implementation of the Monte Carlo method. The second experiment measures a different area of the dot grid target. The measurement results are corrected according to the coefficients determined in the first experiment. The mean residual between the measured points and their corresponding certified values reduced 29.6% after the correction. The sum of squared errors reduced 47.7%. The methods and the algorithms for raw data processing, such as data partition, fittings of dots’ centres, K-means clustering, etc., are the same for both two experiments. The experimental results demonstrate that our method for the correction of the errors produced by the movement of lateral stage of confocal microscope is meaningful and practicable.

This paper studies the performance improvement of the IEEE 802.15.4 nonbeacon-enabled mode originated by the inclusion of the Request-To-Send/Clear-To-Send (RTS/CTS) handshake mechanism resulting in frame concatenation. Under IEEE 802.15.4 employing RTS/CTS, the backoff procedure is not repeated for each data frame sent but only for each RTS/CTS set. The maximum throughput and minimum delay performance are mathematically derived for both the Chirp Spread Spectrum and Direct Sequence Spread Spectrum Physical layers for the 2.4 GHz band. Results show that the utilization of RTS/CTS significantly enhances the performance of IEEE 802.15.4 applied to healthcare in terms of bandwidth efficiency.

Chikungunya virus is an Alphavirus belonging to the family Togaviridae that is transmitted to humans by an infected Aedes mosquito. Patients develop fever, inflammatory arthritis, and rash during the acute stage of infection. Although the illness is self-limiting, atypical and severe cases are not uncommon, and 60% may develop chronic symptoms that persist for months or even for longer durations. Having a distinct periodical epidemiologic outbreak pattern, chikungunya virus reappeared in Thailand in December 2018. Here, we describe a cohort of acute chikungunya patients who had presented to the Bangkok Hospital for Tropical Diseases during October 2019. Infection was confirmed by real-time RT-PCR using serum collected at presentation to the Fever Clinic. Other possible acute febrile illnesses such as influenza, dengue, and malaria were excluded. We explored the sequence of clinical manifestations at presentation during the acute phase and associated the viral load with the clinical findings. Most of the patients were healthy individuals in their forties. Fever and arthralgia were the predominant clinical manifestations found in this patient cohort, with a small proportion of patients with systemic symptoms. Higher viral loads were associated with arthralgia, and arthralgia with the involvement of the large joints was more common in female patients

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess of energy that would be otherwise lost in pressure reducing valves or head loss chambers. In on-grid applications, there are many research works and implementations of PATs. However, there is still limited research for the optimization of efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs, by optimizing and analysing the energy efficiency and stability for a wide range of operation. A self-excited induction generator (SEIG) coupled to the PAT is provided to cover the conventional type of electric ma-chines typically coupled to the hydraulic pumps. In this context, a methodology is proposed, based on the hydraulic, mechanical and electric subsystems, to define the PAT-SEIG operational area, to maximize energy conversion and the system efficiency. This will guarantee stable electric output quantities when operating in a DC off-grid system. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers are suitable to design and optimize an off-grid PAT-SEIG system and to define the best hydraulic machines, electronic equipment, and all different control elements to maximize energy conversion within the target of operational limits. To develop and verify this new methodology, two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions. The proposed solution proved to be resilient under a variable load regime. The system's maximum efficiency can be adapted using different capacitor values for the excitation of the SEIG, and the operational range is limited to the electric components. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system were between 0.7 and 0.8 p.u.

The three-axis stabilization geostationary remote sensing satellite features high time resolution and plays a significant role in weather forecasting and environmental monitoring. With the future development of satellite technology, we propose a rapid geolocation algorithm for three-axis stabilization geostationary remote sensing satellites from the perspective of software algorithms. The method assesses conventional remote sensing data geolocation methods and the coordination group for meteorological satellites (CGMS, Coordination Group for Meteorological Satellites) nominal grid publishing format. First, the initial incident viewing vector of each detector on the sensor was constructed. Then, by reflecting the satellite platform's east-west and north-south mirrors, the outgoing vector of the payload coordinate system was obtained. This vector was converted to the satellite body coordinate system and, subsequently, to the satellite orbit coordinate system. The conversion of the earth-fixed coordinate system and the mirror rotation angle under ideal CGMS grid conditions were obtained, and the mirror rotation angle was converted to the corresponding CGMS nominal grid position to complete data positioning. The algorithm omits the complex process of calculating the intersection point between the viewing vector and the geodetic ellipsoid sphere from the earth-fixed coordinate system, resulting in an 83.3% improvement in computational efficiency without compromising positioning accuracy compared with conventional geolocation methods. This algorithm greatly improves the processing efficiency of geostationary meteorological satellite positioning, but its limitation is that the data processing results must be released in the CGMS nominal grid format.

Continuous greenhouse gas emissions are causing global warming and impacting the habitats of many animals. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable is challenging. To address the issue, we propose a grid dispatching technique that combines prediction technology, reinforcement learning, and optimization technology. Prediction technology can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the power system, the aggregation of distributed energy resources increases uncertainty, particularly due to the fluctuating generation of renewable energy. This requires the use of advanced predictive control techniques to ensure long-term economic and decarbonization goals. In this paper, we present a real-time dispatching framework that integrates deep learning-based prediction, reinforcement learning-based decision-making, and stochastic optimization techniques. The framework can rapidly adapt to target uncertainty caused by various factors in real-time data distribution and control processes. The proposed framework achieved global Champion in the NeurIPS Challenge 2022 competition and demonstrated its effectiveness in practical scenarios of intelligent building energy management.

Besides the increase in global energy demand, access to clean energy, reduction in greenhouse gas emissions caused by conventional power generation sources, energy security, and availability of electricity in remote villages in emerging nations are some of the factors that foster the use of renewable energy sources (RESs) in generating electricity. One of the aims of initiating microgrids (MGs) is to maximize the benefits of RES and alleviate the associated grid integration issues. Microgrids are made up of RES connected to electrical loads within clearly delineated electrical limits that operate as individual controllable units on the electrical network. It can operate in-dependently as well as in connection with the grid. The paper presents an overview of microgrids and investigates the system's performance when connected to and disconnected from the grid. Furthermore, in both modes of operation, the functioning and behavior of system components such as the bidirectional DC-DC converter and energy storage system (ESS) were evaluated. The architecture of the proposed microgrid consists of a small hydropower plant, a wind farm, and a battery energy storage system (BESS). The microgrid under investigation is modeled and simu-lated using MATLAB and Simulink.

Energy is a fundamental ingredient in economic development and energy consumption is an index of prosperity and the standard of living. The consumption of energy has increased significantly in the last number of decades, as the standard of living has improved. Renewable energy is a sustainable and clean source of energy derived from nature. Renewable energy technology is one of the solutions, which produces energy by transforming natural resources into useful energy forms. When you do something for the first time in the world, you never attain a perfect product or solution from the start. In Central Greece Lamia city, we are the first to design and implement an off-grid energy system using hydrogen energy storage technology and phase change materials for a house residence. The action plan for energy efficiency, a series of directives and incentive mechanisms, mandatory energy certification of buildings, indicate the urgent need to reduce energy consumption in buildings, which results in a more comfortable living, long service life of buildings, which in turn preserves the environment.

Banana bunchy top virus (BBTV) was recently detected in Mozambique and appears to be limited to the provinces of Gaza, Maputo and Zambezia, but it has great potential to spread to other provinces. Despite its importance, nothing is known about the BBTV isolates that occur in Mozambique. In this study, the sequences of the S and R genes of 40 isolates chosen as representatives of samples collected previously from eleven farms of the four administrative posts of the district of Chóckwè, Gaza province, were sequenced and analyzed. The S-DNA nucleotide sequences of the analyzed isolates were highly conserved, with identity ranging from 97% to 100%. The same was observed for the R-DNA sequences, with most identities ranging between 98% and 100% among the isolates from Chókwè and above 90% when compared to the isolates from GenBank. The phylogenetic analysis showed that the Mozambican BBTV isolates belong to the Pacific-Indian Oceans (PIO) group, showing greater proximity to the isolate JQ820453 from Malawi than to the isolates from sub-Saharan countries, which were grouped in a distinct subclade. This is the first study conducted to determine the molecular characteristics of BBTV isolates present in Mozambique.

The study aimed to assess the prevalence of the frailty syndrome in older patients hospitalized in the geriatric ward depending on the diagnostic criteria used, the feasibility of particular diagnostic scales in hospitalized patients, and their compatibility. Material and methods: Patients admitted to the Department of Geriatrics of Hospital of the Ministry of the Interior and Administration in Bialystok within eight months were included in the study. Four diagnostic scales were used to identify the frailty syndrome: Fried criteria, 7-point Clinical Frailty Scale (CFS), 40-item Frailty Index (FI), and FRAIL Scale. The compatibility of the scales was determined using Cohen's Kappa statistics. Results: 416 patients (mean age 81.2 ± 6.91 years, 77.4% women) were included in the study. Depending on the scale, the prevalence of frailty syndrome varied from 26.8% ( FRAIL Scale), 52.3% (Clinical Frailty Scale), and 58.1% (Fried criteria) up to 62.9% (Frailty Index). We observed the highest feasibility for CFS (100%), intermediate for Frailty Index (95.2%), FRAIL Scale (89.7%), and the lowest for Fried scale (79.8%). The highest level of agreement was found between the CFS and Fried scale, with 70.2% consistent ratings (Cohen Kappa 0.49). Conclusions: Patients in the geriatric ward are characterized by a high prevalence of frailty, although it differs depending on the criteria. The Frailty Index found the highest prevalence and the lowest in the case of the FRAIL scale. The feasibility of scales and their comparability differed; the most difficult to use in daily practice was the Fried scale, while the Clinical Frailty Scale was determined feasible in all patients. The highest agreement was observed between the Fried criteria and the Clinical Frailty Scale and the lowest between the FRAIL scale and the remaining ones.

The determination and classification of seafloor sediment types are crucial for the exploitation of marine resources, construction of marine engineering, and maintenance of marine ecological en-vironments. Automatic classification of seafloor sediment based on acoustic telemetry data is an important method to quickly understand the type of a large range of sediment. Currently, most studies on sediment classification are based on multi-beam backscattering intensity data, which is a relatively single data type. Besides, the low-dimensional details of standard U-Net gradually weaken in the propagation process, limiting the accuracy of sediment classification. Therefore, this study proposes an automatic classification method of seafloor sediment types based on an im-proved U-Net and K-means clustering algorithm, using multi-beam water depth, sub-bottom profile, and sample test data in the Northern Slope of the South China Sea. Six sediment types, including gravelly muddy sand, sand, silty sand, less muddy silt, muddy silt, and silty mud, were identified in the study area. Additionally, the study area was divided into four sedimentary en-vironment zones, including the shelf sedimentary area, the upper shelf slope break sedimentary area, the lower shelf slope break mixed sedimentary area, and the slope deposition area, based on the results of sediment classification and geological background. The sedimentary environment zones were found to be distributed along the trend of the shelf slope break line. The results of this study not only provide an important supplement to the existing classification methods of seafloor sediments but also contribute to the understanding of the sedimentary environment and process of the Northern Slope of the South China Sea.

– Every physical theory involving quantum fields does require a model of the quantum vacuum. The vacuum associated to quantum gravity must incorporate the prescriptions from both the theory of relativity and quantum physics. In this work, starting from the hypothesis of nucleation of sub-Planckian bubbles from a de Sitter vacuum, we study the necessary conditions to obtain baby universes, black holes and particles. The de Sitter-Planck background is described by an “infinite” Quantum Boltzmann statistics that generates fermions and bosons, and manifests itself as a deformation of the geometry that leads to a generalized uncertainty principle and a unified expression for the generalized Compton wavelength and event horizon size, drawing a connection between quantum black holes and elementary particles, the latter seen as a collective organization of the bubbles of the vacuum described by the generalized Compton wavelength. The quantum thermodynamics of black holes is then outlined and the physical history of each bubble is found to depend on the cosmological constant described in terms of thermodynamic pressure. Finally, a suggestive treatment of the Casimir effect is provided in the de Sitter-Planck background with Quantum-Boltzmann statistics and wormholes are explored as bubble coalescence processes.

Fertility management techniques being promoted in sub-Saharan Africa (SSA) seek to grow indigenous vegetables economically and sustainably. This study was conducted in a phytotron chamber and compared yield, soil carbon (C) speciation and greenhouse gas (nitrous oxide (N₂O) and carbon dioxide (CO₂)) emissions from SSA soils of two ecoregions; the dry savanna (lna, Republic of Benin) and rainforest (Ife, Nigeria) cultivated with local amaranth (Amaranthus cruentus) under manure (5 t/ha) and/or urea (80 kg N/ha) fertilization. Vegetable yield ranged from 1753 kg/ac to 3198kg/ac in the rainforest, RF, soils and 1281 kg/ac to 1951 kg/ac in the dry savanna, DS, soils. Yield in the urea treatment was slightly higher compared to the manure+urea treatment, but the difference was not statistically significant. Cumulative CO₂ emissions over 21 days ranged from 497.06 to 579.47 g CO₂ in the RF, and 322.96 to 624.97 g CO₂ in the DS, while cumulative N₂O emissions ranged from 60.53 to 220.86 mg N₂O in the RF, and 24.78 to 99.08 mg N₂O in the DS. In the RF samples, the combined use of manure and urea reduced CO₂ and N₂O emissions but led to an increase in the DS samples. ATR-FTIR analysis showed that the combined use of manure and urea increased the rate of microbial degradation in the soils of the DS, but no such effect was observed in soils of the RF. We conclude that combining manure and urea fertilization has different effects on soils of the two ecoregions, and that RF farmers can reduce agricultural emissions without compromising soil productivity and yield potential.

In this work it is acknowledged that important attempts to devise an emergent quantum (gravity) theory require space-time to be discretized at the Planck scale. It is therefore conjectured that reality is identical to a sub-quantum dynamics of ontological micro-constituents that are connected by a single interaction law. To arrive at a complex system-based toy-model identification of these micro-constituents, two strategies are combined. First, by seeing gravity as an entropic phenomenon and generalizing the dimensional reduction of the associated holographic principle, the universal constants of free space are related to assumed attributes of the micro-constituents. Second, as the effective field dynamics of the micro-constituents must eventually obey Einstein’s field equations, a sub-quantum interaction law is derived from a solution of these equations. A Planck-scale origin for thermodynamic black hole characteristics and novel views on entropic gravity theory result from this approach, which eventually provides a different view on quantum gravity and its unification with the fundamental forces.

When a grid-forming (GFM) inverter is connected to a low or medium voltage weak power grid, the line impedance with resistive-inductive characteristics will cause power coupling. Typical GFM decoupling control strategies are designed under nominal line impedance parameters. However, there are deviations between the nominal line impedance and actual parameters, resulting in poor decoupling effects. Aiming at this problem, this paper proposes a power decoupling strategy based on a reduced-order extended state observer (ESO). Firstly, the power dynamic model of GFM is established based on the dynamic phasor method. Then, the model deviation and power coupling due to line impedance parameter perturbation are estimated as internal disturbances of the system, and the disturbances are compensated on the basis of typical power control strategy and virtual impedance decoupling. Good decoupling performance is obtained under different impedance parameters, improving the control strategy's robustness. Finally, the effectiveness of the proposed method is verified by the results of RT Box hardware-in-the-loop experiments.

This paper provides a study on the development of solar energy, its prospects, and its impacts in Africa. Although the main focus is on technologies adopted for different uses (solar farms, mini-grids, off-grid, solar houses, irrigation, lighting, etc.), we also consider some aspects that increase the adoption of solar energy, such as legislation and business models. Finally, we will examine current and future solar projects managed by NGOs and international companies and how they benefit to leveraging social and economic standards, quality of life as well as capacity building in Africa. This study shows the plethoric interest of institutions outside Africa in the market of solar energy in the continent and that there is no African strategy to increase its independency toward a sustainable development particularly through energy transition.

A robust explicit model predictive control (EMPC)-based frequency adaptive grid voltage sensorless control is developed for a grid-connected inverter (GCI) via a linear matrix inequality (LMI) approach under the model parameter uncertainties and non-ideal grid environment. The control scheme is achieved by an enhanced prediction model rather than the conventional prediction model provided by fixed parameters to maintain the quality of grid currents injected into the grid when the model parameters are varied. Furthermore, an LMI-based observer is integrated with the disturbance observer to improve the reference tracking performance and reject disturbances. The proposed observer is employed for the grid frequency adaptive control without need of grid voltage sensors. The proposed current controller and observer employ the LMI scheme to maintain a stable and robust operation of the GCI. The discrete-time frequency response and pole-zero map analyses are used to investigate system stability and robustness against parameter uncertainties. Comprehensive simulation, experimental results and analyses clearly demonstrate the robustness of the proposed control scheme under various adverse test conditions with unexpected grid and system uncertainties.

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

The arrival of new developments in the field of power electronics circuit’s applications in power systems builds new control strategy to improve voltage quality for power grid by overcome interruptions. Wind energy resources are unconventional resources and get advanced problems to power grid when it is connected. Uncertainty of load sharing, peak energy management, climatic conditions, wind velocity and wind energy injected into power grid leads many of power quality problems on power grid based on the existing guidelines specified in IEC-61400 standard. This system plans efficient operation of DFIG to eliminate the voltage collapse and mismatch frequency to power grid. The DFIG connected diode rectifier and rotor side converter wind generator works as an Efficient Static Synchronous Compensator (E-SATCOM) for supplying the demand of reactive power for power grid to mitigate PQ problems. The benefit of using a combined controller was verified by simulink/Matlab and its simulated results used Doubly Fed Induction Generator (DFIG), wind Turbine. This simulation results gives good quality transient and stable state response to manage and support reactive power for both symmetrical and unsymmetrical faults in connection of grid codes to provide continuous quality of power supply, multiple wind generators are required for 140 bus power grid.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

The COVID-19 pandemic had a significant impact on the mental health of individuals globally, and primary healthcare (PHC) nurses play a critical role in providing mental healthcare services. However, limited research has explored the experiences of PHC nurses in providing mental healthcare services during the COVID-19 pandemic. This study explored the experiences of PHC nurses in providing mental healthcare services during the pandemic in Durban, South Africa. The aim was to identify the challenges faced by healthcare providers and the potential for innovative approaches to improve access to care. A qualitative, exploratory design guided the study, and data were collected through in-depth interviews with twelve PHC nurses purposively selected. Thematic analysis was used to analyse the data. Findings from interviews with primary healthcare nurses reveal that the pandemic exacerbated existing challenges, including medication adherence issues, fear and uncertainty among patients, vaccine hesitancy, decreased clinic visits, and the mental and emotional toll on both patients and healthcare workers. PHC nurses adapted their services by increasing outreach efforts, prioritising patient care, and utilising technology and non-governmental organisations’ (NGO) support. Challenges included reduced patient visits, complexities in healthcare provision, and lack of adequate support. Positive changes observed include increased mental health awareness among healthcare professionals and younger generations. Recommendations include implementing outreach and awareness campaigns, providing accurate information about COVID-19 and vaccinations, and promoting cultural sensitivity in mental healthcare provision.

Thrombosis is characterized by the pathological formation of fibrin clots within a blood vessel, leading to the obstruction of blood flow. Fibrinolytic enzymes from microorganisms have exhibited promising effects to dissolve clots in a more efficient and safe way. Then, the aim of this study was to evaluate the biomass and fibrinolytic enzyme production of Tetradesmus obliquus under autotrophic and mixotrophic conditions using different concentration of corn steep liquor (CSL). Different extraction and precipitating methods were tested, and the enzyme was purified by ion exchange chromatography. More advantageous culture condition was mixotrophic using 0.25% CSL, showing the highest values of biomass productivity (Px = 169.3 ± 44.36 mg∙L-1day-1) and specific growth rate (µmax = 0.17 ± 0.00 day-1), and significant fibrinolytic production (391.34 ± 40.03 U∙mg-1). Moreover, fibrinolytic activity was higher when extracted by homogenization and precipitated using acetone, which exhibited clear zone of fibrin degradation in the fibrinolytic plate assay. Additionally, the purified enzyme showed specific activity of 1176.90 ± 140.37 U∙mg-1 and molecular weight around 97 kDa. Finally, the enzyme has higher enzymatic activity than various fibrinolytic enzymes, and the obtained enzyme has potential to be developed as a therapeutic agent in thrombosis treatment. Additional studies are need to investigate the biochemical properties and biological profile of this enzyme.

Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system's required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system's backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

Taking into consideration the arising resistance towards the currently available antimycobacterial, there is still an unmet need for the development of new chemotherapeutic agents to combat the infectious agent. This study presents the X-ray single-crystal analysis to verify the structure of leading sulfonyl hydrazone 3b, which have proven their potent antimycobacterial activity against Mycobacterium tuberculosis H37Rv with MIC value of 0.0716 μM, respectively, low cytotoxicity and very high selectivity indexes (SI=2216) and fully characterized by NMR and HRMS methods. Furthermore, this study assessed the ex vivo antioxidant activity, acute and subacute toxicity, and in vitro inhibition capacity against enoyl-ACP reductase of hydrazones 3a and 3b, as 3a was identified as the second leading compound in our previous research. Compared to isoniazid, compounds 3a and 3b demonstrated lower acute toxicity for intraperitoneal administration, with LD50 values of 866 and 1224.7 mg/kg, respectively. Subacute toxicity tests, involving the repeated administration of a single dose of the test samples per day, revealed no significant deviations in hematological and biochemical parameters or pathomorphological tissues. The compounds exhibited potent antioxidant capabilities, reducing malondialdehyde (MDA) levels and increasing reduced glutathione (GSH). Enzyme inhibition assays of the sulfonyl hydrazones 3a and 3b with IC50 values of 18.2 µM and 10.7 µM, respectively and molecular docking studies revealed enoyl acyl carrier protein reductase (InhA) as the possible target enzyme of the compounds to show their antitubercular activities. In conclusion, the investigated sulfonyl hydrazones display promising antitubercular drug-like properties and warrant further investigation.

In this paper, new types of M-fractional wave solutions of mathematical physics model named as truncated M-fractional (1+1)-dimensional non-linear simplified Modified Camassa-Holm model are achieved by applying the modified simplest equation (MSE), Sardar sub-equation and generalized Kudryashov techniques. The gained solutions containing dark, bright, periodic and mixed wave solitons. Effect of fractional order derivative is also discussed. Achieved wave solitons are verified by Mathematica tool. Few of the gained wave solitons are also described through 2-dimensional, 3-dimensional and contour graphs through Mathematica tool. The gained solutions are helpful for the further development of concerned model. Finally, these techniques are simple, fruitful and effective to deal with non-linear FPDEs.

In light of the current situation where no testing equipment is available for measuring thermal deformation of objects, this paper proposes a novel method for accurate and precise measurement. The method overcomes the limitations of previous approaches that relied on pitch angle. By utilizing the principle of biplane multiple reflections, a bivariate laser spot displacement analysis algorithm is devised to attain highly precise measurements of bivariate angles. Additionally, a temperature gradient comparison algorithm is introduced to calculate the indicator test results under specific temperature conditions. To validate the effectiveness and reliability of this method, a testing system is constructed and utilized. The results demonstrate that the thermal deformation angle change test achieves an impressive accuracy of 0.015″ and a rate of thermal deformation angle change of 0.3247″/°C. These values are in close agreement with the previously simulated analysis result of 0.359″/°C, with only a relative error of 9.55%. Therefore, the test results confirm the efficacy and reliability of this testing method along with the feasibility of the algorithm processing.

An off-grid solar energy storage system (ESS) in National Pingtung University of Science and Technology (NPUST) was built and officially operated on Jun. 16th 2022. The system is installed in a 40' general container with PV panels of solar power 8250 W_p on top of the container. The ESS is made by repurposed lithium iron phosphate (LFP) batteries of 20 kWh capacity, where a battery management system (BMS) is adopted to ensured the safety of the battery system. An energy management system (EMS) is built to receive, process, analyse, store, and output the energy information. In this manuscript, a brief engineering report about the system is given.

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

It is widely believed that the dramatic transformation of the spherical outflows of AGB stars into the extreme aspherical geometries seen during the planetary nebula (PN) phase is linked to binarity and driven by the associated production of fast jets and central disks/torii. The key to understanding the engines that produce these jets and the jet-shaping mechanisms lies in the study of objects in transition between the AGB and PN phases. I discuss the results of our recent studies with high-angular-resolution (with ALMA & HST) and at high-energies (with GALEX, XMM-Newton & Chandra) of several such objects, which reveal new details of close binary interactions and high-speed outflows. These include two PPNe (the Boomerang Nebula and IRAS16342-3814), and the late carbon star, V Hya. The Boomerang is notable for a massive, high-speed outflow that has cooled below the microwave background temperature, making it the coldest object in the Universe. IRAS16342 is the prime example of the class of water-fountain PPNe (very young PPNe with high-velocity H₂O masers) and shows the signature of a precessing jet. V Hya ejects high-speed bullets every 8.5 years associated with the periastron passage of a companion in an eccentric orbit. I discuss our work on AGB stars with strongly-variable high-energy (FUV, X-ray) emission, suggesting that these objects are in the early stages of binary interactions that result in the formation of accretion disks and jets.

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development. One of the solutions seen to ensure energy security, expand energy access and promote a low carbon future in this isolated islands is the use of renewable energy sources. This study wishes to determine the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 9-year on-site wind speed data to develop high resolution wave energy model using SWAN wave model, and assessed its annual energy production through matching with wave energy devices. Results shows that average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m respectively, while southwest monsoon which is sheltered by the mainland resulted to a lesser outcome, Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the wave energy resource (Bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the observed values (average r = 0.9). Its annual energy production is highest at Station 5, with AEPWaveBouy = 43.761 MWh, AEP-Pelamis = 216.786 MWh and AEPWave Dragon = 2462.66 MWh. At present, the minimum requirement for a wave energy development to be feasible is 5 kW/m, which in this case, Cuyo Island falls short, but with the continuous evolution of wave energy converters, applications on milder re-sources will soon materialized.

This article presents a unique set of voltage and current data from a public building and acquired using a hybrid measurement solution that combines Python and Grafana. The transversal purpose consists of contributing to the community with a vision of the quality of the supply more oriented to the monitoring of the state of the network, providing a more realistic vision, which allows a better understanding, and the adoption of the best decisions to achieve the efficient energy management and thus optimize the operation and maintenance of power systems. The work focuses on higher order statistical estimators that, combined with exploratory data analysis techniques, improve the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination in the line of low-cost measurement solutions. It also incorporates the underlying benefit of the contribution to industrial benchmarking. The paper also includes a computational comparison between Python and LabVIEW to elicit the performance of the measurement solution.

Computation of Common Middle Point seismic sections and their subsequent time migration and diffraction imaging provides very important knowledge about the internal structure of 3D heterogeneous geological media and are key elements for successive geological interpretation. Full-scale numerical simulation, that computes all single shot seismograms, provides a full understanding of how the features of the image reflect the properties of the subsurface prototype. Unfortunately, this kind of simulations of 3D seismic surveys for realistic geological media needs huge computer resources, especially for simulation of seismic waves’ propagation through multiscale media like cavernous fractured reservoirs. In order to significantly reduce the query of computer resources we propose to model these 3D seismic cubes directly rather than the shot-by-shot simulation with subsequent CMP stacking. To do that we modify the well known "exploding reflectors principle" for 3D heterogeneous multiscale media by use of the finite-difference technique on the base of grids locally refined in time and space. To be able to simulate realistic models and acquisition we develop scalable parallel software, which needs reasonable computational costs. Numerical results for simulation of Common Middle Points sections and their time migration are presented and discussed.

This contribution considers an improved control scheme for three-phase two-stage grid-tied photovoltaic (PV) power system based on integral sliding mode control (ISMC) theory. The proposed control scheme consists of maximum power point tracking (MPPT), DC-Link voltage regulation and grid currents synchronization. A modified voltage-oriented maximum power point tracking (VO-MPPT) method based on ISMC theory is proposed for design of an enhanced MPPT under irradiation changes. Moreover, a novel DC-Link voltage control based on ISMC theory is proposed in order to achieve good regulation of DC-Link voltage over its reference. To inject the generated PV power into the grid with high quality, a voltage oriented control based on space vector modulation (SVM) and ISMC (VOC-ISMC-SVM) has been developed to control the grid currents synchronization. Numerical simulations are performed in Matlab/SimulinkTM environment in order to evaluate the proposed control strategy. In comparison with conventional control scheme, the developed control strategy provides an accurate MPP tracking with less power oscillation as well as a fast and an accurate DC-Link regulation under climatic conditions variations. Moreover, the transfer of the extracted power into the grid is achieved with high quality.

The article obtains large deviation asymptotic for sub-critical communication networks modelled as signal-interference-noise-ratio(SINR) random networks. To achieve this, we define the empirical power measure and the empirical connectivity measure, as well as prove joint large deviation principles(LDPs) for the two empirical measures on two different scales. Using the joint LDPs, we prove an Asymptotic equipartition property(AEP) for wireless telecommunication Networks modelled as the subcritical SINR random networks. Further, we prove a Local Large deviation principle(LLDP) for the sub-critical SINR random network. From the LLDPs, we prove the large deviation principle, and a classical McMillan Theorem for the stochastic SINR model processes. Note that, the LDPs for the empirical measures of this stochastic SINR random network model were derived on spaces of measures equipped with the $\tau-$ topology, and the LLDPs were deduced in the space of SINR model process without any topological limitations. We motivate the study by describing a possible anomaly detection test for SINR random networks.

This paper examines some of the largely neglected areas of opportunity to utilize skeletal space structure systems in support of modular, industrialized, economical, sustainable and digital future of the construction industry. In this context, the feasibility of future use of skeletal space structures is studied for a few classes of engineering structures, namely, residential apartment buildings, and offshore platforms, along with their suitability for reconstruction, renovation, modernization and retrofit of damaged buildings and urban areas of cultural heritage significance. Finally, the particulars of lean project management of space structures are discussed with emphasis on engineering and economic factors, production management, environmental aspects, quality management, and reliability, maintainability and sustainability considerations. This article concludes that skeletal space structures can fulfil many of the essential construction requirements of modern societies, especially those facing environmental challenges, all while allowing for design flexibility and mass customization.

In this paper, we study the performance of various deep reinforcement learning algorithms to enhance the energy management system of a microgrid. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a set of thermostatically controlled loads, a set of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate among the different flexible sources by defining the priority resources, direct demand control signals, and electricity prices. Seven deep reinforcement learning algorithms were implemented and are empirically compared in this paper. The numerical results show that the deep reinforcement learning algorithms differ widely in their ability to converge to optimal policies. By adding an experience replay and a semi-deterministic training phase to the well-known asynchronous advantage actor-critic algorithm, we achieved the highest model performance as well as convergence to near-optimal policies.

Satellite altimetry is successfully developing during the past three decades for the sea level, ocean dynamics, coastal oceanography, planetary waves, ocean tides, wind and wave, ice cover, Earth’s gravity field, and climatology research. We propose a new essential add-on of satellite altimetry related to the peculiarities of the orbits of the Topex/Poseidon and Jasons’ satellite missions which were not mentioned before in the scientific publications. Derived subsets of “self-crossover” and “inter-crossover” points in sub-polar latitudes are discussed in detail in the context of water exchange, and wind-wave dynamics, and potential challenges to be solved. The relatively short time lags between measurements at these crossovers provide additional information on anomalies of magnitudes and directions of ocean currents, and characteristics of wind-driven waves. Resulting data snapshots with constant space and time intervals can be regarded as time series of virtual buoys, an analog of continuous buoy measurements of the sea level, wind speed, and wave height. Areas of the World Ocean where these specific crossovers occur are described in the context of water exchange, wind wave studies, and potential challenges to be solved. The value of these special crossovers for studies and monitoring of the sub-polar seas is illustrated by a case study.

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must carefully be evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis, and energy system modelling to optimize decision-making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends signifi-cantly on three factors: 1. the residential electricity consumption patterns that are influenced by the socio-economic composition of the community, 2. the degree of flexibility of i) PUE assets and ii) operational preferences of the PUE user, and 3. the capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy system can reduce the financial risk of operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project´s financial sustainability, and is achieving the overall objectives of decentralized energy system planning.