The mucopolysaccharidoses (MPS) are a group of 11 lysosomal storage diseases (LSDs) produced by mutations in the enzymes involved in the lysosomal catabolism of glycosaminoglycans. Most of the mutations affecting these enzymes may lead to changes in processing, folding, glycosylation, pH stability, protein aggregation, and defective transport to the lysosomes. The use of small molecules, called pharmacological chaperones (PCs), that can restore the folding, trafficking and biological activity of mutated enzymes has been extensively explored in LSDs as a therapeutic alternative. PCs have the advantage of wide tissue distribution, potential oral administration, lower production cost, and fewer issues of immunogenicity. In this paper, we will review the advances in the identification and characterization of PCs for the MPS. These molecules, mainly based in molecules mimicking the enzyme substrates, have been described for MPS II, IVA, and IVB, showing a mutation-dependent enhancement of the mutated enzymes. Although the results show the potential of this strategy, further studies should focus in the development of disease-specific cellular models that allow a proper screening and evaluation of the identified PCs. In addition, in vivo evaluation, both pre-clinical and clinical, should be performed, before they can become a real therapeutic strategy for the treatment of MPS patients.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

Full-scale/model test comparison studies to validate the traditional ABL wind tunnel simulation technique are reviewed. According to the literature review, notable discrepancies between full-scale measurement results and model test results were observed by most performed comparison studies, but the causes of the observed discrepancies were not revealed in a scientific way by those studies. In this regard, a new research scheme for future full-scale/model test comparison studies is proposed in this article, which utilizes the multiple-fan actively controlled wind tunnel simulation technique. With the new research scheme, future full-scale/model test comparison studies are expected to reasonably disclose the main problems with the traditional ABL wind tunnel simulation technique, and the technique can be improved correspondingly.

The advent of RNA-sequencing (RNA-Seq) technologies has markedly improved our knowledge and expanded the compendium of small non-coding RNAs, most of which derive from the processing of longer RNA precursors. In this review article, we will discuss about the biogenesis and function of small non-coding RNAs derived from eukaryotic ribosomal RNA (rRNA), called rRNA fragments (rRFs), and their potential role(s) as regulator of gene expression. This relatively new class of ncRNAs remained poorly investigated and underappreciated until recently, due mainly to the a priori exclusion of rRNA sequences—because of their overabundance—from RNA-Seq datasets. The situation surrounding rRFs resembles that of microRNAs (miRNAs), which used to be readily discarded from further analyses, for more than five decades, because we could not believe that RNA of such a short length could bear biological significance. As if we had not yet learned our lesson not to restrain our investigative, scientific mind from challenging widely accepted beliefs or dogmas, and from looking for the hidden treasures in the most unexpected places.

Installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of its possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has practically decarbonized its electrical matrix, incorporating wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country's Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a transportation fuel for both domestic consumption and export. Combining the country's strong base of wind experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms' ecological studies, and examines offshore wind farms' global environmental and social impacts. Finally, it proposes suggested studies for environmental approval for offshore wind farms.

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed and implemented product in this study serves as a proof of concept that could be replicated by WMO Members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for input selection based on large-scale indicators to enhance Artificial Intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction of wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead.

The energy transition to wind and solar opens opportunities for green hydrogen as the wind and solar generation tends to bring electricity prices down to very low levels. This paper evaluates whether green hydrogen can integrate well with wind and solar PV to help with improved management of the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during surplus renewable energy periods. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used systems engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, but without those changes, our modelling suggested it would be financially viable within this decade

In the present work, a methodology for wind field reconstruction based on the Meteo Particle Model (MPM) from Numerical Weather Prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP Limited Area Models (LAMs) are run at spatial resolution of about 1 km, this level of information is not sufficient for many applications, for example to support drone operation in urban contexts. The coupling of MPM with the NWP Limited Area Model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values in the generical point considered for the investigation. The numerical results obtained reveal a good behavior of the method in reproducing the general trend of wind speed, as confirmed also by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of spatial and temporal resolution even if the MPM inherits the bias that inevitably affects the COSMO output.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Extensive surface and upper air measurements of a typhoon over the northern part of the South China Sea, namely, Typhoon Talim in July 2023, are documented and analyzed in this paper. A number of features have been observed from the upper air measurements. First, the log law and the power law are found to be appropriate in fitting the wind profiles of the typhoon in the first 1000 m or so above the sea surface. A low level jet is observed in the lower troposphere from the observations of the radar wind profilers. The paper is also novel from the perspectives that the vertical wind profile from a Doppler LIDAR on an offshore platform over the northern part of the South China sea, and that ocean radar data are used to analyze the surface wind observations of a typhoon in the region. The results of this paper would be useful in understanding the structure of tropical cyclones, e.g. in wind engineering applications.

Currently, there are different types of technology for the production of electricity that use various energy sources, this causes the establishment of generation centers that provide from a small amount to tens of megawatts of electrical energy. These centers are built to supply electricity to nearby loads through networks integrated into a large electrical system or in isolation with their electrical system. The technological evolution, the different energy sources, and the generation centers close to the consumers entail what has been called a distributed generation (DG), the DG carries with it aspects that need to be analyzed. In this paper, the impact of wind generation on the medium voltage energy distribution network is studied, it was determined that the addition to the distribution network of power by wind turbines less than the transmission center does not produce an impact on the network. Simulated results obtained using SIMULINK and DIgSILENT are presented and discussed.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

SStatistical characteristics of the wind speed in Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at 10 m height above the ground. The cumulative mean wind speed at measurement height was shown to be 4.53 m/s with standard deviation of 2.32 m/s. Prevailing wind direction is shown to be char-acterized by cumulative mean azimuth of 226° with standard deviation of 79.76°. The results were extrapolated to 70-meter height in order to estimate wind characteristics at hub height of a me-dium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly and seasonally, demonstrating a good match with histogram-based statistical repre-sentations. Shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind tur-bines energy yield calculations. The novelty of the current paper is related to gathering wind statistics for a certain area (Samaria) we are not aware of any published statistics regarding wind velocity and direction in this area. The data may be interesting for potential regional wind energy development in which the obtained Weibull distribution can be used in calculations of expected power generation of particular turbines with known power dependence on velocity. We also point out that the fact that realistic wind velocity statistics is well described by an analytic formula (Weibull distribution) is not trivial, and in fact the fit may have been poor.

This study examines the effect of surface currents on the bulk algorithm calculation of wind stress estimated using the scatterometer data during 2007-2020 in the Indian Ocean. In the study region as a whole the wind stress decreased by 5.4% by including currents into the wind stress equation. The most significant reduction in the wind stress is found along the most energetic regions with strong currents such as Somali Current, Equatorial Jets and Aghulhas retroflection. A highest reduction of 11.5% is observed along the equator where the Equatorial Jets prevail. A sensitivity analysis has been carried out for the study region and for different seasons to assess the relative impact of winds and currents in the estimation of wind stress by changing the winds while keeping the currents constants and vice versa. The inclusion of currents decreased the wind stress and this decrease is prominent when the currents are stronger. This study showed that equatorial Indian Ocean is the most sensitive region where the current can impact on wind stress estimation. The results showed that uncertainties in the wind stress estimations are quite large at regional levels and hence better representation of wind stress incorporating ocean currents should be considered in the ocean/climatic models for accurate air-sea interaction studies.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

The survey findings reveal that rivers worldwide carry an annual sediment load of 15 billion tons into the sea, and the Yellow River basin alone contributes 1.6 billion tons of sand. Therefore, understanding the science of wind and sand in the Yellow River is crucial to ensuring the safe development of similar basins across China and the world. This study examines the midstream wind and sand area of the Xiliugou tributary, a part of the upper Yellow River. The researchers used a stepped sand collector combined with an anemometer to measure the sand transport flux at 0-50 cm height on various underlying surfaces of the basin. Then, they estimated the amount of wind and sand entering the Yellow River using a function model based on the measured factors. Furthermore, the team analyzed the particle size composition of wind-eroded sand to better understand the principles of wind and sand erosion and accumulation in the basin. The results of the study show that the sand transport flux per unit area varies significantly across different underlying surfaces. Moreover, the contribution of moving sandy land, semi-fixed sand, and fixed sand to wind and sand deposition in the Yellow River basin was 77.08%, 15.30%, and 7.62%, respectively. The vertical change of sand transport rate on the basin's surface demonstrates that the total sand transport rate is an exponential function of wind speed. Based on this relationship, the researchers estimated that the total annual average wind and sand entering the Yellow River basin via the Xiliugou tributary is approximately 8.09×105t. Due to the basin's unique geography and sand source, the particle size composition of wind-eroded sand differs between the east and west sides of the river channel. On the west side, desert sand, mainly fine sand, and very fine sand constitutes the sand source. Conversely, on the east side, sand collected in the riverbed by secondary wind erosion is the primary sand source. Furthermore, human activities have disturbed the grain composition, mainly comprising powder and clay particles. This phenomenon, known as "wind-blown mud and water-washed sand," is evident in this geographical unit. In conclusion, the Yellow River basin still faces significant ecological security hazards. Understanding the coupling relationship between desert-basin-sand and wind is the foundation for effectively controlling wind and sand flow into the Yellow River basin.

With the rapid development of global offshore wind power, the demand for offshore wind power operation and maintenance is also increasing. Wisdomization of offshore wind farms is a practical need to improve the operation level and benefit of offshore wind farms. This paper first introduces the current development situation and characteristics of global offshore wind power, and expounds the current situation and main challenges of offshore wind power operation and maintenance market. Therefore, our paper discusses the innovation of offshore wind power operation and maintenance from the aspects of operation and maintenance management of offshore wind power, monitoring and analysis technology of units, far-reaching wind field monitoring and operation and maintenance risks. Then, combined with information technology and lean management concept, a smart operation and maintenance management platform for wind farms in far-reaching sea areas is built to explore centralized and intelligent operation and maintenance management mode, improve operation and maintenance efficiency of wind farms in far-reaching sea areas, and minimize operation and maintenance costs. Finally, through the research on the characteristics of 5G technology, combined with the practical experience of operation and maintenance, and in view of the characteristics of offshore wind farms, we analyze and propose several typical application scenarios of 5G technology in the intelligent operation and maintenance of offshore wind farms, which provides a new solution for the efficient operation and maintenance of offshore wind farms.

Colorectal cancer (CRC) is the third most commonly diagnosed type of cancer worldwide and is responsible for numerous deaths. 5-fluorouracil (5-FU) is an effective chemotherapy drug commonly used in the treatment of CRC, either as monotherapy or in combination with other drugs. However, half of CRC cases are resistant to 5-FU-based therapies. To contribute to the understanding the mechanisms underlying CRC resistance or recurrence after 5-FU-based therapies, we performed a comprehensive study integrating in silico, in vitro and in vivo approaches. We identified differentially expressed genes and enrichment of pathways associated with recurrence after 5-FU-based therapies. Using these bioinformatic data as a starting point, we selected a group of drugs that restored 5-FU sensitivity to 5-FU resistant cells. Interestingly, treatment with the novel Rac1 inhibitor, 1A-116, reversed morphological changes associated with 5-FU resistance back to a control-like status. Moreover, our in vivo studies have shown that 1A-116 affected tumor growth and the development of metastasis. All our data allowed us to postulate that targeting Rac1 represents a promising avenue for the development of new therapies for patients with CRC resistant to 5-FU-based therapies.

In recent decades, there has been a startling rise in the number of cancer patients worldwide, which has led to an amazing upsurge in the development of novel anticancer treatment candidates. On a positive note, arylpiperazines have garnered attention in cancer research due to their potential as scaffolds for developing anticancer agents. These compounds exhibit a diverse array of biological activities, including cytotoxic effects against cancer cells. Indeed, one of the key advantages of aryl piperazines lies in their ability to interact with various molecular targets implicated in cancer pathogenesis. Here, we focus on the chemical structures of several arylpiperazine derivatives, highlighting their anti-proliferative activity in different tumor cell lines. The modular structure, diverse biological activities, and potential for combination therapies of arylpiperazine com-pounds make them valuable candidates for further preclinical and clinical investigations in the fight against cancer. This review, providing a careful analysis of different arylpiperazine deriva-tives and their biological applications, allows researchers to refine the chemical structures to improve potency, selectivity, and pharmacokinetic properties, thus advancing their therapeutic potential in oncology.

Blockchain technology (BCT) has the potential for sustainable development, enhancing business efficiency, reducing costs, and strengthening data security. Despite its potential, adoption among small and medium-sized enterprises (SMEs) is low due to the need for significant changes in technology infrastructure and the challenge of interoperability. A cross-sectional survey was done with 226 Hong Kong SMEs. The PLS-SEM results highlighted favorable transformation conditions as the most critical factor for SMEs considering the adoption of BCT. These conditions address both internal and external readiness for adopting the disruptive strategy. Other contributing factors to BCT adoption include trust in BCT developers, perceived usefulness and ease of use, government support, and perceived security. The research results contributed to the literature on disruptive technology acceptance, adoption, transformation conditions, technology readiness, and SMEs’ technology adoption. Government-supported policies and initiatives can promote BCT adoption, including educational programs, case studies, and resources for readiness. Financial support and regulatory frameworks can further encourage adoption. BCT developers can demonstrate the value of their systems, effectively communicate potential ROI and cost savings, and provide user-friendly interfaces to increase SMEs’ intention to adopt BCT.

Plant diseases and pests reduce crop yields, accounting for global crop losses of 30% to 50%. In conventional agricultural production systems, these losses are typically controlled by applying chemical pesticides. However, public pressure is mounting to curtail agrochemical use. In this context, employing beneficial endophytic microorganisms is an increasingly attractive alternative to the use of conventional chemical pesticides in agriculture. A multitude of fungal endophytes are naturally present in plants, producing enzymes, small peptides and secondary metabolites due to their bioactivity, can protect hosts from pathogens, pests and abiotic stresses. The use of beneficial endophytic microorganisms in agriculture is an increasingly attractive alternative to conventional pesticides. The aim of this study was to characterize fungal endophytes isolated from apparently healthy, feral wine grapes in eastern Canada that have grown without agrochemical inputs for decades. Host plants ranged from unknown seedlings to long-lost cultivars not widely propagated since the 1800s. HPLC-MS was used to identify unique endophyte-derived chemical compounds in the host plants, while dual-culture competition assays showed a range in endophytes’ ability to suppress the mycelial growth of Botrytis, which is typically controlled in viticulture with pesticides. Twelve of the most promising fungal endophytes isolated were identified using multilocus sequencing and morphology, while DNA barcoding was employed to identify some of their host vines. These fungal endophyte isolates, which consisted of both known and putative novel strains, belonged to seven genera in six families and five orders of Ascomycota. Exploring the fungal endophytes in these specimens may yield clues to the vines’ survival and lead to the discovery of novel biocontrol agents.

Small bowel non-Meckelian diverticulosis is a rare condition with only few published cases despite being described over 200 years ago. In the midst of the COVID-19 pandemic, studies suggested that a lot of patients may experience gastrointestinal manifestations. Intestinal symptoms could worsen the inflammation and infection associated with small bowel diverticulitis. Here, we present two cases: one with inflammation and rupture in a COVID-19 patient and the other as an asymptomatic detection. Furthermore, we provide a mini-review of the literature to emphasize the importance of considering this entity in the differential diagnosis of an acute abdomen.

We previously showed that feeding mice a diet containing 1% mantle tissue decreased food consumption, leading to death. We also isolated and identified toxic substances in the mantle tissue. In the present study, we investigated the characteristics and stability of mantle tissue toxicity. Treatment of mantle tissue with 1 mM HCl, 1 mM NaOH, 1 mM dithiothreitol, and 1 mM H2O2 and heating did not reduce the toxicity of mantle tissue in mice. These results suggest that mantle toxins are stable in tissues, particularly when exposed to acidic and digestive enzymes. We examined whether mantle tissue exhibited acute toxicity. Diets containing 1% and 20% mantle extract showed similar levels of toxicity, demonstrating that feeding of mantle tissue does not lead to acute toxicity. Finally, we examined the toxicity of the mantle tissue against small intestinal tissue. Chronic feeding of mantle tissue to mice changed the color of the small intestine. Real-time PCR analysis showed that mantle tissue feeding caused changes of inflammation and endoplasmic reticulum stress markers in the small intestine. These results suggest that feeding of mantle tissue causes toxicity after causing initial damage to the small intestinal tissue.

To verify the reliability of the proposed stitching linear scan method for roundness measurement of small cylindrical workpieces, measurement uncertainties of the workpieces with various dimension are conducted. The cross-sectional circle of a cylindrical workpiece is divided into several equal arcs to be measured. Thus, the arc profile can be characterized according to the measured arcs. The roundness profile of the cylindrical part can be obtained by stitching these arc profiles together. Firstly, the measurement uncertainty of the workpiece with a diameter 1.5 mm and length 5.8 mm is evaluated to be 0.095 μm which can meet the uncertainty target of 0.1 μm. Considering the uncertainty of θx and θz which are the alignment around X-axis and Z-axis respectively changing with workpiece dimension changing, relevant uncertainties are carried out. As a result, there is no limitation with respect to workpiece diameter theoretically If the alignment can reach θz = θx = ±0.1° without considering the measurable range of the machine.

Homocysteine (Hcy) is a sulfur-containing amino acid that is generated during methionine metabolism. Hyperhomocysteinemia (HHcy) is typically defined as levels >15 micro mols/L. Elevated plasma levels of Hcy can be caused by the deficiency of either vitamin B12 or folate. The active role of homocysteine is quite ambivalent: many studies detected its potential impact on neurological events; others try to identify it as one of the possible risk factors of cardiovascular events, but with a complementary and secondary role. HHcy has been reported in many neurologic disorders, including cognitive impairment and stroke, independent of long-recognized factors such as hyperlipidemia, hypertension, diabetes mellitus, and smoking. Nowadays, homocysteine could be considered as a possible link between a common vascular risk factor and potential alterations in degenerative neuronal disorders. HHcy-induced oxidative stress, endothelial dysfunction, inflammation, smooth muscle cell proliferation, and endoplasmic reticulum stress; all these aspects have been considered to play an essential role in the pathogenesis of several diseases, including atherosclerosis, major stroke, and vascular dementia. Specific models of astrocytes impairment in HHcy-mice, which mimic small vessel disease, have been developed with a three-step investigation (at 6, 10, 14 weeks of B6, B9, and B12 detrimental diet in wild type HHcy mouse). These studies found out that after ten weeks on a diet (at the most after 14 weeks), end-feet disruption occurs. This phenomenon is concomitant to reduced vascular labeling for aquaporin -4-water channels, lower protein/mRNA levels for Kir4.1, and BK potassium channels, associated with a higher expression of MMP-9. The most exciting finding is that microglial activation in this mice model was evident since the precocious time of observation (6-week time) and precedes astrocytic changes. Our research aims to review the possible role of HHcy in neurodegenerative disease and small-vessel disease and to understand its pathogenic impact.

Small-scale fishers are considered as one of the most vulnerable communities in Bangladesh but very few studies focused on the livelihood sustainability and vulnerabilities of this professional group. A fieldwork in lower Padma and upper Meghna hilsa sanctuaries identifies different livelihood characters and associated vulnerabilities of the fishers. A conceptual framework known as Sustainable Livelihood Approaches (SLA) has been introduced to analyses the qualitative and quantitative data. The insights of the livelihood strategies provide on small-scale fishers and fisheries management have been explained and explored. Fishers are found solely dependent on fishing, economically insolvent and neglected. In addition, some socio-economic abstractions such as low income, credit insolvency, lack of substitute earning flexibility make them more vulnerable. A number of effective suggestions are elicited from fishers’ perceptions, the implementation of which is crucial to ensure livelihood sustainability of the small-scale fishers.

Alfalfa, an important legume forage, is an ideal crop for sustainable agriculture and a potential bioenergy plant. Drought, one of the most common environmental stresses, substantially affects plants’ growth, development and productivity. MicroRNAs (miRNAs) are newly discovered gene expression regulators that have been linked to several plant stress responses. To elucidate the role of miRNAs in drought stress regulation of alfalfa, a high-throughput sequencing approach was used to analyze 12 small RNA libraries comprising of 4 samples, each with 3 biological replicates. We identified 348 known miRNAs, belonging to 80 miRNA families, from the 12 libraries and 281 novel miRNAs using Mireap software. 18 known miRNAs in roots and 12 known miRNAs in leaves were screened out as drought-responsive miRNAs. Except for miR319d and miR157a which were upregulated under drought stress, the expression pattern of drought-responsive miRNAs were different between roots and leaves in alfalfa. This is the first study discovering miR157a, miR1507, miR3512, miR3630, miR5213, miR5294, miR5368 and miR6173 are drought-responsive miRNAs. Target transcripts of drought-responsive miRNAs were computationally predicted. All 447 target genes for the known miRNAs were predicted using an online tool. This study provides a significant insight on understanding drought-responsive mechanisms of alfalfa.

In this paper, dual-rotor counter-rotating (CR) configurations of vertical axis wind turbines (VAWT) are briefly inspected and divided into three types. This investigation was focused on one of these types – the CR-VAWT with co-axial rotors, in which two equal rotors are placed on the same shaft, displaced from each other along it and rotate in opposite directions. For this CR-VAWT with three-blade H-Darrieus rotors, the properties of the design in terms of aerodynamics, mechanical transmission and electric generator, as well as control system are analyzed. A new direct-driven dual-rotor (DR) permanent magnet synchronous generator (PMSG) was proposed, in which two built-in low-power PM electric machines have been added. They perform two functions – start-up and overclocking of the rotors to the angular velocity at which the lifting force of the blades is generated and stabilizing the CR-VAWT work as wind gusts act on the two rotors. Detailed in this paper is the evaluation of aerodynamic performance of the CR-VAWT via 3D computational fluid dynamics (CFD) simulations. The evaluation was conducted using the CONVERGE CFD software with the inclusion of the actuator line model for the rotor aerodynamics, which significantly reduces the computational effort. Obtained results show that both rotors, while they rotate in opposite directions, had a positive impact on each other: the power coefficients of the upper and lower rotors in the CR-VAWT increased by 5.5% and 13.3% respectively compared to the single-rotor VAWT at the optimal distance between the rotors of 0.3 from a rotor height.

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Sudden variation of aerodynamic loads is the potential source of safety accidents of high-speed train (HST). As a follow-up investigation on the aerodynamic response of a HST that enters a tunnel under crosswind environment, this paper focuses on the transient response of a HST’s safety indices based on the train–track coupling interaction model. Firstly, a wind–train–track coupling dynamic model is proposed by introducing transient aerodynamic loads into the vehicle–track system. Secondly, the temporal evolution of safety coefficients indicates that the train’s safety risk increases during tunnel entry with crosswind. Results show that the derailment coefficients and wheel load reduction rate during tunnel entry are not only larger than those in open air but also those inside the tunnel due to the sudden disappearance of wind excitation at the tunnel entrance. In addition, the characteristic wind curve, which is the wind velocity against the train speed, is presented for application based on the current specification of the safety criteria threshold. The investigation will be useful in assessing the safety risk of a running train subjected to other aerodynamic attacks, such as the coupling effect of infrastructure scenario and crosswind in windy area.

In the past decade, RNA fragments derived from full length small nucleolar RNAs (snoRNAs) have been shown to be specifically excised and functional. These sno-derived RNAs (sdRNAs) have been implicated as gene regulators in a multitude of cancers, controlling a variety of genes post-transcriptionally via association with the RNA-induced silencing complex (RISC). In this review, we have summarized the literature connecting sdRNAs to cancer gene regulation. SdRNAs possess miRNA-like functions, and are able to fill the role of tumor-suppressor or tumor-promoter in a tissue context-dependent manner. Indeed, there are many miRNAs that are actually derived from snoRNA transcripts, meaning that they are truly sdRNAs and as such are included in this review. As sdRNAs are frequently discarded from ncRNA analyses, we emphasize that sdRNAs are functionally relevant gene regulators and likely represent an overlooked subclass of miRNAs. Based on the evidence provided by the papers reviewed here, we propose that sdRNAs deserve more extensive study to better understand their underlying biology and to identify previously overlooked biomarkers and therapeutic targets for a multitude of human cancers.

Presently, Jordan confronts a double substantial challenge of enhancing energy security and mitigating greenhouse gas emissions. One of the most encouraging avenues to attain the objectives of sustainable development, energy security, and environmental conservation is to enhance the integration of renewable energy into the generation of electricity. In pursuit of this goal, the Jordanian government intends to expand investments in the green energy sector, with solar and wind energy poised to play a key role in satisfying energy demands and cultivating an environmental sustainability. This paper aims to examine the distinctive dynamics, challenges, obstacles, and potential solutions corresponding to the establishment of community solar and wind farms in Jordan suburban areas. It aims to highlight the opportunities and barriers influencing the track of sustainable energy adoption in the country. Through a comprehensive analysis of the Jordanian context, the goal is to extract insights that can guide policies and practices. Additionally, the findings may serve as a valuable benchmark for other regions confronting similar challenges on their pursuit for a sustainable energy future.

Climate research shows that extreme weather events and their effects are increasing in frequency, severity, and impact. Extreme wind events can cause massive societal and economic damage. High wind occurrences and their causes must be identified because they are hard to define and understand. Extreme wind modeling affects wind power plant planning, installation, and building modeling. This applies to city, vegetation, and vegetational area modeling. The scientific literature utilizes statistical, dynamic (Weather Research and Forecasting-WRF), and machine learning methods to identify extreme wind speeds. This study explores the relationship between lower-level extreme wind speeds and upper-level parameters like Z500, and T850 based on cluster analysis. The aim is to explore the correlation between upper-level atmospheric dynamics and lower-level wind behavior. K-Means cluster analysis method was employed to classify Turkish regions with similar severe wind episodes. WRF model simulation findings from NEWA (New European Wind Atlas) project (2019) were used for that purpose. The NEWA project's wind speed data at 100m a.g.l. was used for cluster analysis. We simplify the process by grid point reduction and applying the top 2% percentile as threshold value. Self-Organized Maps (SOM) clustering was also applied using the upper-level variables.

The maritime sector, grapples with environmental challenges, but the adoption of "cold ironing" emerges as a promising solution, signifying a shift toward sustainability by providing energy to ships at port, reducing reliance on fossil fuels, and significantly lowering emissions, contributing to global climate change efforts. This transition towards renewable energy is especially crucial for islands like Lesvos, addressing challenges of high energy costs and environmental degradation resulting from reliance on imported fossil fuels, highlighting the potential for positive economic and environmental impacts. The existing literature highlights a research gap in the development of Renewable Energy Sources (RES) to support cold ironing operations, specifically due to the absence of specific data for the power demand needed in such operations and the lack of monitoring data for accurate calculations. This study's primary objective is to utilize real monitoring data from the port of Lesvos to evaluate the power demand for a potential cold ironing application. Additionally, the study aims to analyze the implementation of a wind power park to meet the energy needs of an electric port and evaluate the potential reduction in CO2 emissions. The database of Marine Traffic S.A. was utilized, while the determination of the energy used in-port per ship depended on factors such as the number of arrivals, the duration of hoteling, and the type of engine used by the ships. In addition, the software RETSCREEN was used for the energy analysis of a simulated 20 MW wind park that can support the in-port operations. The combined monthly energy demand for both main and auxiliary engines of shipping vessels at Mytilini port was assessed to be 6118 MWh and averaging a power demand equaled to 8.2 MW. The simulation analysis indicates that the annual electricity delivered to the port can be72080 MWh, with a significant portion remaining unused and exported to the grid, amounting to 14956 MWh annually. Due to demand fluctuations, in-port activities require an additional 924 MWh from the main electrical grid, which highlights the importance of additional measures like energy storage and demand-response practices for the gradual transition to 100% Renewable Energy Sources (RES) operations.

This paper is devoted to the analysis of fluctuations in the solar wind plasma and interplanetary magnetic field parameters observed by Solar Orbiter and WIND spacecraft at different scales, ranging from ~10^3 to 10^7 km. We investigated two long observation intervals when the spacecraft were near the Sun-Earth line, during which the distance between the spacecraft was 0.1 and 0.5 AU respectively. It is analyzed how the fluctuation properties in different types of SW, associated with quasi-stationary and transient solar phenomena, transform on the way from the Sun to the Earth. It is shown that the time series behavior of the bulk speed subject to the least change even at the large distance between the observation points, while the time series of the IMF magnitude, components and proton density were significantly transformed even at the relatively short distance. Regardless of the assumed change in the absolute value of the parameters, the conducted statistical analysis shows that the characteristics of their fluctuations (including the level of intermittency) remains near constant regardless of the distance between the observation points and depends mostly on the type of the solar wind.

The close proximity of different buildings heights can cause disturbances in the working of the smoke and ventilation ducts of lower buildings, threatening the health and even the lives of residents. To define the influence of high-rise building on the work of the ducts of the neighbouring double-storied building, experimental investigations in the wind tunnel were conducted. On this basis, empirical equations and graphs were developed leading to determining the aerodynamic coefficients considering different wind directions and the height of ducts. The direction of the wind reveals a greater influence than the height of the ducts. Properly using deflectors or increasing the height of the duct ensures maintaining static rarefaction in the area of smoke and ventilation ducts. The creation of rarefaction ensures the reliability of the natural ventilation system and the safety of the health of residents.

The overcrowding of high-rise buildings in the city adversely affects the wind environment by changing the air currents in the surrounding areas. In particular, extreme climate phenomena caused by climate change are stronger and more frequent, causing social damages in cities. To comprehensively better understand the wind properties around high-rise buildings, actual filed measurement is necessary to determine the environment assessment of wind effect. We performed the on-site measurement on LCT residential complex regions (411.6m tall) with highly concentrated high-rise buildings in coastal city Busan, South Korea under extreme weather conditions such as typhoon invasions to determine wind fluid characteristics. In the field monitoring, five anemometers were installed to analyze the wind environment around high-rise buildings when typhoon 'Hinnamnor' invaded. Compared to the nearby weather station operated by KMA(Korea Meteorological Administration), the gust was 3.7 times stronger and the max 1 min-mean wind speed was 3.1 times stronger, and the characteristics of the downward wind and the canyon wind were shown depending on the location characteristics of the point. Turbulence intensity decreased as the wind speed increased and converged to a certain value. Likewise, the gust factor also decreased as the wind speed increased and converged to 2.0, which is considered to be the parameter that best represents the intensity of the instantaneous gust caused by the skyscraper wind effect.

Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

In this paper we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of methods produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warmstart procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and and the peak computation time by a factor of 8, compared to when no homotopy is applied. Overall, the CPU timings are competitive to timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of 3. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.

This paper presents the super-twisting algorithm (STA) direct power control (DPC) scheme for the control of active and reactive powers of grid-connected DFIG. Simulations of 5 KW DFIG has been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC). The proposed controller schemes with fixed gains are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effe This paper presents a comparative study of two approaches for the direct power control (DPC) of doubly-fed induction generator (DFIG) based on wind energy conversion system (WECS). Vector Control (VC) and Sliding Mode Control (SMC). The simulation results of the DFIG of 5 KW in the presence of various uncertainties were carried out to evaluate the capability and robustness of the proposed control scheme. The (SMC) strategy is the most appropriate scheme with the best combination such as reducing high powers ripple, diminishing steady-state error in addition to the fact that the impact of machine parameter variations does not change the system performance. cts of parametric uncertainties not affecting system performance.

The incorporation of wind generation introduces challenges to the operation of the power system due to its uncertain characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal Kriging and analog approaches are used to forecast wind power generation and used as input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal Kriging takes into account the spatial and temporal information given by the database to enhance wind forecasts. We evaluate the performance of using the spatio-temporal Kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results show that the use of Kriging-based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

This paper reassesses the detrimental effect on fatigue performance due to thicker sections based on extensive fatigue strength test database, taken from research program worldwide over the past half of a century in offshore oil & gas and renewable industry. The data entries in the database have been evaluated to ensure its data integrity. Statistical analyses on these S-N data are performed with or without the thickness correction at different exposure level to corrosive environment, in order to re-evaluate the suitability of current standards in regard to the thickness effect. The study has concentrated on T-joint, transverse butt welded joint and tubular joint as these are the most commonly used joint types in the offshore wind industry. The analysis indicates general agreement of fatigue strength with the thickness effects in current standard for in air conditions but great conservatism for corrosive environment.

In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind speed. Further comparison of the SAR retrieved sea surface wind speed with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

Monin-Obukhov similarity theory (MOST) overestimates wind shear in some atmospheric stable conditions, i.e. Richardson number $R_f<0.25$. The overestimated wind shear that leads to an under-predicted friction wind speed and a lower ambient turbulence intensity for a given hub-height reference wind speed and a given roughness length, could influence wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we firstly show that MOST predicts a larger wind shear for the atmospheric stability parameter $\zeta>0.1$ and proposes new flow similarity functions without constraining $R_f$ to limit the overestimated wind shear by MOST. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier–Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in larger wake deficits and slower wake recovery rate with a square root of the mean-squared-error (RSME) of wake deficit in the range of 0.07-0.18. This overestimated wake effect is improved by applying the new similarity functions and the RSME of wake deficit is averagely reduced by 0.05. Finally, we check the role of the under-predicted turbulence intensity playing in the larger wake deficit predicted by models based MOST. Additional numerical simulations using the modified turbulence model are carried out, in which the roughness length is reduced to impose a hub-height ambient turbulence intensity equivalent to the MOST case. Simulation results show that reducing turbulence intensity enhances wake effects, however, it cannot reproduce the large wake deficit predicted by models based on MOST, which suggests that the overestimated wake effect by MOST could be also related to the overestimated wind shear.

Atmospheric stability affects wind turbine wakes significantly. High-fidelity approaches such as large eddy simulations (LES) with the actuator line (AL) model which predicts detailed wake structures, fail to be applied in wind farm engineering applications due to its expensive cost. In order to make wind farm simulations computationally affordable, this paper proposes a new actuator disk model (AD) based on the blade element method (BEM) and combined with Reynolds-averaged Navier–Stokes equations (RANS) to model turbine wakes under different atmospheric stability conditions. In the proposed model, the upstream reference velocity is firstly estimated from the disk averaged velocity based on the one-dimensional momentum theory, and then is used to evaluate the rotor speed to calculate blade element forces. Flow similarity functions based on field measurement are applied to limit wind shear under strongly stable conditions, and turbulence source terms are added to take the buoyant-driven effects into consideration. Results from the new AD model are compared with field measurements and results from the AD model based on the thrust coefficient, the BEM-AD model with classical similarity functions and a high-fidelity LES approach. The results show that the proposed method is better in simulating wakes under various atmospheric stability conditions than the other AD models and has a similar performance to the high-fidelity LES approach however in much lower computational cost.

Knowledge about laminar-turbulent transition on operating multi-megawatt wind turbine blades needs sophisticated equipment like hot-films or microphone arrays. Contrarily thermographic pictures can easily be taken from the ground and temperature differences indicate different states of the boundary layer. The accuracy however, still is an open question, so that an aerodynamic glove known from experimental research on aero-planes was used to classify the boundary-layer state of a 2 megawatt wind turbine blade operating in the orthern part of Schleswig-Holstein, Germany. State-of-the-art equipment for measurering static surface pressure was used for monitoring the lift distribution. To distinguish laminar and turbulent parts of the boundary layer (suction side only) 48 microphones were applied together with ground-based thermographic cameras from two teams. Additionally, an optical camera mounted on the hub was used to survey vibrations. During start-up (from 0 to 9 rpm) extended, but irregularly shaped regions of a laminar boundary layer were observed which had the same extension measured both with microphones and Thermography. When an approximately constant rotor rotation (9 rpm corresponding to approximately 6 m/s wind-speed) was achieved, a flow transition was visible at the expected position of 40 % chord length on the rotor blade, which was fouled with dense turbulent wedges and an almost complete turbulent state on the glove was detected. In all observations, quantitative determination of the flow transition positions from thermography and microphones agree well within their accuracy.

We present a comprehensive database of near-shore wind observations that were carried out during the experimental campaign of the RUNE project. RUNE aims at reducing the uncertainty of the near-shore wind resource estimates from model outputs by using lidar, ocean, and satellite observations. Here we concentrate in describing the lidar measurements. The campaign was conducted from November 2015 to February 2016 at the west coast of Denmark and comprises measurements from eight lidars, an ocean buoy and three types of satellites. The wind speed was estimated based on measurements from a scanning lidar performing PPIs, two scanning lidars performing dual synchronized scans, and five vertical profiling lidars, of which one was operating offshore on a floating platform. The availability of measurements is highest for the profiling lidars, followed by the lidar performing PPIs, those peforming the dual setup, and the lidar buoy. Analysis of the lidar measurements reveals good agreement between the estimated 10-m wind speeds, although the instruments used different scanning strategies and measured different volumes in the atmosphere. The campaign is characterized by strong westerlies with occasional storms.

Current global commitments to reduce emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfill its obligations. However, no scoping study has yet been carried out. The methodology presented here addresses this research need. It employs Analytical Hierarchy Process and pairwise comparison methods in a Geographical Information Systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked, then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint, to deliver maximum turbine number. Ten favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/yr of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the paper, giving an initial plan for installation in these locations.

Background: Lung cancer remains the leading cause of cancer-related mortality globally, predo-minantly affecting older individuals. Despite the increasing use of immune checkpoint inhibitors (ICIs) like nivolumab in non-small cell lung cancer (NSCLC), the efficacy and safety in elderly pa-tients, particularly those aged 65 and above, remain underexplored due to their underrepresenta-tion in clinical trials. Methods: This retrospective study analyzed data from 60 elderly patients (≥65 years) with metastatic NSCLC who received nivolumab as second-line or later therapy between January 2020 and May 2023. Results: The median age was 67 years, with a predominance of males (78%). Nivolumab was administered for a median of 8 cycles, with 33.3% of patients recei-ving 15 or more cycles. The median OS was 23 months, and the 1-, 3-, and 5-year survival rates were 93.3%, 54.1%, and 18.6%, respectively. Multivariate analysis identified adenocarcinoma histology, fewer than 15 cycles of nivolumab, and non-response to prior therapies as independent predictors of poor OS. Nivolumab treatment was generally well-tolerated, with 45% of patients experiencing at least grade 1 toxicity. Conclusion: Nivolumab is effective and well-tolerated in elderly patients with metastatic NSCLC, providing survival benefits comparable to those observed in younger populations. The number of treatment cycles and initial response to therapy are key determinants of survival, underscoring the importance of continued treatment in this age group.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the CBAM attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.

A 58 year old female presented with abdominal pain, vomiting and constipation. Laboratory tests indicated elevated white blood cell count and C-reactive protein levels. Imaging via CT scan revealed a large cystic mass in the right ovary, abscesses and generalized small bowel distension, which initially led to a diagnosis of ovarian cancer with peritoneal carcinomatosis. Despite conservative management, the patient’s condition did not improve, prompting a laparotomy. Intraoperative findings included generalized peritonitis, significant small bowel dilation due to inflammatory adhesions and a perforated dermoid ovarian cyst. The cyst was resected and a prophylactic ileostomy was installed. Histopathological examination confirmed the diagnosis of a benign dermoid ovarian cyst. This case illustrates the rare presentation of a perforated dermoid cyst mimicking peritoneal carcinomatosis and emphasizes the importance of considering such complications in the differential diagnosis of bowel obstruction and peritoneal disease. Early recognition and appropriate surgical intervention are crucial for optimal outcomes.

Bartonella species, intracellular parasitic zoonotic pathogens, has drawn increasing attention for their diverse host range and associated human diseases. This study investigates Bartonella infection in small mammals in Yunnan Province, China, focusing on tissue tropism. 333 small mammals were sampled from 13 species, 3 orders, 4 families, and 4 genera in Heqing and Gongshan Counties. Conventional PCR and real-time quantitative PCR (qPCR) were utilized for detection and quantification, followed by bioinformatic analysis of obtained DNA sequences. Results show a 31.5% detection rate, varying across species. Notably, Apodemus chevrieri, Eothenomys eleusis, Niviventer fulvescens, Rattus tanezumi, Episoriculus leucops, Anourosorex squamipes, and Ochotona Thibetana exhibited infection rates of 44.4%, 27.7%, 100.0%, 6.3%, 60.0%, 23.5%, and 22.2%, respectively. Genetic analysis identified 30, 10, and 5 strains based on ssrA, rpoB, and gltA genes, with nucleotide identities ranging from 92.1% to 100.0%. Bartonella strains were assigned to B. grahamii, B. rochalimae, B. sendai, B. koshimizu, B. phoceensis, B. taylorii, and a new species identified in the Episoriculus leucops (GS136). Tissue distribution analysis revealed varied copy numbers across different tissues, with the highest load in spleen. These findings underscore Bartonella's diverse species and host range in Yunnan Province, highlighting its broad tissue tropism in small mammals.

Peste des Petits Ruminants (PPR) is one of the major transboundary animal diseases that severely impact livestock production and trade worldwide. An investigation was carried into the seroprevalence and risk factors of PPR in small ruminants, camels, and wild ruminants in Bahrain. A total of 1240 serum samples were collected from 119 sheep flocks and 1224 serum samples from 125 goat herds. In addition, 100 sera from camels and 38 sera from wild ruminants, were collected. Serum samples were tested for the presence of PPR specific antibodies using a competitive ELISA. Flock management and health information of the flocks and herds were collected using a semi-structured questionnaire. The individual prevalence of PPR in sheep and goats was 26% and 25.5%, respectively. The flock or herd level prevalence of PPR was 22.7% and 29.6% in sheep and goats, respectively. None of the tested camels or wild ruminants were positive for PPR-specific antibodies. Sheep and goats from the Northern governorate were more likely to be seropositive to PPR than in other governorates. On the individual animal level, the univariate analysis suggested; age (&lt;12 months), sex (male), and health status (weak animals) as risk factors for PPR seropositivity. On the flock/herd level, the multivariable logistic regression analysis identified large flock/herd size and semi-intensive farming as risk factors for PPR seropositivity.

We present the first evidence of lethal interspecific aggressive behavior against Burmeister’s porpoise Phocoena spinipinnis, in a non-predatory context, and the first indications of such behavior in a cetacean from Chilean waters. Two cases, both adult males, are described. Analysis of the fresh tooth rakes, specifically the inter-rake distance, points to common bottlenose dolphin Tursiops truncatus as aggressor. Interdental distances measured on skulls allowed us to exclude Orcinus orca, Globicephala melas, Lagenorhynchus obscurus and Pseudorca crassidens. Evidence of severe blunt trauma, including extensive subdermal hematomas, rupture of pleura and extrusion of lung parenchyma, underscores the level of violent aggression. The reason is unknown but Tursiops truncatus is well-documented to occasionally harass and kill harbour porpoises Phocoena phocoena in the Northern Hemisphere. Considering the scarce information available on the biology and behavioral ecology of Burmeister’s porpoises in Chile and elsewhere, we recommend more systematic field research. Also, as to help formulate effective management measures that can mitigate the main threat for this species in Chile (as in Peru), i.e. fisheries interactions. In Chile, P. spinipinnis is classified as Data Deficient (D.S. N°06-2017), so its real conservation status is unknown.

Micro and small wood-processing enterprises represent the heart of the European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the pro-vision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is nonparametric, linear-programming-based approach, commonly used to analyse the efficiency of organizational units. This method allows estimating the economic efficiency of a certain eco-nomic system without assumptions about the functional form between resources and products. Furthermore, DEA determines the efficiency frontier and gives results of whether an enterprise, i.e., a Decision Making Unit (DMU) is efficient or not. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterpris-es in the EU countries and reveal the hidden inputs that facilitate efficiency generation. The eco-nomic efficiency evaluation was carried out on the basis of the official statistical data for the mi-cro and small wood-processing companies in the EU member states for the period 2015-2020 by performing a two-stage DEA analysis. The data used were standardized by value per employee. In addition to the first stage of DEA, fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically-efficient entities. The only contextual variable affecting the eco-nomic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase of the investments.

In this study, we aimed to identify blood lymphocytes as a prognostic factor for survival in patients with locally advanced stage III non-small cell lung cancer (NSCLC) treated with concurrent chemoradiotherapy (CCRT). We conducted a secondary analysis of 196 patients enrolled in the Korean Radiation Oncology Group 0903 phase III clinical trial to evaluate the prognostic significance of circulating blood lymphocyte levels. The median total lymphocyte count (TLC) reduction ratio during CCRT was 0.74 (range: 0.29 – 0.97). In multivariate analysis, patient age (p = 0.014) and gross tumor volume (GTV, p = 0.031) were significant factors associated with overall survival, while TLC reduction (p = 0.018) and pretreatment neutrophil-to-lymphocyte ratio (NLR; p = 0.010) were associated with progression-free survival (PFS). In multivariate logistic regression analysis, pretreatment NLR, GTV, and heart V20 were significantly associated with TLC reduction. TLC reduction during CCRT is a significant prognostic factor for PFS, and heart V20 is significantly associated with TLC reduction. Thus, constraining the volume of the radiation dose to the whole heart must be prioritized to reduce TLC.

Recent advances in embedded antenna and sensor technologies for 5G communications have galvanized a response toward the investigation of their electromagnetic (EM) performance for urban context and civil engineering applications. This article quantitatively investigates the effects of material loading on an evolved antecedent-based design of a hexagonal-stubbed Complementary Split-Ring Resonator (CSRR)-loaded antenna through simulation and experimentation. The optimized antenna design is first conceptualized within a simulation environment to achieve EM resonance at 3.50 GHz before delving into the analysis of operational performance characteristics. As a proof-of-concept, a physical antenna prototype is fabricated on a printed circuit board for the in-situ evaluation of S11 parameter plot. Subsequently, a simulation-based parametric study is conducted on antenna prototypes embedded into Ordinary Portland Cement pastes with varying weight percentages of iron(III) oxide. Simulation-derived and experimental results are mutually verified, achieving a systemic downward shift in resonant frequency and corresponding variations in impedance matching induced by changes in the loading reactance. Finally, an inversion modeling procedure is employed using perturbation theory to extrapolate the relative permittivity of the dielectric embedding materials. Our proposed analysis contributes to optimizing concrete-embedded 5G antenna sensor designs and establishes a foundational framework for estimating unknown EM parameters of cement-based composites.

Background: Lipid disorders are one of risk factors for cardiovascular diseases. The aim of the study was to determine the lipid profile in early childhood in the group of Polish children born small for gestational age (SGA). Materials and Methods: The study included 140 children (93 SGA and 47 controls) aged 5 to 11 years. All children underwent a physical examination and blood tests for the glucose and lipid profile. SGA group was divided in subgroups: symmetrical and asymmetric intrauterine growth restriction (IUGR). Results: Blood sample analysis revealed higher levels of total cholesterol levels (SGA group 190.61 +/- 24.66 mg/dl vs control 143.23 +/- 23.90; p&lt;0.001). The analysis of particular fractions of cholesterol showed significantly higher mean values of triglycerides, LDL-cholesterol as well as lower mean values of HDL-cholesterol in children born as SGA. Children in both groups did not differ significantly according to weight and body mass index. A statistically significantly higher glucose concentration was obtained in the group of children with IUGR with symmetrical type of IUGR. Conclusions: Children born SGA even in their early childhood, without overweight or obesity, have abnormal lipid profile, which may be a risk factor of cardiovascular diseases in adulthood.

EGFR tyrosine kinase inhibitors (TKIs) are the preferred initial treatment for non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. However, remission is transient, and no further effective treatment options are available for EGFR-TKI-advanced EGFR-mutant NSCLCs. Immunotherapy with immune checkpoint inhibitors (ICIs) induces sustained cancer remission in a subset of NSCLCs. However, ICI therapy exhibits limited activity in most EGFR-mutant NSCLCs. Mechanistically, the strong oncogenic EGFR signaling in EGFR-mutant NSCLCs contributes to the non-inflamed tumor immune microenvironment (TIME), characterized by a limited number of CD8+ T cell infiltration, a high number of regulatory CD4+ T cells and a high number of inactivated infiltrated T cells. Besides, EGFR-mutant NSCLC patients are generally non-smokers with low levels of PD-L1 expression and tumor mutation burden. However, current understanding only partially explains why a small population of EGFR-mutant NSCLCs durably respond to ICI therapy, resulting in many researchers actively working in this field. This review reviews the hope seen from pre-clinical studies and clinical trials which may be adopted to improve the outcome of ICI therapy in EGFR-mutant NSCLCs. Besides, the underlying mechanisms leading to the inferior clinical outcome of ICI therapy in EGFR-mutant NSCLCs are discussed.

The pathogenesis of Cerebral Small Vessel Disease (CSVD) is largely unknown. Endothelial disfunction has been suggested as the turning point in CSVD development. In this study we tested the effect of plasma from CSVD patients on human cerebral microvascular endothelial cells with the aim of describing the pattern of endothelial activation. Plasma samples from 3 groups of young subjects have been tested: PTs (subjects affected by early stage CSVD); CTRLs (control subjects without abnormalities at MRI scanning); BDs (blood donors). Human Brain Endothelial Cells 5i (HBEC5i) were treated with plasma and total RNA was extracted. RNAs were pooled to reduce gene expression-based variability and NGS analysis was performed. Differentially expressed genes were highlighted comparing PTs, CTRLs and BDs with HBEC5i untreated cells. No significantly altered pathway was evaluated in BD related treatment. Regulation of p38 MAPK cascade (GO:1900744) was the solely pathway altered in CTRLs related treatment. Indeed, 36 different biological processes turned out to be deregulated after PTs treatment of HBEC5i, i.e., the cytokine-mediated signaling pathway (GO:0019221). Endothelial cells activate inflammatory pathways in response to stimuli from CSVD patients’ plasma, suggesting the pathogenetic role of neuroinflammation from the early asymptomatic phases of cerebrovascular disease.

Small molecule kinase inhibitors (SMKIs) are one of the heightened concerns in the field of drug research and development. There are seventy-nine (up to July 2023) small molecule kinase inhibitors have been approved by FDA and hundreds kinase inhibitors candidates in clinical trials, which shed light on the treatment of some major diseases. As an important strategy of drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods, such as docking, molecular dynamic, pharmacophore have been applied to the design and optimization of small molecule kinase inhibitors. In the review, we provide an overview of recent advances in CADD and SMKIs; and the application of CADD for discovery of SMKIs.

Appropriate use of perioperative antimicrobials can significantly reduce the risk of postoperative infections. However, inappropriate antimicrobial use can result in the creation of multidrug resistant bacteria, increased costs, host flora disruption, side effects, and increased risk of hospital-acquired infections This survey evaluated the current perioperative use of antimicrobials in small animals by Spanish veterinarians using a web-based questionnaire. Responses were represented using descriptive statistics and a statistical analysis of the association between demographic data and perioperative antimicrobial use was performed. Preoperative antimicrobials were administered in clean surgery by up to 68.3% of participants, 81.0% in clean-contaminated surgery and 71.3% in dirty surgery, while in the postoperative period, antimicrobials were administered by up to 86.3% of participants in clean surgery, 93.2% in clean-contaminated surgery and 87.5% in dirty surgery. Factors considered “very important” for antimicrobial selection were the degree of wound contamination, patient immunosuppression, and use of prosthesis. The most frequently used antimicrobial was beta-lactamase-resistant penicillin. Postoperative antimicrobial use was associated with participants without postgraduate training. This study highlights an overuse of antimicrobials in perioperative procedures in small animal surgery in Spain. Therefore, evidence-based guidelines and further education regarding the correct use of antimicrobial prophylaxis are recommended.

Traditional camera sensors rely on human eyes for observation. However, the human eye 1 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3 Target recognition technology is an important technology to judge the target category in camera 4 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8 preserve the context feature information. The feature fusion network was improved to effectively 9 combine shallow information and deep information. A new network structure was proposed to 10 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15 more accurate than YOLOv8.

Small RNAs (sRNA) are epigenetic regulators of essential biological processes that have been linked to the onset and progression of leukemia, including acute adult T-cell leukemia-lymphoma (ATLL) caused by the retrovirus Human T-lymphotropic virus type 1. Here, we describe the sRNA profile of a 30-year-old female with ATLL at diagnosis and after maintenance therapy, with the goal of correlating their expression levels with response to therapy.

We develop the theory of Heyting small spaces and prove a new version of Esakia duality for such spaces. To do this, we notice that spectral spaces may be seen as sober small spaces with all smops compact and introduce the method of the standard spectralification. This helps to understand open continuous definable mappings between definable spaces over o-minimal structures.

The accumulation of process waste in the production line causes fluctuations, bottlenecks, and increased inventory in workstations disrupting process flow. In this paper, the aim is the use a simulated value stream mapping (SVSM) as a lean assessment tool for decision-making in the continuous improvement process to influence and provide consideration and consistency on productivity improvement in the production system. The proposed methodology applied discrete event simulation for production process operations improvement to eliminate non-value adding times and provides good quality products at the lowest cost and highest efficiency. The results are the analysis of the current state of the production system in a South African truck manufacturing industry small and medium enterprise (SMEs) as a potential solution for the production system future state. The identified non-value adding times in the 6 most critical workstations was eliminated by SVSM which resulted in a productivity improvement of 4%, most importantly bringing the productivity to 95% and total cycle time improvement to 451 for small units and 466 for large units. The results proposed combined VSM and Simulation techniques which enhance the LEAN application by DES to increase productivity and performance improvement to remain competitive in the global economy.

Medical and translational scientific research requires the use of animal models as an initial approach to the study of new therapies and treatments, but when the objective is an exploration of translational potentialities, classical models fail to adequately mimic problems in humans. Among the larger animal models that have been explored more intensely in recent decades, small ruminants, namely sheep and goats, have emerged as excellent options. The main advantages associated to the use of these animals in research works are related to their anatomy and dimensions very similar to those of humans in most physiological systems, in addition to their low maintenance and feeding costs, tendency to be docile, long life expectancies and few ethical complications raised in society. The most obvious disadvantages are the significant differences in some systems such as the gastrointestinal, and the reduced amount of data that limits the comparison between works and the validation of the characterization essays. Despite everything, recently these species have been increasingly used as animal models for diseases in different systems, and the results obtained open doors for their more frequent and advantageous use in the future. The purpose of this review is to summarize the general principles related to the use of small ruminants as animal models, with focus on regenerative medicine, to group the most relevant works and results published recently and to highlight the potentials for the near future in medical research.

Shrinking farm size and fragile farm resources pose a significant challenge to the sustainability of small-scale farms. Efficient resource utilization in small-scale farms is crucial to achieving farm sustainability through endogenous mechanisms. However, the precise mechanisms to integrate physical resources to achieve farm sustainability are not very clear yet. By capturing the interaction among farm resources as a network phenomenon, we identify the discrete resource interactions (RIs) in different types of small-scale farms of Indian Sundarbans, which are associated with higher farm sustainability. Thirty-two linkages, 11 reciprocal linkages, 22 triads, and three ‘core elements’ that occurred and cooccurred on highly sustainable farms are found to be critical in achieving farm sustainability. Using the properties of resource interaction networks as explanators of farm sustainability, we anticipate that sustainability in small-scale farms can be achieved by strategically creating new RIs on the farm. However, there may be limitations to such achievement depending on the nature of RI and type of farm. The analytical approach helps to understand the structural basis of sustainability in small-scale farms, and this approach can be used to achieve farm sustainability through the strategic integration of existing farm resources in the smallholder systems.

Although researchers have investigated widely the impact of IOD phases on human lives, only a few have examined such impacts on fisheries. In this study, we analyzed the influence of negative (positive) of IOD on a chlorophyll a (Chl-a) concentration as an indicator of phytoplankton biomass and small pelagic fish production in the eastern Indian Ocean (EIO) off Java. We also conducted field surveys in the EIO off Palabuhanratu Bay at the peak (October) and the end (December) of the 2019 positive IOD phase. Our findings show that the Chl-a concentration had a strong and robust association with the 2016 (2019) negative (positive) IOD phases. The negative (positive) anomalous Chl-a concentration in the EIO off Java associated with the negative (positive) IOD phase induced strong downwelling (upwelling), leading to the preponderant decrease (increasing) of small pelagic fish production in the EIO off Java.

Aminoacyl-tRNA synthetases (ARSs) are essential enzymes for translating amino acids for protein synthesis. Their function in pathogen-derived infectious diseases has been well established, which has led to development of small molecule therapeutics. The applicability of ARS inhibitors for other human diseases such as fibrosis has recently been explored in the clinical setting. There are active studies to find small molecule therapeutics for cancers. Studies on central nervous system (CNS) disorders are burgeoning as well. In this regard, we present a concise analysis of the recent development of ARS inhibitors based on small molecules from the discovery research stage to clinical studies as well as a recent patent analysis from the medicinal chemistry point of view.

We present a summary of our protocol for employment of small-molecule circadian rhythm modulators. The abilities of compounds to affect oscillations have received significant attention. We outline assessments of circadian changes using indirubin-3’-oxime (IO), 5-iodo-indirubin-3’-oxime (5I-IO), and 5-sulfonic acid-indirubin-3’-oxime (5SA-IO), analyzing effects on Bmal1 and Per2 oscillations and oncogenic features.

In general, remote sensing has proven to be a powerful tool in the overall understanding of natural and anthropogenic phenomena. Satellites have become useful tools for tasks such as characterization, monitoring, and the continuous prospecting of natural resources. This research aims to analyze spatial dynamic and destructive on coral reefs area and correlation between live coral reduction and population on small islands. Landsat MSS, TM, ETM, and OLI-TIRS are used to spatial analyze of coral reef dynamics from 1972 to 2016. The image processing includes gap-filling, atmospheric correction, geometric correction, image composite (true color), water column correction, unsupervised classification, reclassification, accuracy assessment. The statistical analysis identifies the relationship between dynamic population data with a reduction of live coral, namely Principal Component Analysis (PCA) and Multiple Regression Analysis. The effect of the population shows a positive correlation with the reduction in the area of live coral, although it is significant. The fact is the practice of coral destruction on an island; it is usually not only caused or carried out by residents who live on the island but also carried out by other residents of different islands.

We propose a new estimator for creating expansion factors for survey plots in the USDA Forest Inventory and Analysis program. This is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data in that it produces a single set of expansion factors that can have general purpose use to produce small area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such of GREG estimation, in that it is guaranteed to produce positive expansion factors. We extend the previous method here to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

Sustainable Development Goal (SDG) 6.2 sets an ambitious target of leaving no one without adequate sanitation by 2030. The key concern is the lack of local human and financial capital to fund the collection of reliable information to monitor progress towards the goal. As a result, national and local records may be telling a different story of the proportion of safely managed sanitation that counts towards achieving the SDGs. This paper unveils such inconsistency in sanitation data generated by urban authorities and proposes a simple approach for collecting reliable and verifiable information on access to safely managed sanitation. The paper is based on a study conducted in Babati Town Council in Tanzania. Using a smartphone-based survey tool, city health officers were trained to map 17,383 housing units in the town. A housing unit may comprise of two or more households. The findings show that 5% practice open defecation, while 82% of the housing units have some forms of sanitation. Despite the extensive coverage, only 31% of the faecal sludge generated is safely contained, while 64% is not. This study demonstrates the possibility of using simple survey tools to collect reliable data for monitoring progress towards safely managed sanitation in the towns of global south.

In this paper, the linear nonequilibrium thermodynamic approach is used to analyze energy processes in the wind energy conversion system (WECS), with a directly connected vertical axis wind turbine (VAWT) and vector controlled permanent magnet synchronous generator (PMSG). Both elements are considered as linear universal energy converters (EC) linearized at the points of the given range of common to VAWT and PMSG angular velocity. The dependences on the angular velocity of the coupling coefficients between the ECs input and output and the dimensionless parameters of their operating modes were received. This allows for choosing the optimal points of ECs operation according to the specified criteria and obtaining indicators of their maximum efficiency. To assess the quality of the cascade connection of two ECs, the appropriate coefficient was introduced, the dimensionless parameters and characteristics of the equivalent EC were obtained, and the conditions of its maximum efficiency were determined. Analysis of the VAWT-PMSG connection quality showed reserves for improving WECS efficiency. The impact of VAWT and PMSG variable parameters on the quality of this connection is studied, and further research directions are shown. The proposed thermodynamic approach allows an effective search for solutions for the efficiency improvement of various systems with energy transfer and transformation.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

In this paper, the active front-end (AFE) converter topology for the total harmonic distortion (THD) reduction in a wind energy system (WES) is used. A higher THD results in serious pulsations in the wind turbine (WT) output power and in several power losses at the WES. The AFE converter topology improves capability, efficiency and reliability in the energy conversion devices; by modifying a conventional back-to-back converter, from using a single voltage source converter (VSC) to use pVSC connected in parallel the AFE converter is generated. The THD reduction is done by applying a different phase shift angle at the carrier of digital sinusoidal pulse width modulation (DSPWM) switching signals of each VSC. To verify the functionality of the proposed methodology, the WES simulation in Matlab-Simulink® is analyzed, and the experimental laboratory tests using the concept of rapid control prototyping and the real-time simulator Opal-RT® Technologies is achieved. The obtained results show a type-4 WT with total output power of 6MVA, generating a THD reduction up to 5.5 times at the WES.

Due to its high short-term global warming potential (GWP) compared to carbon dioxide, methane (CH₄) is a considerable agent of climate change. This research is aimed at analyzing data on methane gathered at the GAW (Global Atmosphere Watch) station of Lamezia Terme (Calabria, Southern Italy) spanning seven years of continuous measurements (2016-2022) and integrating the results with key meteorological data. Compared to previous studies on detected methane mole fractions at the same station, daily-to-yearly patterns have become more prominent thanks to the analysis of a much larger data set. Overall, the yearly increase of methane at the Lamezia Terme station is in general agreement with global measurements by NOAA, though local peaks are present, and an increase linked to Covid-19 is identified. Seasonal changes and trends have proved to be fully cyclic, with the daily cycles being largely driven by local wind circulation patterns and synoptic features. Outbreak events have been statistically evaluated depending on their weekday of occurrence to test possible correlations with anthropic activities. A cross analysis between methane peaks and specific wind directions has also proved that local sources may be deemed responsible for the highest mole fractions.

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real time pricing (RTP) schemes. In this paper, we present a new method to optimise the size and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. The storage in the PHS is used to supply load consumption and/or energy arbitrage. Further, both load supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. The PHS is not worth considering with the present cost of components.

Achieving Net Zero emissions target is driving up the need for offshore wind as an alternate energy source. Ireland has the capacity to produce upwards of 30GWof power through offshore wind alone. Wind resource assessments are extremely vital in determining the long term trends of a site. The instruments used for this often suffer breakdowns or miss readings which impacts the long term trend analysis. Wind time series data from Ireland’s Marine Institute is used which is shown to have significant gaps in it’s 20 years of service. Data imputation is necessary to fill in these gaps as accurately as possible. This paper compares LSTMs and ARIMA as two competing methodologies for data imputation. LSTMs are shown to be marginally superior to ARIMA imputation with a mean squared error of 0.45 compared to that of ARIMA of 0.60.

This work is aimed at investigating the capabilities and limits of the mid-fidelity numerical solver DUST for the evaluation of wind turbines aerodynamic performance. In particular, this study was conducted by analysing the benchmarks NREL- 5MW and Phase VI wind turbines, widely investigated in literature by from experimental and numerical activities. The work started by simulating simpler configuration of the NREL- 5MW turbine to progressively integrate complexities such as shaft tilt, cone effects and yawed inflow conditions, offering a detailed portrayal of their collective impact on turbine performance. A particular focus was then given to the evaluation of aerodynamic responses from the tower and nacelle, as well as aerodynamic behavior in yawed inflow condition, crucial for optimizing farm layouts. In the second phase, the work was focused on NREL Phase VI turbine due to the availability of experimental data on this benchmark case. Comparison of DUST simulations results with both experimental data and high-fidelity CFD tools shows the robustness and adaptability of this mid-fidelity solver for this applications, thus opening a new scenario for the use of such mid-fidelity tools for the preliminary design of novel wind turbines configurations or complex environments as wind farms, characterised by robust interactional aerodynamics.

Floating wind is becoming an essential part in terms of renewable energy. Therefore, highlighting the perspectives in developing the floating wind platforms is very important. In this paper, we focus on floating wind concepts and projects around the world. This will give a taste to the reader about what is going on in terms of the projects around the world. The main aim of this work is to further explain the collected data regarding the floating wind concepts and projects, and further classify them in terms of cost, power capacity, wind speed, water depth, and distance to shore.

This study investigates the spatial and temporal distribution of litter at 7 beach sectors in Cadiz Bay, Southwest Spain. Two ten-day surveys carried out in autumn 2022 and spring 2023 revealed a total of 4,199 and 4,634 items, respectively, with plastic being the predominant material (71.13% in autumn, 88.39% in spring). Litter quantities varied significantly among beaches, La Puntilla showing the highest abundance with 0.48 ± 0.25 items m-1 in autumn and 1.34 ± 0.61 items m-1 in spring. The lowest abundance was recorded at sector 1 of Valdelagrana beach (0.08 ± 0.06 items m-1) during autumn and at El Castillito beach (0.08 ± 0.04 items m-1) during spring. Statistical analyses did not reveal significant differences in litter quantities between autumn and spring, however high significant interaction was shown considering both season and beach lo-cation. Seasonal differences were also observed in litter composition. Litter categories increased from 90 in autumn to 107 in spring. The top 10 litter categories varied between seasons and included cigarette butts, plastic fragments and plastic packaging. Cigarette butts were principally abundant in autumn because re-lated to summer beach users and wet wipes were particularly observed in spring because linked to wastewaters and river discharges especially relevant in winter months. Hydrodynamic and wind conditions were analyzed, revealing complex relationships with litter abundance.

Super Typhoon Saola came very close to Hong Kong on 1 and 2 September 2023, necessitating the issuance of No. 10 hurricane signal, the highest tropical cyclone warning signal, in Hong Kong. While there were widespread damages in Hong Kong, no people were killed in the event with effective early warning. It is rare that a super typhoon came very close to Hong Kong and this paper is the first part in the series of the documentation of Saola to summarize the interesting observations of Saola near Hong Kong for future reference by weather forecasters, including sur-face observations, upper air observations, microburst alert from weather radars, and turbulence intensity based on spectral width measurement of radars.

An axial wind turbine's performance is analyzed numerically using geometrical and operational parameters. The performance of an axial wind turbine is simulated using three-dimensional computational fluid dynamics (CFD) and compared with experimental data, which confirms the validity of the assumptions and method used. A study of the effects of air in clean and sand particlesy conditions is then conducted. A study was also conducted on mechanical power and the impact of the blade tip on the extension of the vortex. It has been found that different sand particle sizes have a significant impact on wind performance numerically from 0.1 to 0.9, and the results indicate that mechanical power and thrust increase as wind velocity increases, but larger sand particle sizes reduce power, thrust, and torque as wind velocity increases. The turbulence intensity causes the pressure distribution on the walls to be irregular, thereby increasing mechanical power and propulsion force. Moreover, as the wind velocity increases, the tip speed ratio increases and the vortexes are subsequently extended.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

The quantile estimation of extreme wind speed is needed in various environmental fields such as climatology, design of structures, renewable energy sources and agricultural operations. These calculations are crucial for the coding of wind speed. In this study, the required wind speed series of 16 stations in Khyber Pakhtunkhwa, Pakistan, was obtained from the NASA official website and measured in meters per second (m/s) at a 10-meter distance. A Regional Frequency Analysis of 16 AMWS stations was performed using L-moments. The quantile estimates of extreme wind speed are needed for various areas of interest using Regional Frequency Analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meter per second (m/s). A Regional Frequency Analysis of AMWS using L-moments is performed utilizing wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions are chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

To deal with the uncertainties in modeling offshore wind turbines, we proposed a parameter inversion method for the pile-soil interaction model based on structural health monitoring results and the numerical model. The proposed parameter inversion method has a numerical model, an objective function selected using both the numerical and identified results, and an inverse optimization using a random search algorithm in the assumed parameter space. The parameter results in the minimum optimization objective function are identified as the in-situ parameter. The proposed method is confirmed to converge after some iterations, whatever the initial parameter values are. However, different initial parameter cases may converge to slightly different optimal parameters, implying the pile results are sensitive to geological parameters. Moreover, a comparison with the original design results shows design redundancy or risks. Though the proposed method has several flaws, it can shed some light on the influence of uncertainties in offshore wind turbines, such as soil parameters in geological surveys.

The aim of this paper is to provide a new perspective on the conditions for the multiple dimensions of concerns, and to understand why some wind power projects run smoothly while others lead to unexpected outcomes due to local resistance. The paper described how multiple community concerns about wind power project planning can be described using conflict management theory and by using different procedures to handle tangible and intangible concerns. Policy recommendations are given on how: to minimise overflowing and the risk of the conflicts; to avoid “boxing” community concerns in categories; to give more attention to the intangible and less calculable concerns; to secure appropriate and early planning with proactive involvement of local communities.

In recent years, excessive heat in the urban texture has become the main problem in the humid and calm wind city of Babolsar with high density, especially in high-rise areas. Therefore, in order to create comfort in this region, it is necessary to establish and continue the wind circulation in space with an environmentally compatible and optimal configuration. The study applies combination of literature, field measurement, experimental validation of CFD simulation output, and comparative analysis. After field measurement and validation of FLOW-3D simulation software (V11.2.2), the relationship between these parameters (height, the width of passages, enclosure between buildings, and buildings' orientation) will be studied which affects the wind’s velocity and direction. The factors of the buildings' orientation and enclosure based on the passages' width have opposite reactions in the direction of the prevailing wind especially from perpendicular side. In this study, two effective factors are on wind velocity: 1- The orientation of the buildings towards the wind flow by creating permeability 2- Reducing the enclosure by increasing the width of the passage's perpendicular to the wind flow (w '= 3w, E' = 0.33E). According to the theoretical and practical study, first, the creation of permeability in the body of the block and the separation of buildings instead of aggregation has been studied, and then reducing the confinement of streets perpendicular to the wind flow has been discussed as effective solutions to improve the wind velocity and circulation between the urban environment.

Oil palm is one of the most important crops in Malaysia. Lately, the production of oil palm has been reduced due to a variety of factors, including the weather and climate. Temperature, wind speed, relative humidity, sunshine, and rainfall distribution all have an impact on palm tree growth and development, which in turn has an impact on oil palm production. This paper aims to investigate the effects of some weather elements (temperature, wind speed, relative humidity, sunshine, and rainfall) on oil palm production in Peninsular Malaysia. Data were analyzed using the Statistical Package for Social Sciences (SPSS 20.0 version), with descriptive statistics, and multiple linear regression (MLR). The MLR model determined the strength of the relationship between oil palm yield (dependent variable) and the changing variables of temperature, sunshine, wind speed, relative humidity, and rainfall (independent variables). The findings revealed that temperature, wind speed, relative humidity, sunshine, and rainfall have a low impact on oil palm production and yield turnover. The R2 value of 0.202 shows that the independent variables explained only 20.2% of the fluctuation in palm oil production. The study recommends working within an integrated approach involving scientific research, planting, improving variety, improving regional academic leadership, and engaging private and public stakeholders, emphasized collaborative efforts with researchers in consumer countries, and strengthening the capacity of growers to best agroecological practices.

Smart cities will have a strong impact on the future of renewable energies as terms like sustainability and energy saving will be more common. In this sense, both of wind and hydrokinetic compact-size turbines, can play an important role in urban communities by providing energy to nearby consumption points in an environmentally suitable way. This work presents the experimental evaluation for a vertical-axis turbine Darrieus type, operating in an open-field wind tunnel and a confined water channel. Power and characteristic curves have been obtained for all test conditions, also the effect of turbine blockage has been evaluated under blockage values ranging from 6.8% to 35%. The peak power coefficient for the confined flow condition reached a value of 0.31 which is 1.5 times higher than the peak one for the experimental open field condition at the same Reynolds number and a blockage of 20%. Finally, two blockage correction equations have been applied to the water channel tests, which gave values quite similar to the results obtained from the wind tunnel.

Wind is one of the factors that has a great influence on suspended matter in lakes, especially in shallow lagoons. In order to know how wind affects the water in Albufera of Valencia, a shallow coastal lagoon, the measured variables of turbidity and transparency have been correlated with the estimates by processing Sentinel-2 satellite images with the Sen2Cor processor. Data from four years of study show that most of them are light to gentle easterly breezes and moderate to fresh westerly breezes. The results obtained show significant correlations between the measured variables and those obtained from the satellite images for total suspended matter and water transparency and with the average daily wind speed. There is no significant correlation between wind and chlorophyll a. Moderate to fresh breezes resuspend the fine sediment reaching concentration values from 100 to 300 mg L-1 according to satellite data. However, it is necessary to obtain field data for the values of moderate and fresh winds, as for now there are no experimental data to verify the validity of the satellite estimates.

Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. This study aimed to develop an AM detection method using a random forest approach. The method was developed and validated on 6,000 10-second samples of wind farm noise manually classified by a scorer via a listening experiment. Comparison between the random forest method and other widely-used methods showed that the proposed method consistently demonstrated superior performance. This study also found that a combination of low-frequency content features and other unique characteristics of wind farm noise play an important role in enhancing AM detection performance. Taken together, these findings support that using machine learning-based detection of AM is well suited and effective for in-depth exploration of large wind farm noise data sets for potential legislative and research purposes.

In-cloud ice mass accretion on wind turbines is a common challenge faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests--including the cloud water response to surface roughness and albedo effects related to them--must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and to present date not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME 3D NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insulation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In early season cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

The application of naturally ventilated pig buildings (NVPBs) with outdoor exercise yards is on the rise mainly due to animal welfare considerations, while the issue of emissions from the buildings to the surrounding environment is important. Since air pollutants are mainly transported by airflow, the knowledge on the airflow characteristics downwind the building is required. The objective of this research was to investigate airflow properties downwind of a NVPB with a roofed outdoor exercise yard for roof slopes of 5°, 15°, and 25°. Air velocities downwind a 1:50 scaled NVPB model were measured using a Laser Doppler Anemometer in a large boundary layer wind tunnel. A region with reduced mean air velocities was found along the downwind side of the building with a distance up to 0.5 m (i.e. 3.8 times building height), in which the emission concentration might be high. It was found that a smaller roof slope (i.e. 5° slope) resulted in a higher and shorter wake zone and thus a shorter air pollutant dispersion distance. It was concluded that a smaller roof slope could contribute to the dilution of air pollutants and a lower air pollutant concentration near the ground.

Tidal stream energy, due to its high level of consistency and predictability, is one of the feasible and promising type of renewable energy for future development and investment. Applicability of Blade Element Momentum (BEM) method for modeling the interaction of turbines in tidal arrays has been proven in many studies. Apart from its well-known capabilities, yet there is scarcity of research using BEM for the modeling of tidal stream energy farms considering full scale rotors. In this paper, a real geographical site for developing a tidal farm in the southern coasts of Iran is selected. Then, a numerical methodology is validated and calibrated for the selected farm by analyzing array of turbines. A linear equation is proposed to calculate tidal power of marine hydrokinetic turbines. This methodology narrows down the wide range of turbine array configurations, reduces the cost of optimization and focuses on estimating best turbine arrangements in a limited number of positions.

In 2015, the new installed capacity of global renewable energy power generation exceeded the newly installed capacity of conventional energy power generation, marking a structural change in the construction of the global power system. With the continuous improvement of wind energy utilization technology, the global wind power industry has developed rapidly in recent years. The world's available wind energy is 20 billion kilowatts and has become one of the most economical green power. In China, wind power has become the third largest source of electricity, with the installed capacity increasing from 3.1% in 2010 to 9.2% in 2017. In 2017, China's new installed capacity was 19,660 MW, accounting for 37.45% of the world's new installed capacity. This paper evaluates and compares the efficiency of wind power industry in the four regions of eastern, central, western and northeastern China through EBM models based on radial and non-radial factors. This paper discusses the contribution of China's wind power industry to CO2 emission reduction from the relationship between installed capacity efficiency and CO2 emission reduction efficiency. The conclusions show that the overall efficiency score and ranking of wind power in 2013-2017 is the best in the eastern region, followed by the northeast region and the western and central regions.