Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the Land Use/Cover Change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario1), ecological development priority(Scenario2), and cropland development priority(Scenario3). Climate change scenarios were constructed using CMIP6 data, representing low stress (SSP119), medium stress (SSP245), and high stress (SSP585) conditions. The results show the following: (1) From 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) Water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) By 2030, the water yield of Scenario1 is highest among different land use scenarios, marginally surpassing ecological development priority by 0.5%; (4) Climate scenarios reveal significant disparities, with SSP126 yielding 67% higher water production than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

In eastern part of Ethiopia groundnut is very commonly intercropped with sorghum. Even though intercropping of sorghum with groundnut is practiced in the eastern part of Ethiopia the population density at what ratio should not be known. Therefore determining the population ratio is found to be crucial to draw management options. The study was conducted at two locations at Fadis (on station) and Babile (sub- station). Sorghum variety (Teshale) and groundnut (Fetene) were used as planting materials. All agronomic data were collected and analyzed by using GenStat software. Significant different were obtained among the treatments. The research result showed that, there were significance difference for both sorghum and ground nut yield per hectare in the years (2014 and 2015) among treatments. The result over time(in 2014 and 2015) at Fadis and Erer showed that the highest sorghum and groundnut yield per hectare were obtained/harvested from the intra row spacing of 25cmx20cm and 30cmx20cm intercropping sorghum with ground nut system with (1.27 and 1.31) respectively. The highest LER (1.31) and highest GMV (10218.00 ETBr/ha) were obtained from the intercropping of 30cmx20cm (Sorghum & groundnut). Clearly showed that, with intercropping of sorghum and Groundnut, it is possible to produce additional yield of sorghum without significant reduction in groundnut yield. As a result of this, the intra spacing of the main crop (sorghum) 25cm and 30cm and for the subsidiary crop (groundnut) 20cm was recommended for further production in the study areas of eastern Harerghe zone and similar agro-ecologies.

Against the background of the sky, imaging and perception of aircraft are cru- cial. Among these, the location of aircrafts is not a very difficult problem in general, because that deep learning algorithms and object detection models based on convolutional neural network(CNN) are ever-evolving. In order to have a better identification of various aircrafts, or to accurately locate different types of flying targets in the sky, it becomes significant to distinguish and recognize different types of aircrafts. One question is that, distinguishing and recognizing between sub-categories of aircrafts pose great challenges. Although fine-grained recognition focuses on exploring and studying such problems, aircrafts under different sub-categories and granularities lead us to rethink the application of features. We noticed that features in swin-transformer demonstrates the under- standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a better understanding of features encoding and use, which are inspired by the feature encoding and computation in swin-transformer. In our paper, our ap- proach has achieved effects on features encoded graphically, manifested from the architecture design and convolutional neural network computation, and outper- forms other famous fine-graiend classification models on this issue. Not only approach we proposed has demonstrated superior performance in fine-grained aicraft classification, but also the mechanisms of the feature encoding under different sample space partitions is revealed for this issue, which provides a re- search for the representation of aircraft fineness characteristics. The relationship between representation orientation of aircraft feature information under various grained divisions, shows that the recognition of different categories according to man-made, such as aircraft, can be achieved by means of the feature represen- tations studied in this paper, for more specific defined classification tasks and various man-made targets partition criteria, which may influences the princi- ple of design for calculation and feature extraction in fine-grained classification models.

Accurate assessment of shale gas reserves hinges on a detailed characterization of gas and water distribution within shale reservoirs. Despite its importance, the nanoscale behavior of water and methane in deep shale formations, especially under elevated temperatures and pressures, remains poorly understood. This study proposes a novel molecular modeling approach to represent the complex nanocomposite porous media found in deep shale matrices. Leveraging quantitative data from experimental analyses of shale pore composition, we developed a molecular model that accurately reflects the intricate pore-slit structure within a composite of kerogen and illite, common constituents of shale. Employing a hybrid grand canonical Monte Carlo and molecular dynamics simulation technique, we investigated the micro-scale distribution and preferential adsorption sites of water and methane within the shale matrix. Our research further elucidates the impact of water on the pore structure of shale and delineates the co-occurrence patterns of water and methane. The simulations reveal that water and methane share similar adsorption preferences on kerogen surfaces, while methane does not exhibit specific adsorption preferences on water-bearing illite. We also find that the presence of water significantly alters the illite pore space, leading to a reduction in larger pore volumes and a concomitant increase in smaller pores. The decrease in methane content with increasing water content is more pronounced in the shrinkage cracks compared to the kerogen matrix pores and is particularly notable in the macropores of illite. The findings underscore the differential sensitivity of methane content to water levels across various shale pore types. These insights contribute to a deeper theoretical understanding of gas and water distribution mechanisms in deep shale reservoirs, offering valuable support for the evaluation of deep shale gas resource potential.

: This study focuses on optimizing chlorophyll extraction techniques and strategically placing leaf disks to enhance chlorophyll concentration in sesame (Sesamum indicum L.) leaves. Thirty sesame genotypes, categorized into light green (LG), middle green (MG), and deep green (DG) pigment groups based on leaf coloration, were selected from a larger pool of field-grown accessions. The investigation involved determining optimal SPAD value index measurements, quantifying pigment concentrations, exploring extraction solvents, and selecting suitable leaf disk positions. Significant variations in chlorophyll content were observed across genotypes, greenness categories, and leaf disk positions. The categorization of genotypes into DG, MG, and LG groups revealed a correlation between leaf appearance and chlorophyll content. The study highlighted a consistent relationship between carotenoids and chlorophyll, indicating their role in adaptation to warm environments. Examination of leaf disk positions revealed a significant chlorophyll gradient along the leaf blade, emphasizing the need for standardized protocols. Chlorophyll extraction experiments identified DMSO and 96% ethanol, particularly the later incubated for 10 minutes at 85°C, as effective choices. This recommendation considers factors like cost-effectiveness, time efficiency, safety, and environmental regulations, ensuring consistent and simplified extraction processes. For higher chlorophyll extraction, focusing on leaf tips and the 75% position along the sesame leaf blade is suggested, consistently yielding higher chlorophyll content. Our findings therefore, underscores insightful knowledge on chlorophyll gradients and encouraging for standardized protocols, that enabling researchers to refine experimental designs for precise and comparable chlorophyll measurements. Recommended solvent choices ensure reliable outcomes in plant physiology, ecology, and environmental studies.

Carbon dioxide (CO2)miscible fracturing huff and puff technology plays a pivotal role in enhancing the recovery rate of crude oil, particularly in reservoirs with challenging physical properties, strong water sensitivity, high injection pressure, and complex water injection dynamics. In this study, the oil-increasing mechanism and huff and puff effect of CO2 miscible fracturing fluid are investigated through a comprehensive experimental approach. Specifically, experiments on PVT gas injection expansion, minimum miscible pressure, and CO2 miscible fracturing huff and puff are conducted on the G fault block reservoir in J Oilfield. The findings demonstrate that injecting CO2 into reservoirs leads to oil volume expansion, viscosity reduction, and saturation pressure increase. Additionally, the inclusion of solubilizers and viscosity reducers further enhances the efficiency of crude oil extraction. Solubilizers not only improve oil recovery but also reduce the minimum miscible pressure required for effective CO2 dispersion. Furthermore, the shut-in time, permeability, and huff and puff method all have considerable impacts on huff and puff recovery rate. This study offers valuable technical insights, supporting the application of CO2 miscible fracturing huff and puff technology to enhance oil recovery in low-permeability reservoirs.

In the process of oil and gas development and production, when the flow rate in the wellbore is large, the materials underground will be eroded, causing the operation to be unable to proceed smoothly and reducing the efficiency of oil and gas production. Therefore, optimizing the anti-erosion coating under high-speed flow conditions in wellbore is a very interesting thing. It can greatly improve operational efficiency. In this paper, laser cladding was used to prepare the new coating used in the PDC drill bit. Then, the effects of tungsten carbide and multi-layer graphene on the microstructure and properties of the tungsten carbide coating were studied. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding WC, the hardness has significantly improved. However, when the WC content further increases more than 30%, the increase in hardness is relatively small. In addition, when the content of graphene is more than 0.3%, the branched structure became thicker, and not only the segregation of Cr, Si and W became very obvious again, but also the segregation of Fe occurred at the Ni enrichment site. The research results contribute to improving oil and gas production and operational efficiency under high-speed fluid flow conditions in the wellbore.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

This study aims to investigate the interfacial tension, emulsion stability, and the impact on oil displacement effectiveness of mixed systems containing nonionic surfactants and amphoteric surfactants at different ratios. To achieve this, three different emulsion stability formulations were prepared by mixing betaine and nonionic surfactants in ratios of 1:1, 3:2, and 3:1 respectively. By adjusting the proportions of betaine and nonionic surfactants, variations in interfacial tension and emulsion formation among the three mixed systems were studied to analyze the mechanism of interaction between betaine and nonionic surfactants and evaluate their effects on crude oil displacement and enhanced oil recovery in reservoirs. Subsequently, microscopic and macroscopic oil displacement experiments were conducted on the three mixed systems, along with experiments on emulsion flow resistance. The aim was to verify the oil displacement effectiveness of different mixed systems and further understand their potential applications in reservoirs. By comprehensively analyzing the experimental results, optimal ratios and conditions for the mixed systems were determined. The experimental results indicate that the emulsification system formed by mixing betaine and nonionic surfactants at a ratio of 3:1 achieves molecular synergies through electrostatic interactions and mixed adsorption with betaine, resulting in a compact arrangement of interface membranes and demonstrating a synergistic effect in reducing interfacial tension to an ultra-low value of 10 -4 mN/m. Additionally, as the proportion of betaine surfactant increases, the viscosity of the aqueous phase liquid film increases, the rate of thinning of the liquid film slows down, the rate of water release slows down, and the stability of the emulsion increases. Microscopic oil displacement experiments verified that the three mixed systems increase oil washing efficiency through low interfacial tension and emulsification stripping action, thereby increasing the oil displacement mechanism's coefficient. Emulsion flow resistance experiments demonstrated that as the proportion of betaine surfactant increases, the system exhibits better plugging ability. However, macroscopic oil displacement experiments revealed differences in compatibility between different emulsion stability solutions and rocks with different permeabilities. The mixed system of betaine and nonionic surfactants at a ratio of 3:1 exhibits the best emulsification plugging ability in rocks with a permeability of 109mD, while the 1:1 mixed system performs best in rocks with a permeability of 12mD. This holds significant theoretical and practical significance for reservoir development and enhanced production.

Water quality assessment in pastoral of Tibetan Plateau, which is water sources for about 40% of world's population and the runoff-yield area of Yellow rivers, is very important. In this paper, Xiahe county which belongs to Tibetan Plateau pastoral was investigated. Six parameters(via, chloride, COD, ammonia nitrogen, nitrate, fluoride, sulfate) were selected to assess the water quality and health degree by using fuzzy comprehensive evaluation methods. The hydrochemical type in surface water was of HCO3--Mg2+-Ca2+ type. The cations and anions in surface water were mainly from weathering and dissolution of carbonate rock. Results showed that the water quality in all 69 sampling sites was all of class Ⅰ. The integrated health degree reached more than 0.85 and the health rate were 100%. Although ammonia nitrogen was regarded as the main contamination factor, but it had a little effect on the entire body of water. Overall, the surface water qualities of most samples in Xiahe County was good condition.

Alternative fertilizations are essential to minimize the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic-inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different treatments were set up: control (CK); conventional fertilization (CF); optimized fertilization (OF); organic fertilizer replacing 25% (T1) and 50% (T2) of OF; and organic nitrogen (N) replacing 25% (M1) and 50% (M2) of OF-N. Overall, significant increases occurred in the yields of COIF crops. Compared with the CF, the highest wheat and rice yields happened in M1 treatment (approximately by18.5%) (P < 0.05). COIF slightly alleviated soil acidification, and improved the cation exchange capacity (CEC) of the study soils. Besides, COIF treatments significantly increased the contents of total phosphorus, total potassium, available phosphorus, and available potassium by 6.35 to 16.9 %, 3.17 to 10.9 %, 5.53 to 28.7 %, and 2.6 to 12 %, respectively (P <0.05). Nevertheless, negligible increases took place in Cd content of COIF soils compared with the CK. Altogether, our results concluded on 25% replacement of OF-N by organic N (M1) to effectively improve the fertility/ecological sustainability of the study soils.

Asphaltene-resin-paraffin deposition (ARPD) is a complicated and prevalent issue in oil and gas industry, impacting the efficiency, integrity of petroleum extraction, production, transportation and processing systems. Considering all witnessed ARPD problems in Azerbaijan oil fields, this paper proposed a chemical method and optimized the type and the concentration of chemical inhibitors. And then the effect of selected chemical reagent on inhibiting the ARPD amount and thus en-hancing oil recovery was detected by reservoir simulation during both waterflooding and CO2 flooding production. Three new chemical compounds (namely, Chemical-A, Chemical-B and Chemical-C) were examined in laboratory conditions and their impact on rheological properties of high-paraffin oilfield samples of Azerbaijan (X, Y and Z) were investigated. Experimental results show that Chemical-C with concentration of 600 g/t has the best efficiency for alleviating the problems. After adding Chemical-C to the crude oil, the freezing point of oil is decreased from 12 °C to (-4) °C, ARPD amount is declined from 0.185 to 0.016 g and oil effective viscosity is reduced from 16.2 mPa·s to 3.1 mPa·s. It is determined that for water and CO2 flooding, higher injection pressure resulted in reduced asphaltene precipitation. Adding selected ARPD inhibitor, the oil recovery for waterflooding can increase from 52% to 62%, while it can rise from 55% to 68% for CO2 flooding.

With the development of economy, maritime transportation has increased rapidly, which has had a significant impact on the marine environment and resources. In recent years, Synthetic Aperture Radar (SAR) has been widely used to observe maritime environmental incident. Through the large coverage area of SAR, marine environmental emergencies, especially oil pollution, can be better monitored and responded to. This study first proposed an improved Full-scale Aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas. The convolutional block attention module (CBAM) in FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached 84.55% mIoU (mean Intersection over Union), which was 17.15% higher than that of the original U-Net model. The improved FA-MobileUNet model was then examined by some oil pollution incidents. According to the detection results, if the oil spill was suspected to be caused by a ship, the corresponding Automatic Identification System (AIS) data were collected based on the acquisition time and geo-location of the SAR image to search for the ship suspected of causing oil pollution. Finally, some oil pollution incidents in Taiwan marine area were collected to verify the effectiveness of the proposed model for marine oil pollution monitoring. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

This paper addresses the lack of analytical tools for visual data analysis in IEEE Xplore, an abstract database of publications on technical problems in electronics and electrical engineering. The openness of the platform and the good fillability of a large number of bibliometric record fields make it attractive for visual data analysis. The topic of machine learning and artificial intelligence applied to power systems, which are critical issues in the energy transition process, is chosen as an example for visual data analysis. The programs and utilities used for data analysis were VOSviewer, Scimago Graphica, FP-Growth algorithm, Inkscape, and MultiDendrograms. The use of IEEE Terms field data to identify publication topics in the exported data is analyzed in the most comprehensive way. This paper can be considered a reference for professionals interested in bibliometric analysis and visualization of IEEE Xplore data.

Objectives. The aim of the study is to identify relevant research topics based on pooled bibliometric data of International Journal of Hydrogen Energy publications from 2022-2024, collected from The Lens and ScienceDirect platforms. Data source. Bibliometric records of International Journal of Hydrogen Energy articles published in 2022-2024 from The Lens and ScienceDirect platforms were used. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility. Results. The study showed the feasibility of merging bibliometric records from the open platforms ScienceDirect and The Lens, which complement each other. The 'Fields of Study' data of The Lens can be rationally interpreted as system keywords similar to Scopus Index Keywords. The possibility of using 'Fields of Study' data in a method similar to bibliographic coupling is shown. The practicality of using an Alluvial diagram to show the co-occurrence of the four terms was demonstrated. The study also emphasizes the advisability of joint use of VOSviewer and Scimago Graphica programs for complex visualization of bibliometric analysis results. The study identified two dominant publication topics in International Journal of Hydrogen Energy for 2022-2024, which can be described in terms of: 'Catalysis, Hydrogen, Physical_chemistry, Chemical_engineering, Nanotechnology, Electrochemistry, Organic_chemistry, Materials_science' and 'Electrical_engineering, Hydrogen_production, Renewable_energy, Environmental_science, Hydrogen_economy, Hydrogen, Engineering'.

Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.

Background. Keyword co-occurrence analysis is a crucial tool for comprehending research trends, identifying relevant studies, and gaining insight into the connections between various concepts and topics. Objective. This study focuses on analyzing the co-occurrence of keywords using FP-growth algorithm and direct search methods. Materials and methods. The methodology involved extracting bibliometric data of Energies journal for 2023-2024 from MDPI publisher platform, keyword lemmatization and keyword co-occurrence estimation. Clustering and visualization were performed using Multidendrograms and Scimago Graphica software. Results. The results showed that the FP-growth algorithm can achieve a close match with the direct search results, which facilitates data preparation for clustering. In addition, finding the co-occurrence of three or more keywords significantly reduced the number of possible combinations, which allowed the identification of specific research topics. Conclusions. This study highlights the usefulness of the FP-growth algorithm in keyword analysis and provides insights into ways to refine search queries to abstract databases for the purpose of designing and writing literature and systematic reviews.

Relevance of the subject. Keyword selection is critical to finding relevant literature and requires justification for querying abstract databases and other sources to ensure accuracy and completeness. The topic of Energy Systems is of great importance due to the introduction of renewable energy sources, modern control methods, and complex energy systems during the energy transition. The aim of this study. The goal of this paper was to identify keywords that would be useful to subject matter experts when collecting literature on a particular topic, based on entries in the IEEE Terms field. Data. Bibliometric data were exported from the IEEE Xplore platform in the following order: for 2019-2023, 2000 records sorted by relevance, for 2024, 1680 records current as of April 11, 2024. Analytical methods applied and software used. VOSviewer, Scimago Graphica implementing the Clauset-Newman-Moore algorithm and agglomerative hierarchical clustering method implemented in Multidendrograms. Results. The main issues of the Energy Systems topic are presented in tabular and graphical formats. The fpgrowth utility offers flexible data preparation options, which makes it worthwhile to conduct a separate study to analyze the score of co-occurrence terms given by its algorithm.

Purpose of publication: - Preparation of bibliometric data exported from the Scilit platform on energy efficiency and conservation for further analysis to identify relevant research topics. - To identify potential issues in the processing of data exported from the Scilit platform. - Providing colleagues with the opportunity to use the prepared data and examples of their analysis for independent research on topical issues of energy efficiency and energy conservation using materials provided by the Scilit platform. Research Materials: Files in CSV and RIS formats exported from Scilit for the query "energy conservation OR efficiency" in Common Fields [Title, Abstract, Keyword], using filters: Content Type → Journal Article; Year→2021-2023; Subject → Industrial Engineering (29.8K), Energy and Fuel Technology (9.8K), Manufacturing Engineering (9.2K). A total of 30K records sorted by their relevance (10K for each year) were exported. Data are current as of 14-03-2024. Methods: Preprocessing of title, annotation, and keyword field texts using lemmatization dictionaries collected on GitHub, removal of keywords taken from GATE and spaCy, and "manual" editing. Using VOSviewer to analyze publication topics by clustering keywords based on their co-occurrence. Using Scimago Graphica to build bubble diagrams.Application of the GSDMM algorithm for clustering bibliometric records by title and annotation texts. Creation of a dictionary for this algorithm using the keyword field.Use of the Carrot2 demo version and the NMF algorithm for a more detailed analysis of the topics of the record clusters obtained from GSDMM. Results: are presented in the form of initial and interim tables and graphs obtained in the course of this study. The full tables are provided as references to the attached materials. Supplementary material for this preprint on figshare: Chigarev, Boris (2024). Supplementary material for preprint "Analyzing the Possibilities of Using the Scilit Platform to Identify Current Energy Efficiency and Conservation Issues". figshare. Dataset. https://doi.org/10.6084/m9.figshare.25574058.v1

With the offshore oil-gas fields entering decline phase, high-efficiency separation of oil-gas-water mixtures becomes a significant challenge. As essential equipment for separation, the three-phase separators play a key role in Offshore Oil-Gas production. However, level control is critical in the operation of three-phase gravity separators on offshore facilities, as it directly affects the efficacy and safety of the separation process. This paper introduces an advanced deep deterministic policy gradient with adaptive learning rate weights (ALRW-DDPG) control algorithm, which improves the convergence and stability of the conventional DDPG algorithm. An adaptive learning rate weight function is meticulously designed, and an ALRW-DDPG algorithm network is constructed for simulating three-phase separator liquid level control. The effectiveness of the ALRW-DDPG algorithm is subsequently validated through simulation experiments. The results show that the ALRW-DDPG algorithm achieves a 15.38% improvement in convergence rate compared to the traditional DDPG algorithm, and the control error is significantly smaller than that of PID and DDPG algorithms.

Sentinel eggs used to monitor field parasitism of stink bug pests (Hemiptera: Pentatomidae) can only be deployed for a few days to avoid releasing the pest in the monitored area. Using sterile eggs removes the risk of accidental pest introduction and extends deployment time. Freezing the eggs before deployment is one common method for sterilizing sentinel eggs. However, some egg parasitoid species have lower to no parasitism on frozen eggs. In this study, x-ray irradiation was used to sterilize Bagrada hilaris sentinel eggs intended for monitoring parasitism by Gryon aetherium (Hymenoptera: Scelionidae), the most promising biological control candidate. In this case, freezing sentinel eggs is not recommended because G. aetherium has low levels of parasitism on frozen eggs. Doses as low as 10 Gy induced 100% sterility. Irradiated eggs successfully sustained the development of G. aetherium and Ooencyrtus californicus (Hymenoptera: Encyrtidae), another egg parasitoid attacking B. hilaris, and parasitism levels were comparable to fresh eggs up to seven days. In addition, G. aetherium showed no preference for fresh non-irradiated over seven-day-old irradiated eggs. Our results indicate that x-ray irradiation is a suitable alternative to produce safe and reliable sentinel eggs to monitor egg parasitism of B. hilaris and possibly other species.

The alternating stress caused by periodic high-pressure injection and extraction in gas storage can potentially induce fault slippage, compromising the sealing integrity of faults within these facil-ities. Understanding the mechanical behavior of faults under alternating stress is crucial for en-suring the long-term stability and safety of gas storage operations. To explore the impact of fault dip angle and fault gouge strength on fault slip characteristics, fault samples were prepared with uniaxial compressive strengths of 20.1, 30.2, 42.4, and 51.4 MPa at two distinct dip angles. Triaxial compression experiments were conducted under alternating stress conditions corresponding to operational pressures at a specific gas storage in China. Results indicate that faults with dip angles of 30° and 45° tend to fail at their weakest points. Increasing strength of fault gouge shifts failure mechanisms from interfacial failure between gouge and surrounding rock towards internal gouge failure, often accompanied by shear failure across sections, resulting in characteristic "X"-shaped conjugate shear failures. The decrease in the ratio of bedrock strength to fault gouge strength elucidates the observed phenomena of initial reduction followed by increased fault deformation. Transition points for faults with 30° and 45° dips occur around the strength ratios of 1.7/1 and 1.2/1, respectively. Fault damage exhibits a negative correlation with fault gouge strength and a positive correlation with fault dip angle. Samples with higher strength fault gouge at a 30° dip angle generally incur less damage compared to those with lower strength fault gouge at a 45° dip angle. Moreover, higher maximum static friction coefficients denote greater fault resistance to slipping, with 30° faults consistently demonstrating superior resistance compared to 45° faults. Additionally, higher strength fault gouge consistently enhances slip resistance under identical dip angles.

A Partial discharge (PD) occurrence in high-voltage (HV) and medium-voltage (MV) power assets is a warning sign of insulation system degradation or defective manufacturing/assembly, that may cause catastrophic damage and eventually lead to the breakdown of the power equipment. Ultra-high frequency (UHF) PD measurement technology has extensively been used to monitor the insulation health of the HV or MV equipment due to its high immunity against ambient noise and conducted disturbances. This paper presents experimental validation of a new design of a UHF planar rectangular microstrip antenna that can be used for PD monitoring of HV and/or MV power equipment, such as power transformers. The designed planar rectangular microstrip antenna has a compact size of 80mmx70mmx1.6mm. Laboratory experiments were performed to validate the antenna sensitivity to PD activity caused by insulation defects created inside an oil-filled transformer tank model. PD measurement results confirm that the proposed rectangular microstrip antenna has high sensitivity and high signal-to-noise ratio (SNR), thanks to its capability to suppress external noise disturbances, especially during online PD monitoring. Thus, the proposed UHF antenna seems to be a promising and alternative PD sensor that can be employed in UHF PD monitoring systems of HV or MV power equipment.

Abstract: Toxicity of emerging organic pollutants to photosystems of aquatic plants are still not well clarified. This study aimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by SECM (scanning electrochemical microscopy). The protocol was also checked by confocal laser scanning microscopy (CLSM), the traditional Clark oxygen electrode method and chlorophyll fluorescence technique. The typical persistent organic pollutant 2,4-dichlorophenol (2,4-DCP) in water environment and the common aquatic Microsorium pteropus (M. pteropus) were chosen as the model organic pollutant and tested plant, respectively. It was found that the SECM method could well discriminated the responses of stoma micromorphology and spatial pattens of photosynthetic oxygen evolution on single stoma. The shape of stoma blurred with increasing 2,4-DCP concentration, which was in good agreement with the CLSM images. The dose-response curves and IC50 values obtained from the SECM data were verified by the data measured by the traditional Clark oxygen electrode method and chlorophyll fluorescence test. The IC50 value of single stoma oxygen emission of 24 h exposed plant leave, which was derived from the SECM current data (32535 μg L-1), was close to those calculated from the maximum photosynthetic efficiency (Fv/Fm) measured by chlorophyll fluorescence test (33963 μg L-1), and the Clark oxygen electrode method photosynthetic oxygen evolution rate (32375 μg L-1). The 72 h and 96 h 2,4-DCP exposure data further confirmed the reliability of the nanoscale stoma oxygen emission mapping methodology for ecotoxicological assessment. In this protocol, the procedures for how to collect effective electrochemical data and how to extract useful information from the single stoma oxygen emission pattern were well established. This study showed that SECM is a feasible and reliable ecotoxicological tool for evaluation of toxicity of organic pollutants to higher plants with unique nanoscale visualization advantage over the conventional methods.

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of Particulate Matter with an aerodynamic diameter of 2.5μm (PM2.5) and 10μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist the main cause for many diseases. Based on the existence of statutory limits, by the World Health Organization (WHO), for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that will have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict the air pollutants concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural networks (ANNs) models. In the frame of the specific work, a significant number of ANNs was developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. Results showed that ANNs present a re-markable prognostic ability in order to forecast the air pollution levels in an urban environment.

In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

In this work, the properties of a composition based on synthetic rubber ethylene-propylene ter-polymer and polymers with functional groups of the mixture are studied. Low efficiency of some compositions based on ethylene and propylene copolymer limits their use in the production of tires and rubber products. Therefore, we conducted a study to improve the disadvantages of SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) The data obtained showed that a number of its indicators of rubber based on them were improved. Tear resistance, resistance to repeated stretching increased, adhesion of the composition to metal, chemical resistance increased, the degree of swelling in benzene and gasoline-benzene mixture decreased. Compositions based on the mixture SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) +Dioctyl phthalate: (70:20:10) have high resistance to swelling in an aggressive environment (Benzene, gasoline, alcohol, acid, oil, etc.) mixture.

As waterflooding multi-layer reservoirs get into the high-water-cut stage, inter-layer conflicts become increasingly serious, leading to a worsening development effect over time. Production layers regroup is an effective approach for resolving inter-layer conflicts and improving waterflooding efficiency. At the current stage, there are limitations for most of the methods of production layers regroup. This article proposes a smart method for optimizing the layer regroup plan based on genetic algorithm. Comprehensively considering various factors that affect the regroup of layers, based on the combination principle of "smaller intra-group variance and larger inter-group variance of each influencing factor are expected ", genetic algorithm is used to calculate the fitness value of the initial combination schemes, the advantageous schemes with higher fitness values are selected as the basis of the next generation. Then crossover and mutation operations are performed to those advantageous schemes to generate new schemes. Through continuous selection and evolution, until the global optimal solution with the highest fitness value is found, the optimal combination scheme is determined. Comparative analysis with numerical simulation results demonstrates the reliability of this intelligent method, with an increased oil recovery of 4.34% for the sample reservoir. Unlike selecting a preferable plan from a limited number of predefined combination schemes, this method is an automatic optimization to solve the optimal solution of the problem. It improves both efficiency and accuracy as compared to conventional reservoir engineering methods, numerical simulation methods and most of the mathematical methods, thus could provide effective guidance for EOR strategies of waterflooding reservoirs in high-water-cut stage.

Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hot spot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of the mountain’s side that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites.

Particulate matter (PM) is one of the problems faced in environmental science. It has health effects on man and animals in both developed and developing countries. Research and efforts have been on it several years back. Policy statements and efforts have been published. This review paper is an added information on air pollution. In it, efforts were made in discussing these: classification, effects, methodology, case studies and source apportionment. It is hoped that this paper would contribute to existing knowledge on PM.

Pipelines are widely used globally, particularly in Nigeria, to transport raw and processed hydrocarbon fluids, including crude oil, from the point of production to the shore for export, processing facilities and distribution of refined products to depots for sales and consumption. Although pipelines are designed to withstand environmental and physical shocks, intermittently, pipelines are exposed to natural or human-induced conditions that damage pipelines and disrupt their functionality. These disruptions could result from operational defects or vandalism, causing an economic loss of up to US$ 658.5 million as observed in the Niger Delta from 1990 to 2022. Furthermore, product loss of about 705,100 barrels observed over the same period has caused environmental and social impacts, damaging the natural environment, destroying livelihoods supported by the ecosystem, and deteriorating the health of residents as documented in several literatures. To mitigate these issues and ensure the safe and reliable conveyance of crude oil in pipelines, measures must be taken to timely detect spillages and intervene during oil spillages to minimize damage and loss. Such detection methods include external, visual/ biological and internal/computation-based approaches. This paper uses a mixed-method approach to review the literature on the causes and impact of crude oil pipeline leakage in the Niger Delta region of Nigeria and highlights some methods that could be applied to detect pipeline leakage, as well as their strengths and inefficiencies. This paper further assesses the joint investigation visit mechanisms for oil spill response used in the oil and gas industry in Nigeria and identified that between 2006 to 2021, it took an average of 4 days to report oil spill occurrence, and 21 days to respond. Also, for 18 percent of the spill occurrences in that period, there were no JIV. This study suggests that the gap in the JIV mechanism can be addressed by integrating technology to enhance detectability and minimize the cost of monitoring. The paper also guides the way forward to develop and improve the reliability of pipeline leak detection systems.

This study is aimed at demonstrating application of vegetation spectral techniques for detection and monitoring of impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G/NIR) and green-shortwave infrared (G/SWIR) from the spill sites (SS) and non-spill (NSS) sites in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G/NIR and G/SWIR indicated certain level difference between vegetation condition at the SS and the NSS were significant with p-value <0.5 in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G/NIR - p-value 0.01 and GSWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G/NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post—spill analysis show that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique is essential for real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in the mangrove forest.

Pipelines are widely used for transportation of hydrocarbon fluids over millions of miles over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distributions deport. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses on pipelines leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

In order to establish the formation patterns of the Co–Mg oxide system, samples with different Co:Mg ratios and heat treatment temperatures were synthesized and studied. Study of the sam-ples confirmed the phase transition of MgxCo2–xO4 spinels into corresponding solid solutions at 800–900 °C. The similarity of formation patterns for different compositions is shown. The rock-salt oxide in low-temperature samples is an anion-modified paracrystalline phase that forms a “true” solid solution only upon spinel decomposition. TPR profiles of decomposed Co3O4 spinel show surface Co3O4 peaks and a wide peak corresponding to well-crystallized CoO, while par-tial Co3O4 TPR up to 380 °C results in dispersed and amorphous CoO. High-temperature non-stoichiometric samples are poorly reduced, indicating their low oxygen reactivity. Spinel reoxidation after heat treatment to 1100 °C by calcination at 750 °C showed complete regenera-tion for MgCo2O4–Co3O4 samples and its absence in case of an excess of MgO relative to stoi-chiometry. Based on the data obtained, it can be concluded that the processes governing the formation of Co–Mg oxide systems are determined by both Co:Mg ratio and the heat treatment conditions, potentially providing the possibility of fine tuning for specific catalytic processes.

Environmental estrogen pollution and estrogen effects on the female reproduction system are well-recognized scientifically. Among the estrogens, 17 β-estradiol is a priority in environmental estrogen pollution, and it is also a major contributor to estrogen which regulates the female reproduction system. 17 β-estradiol is carcinogen and has a tumor promotion effect relating to breast cancer, lung cancer and others. It also affects the psychological well-being such as depression, fatigue and others. Thus, a simple method of detection of 17 β-estradiol will be important for both environmental estrogen pollution and women’s health care. This study demonstrates a simple-use, cost effective 17 β-estradiol biosensor system which can be used for both environment and women’s health care applications. The bio-recognition mechanism is based on the influence of the redox couple, K₃Fe (CN) ₆/K₄Fe (CN) ₆ by the interaction between 17 β-estradiol antigen and its α-receptor (ER- α; α-estrogen antibody). The transduction mechanism is an electrochemical analytical technique, differential pulse voltammetry (DPV). The levels of 17 β-estradiol antigen studied was between 2.25 pg/mL to 2,250 pg/mL, Phosphate buffered saline (PBS), tap water from the Cleveland regional water district, and simulate urine were used as the test media covering the potential application areas for 17 β-estradiol detection. An interference study by testosterone which has a similar chemical structure and molecular weight as those of 17 β-estradiol was carried out, and this 17 β-estradiol biosensor showed excellent specificity without any interference by similar chemicals.

Heavy metal pollution poses a significant threat to ecosystems and human health worldwide. In response to this pressing issue, bioremediation has emerged as a promising approach for mitigating contamination. This paper adopts a bibliographic method to explore the key techniques and applications of bioremediation. Furthermore, we delve into the most prominent threats to human environmental health and the corresponding remediation methods. Additionally, we discuss the future trajectory of bioremediation research, with a particular focus on the integration of big data and machine learning technologies. These advanced methodologies hold great potential for enhancing the effectiveness and efficiency of environmental remediation efforts in the face of escalating pollution challenges.

Analyses of the three predominant types of sedimentation – clastic, carbonate and black shale – revealed their discontinuity/continuity in different facies environments of the Middle and Late Devonian of the central part of the Volga-Urals petroleum province, namely (1) in depressions, both condensed and non-condensed, (2) on the slopes of depressions and (3) in shallow water environments represented by tectonic arches or localised biohermal uplifts. The regional zonation of conodonts, correlated with the global chronostratigraphic scheme, substantiates the stratigraphic completeness of the general Devonian sequence in this area. In particular, the sedimentation of organic-rich black shales, traditionally considered as source rocks, lasted more than 29 million years, starting from the Late Eifel and ending at the Devonian-Carboniferous boundary. There was only one hiatus in the deposition of both black shales and carbonate sediments, lasting about 2.5 million years at the end of the Frasnian. That interval is dominated by clastic sediments, which are considered to be the most productive reservoirs of the Volga-Ural province. In the time interval from the beginning of the Eifel to the end of the Frasnian (about 17 Ma), (a) clastic sediments show the highest accumulation rates; (b) the most complete sequences occur in depressions; and (c) arches and uplifts show the most significant gaps in the geological record. During Famennian, (a) clastic sedimentation has completely ceased; (b) differences between the slopes of troughs and shallow-water environments have diminished, resulting in stable accumulation of carbonate sediments in both settings; (c) the decrease in sequence thickness (up to 50 m) in depressions suggests reduced accumulation rates of deep-water organic-rich black shales. The results help to understand the complex spatial relationship between different coexisting sedimentation types and focus attention to the incompleteness of the geological record in different environments.

High-precision conical threads are widely used in responsible joints in the drilling of oil and gas wells. Difficult underground drilling conditions due to the occurrence of significant mechanical loads and an aggressive environment in wells require the use of workable materials that are often difficult to machine. The huge need for such connections dictates the use of high-performance cutting tools, which means that high demands are placed on their effective geometric performance. The study describes the theoretical dependence of the profile precision of the tapered drill-string tool-joint threads of all standard sizes on the complex of a cutting tool geometric parameters and cutting tool setting accuracy. The experiments include an assessment of the geometric accuracy of the profile of the obtained NC23 thread connector, depending on the values of the installation angles and the declared tangential displacement of the standard threaded carbide insert.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

Soluble inorganic pyrophosphatase (sPPase), a pyrophosphate hydrolase, has been shown to play an important role in several physiological processes such as plant growth and development, metabolic activities, and response to abiotic stresses, but sPPase has been less studied in woody plants. In order to preliminarily verify whether the soluble inorganic pyrophosphatase in Eucommia ulmoides Oliver (E. ulmoides) has a role in resistance to drought stress, we amplified the EuSIP5 (E. ulmoides soluble inorganic pyrophosphatase 5) cDNA sequence from E. ulmoides by RT-PCR. In order to study the drought resistance of the Eucommia ulmoides soluble inorganic pyrophosphatase 5 (EuSIP5) gene, the cDNA sequence of the EuSIP5 gene was amplified from Eucommia ulmoides Oliver by RT-PCR. Bioinformatic analysis indicated that EuSIP5 might be an unstable amphipathic protein, mainly located in the cytoplasm. E. ulmoides, the highest expression of the EuSIP5 gene was found in male leaves and pericarp from April to October, and in leaves of E. ulmoides in July and September. During drought, the expression of EuSIP5 in seedling E. ulmoides leaves was significantly higher than that of the control. An overexpression vector harboring the EuSIP5 was constructed and introduced into the tobacco (Nicotiana tabacum cv. Xanthi). We found that during drought stress, the appearance of wilting was delayed by 4 days in transgenic tobacco plants (OE) compared with wild type plants (WT), and the expression of drought-related genes DET2, CYP85A1, P5CS, ERF1 and F-box was found to be increased in transgenic tobacco leaves, as well as the enzyme activities of the protective enzymes: peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) were significantly higher than those of the WT, and the malondialdehyde (MDA) content was lower than the WT. These results indicated that the introduction of EuSIP5 gene into tobacco increased the expression of drought-related genes, enhanced the anti-membrane oxidation ability of tobacco, and reduced the oxidative stress damage, thus improving the drought resistance of tobacco.

It is concerning that Cameroon's urban centers are experiencing an alarming rise in air pollution as a result of urbanization, population growth, industrial activity, and transportation emissions. We examined the causes, health impacts, research methodology, case studies, and government measures to address the urgent issue of air pollution in Cameroon's urban regions. We have emphasized how Cameroon's air pollution levels have significantly increased as a result of causes like population expansion, urbanization, industrial emissions, and automobile exhaust A thorough discussion of the negative health impacts of extended exposure to air pollution is included, with special attention to the sensitivity of the old and young. These effects include respiratory disorders, cardiovascular ailments, and early mortality. We emphasized the pressing need for all-encompassing strategies to address air pollution, such as increased public awareness campaigns, stronger emissions regulations, the development of renewable energy sources, and better waste management. The techniques employed in studies on air pollution, like as epidemiological research and air quality monitoring, are also explained to provide light on the unique health dangers that Cameroonian urban dwellers must contend with. We emphasized in our conclusion the significance of joint efforts by legislators, medical professionals, and the general people to reduce air pollution and protect public health in Cameroon in order to create a cleaner and healthier future. In order to protect public health in the future, it is imperative that Cameroon's air pollution be addressed.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Air pollution is a critical issue impacting urban environments, leading to severe health problems and environmental degradation. This comprehensive review examines the potential of green systems—specifically green walls, active green walls, and urban greenery systems—to mitigate atmospheric pollutants such as particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO2). By systematically analyzing 44 recent studies, the review highlights the pollutant capture efficiency of various green technologies and plant species in both indoor and outdoor settings. Active green walls, particularly those utilizing plant species such as Chlorophytum comosum and Sansevieria trifasciata, were found to be highly effective, with VOC reduction efficiencies up to 96.34%, PM reductions by 65.42%, and CO2 reduction rates reaching 4.8% under optimal conditions. The review identifies key strengths in current research, including diverse experimental setups and the use of sophisticated measurement techniques, but also notes significant limitations such as variability in experimental conditions and a lack of long-term performance data. The study underscores the importance of proper maintenance to sustain green systems' efficacy and highlights the potential issue of pollutant resuspension, which remains under-researched. Practical implications for urban planning are discussed, advocating for the integration of effective green systems into urban infrastructure to enhance air quality and public health. Recommendations for future research include the need for standardized metrics, long-term studies, economic feasibility analyses, and real-world validation of simulation models to better understand and optimize green systems for urban air pollution mitigation.

Metals are particularly sensitive to some pollutant gases. Many museum showcases and store rooms present atmospheres that can corrode cultural heritage artefacts containing metals. Whilst numerous strategies have been reported to mitigate such situations, avoiding them is preferable. Several approaches to testing materials used in construction, fitting out or dressing are used. The relative merits and drawbacks will be discussed. Several parameters of the most widely used, the accelerated corrosion ‘Oddy’ test have been investigated. The influence of abrasive on subsequent corrosion of lead and copper coupons have been assessed. Quantification methods for tested coupons are reviewed. The influence of test duration and temperature were assessed through comparison with real life, long term experience of materials behaviour. Direct contact tests with the material tested touching the test material has been investigated. Several materials present in artefacts are known to potentially cause corrosion when enclosed with metals in other artefacts. A number of situations have been investigated with pollution and RH and some corrosion rate measurements. Ways to isolate artefacts or mitigate are explored and a decision support model has been developed.

In order to develop novel botanical insecticides, the joint action of Ligusticum chuanxiong oil (LCO) and lemongrass oil (LO) against Aphis citricola van der Goot was determined systematically indoors and outdoors. The chemical profiles of LCO and LO as determined by gas chromatography- mass spectrometry analysis revealed that main compounds from LCO were Z-Ligustilide (44.58%) and Senkyunolide A (26.92%), and that of LO were geranial (42.16%) and neral (32.58%), respectively. The mixture of LCO and LO showed significant synergy against A. citricola, with a common-toxicity coefficient (CTC) value of 221.46 at the optimal ratio of LCO to LO (4: 1, w/w). Based on the results of solvents and emulsifiers screening, L. Chuanxiong oil · Lemongrass oil 20% emulsifiable concentrate (20% LCO · LO EC) was developed, which was confirmed to meet the requirements of a commercial pesticide by quality test. Field trials indicated that the insecticidal activity of the diluted 20% LCO · LO EC (1000 fold dilution) was comparable to conventional pesticide (20% imidacloprid EC) on A. citricola 7 days after application. Thus, the mixture of LCO and LO has the potential to be further developed as a botanical pesticide.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

The study aimed to compare the cardiovascular benefits of Omega-3 Polyunsaturated Fatty Acids from fish oil versus krill oil in different forms and dosages. Following PRISMA 2015, data analysis with R software, and databases were searched from 1993 to 2023, (ID PROSPERO: CRD42024502338). Sixty high-quality studies were analyzed, covering various forms of fish oil and krill oil at different dosages (fish oil (ethyl ester-EE, triglyceride-TG, re-esterified triglycerides-rTG, capsules, emulsion-EM); krill oil (phospholipid/free fatty acid-PL/FFA, high concentration phospholipid-HPL, low concentration phospholipid-LPL)). Meta-analysis revealed that krill oil with phospholipid/free fatty acid at doses 2000-2900 mg effectively lowered triglycerides, while fish oil in ethyl ester form at doses above 3000 mg significantly reduced total cholesterol. Fish oil in triglyceride form at doses 2000-2900 mg and other variations positively impacted HDL-C levels. Fish oil in EE form above 3000 mg and triglyceride form below 2000 mg notably reduced LDL-C. However, there was limited effectiveness observed for other interventions. Analysis for LDL-C exhibited publication bias, while other parameters demonstrated reliability (Egger's test). In conclusion, krill oil - PL/FFA, Fish oil - EE above 3000 mg, Fish oil - TG 2000-3000 mg, and fish oil - rTAG 300-1900 mg effectively in reducing heart risk patients.

The effect of varying concentrations of crude oil pollution on the germination and growth parameters of Mucuna pruriens (Var. cochinchinenesis) was investigated. The following treatments were used; 0ml, 100ml, 200ml, 400ml, 800ml 1600ml respectively. The germination of velvet bean was 100% but growth was found to be inhibited by a wide range of treatments (800ml and 1600ml). the lag phase of proceeding growth is also reduced at this treatment. Growth inhibition is attributed primarily to the characteristics of oil, which acts as a physical barrier to water and oxygen uptake. Reduction in the growth and development of plants was observed in the polluted treatments as against the control (non–polluted treatment) because of the deleterious effect of the petroleum products on the morphological parameters (plant height, number of leaves and leaf area) tested for. Velvet beans treated with lower treatment (100ml and 200ml) were observed to be more productive when compared to the control experiment. Consequently, as the crude oil exceeds the plant absorbing capacity at 800ml and 1600ml respectively, the growth retards, chlorosis, presence of tough apical meristem, and defoliation from the base of the plant.

The ubiquitous presence of microplastics (MPs) in the environment poses a threat to ecosystems and increases concerns about their exposure to the human body. However, despite being consumed for health improvement, limited research has been conducted on MPs in functional foods. This study aimed to investigate the occurrence of MPs and the relation of the source of raw material or the manufacture in various omega-3 oil in the Korean market. MPs in omega-3 capsule and raw oil, sourced from both plant-based (PB) and animal-based (AB) sources, were investigated. We developed the method by direct filtration with acetone washing and analyzed MPs greater than 5 μm using microscope-Raman spectroscopy. The average number of MPs was found to be 1.2 ± 1.7 MPs/g for PB raw oil, 2.2 ± 1.7 MPs/g for AB raw oil, 3.5 ± 3.9 MPs/g for PB capsule oil, and 10.6 ± 8.9 MPs/g for AB capsule oil. Polypropylene and polyethylene terephthalate were major specious (83-95 %) found in omega-3 oil. The ratio of size range remained consistent across all groups, with a trend of higher detection rates as the size diminished. The results reveal that the main reason of MPs contamination is not the source of raw material but the manufacturing and packaging process for omega-3 oil.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost, and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the IDW method. This approach can also be used in 3 dimensions to quantitatively evaluate the distribution of light intensity distribution. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific and whether it provides residents with a basis for the protection of their rights.

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks, enabling online detection of corrosion points and microleakage in the tank floor. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. A technical scheme for acoustic emission detection in storage tanks is designed, and the instru-ment is used to carry out acoustic emission testing on the product oil storage tank floor. Based on these test results, location analysis of acoustic emission sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open tank tests confirms that using acoustic emission technology effectively captures corrosion signals from product oil storage tank bottoms. Consequently, it significantly improves inspection efficiency by avoiding unnecessary tank-opening operations. Combining this method with open tank inspection operations such as the leakage magnetic method, one can fully grasp the status of the refined oil storage tanks.

The formation of hydrogen sulfide (H₂S) in petroleum reservoirs by anaerobic microbial activity is called biogenic souring of reservoirs and is a risk in the oil and gas industry as the compound is extremely toxic, flammable and corrosive. In this paper, we present a workflow and the tools to assess biogenic souring from a pragmatic engineering perspective. The retention of H₂S in the reservoir due to the reactions with iron-bearing rock minerals (e.g. siderite) is shown in a theoretical approach here and supported with literature data. Cases are given for two fields under secondary (waterflooding) and tertiary flooding with microbial enhanced oil recovery (MEOR). The use of the Monte Carlo method as a numerical modelling tool to incorporate uncertainties in the measured physical/chemical/ biochemical data is demonstrated as well. A list of studies conducted with different chemicals alone or in combination with various biocides to mitigate biogenic souring provides an overview of potential inhibitors as well as possible applications. Furthermore, the results of static and dynamic inhibition tests using molybdate are presented in more detail due to its promising mitigation ability. Finally, a three-step workflow for the risk assessment of biogenic souring and its possible mitigation is presented and discussed.

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system adjustments and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs. generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO₂ emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and the Rest of PJM region leading to scenario-varying changes in the plant-level release of several air pollutants. Due to air pollution chemistry and transport and socio-demographics, these changes in turn give rise to a reduction of 10 to 182 PM_2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Scenarios that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, vulnerable populations—in both PA and Rest of PJM—benefit most in scenarios that prioritize plant closures in PA near Environmental Justice Areas. These results demonstrate the importance of considering cross-regional linkages and socio-demographics in designing equitable retirement strategies.

There are considerable benefits in applying Artificial Intelligence to automation in modern industries including Oil and Gas Industries. Oil and Gas Industries have many complex systems in their production and flow line. The failure of gas equipment will have serious consequences including financial and environmental issues. Condition monitoring of oil and gas equipment provides the condition of components, machines, systems, equipment, data hardware, and even software. The predictive maintenance and industry 4.0 applications have more advantages in petrochemical industries to make a safer economic environment and this paper addresses the Artificial Intelligence (AI) application of condition monitoring for Oil and Gas Industries. To understand the effectiveness of AI in condition monitoring for oil and gas industries, a case study related to the condition monitoring of drilling machine is conducted and applied Artificial Neural Network (ANN) algorithm to analyze and predict the potential failures. The novelty of this work is the proposal of an approach for tool wear monitoring in drilling using acoustic emission sensors for feature extraction and considering wavelet packet decomposition for further analysis. The extracted features from WPD are given as input for ANN to identify the healthiness of the drill bit and machine. This work aims to find the effectiveness of AI-based condition monitoring in enhancing effectiveness of monitoring and the safety of equipment in Oil and Gas Industries.

The leather industry is one of the most polluting industries in the world due to the large amounts of waste following the raw hides processing, but also due to the high content of chemicals present in their composition. The main problem of chromium-tanned leather solid waste is related to their storage, because of the ability of chromium to leach in soil or water, and also owing to the high ability of trivalent chromium to oxidize to its toxic form, hexavalent chromium. The purpose of this study is to present the latest methods for extracting trivalent chromium from the composition of solid leather wastes. The extraction methods identified in the present study are based on acid/basic/enzymatic hydrolysis and substitution with the help of organic chelators (organic acids and organic acid salts). In addition, the study includes a comparative analysis between the advantages and disadvantages of each identified extraction method. At the same time, the study also presentes alternative chromium extraction methods based on the combination of conventional extraction methods and ultrasound-assisted extraction.

Nowadays, the fingerprinting methodologies of olive oils are dominated. They consider the entire analytical signal, which is acquired and recorded by the analytical instrument, directly from olive oil or isoleted fraction, i,e chromatogram. The shape and intensity of the recorded signal the instrumental fingerprint from the whole olive oil adulteration. Therefore, the methodolygy is based on the chemical composition (Fatty acids and Triglycerides compositions). However, Fatty acids composition as an indicator of purity suggests that linolenic acid content could be used as a parameter for the detection of extra virgin olive oil fraud with 5% of soybean oil. The adulteration could also be detected by the increase of the trans-fatty acid contents with 3% of soybean oil, 2% of corn oil and 4% of sunflower oil. The use of the ∆ECN42 proved to be effective in the Chemlali extra-virgin olive oil adulteration even at low levels: 1% of sunflower oil, 3% of soybean oil and 3% of corn oil. Therefore, compared to classical methods PCA and new approach of using LDA application could represent an alternative and innovative tool for faster and cheaper evaluation of extra-virgin olive oil adulteration.

Pollution spillover is an important issue to improve the water environment of transboundary rivers, which has been aggravated by the decentralization of China's pollution control and promotion system.This paper analyzes the evolution of the pollution reduction mandates and the possible change of water environment in China which are tested with the water quality data of state key monitoring sections in 2004-2014 .In terms of research methods, this paper mainly uses Propensity Score Matching reference with group difference test and OLS. Empirical findings support the association between decentralization and pollution levels. The pollution levels of the monitoring points located at the boundary are significantly higher than that of interior counties. The pollution of tributary is more serious than trunk stream，which quickly reversed after the system changed. Water pollution levels rapidly changes when we compare the monitoring site in front of jurisdictional boundaries with that after the jurisdictional boundaries. We draw the following conclusions that local goverments may manipulate pollution within their jurisdictions and total pollutant control system will exacerbate border pollution, while water quality inspection can reduce marginal pollution.

Infill drilling is one of the most effective methods to improve the performance of poly-mer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved archimedes optimization algorithm with Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize the infill well placement for polymer flooding, an objective function that considered the economic influence of infill drilling was developed. Then, the novel optimization algorithm (HS-AOA) for infill well placement was developed by combining the AOA with the Halton se-quence. The codes were developed in MATLAB and connected to a commercial reservoir simula-tor CMG STARS to carry out the infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into AOA effectively increased the diversity of the initial ob-jects in the AOA and avoided the HS-AOA trapping into the local optimal solutions. The HS–AOA outperformed the AOA. It was an effective approach to optimize the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically im-prove the polymer flooding performance in offshore oil reservoirs. The NPV of the polymer flooding case with infill wells determined by HS-AOA reached 3.5 × 108 $, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for opti-mizing infill well placement for polymer flooding processes. It also can serve as a valuable refer-ence for other optimization problems in the petroleum industry, such as joint optimization of well control and placement.

High level of noise is a disturbance to the human environment. Noise in industries is also an occupational hazard because of its attendant effects on workers’ health. Noise presents health and social problems in industrial operations, and the source is related to the machineries used in the industries. One of the unique features of the noise associated with wood machinery is the level of exposure and duration. Equipment used in a factory can be extremely loud. They can produce noise at decibels high enough to cause environmental health and safety concerns. The mechanically driven transport and handling equipment, cutting, milling, shaping and dust extractor installations in the wood industry generate noise. The sources of noise pollution have increased due to non-compliance with basic safety practices. The increased use of locally fabricated machine in the industry has increased the level of noise and vibration. The effects of industrial noise pollution as discussed include: increase in blood pressure; increased stress; fatigue; vertigo; headaches; sleep disturbance; annoyance; speech problems; dysgraphia, which means reading/learning impairment; aggression; anxiety and withdrawal. As presented in this paper, noise control techniques include; sound insulation, sound absorption, vibration damping and Vibration isolation.

In Tunisia, olive is the most cultivated fruit crop while in the north where rainfall exceeded 400mm.year-1, the olive growing area is in widely extension in marginal soil with high slope gradient. Thus, the inclusion of cover crops is a sustainable solution to increase the ecosystem productivity, improve the soil fertility and oil yields. But, in Tunisia a few study were investigated the effect of legumes cover crop on the olive productivity. Therefore, the aim of this study was to evaluate the short-term (two cropping seasons s 2021 and 2022) effects of different seeded cover crops and soil management practices on the cover crops and soil characteristics, as well as enzymes activities and related microbial diversity. Six intercropping were tested in rainfed olive orchards (cv.Chétoui): Spontaneous vegetation (Spont), Wheat, Vetch, Oat, Fenugreek, mixture Vetch-Oat and compared to control (3 periodic tillage/year without intercropping). During the first cropping season, cover crops were cut as animal feed and only residues were incorporated into the soil. While, during the second year, all cover crops biomass were incorporated into the soil. Results indicate that the dry biomass and carbon uptake were significantly higher in grasses species (Wheat and Oat). But, highest level of nitrogen was found in Fenugreek cover crops. As compared to control, all cover crops including spontaneous vegetation increased soil organic matter and macronutrient level, mainly phosphorus. The most activity of β-glucosidase, phosphatase and arysulphatase was found in the cover crops plot of wheat and oat. In addition, this study improve that the utilization of the carbon sources was dominated by amides, amine and amino-acids in the fenugreek plot; while it was dominated by polymers and carboxylic acids in the case of wheat and oat.

This article deals with special curves similar to those known in mathematics as Lamé curves. Their equations have a certain similarity with the equations of Lamé curves. But these curves are more like ellipses in appearance. Like ellipses, they have major and minor axes. These are such curves that their major and minor semi-axis coincide with an ellipse but are different than an ellipse. The main feature of these curves is that for any triangle whose base coincides with the axis of a given curve and the vertex is on a given curve, the condition aⁿ+bⁿ=cⁿ is obtained. The number “n” is called the degree or rank of this curve. To distinguish this group of curves, we called them Elbe curves. The equations of these lines are also provided in the paper.

Endocrine disruptors are molecules that can interfere with the proper functioning of the endocrine system and lead to harmful effects in living organisms. This review focuses on the impact of synthetic fragrances, which are commonly found in personal care and household products, on the endocrine system. The article discusses the different types of hormones in the body and how they interact with receptors to produce signals. It also explores how endocrine disruptors can interfere with hormone signaling and transport, leading to adverse effects in the body. This work underscores the crucial need for further research into the impact of synthetic fragrances on the endocrine system and the importance of using safer alternatives in personal care and household products.

Improper disposal of waste plastic has caused serious ecological and environmental pollution problems. Transforming plastics into high value-added chemicals can not only achieve efficient recycling of waste plastics, but also an effective way to control white pollution. The catalyst selectively breaks the C-C bond of polyolefin plastic under heat treatment and converts it into liquid fuel, thus realizing sustainable recycling of plastics and has a good development prospect. This review provides a detailed overview of the current development of catalytic pyrolysis, catalytic hydrolysis, solvent decomposition and supercritical hydrothermal liquefaction for cracking plastics to make fuel oil. The reaction mechanism, influencing factors and promoting effects of catalysts in various degradation technologies are analyzed and summarized and the latest proposed tandem reaction for degrading plastics is briefly introduced. Finally, some optimization paths of waste plastic pyrolysis to fuel oil technology are proposed: synergies between mixed raw materials, in-depth exploration of catalysts, design and manufacture of reactors that match the pyrolysis technology. All these are important research directions for promoting the industrialization of plastic pyrolysis to fuel oil.

Waste cooking oil (WCO) recycled asphalt is facing issues regarding insufficient thermal oxidation stability and aging resistance. In this research, glycerol esterification was adopted to pretreat WCO, and the consequences of this treatment on the aging resistance and thermal stability of WCO were analyzed. The impacts of varying levels of esterification of WCO on the high-temperature, low-temperature performances, fatigue properties, as well as aging resistance of recycled asphalt were investigated. Furthermore, the mechanisms of regeneration and anti-aging of deeply esterified WCO recycled asphalt were revealed by Fourier transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) tests. The results indicated that variations in the physical properties of WCO during the aging process were reduced, and its aging resistance was improved following glycerol esterification therapy. The initial thermal decomposition temperature was increased by approximately 115°C, which resulted in the enhancement of thermal stability significantly. Recycled asphalt obtained from deeply esterified WCO exhibited superior high-temperature, low-temperature performances and fatigue properties. Moreover, the thermal oxidation stability and aging resistance of recycled asphalt with deep-esterified WCO could be promoted by reducing the oxidation and volatilization of light components during the aging process.

The existing methods of oil palm Fresh Fruit Bunch (FFB) evacuation from in-field to temporary collection point which typically apply medium to large tractors, often result in unexpected soil compaction. Meanwhile, smaller size equipment indicates low working capacity and unable to perform direct unloading to collection bin. This paper addresses the challenge of FFB evacuation by focusing on preventing soil compaction, increasing working capacity and enabling direct unloading to collection bin. For this purpose, a 6x6WD transporter prototype with a scissor lift mechanism was developed. With the design, the transporter could evacuate FFB in both steep mineral terrain and low ground pressure peatland, able to carry FFB with capacity of 750 kg and provide direct unloading into collection bin. The average field working capacity of the transporter was found to be 15 tons day-1. This research offers insights for future designs of medium-sized FFB transporters with advanced unloading mechanisms.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

Global wastes accumulate at an accelerating rate especially food and slaughterhouse waste constituting a major proportion owing to inefficient management. The high protein concentration of fish waste and keratinaceous byproducts is hazardous, harbouring pathogens, and emitting toxins. Transforming proteinaceous waste into amino acids (AAs) reduces waste bulk and offers a smarter nutrient approach for agriculture. However, this resource remains underutilized triggering eco-system pollution, diseases spreading, and climate change. Currently, small quantities of proteinaceous waste are valorized as animal feed, fertilizer, and in medical, and biotechnological sectors, which cannot solve bulk waste accumulation further, natural AA sources in agriculture are less recognized meanwhile researchers focus on synthetic AAs. Introducing various plant-nutrient formulas enriching fish AA with keratinaceous waste hydrolysate has not been broadly discussed. With this novel approach, using proteinaceous waste resources in agriculture would solve environmental pollution while minimizing land degradation by synthetic fertilizer. In this review, we illustrate the potential and benefits of valorizing proteinaceous waste in bulk through bioconversion. More importantly, we spotlight the biocompatibility of natural AAs and propose an enrichment, to use the ultimate product as catalysts for crops. Consequently, this approach reduces hazardous waste while paving a paradigm change in agriculture.

The utilization of natural waxes to form oleogels has emerged as a new and efficient technique for structuring liquid edible oil into solid-like structures for diverse food applications. The objective of this study was to investigate the interaction between sunflower wax (SW) and monoglycerides (MGs) in olive oil oleogels and to assess their physical characteristics and storage stability. To achieve this, pure SW and a combination of SW with MGs in a 1:1 ratio were examined within a total concentration range of 6-12% w/w. The formed oleogels were characterized based on their microstructure, melting and crystallization properties, textural characteristics, and oxidative stability during storage. All the oleogels were shelf-stable, and as the concentration increased, the hardness of the oleogels also increased. The crystals of SW oleogels were long needle-like, while the combination of SW and MGs led to the formation of crystal aggregates and rosette-like crystals. The differential scanning calorimetry and FTIR showed that the addition of MGs led to different crystal structures. The oxidation results revealed that oleogels had low peroxide and TBARS values throughout the 28-day storage. These results provide useful insights about the utilization of SW and MGs oleogels for potential applications in the food industry.

Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection on images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70 % and 86.5 % with inference times of around 89 ms for an input size of 1280×1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.

Keywords: contamination; bioaccumulation; fish and mollusks tissue; ecosystem components; heavy metals; organochlorine pesticides; polychlorinated biphenyls; polycyclic aromatic hydrocarbons.

Ammonium sulfate has been utilized in agriculture; however, there is a dearth of research on its application in maize cultivation subsequent to the implementation of nitrification inhibitors or coating. This study aims to analyze the impacts of various combinations of ammonium sulfate fertilizers on soil nutrients, plant nutrient uptake, yield, and fertilizer utilization efficiency in maize cultivation to ascertain the optimal and stabilized disposal method for ammonium sulfate. A completely randomized design was employed with five treatments (AU, the control using urea; AS, treatment using ammonium sulfate; ASN, treatment using ammonium sulfate with a nitrification inhibitor; ASG, treatment using oil-coated ammonium sulfate; ASD, treatment using oil-humic acid-coated ammonium sulfate). Results showed that: (1) Compared with AU and AS, ASN, ASG and ASD decreased the leaching rates of TN, NH4+-N and NO3-N to the 10-20 cm soil layer, and more residual N was obtained in 0-10 cm soil layer. The first-order kinetic equation Nt=N0(1-e-kt) can better fit the process of nitrogen accumulation and release, and the N release rate constant is AU>CK>AS>ASH>ASN>ASD; (2) Compared with AU and AS treatments, the plant dry weight, grain dry weight, spike width, spike length, and yields of maize increased by 8.85-11.08%, 12.98-14.15%, 2.95-3.52%, 5.50-5.65%, and 43.21-51.10% under ASG treatment respectively. The impact of effective spike number on maize yield was found to be significant, as demonstrated by the path analysis conducted in this study. Furthermore, the accumulation levels of nitrogen, phosphorus, and potassium within above-ground plants significantly increased under the ASG treatment compared to the AU and AS treatments. The partial productivity of nitrogen, phosphorus, and potassium increased by 1.43-1.51-fold under ASG treatment, while grain nitrogen balance, grain phosphorus balance, and grain potassium balance increased by 1.41-1.58-fold, 1.51-1.95-fold, and 1.15-l.96-fold respectively. The oil-coated ammonium sulfate, therefore, exhibited the optimal slow-release effect of nutrients, thereby achieving superior performance in enhancing maize production and efficiency.

Solvent free copolymerization of epoxides derived from fatty acid esters of waste cooking oil with phthalic anhydride using (salen)CrIII Cl as catalyst and n-Bu4NCl/DMAP as co-catalyst was carried out for the first time under microwave irradiation, where reaction time was reduced from number of hours to minutes. The polyesters were obtained with molecular weight (Mw = 3084-6740 g/mol) and dispersity values (D = 1.18-1.92), when (salen)CrIII Cl/n-Bu4NCl was used as catalysts. While in case of DMAP as a co-catalyst, polyesters with improved molecular weight (Mw = 5537-6925 g/mol) and narrow dispersity values (D = 1.07-1.28) were obtained even at reduced concentrations of (salen)CrIII Cl and DMAP. The obtained products were characterized and evaluated by attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR), proton nuclear magnetic resonance (1H-NMR) spectroscopy, gel permeation chromatography (GPC) and thermogravimetric analysis (TGA) Techniques.

Anomaly detection in equipment processes plays an important role in the oil and gas sector. Algorithms for detecting anomalies in measured data are best understood in computer science and mathematics. Therefore, a possible transfer of knowledge from the latter knowledge area to the former can play a significant role. This paper addresses such a task by analyzing bibliometric data of Computer Science and Mathematics papers published in MDPI journals and publications found on the SPE search platform. It is shown that the main algorithms both extensively studied in MDPI publications and found in SPE publications and reflecting the anomaly detection problem are Random Forest, Support Vector Machine, Long-term Memory Method and Recurrent Neural Network. The main advantages and disadvantages of these methods are briefly described. Examples of classical, highly cited publications describing the work of these algorithms are given. Examples of papers describing their application in the oil and gas industry are given. The sections of SPE disciplines with the largest number of publications using the above algorithms that are frequently used for anomaly detection are presented.

This study proposes and validates a novel combustion control system for Oil-Fired Boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs, and applies Internal Model Control (IMC)-based Proportional-Integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC parameter tuning through experimentation is essential for achieving effective control performance. Experiments conducted on a 3000 kg/h marine oil-fired boiler in actual operation optimized the controller parameters for stability and responsiveness, and validated their effectiveness. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Background: Ice formation on power transmission lines presents significant risks, including structural damage and power outages. Traditional methods for detecting ice on these lines have limitations, particularly in challenging environmental conditions. This study aims to enhance ice detection accuracy by introducing a novel image segmentation and edge detection methodology using improved multi-scale Retinex and advanced morphological operations. Method: The proposed method integrates multi-stage image processing techniques, including image enhancement, grayscale conversion, thresholding, segmentation, object isolation, and edge detection. High-resolution binocular cameras were used to capture images of ice formations on transmission lines. The effectiveness of the method was assessed through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methodologies. Results: The proposed method demonstrated superior performance in both ice detection and thickness measurement. The method effectively identified and isolated ice formations under various conditions with an average accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03%. Additionally, the analysis of ice thickness measurements further highlighted the method’s accuracy, with a notably low root mean squared error (RMSE) of 1.20 mm and mean absolute error (MAE) of 1.10 mm, along with a strong R-squared value of 0.95. These statistical metrics show that the proposed method not only outperforms previous methods in consistency and alignment with manual measurements but also stands out as the most reliable option for tasks requiring precise measurements, offering significant advancements in accuracy and reliability over existing methodologies. Conclusions: The enhanced image segmentation and edge detection approach provides a more accurate and reliable method for monitoring ice formations on power transmission lines.

Comprehensive studies into the various causes of this ubiquitous disease have been prompted by the worldwide increase in cancer incidence. There are many potential causes of cancer, but environmental factors are of particular concern now. This abstract explores the complex link between environmental factors and the growing cancer epidemic, shedding light on the role that our immediate environment plays in the emergence of cancers. The environment can contain carcinogenic agents that gradually influence the course of cancer, including air pollutants, water contaminants, workplace exposures, and industrial activities. The ways through which environmental carcinogens may cause DNA damage and unchecked cell proliferation are discussed in this abstract. Environmental justice and the moral necessity to reduce inequities in exposure and health effects are highlighted to underline the disproportionate impact on vulnerable groups. The study presented here highlights the need for preemptive action to reduce environmental risks. Society can reduce the hidden threat posed by environmental carcinogens through strict legislation, sustainable practices, and community-driven initiatives. In order to lessen the cancer burden and secure a healthier future for all, the authors of this abstract argue that there must be a concerted effort to create surroundings that promote health and well-being.

Traffic congestion is one of the foremost problems confronted by the urban and suburban tenants of today. Traffic congestion increases vehicle emissions and degrades air quality. Urban planners and policy makers have consequently been always investigating choices to alleviate traffic congestion and to enhance air quality. Teleworking is one option that has received significant consideration and has been studied in the recent past. The aim of the study is to explore the relationship between teleworking, air quality and traffic in Switzerland. The analysis relies on panel individual and household level data over the period 2002-2013. We examine five main air pollutants; the sulphur dioxide (SO₂), the ground-level ozone (O₃) the nitrogen dioxide (NO₂), the carbon monoxide (CO) and the particulate matter less than 10 microns (PM₁₀). Based on the fixed effects estimates, teleworking reduces traffic volume by 1.9 per cent. Furthermore, the reduction observed on air pollution is higher for NO₂, CO and PM₁₀ ranging between 3.3-3.7 per cent, followed by O₃ at 2.3 per cent and SO₂ at 2.1 per cent. According to instrumental variable (IV) approach and the two stage least squares (2SLS) method, the effect is higher ranging between 2.6-4.1 per cent. The respective reduction on traffic becomes 2.7 per cent. Overall, the main concluding remark of the study is that teleworking can be a promising tool for urban planning and development, focusing at the traffic volume reduction, and the air quality improvement. Additional policy implications of teleworking and its beneficial effects for the society are further discussed.

In recent times, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks which include Denial-of-Service (DoS), Distributed-Denial-of-Service (DDoS), Man-in-the-Middle (MitM) attacks, and this may have been caused majorly by the integration of open network to Operation Technology (OT) as a result of low-cost network expansion. The connection of the OT to the internet for firmware updates, third-party support, or vendor interventions, has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations and proposes the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network so as to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in the simulation of process control. The outcome of the experiments shows the reliability and functionality of the different machine-learning algorithms in detecting these anomalies with significant precise attack detections identified using a coarse tree algorithm.

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban-rural transition affects PM2.5 are warranted within the framework of urban-rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban-rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban-rural transformation and PM2.5. The results are as follows: (1) Over time, there was a discernible upward tendency in the change in urban-rural areas. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban-rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM2.5 showed an inverted “N-shaped” curve of negative-positive-negative changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM2.5. Currently, an inverted "U-shaped" curve forms the left side of the impact of population transition (PT) on PM2.5. Land development (LT) has a "U-shaped" curve for its effect on PM2.5. This research provides a new perspective on the topic of PM2.5 and its connection to urban-rural integration, which is crucial to understanding the dynamics of this shift. To achieve its goal of high-quality development, it supports regional initiatives to reduce PM2.5 emissions in the Yellow River Basin. Moreover, it can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution.

The growing expansion of the economy and population has resulted in an increased inclination towards the utilization of groundwater resources. Conducting a geophysical survey is a widely employed method for subsurface mapping and the detection of groundwater. A geophysical study was conducted in Nanshan township, Huarong County, located in the Hunan province of the South-Central region of China. The investigation involved the utilization of a 2D Electrical Resistivity Imaging technique employing forward and inverse pole-dipole electrode array. A total of six survey lines were established, each with an electrode distance from the nearest measuring point to exceeding 800 m. The maximum current electrode separation was utilized in this setup. The spacing between the electrical resistivity sounding points was established at regular intervals. The findings from the exploration indicate the existence of multiple faults within the surveyed region. The study additionally identified two regions of structural failure, which occurred due to the convergence of faults oriented in different directions. This convergence led to the fracturing of rocks, an increase in water content, and a decrease in resistivity. The findings from the exploration were utilized in the formulation of five verification target boreholes. The results of this study offer significant insights that can inform future investigations into groundwater exploration endeavors within the region.

In the realm of power delivery to end-users, transformers are indispensable, with their malfunctions leading to substantial economic, safety, and environmental repercussions. The need for persistent surveillance is accentuated for in-situ oil-filled transformers, given the potential degradation of oil and the emergence of related ageing by-products. As the focus tilts towards online detection methodologies for transformer oil ageing, bypassing challenges associated with traditional offline methods such as sample contamination and misinterpretation, fiber optic sensors are gaining trac-tion due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances typical in high-voltage environments. This work delves into the sensitivity analysis of intensi-ty-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Findings underscore that elongating the sensing length detrimentally affects both the linearity response and repeatability, largely attributed to the diminished resistance to external noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity.

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.

Heat transfer fluids are used in various industrial systems to maintain them in perfect operating conditions. Extensive research efforts have been dedicated to enhance the thermal properties of these heat transfer fluids to improve their efficiency. Developing nanofluids is a potential candidate for such enhancements. This study investigates the impact of incorporating TiO2 nanoparticles into two types of oils: transformer oil (NYTRO LIBRA) and virgin coconut oil (manufactured by Govi Aruna Pvt. Ltd.) at different temperatures and with varying volume fractions. The nanofluids were prepared using a two-step method by adding CTAB (Cetyltrimethylammonium bromide) surfactant. To minimize nanoparticle agglomeration, this study employed relatively low-volume fractions. Thermal properties by means of thermal conductivity, thermal diffusivity, and volumetric heat capacity were measured in accordance with ASTM (American Society for Testing and Materials) standard methods, using a multifunctional thermal conductivity meter (LAMBDA thermal conductivity meter). The measured thermal conductivity values were compared with theoretical models and previous research findings. It was confirmed that the modification of thermal properties was enhanced by doping TiO2 nanoparticles with different volume fraction.

Regular regimes of oil and oil products pipelines are studied. The problem is to develop an algorithm of effective pipeline system control that ensures the fulfillment of specified boundary conditions at the system boundaries. The product is pumped in batches, the flow is characterized by a change in temperature and pressure flow parameters over time. The developed solution method uses the principle of decomposition in the sense that each computational iteration is im-plemented in 3 stages, in three nested loops. The inner cycle is connected with the material bal-ance relations, at the next cycle the flow equations are introduced and the fulfillment of the 2nd Kirchhoff's law is monitored. If the flow process is significantly affected by temperature, then an external cycle is also implemented, correcting the solution so that the energy conservation equa-tion is satisfied. In the calculation procedure, some computational difficulties may arise, which consist in choosing an appropriate optimization method. On a specific example, the possibility of using various library programs to solve the problem was checked. The developed method is suita-ble for the analysis of systems of arbitrary topology. Since the method is not time-consuming, it can be recommended for online control of oil and oil products supply systems. The paper opens a new direction in the theory of hydraulic circuits. It uses both traditional methods of continuum mechanics and stochastic models.

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Preventing the rebound of black and odorous water bodies is critical for improving the ecological environment of water bodies. This study investigated the effect and mechanism of in-situ improvement of sediment microenvironment by nitrate in tailwater of wastewater treatment plants combined with aerobic denitrifying bacteria under low-DO regulation (TailN+CFM+LDO). On the 60th day of remediation, the dissolved oxygen and oxidation-reduction potential in the overlying water increased to 5.6 mg/L and 300 mV, respectively, the acid volatile sulfide in the sediment decreased by 70.4%, and the organic matter in the sediment was reduced by 62.7%, in which the HFOM was degraded from 105 g/kg to 56 g/kg, and the potential risk of water reverting to black and odorous conditions significantly decreased. Amplicon sequencing analysis revealed that the relative abundance of electroactive bacteria Thiobacillus and Pseudomonas with denitrification capacity was found to be significantly higher in the TailN+CFM+LDO group than in the other remediation groups. Functional prediction of 16S sequencing results indicated that both the quantity and activity of critical microbial enzymes involved in nitrification and denitrification processes could enhance in the TailN+CFM+LDO group. These results improved our understanding of the improvement of the sediment microenvironment and could thus facilitate its application.

The focus of this work lies in the construction of monitoring system designed in the form of a patch using a long-lasting potentiometric electrolyte concentration sensor from metal electrodes, in a galvanic cell configuration Mg(s)|NaCl(aq)|Cu(s). Initially, a mathematical analysis was conducted on the expected sensor output through the Nernst equation. The experimental phase involved conducting controlled tests in the laboratory using various molar concentrations of sodium chloride (NaCl) in aqueous solution. Results showed that the electrolyte sensor allows for monitoring electrolyte levels in NaCl in the range of 0.02 to 1 M/L, with electrode lifespan exceeding 10 days. On the other hand, when patch is using human sweat during controlled exercise routines, it showed the sensor's capability for monitoring changes in the subjects' hydration levels in real time. The sensor output potential is greater than 1.7 volts when the sensor electrodes are in contact with human sweat, which is equivalent to measuring molar concentrations at normal hydration and moderately high hydration levels. Sensor results indicate that the system can function in a self-powered manner without external batteries. Additionally, the extended lifespan of its electrodes allows for continuous monitoring of imbalances in the human body, showcasing the system capability for long-term health assessment.

The antimicrobial resistance to clinically approved antibiotics is rapidly increasing worldwide. Staphylococcus aureus is a critical contributor to deaths associated with antibiotic resistance. In this regard, the search for natural compounds is quite active, that alone or in combination with other agents can be effective as new antibacterial agents. The antibacterial activity of essential oils has presented an increasing interest during the last years and was effective even on multidrug-resistant strains. The present study aims to investigate the interaction effects of oregano essential oil (OEO) with some conventional antibiotics against Staphylococcus aureus tested by agar methods. For this purpose, we selected S. aureus ATCC 29213 and five clinical isolates representative of the species. The essential oil was analyzed using gas chromatography coupled with mass spectrometry (GC-MS). The analysis of the Oregano essential oil revealed the main component to be carvacrol (81,20%). Concentration 2.5% (v/v) OEO was combined with drugs proposed by EUCAST, 2024, specifically penicillin, cefoxitin, erythromycin, gentamycin and tetracycline. The research results showed that the combination of OEO with penicillin, cefoxitin, erythromycin, and tetracycline enhanced the antimicrobial activity of the antibiotics against S. aureus in almost all cases. The area of suppression remained identical (but not smaller) in the unaffected cases. When comparing the effects of the GEN-OEO combination and GEN against most S. aureus strains, a decrease in antimicrobial activity was reported by the diffusion agar method.

Sesame seeds and their edible oil are highly nutritious and rich in mono- and polyunsaturated fatty acids. Bioactive compounds such as sterols, tocopherols, and sesamol provide significant medicinal benefits. The high oil content (50%) and favorable mono-and polyunsaturated fatty acids balance, as well as sesame's resilience to water stress makes, make it a promising candidate for global agricultural expansion. However, sesame production faces challenges such as low yields, poor response to agricultural inputs, and losses due to capsule dehiscence. To enhance yield, traits like determinate growth, dwarfism, high harvest index, non-shattering capsules, and photoperiod sensitivity are needed. These traits can be achieved through variation or induced mutation breeding. Traditional breeding methods often result in unwanted genetic changes. The CRISPR/Cas9 technology has the potential to suppress detrimental alleles and improve the fatty acid profile by inhibiting polyunsaturated fatty acid biosynthesis. Even though sesame is an orphan crop, it has entered the genomic era, with available sequences assisting molecular breeding efforts. This progress aids in associating single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) with key economic traits, as well as identifying genes related to adaptability, oil production, fatty acid synthesis, and photosynthesis. Additionally, transcriptomic studies can reveal genes involved in abiotic stress responses and adaptation to diverse climates. Quantitative trait loci (QTL) mapping can identify loci linked to crucial traits such as capsule size, seed count per capsule, and capsule number per plant. This paper reviews recent advances in sesame breeding, discusses ongoing challenges, and explores potential strategies for future improvement. Hence, integrating advanced genomic tools and breeding strategies provides promising ways to enhance sesame production to meet global demands.

Erigeron floribundus (Asteraceae) is an herbaceous plant widely used in the Cameroonian traditional medicine to treat various diseases of microbial and non-microbial origin. In the present study we evaluated the in vitro biological activities displayed by the essential oil obtained from the aerial parts of E. floribundus, namely the antioxidant, antimicrobial and antiproliferative activities. Moreover, we investigated the inhibitory effects of E. floribundus essential oil on nicotinate mononucleotide adenylyltransferase (NadD), a promising new target for developing novel antibiotics, and Trypanosoma brucei, the protozoan parasite responsible for Human African trypanosomiasis. The essential oil composition was dominated by spathulenol (12.2%), caryophyllene oxide (12.4%) and limonene (8.8%). The E. floribundus oil showed a good activity against Staphylococcus aureus (IZD of 14 mm, MIC of 512 µg/mL). Interestingly, it inhibited the NadD enzyme from S. aureus (IC50 of 98 µg/mL), with no effects on mammalian orthologue enzymes. In addition, T. brucei proliferation was inhibited with IC50 values of 33.5 µg/mL with the essential oil and 5.6 µg/mL with the active component limonene. The essential oil exhibited strong cytotoxicity on HCT 116 colon carcinoma cells with an IC50 value of 14.89 µg/mL, and remarkable ferric reducing antioxidant power (TEAC= 411.9 μmol TE/g).

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

Detecting volatile organic compounds (VOCs) emitted from different plant species and their or-gans can provide valuable information about plant health and environmental factors that affect them. For example, limonene emission can be a biomarker to monitor plant health and detect stress. Traditional methods for VOC detection encounter challenges, prompting the proposal of novel approaches. In this study, we proposed integrating electrospinning, molecular imprinting, and conductive nanofibers to fabricate limonene sensors. In detail, polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) served here as fiber and cavity formers, respectively, with multi-walled carbon nanotubes (MWCNT) enhancing conductivity. We developed one-step monolithic molecularly imprinted fibers, where S(-)-limonene was the target molecule using electrospinning technique. The functional cavities were fixed using UV curing method, followed by a target mol-ecule washing. This procedure enabled the creation of recognition sites for limonene within the nanofiber matrix, enhancing sensor performance and streamlining manufacturing. Humidity was crucial for sensor working, with optimal conditions at about 50% RH. The sensors rapidly re-sponded to S(-)-limonene, reaching a plateau within 200 seconds. Enhancing fiber density im-proved sensor performance, resulting in a lower limit of detection (LOD) of 137 ppb. However, excessive fiber density decreased accessibility to active sites, thus reducing sensitivity. Remarka-bly, the thinnest mat on the fibrous sensors created provided the highest selectivity to limonene (Selectivity Index: 72%) compared to other VOCs, such as EtOH (used as a solvent in nanofiber development), aromatic compounds (toluene), and two other monoterpenes (α-pinene and linalo-ol) with similar structure. These findings underscored the potential of the proposed integrated approach for selective VOC detection in applications such as precision agriculture and environ-mental monitoring.

Qatar and other Gulf States have a diverse range of marine vegetation that is adapted to the stressful environmental conditions of seawater. The industrial wastewater produced by oil and gas activities adds further detrimental conditions for marine aquatic photosynthetic organ-isms on the Qatari coastlines. Thus, these organisms experience severe stress from both seawater and industrial wastewater. This review discusses the biodiversity in seawater around Qatar, as well as remediation methods and metabolic pathways to reduce the negative impacts of heavy metals and petroleum hydrocarbons produced during these activities. These methods include phyto-stabilization phyto-accumulation, phyto-volatilization, and phyto-degradation. Phy-to-degradation in particular is discussed in detail, including metabolic pathways such as alkane and aromatic-compound degradation pathways. The role of microorganisms that are adjacent to or associated with these aquatic marine organisms is discussed, including rhizosphere biodeg-radation. In this type of biodegradation, exudates are released by plant roots and supply nutrients to microorganisms in the rhizosphere, which enhances their ability to degrade organic pollutants. Special attention is paid to mangrove forests and their roles in remediating shores polluted by industrial wastewater. Seagrasses (Halodule uninervis, Halophila ovalis, and Thalassia hemprichii) and seaweeds (Chlorophyta, Phaeophyta, and Rhodophyta) around the peninsula of Qatar are also discussed.

One of the most concerning global environmental issues is the pollution of agricultural soils by heavy metals (HMs), especially cadmium (Cd), which not only affects human health by taking Cd-containing foods but also impacts the quality of rice. The soil's nitrification and denitrification processes, coupled with the release of volatile organic compounds by plants, raise substantial concerns. The understanding of three aspects of paddy soil under conditions of heavy-metal pollution and water management is currently limited. In this review, we summarized recent literature related to the deleterious effects of Cd on both soil processes related to the N cycle and rice quality, particularly aroma, in different water management practices. Under both continuous flooding (CF) and alternate wetting and drying (AWD) conditions, cadmium (Cd) has been observed to reduce both nitrification and denitrification processes. The adverse effects are more pronounced in continuous flooding (CF) compared to alternate wetting and drying (AWD). Similarly, the alteration in rice aroma is more significant in AWD than in CF. The precise modulation of volatile organic compounds (VOCs) by Cd, remains unclear based on the available literature. Nevertheless, HM accumulation is higher in AWD conditions compared to CF, leading to a detrimental impact on volatile organic compounds (VOCs). The literature concluded that AWD practices should be avoided in Cd contaminated fields to decrease accumulation and maintain the quality of rice. In the future, rhizospheric engineering and plant biotechnology can be used to decrease the transport of HMs from the soil to the plant’s edible parts.

Air pollution is a man-made environmental-related problem and has importance globally among all environmental issues. It is described as an atmospheric situation in which several components appear at high attention above their ambient level and generate a quantifiable effect on vegetation, animals, and people. Air pollution and its control are a universal problem and have become an issue of great trouble for us in current years. However, monitoring and measurement of the concentration of air pollutants are most difficult in assessment to monitoring other components in the environment. Air pollutants are transported to polar regions by different mechanisms such as long-range atmospheric transport or grasshopper effects and via anthropogenic activities. After reaching the polar regions, they settle down and remain as such for a long-time due to their persistent nature. They can distress the environment and wildlife of the polar regions. Generally, dangerous chemicals may cause a risk of well-being effects, whereas lethal pollutants focus on one physiological reaction. We reviewed the available information on air pollution (occurrence, sources, and harmful effects) in the polar regions.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.