The study examines the survival status of Scapharca broughtonii and Scallop Chlamys farreri under diverse seasons, sizes, and mixed breeding ratios. The objective is to explore the feasibility and economic benefits of the mixed breeding patterns of different shellfish varieties. The findings demonstrate that the overwintering mortality rate of mixed-cultured S.broughtonii ranges from 12.04% to 16.85%, which is conspicuously lower than that of individually cultured S.broughtonii. The summer mortality rate of mixed-cultured S.broughtonii with a size of 2.5 - 4.0 cm is significantly lower than that of individually cultured S.broughtonii (P < 0.05). There is no notable difference in the mortality between monoculture and polyculture of C.farreri. There is no significant disparity in the growth of C.farreri and S.broughtonii in monoculture and polyculture. The optimal polyculture size of the two scallops is 2.5 cm, and the ideal polyculture ratio is 1:1. The co-culture model of S.broughtonii and C.farreri in suspension cages has a high input-output ratio (1:1.9). It can effectively lower the winter and summer mortality rates of ark clams, representing an efficacious new methodology for marine shellfish farming.

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) has changed the paradigm associated with the production of Geospatial Information (GI), since due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is an issue that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in literature to studies that analyse the completeness of OSM watercourses. This study presents an analysis of the variation over time in completeness/coverage of the OSM "waterway" feature in the epochs of 2014 and 2023 in a mountainous region included in the Mondego River basin, located in Inland of Portugal. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of this period (nine years) and the fact that, nowadays, digital mobile devices enable access to satellite images, therefore allowing in an easy way the digitalization of entities or objects of the real world remotely. Regarding the indicator’s altitude, slope and location/proximity of settlements, it was not possible to observe their influence on the evolution of the completeness of this geographic entity (OSM waterway).

Abstract: Kiwifruit, a perennial horticultural crop known for its nutritional value, faces significant challenges due to biotic and abiotic stresses, affecting its yield and quality. Leucine-rich repeat receptor-like proteins (LRR-RLPs), a major group of receptor-like proteins in plants, have diverse functions in plant physiology, including growth, development, signal transduction, and stress responses. Despite their importance, the specific roles of kiwifruit LRR-RLPs in response to biotic and abiotic stresses remain poorly understood. In this study, we investigated the expression patterns of kiwifruit LRR-RLPs under various stress conditions, including pathogen stress, heat stress, waterlogging stress, and copper stress. Through genome and transcriptome analyses, we identified a total of 101, 164, and 105 LRR-RLPs in Actinidia Chinensis 'Hongyang', Actinidia eriantha 'Huate', and Actinidia chinensis 'Red5' kiwifruit, respectively, classified into 9 subgroups based on phylogenetic relationships. Synteny analysis revealed that the expansion of kiwifruit LRR-RLPs was primarily attributed to segmental duplication events. Using RNA-seq data from pathogen-infected kiwifruits, we identified specific LRR-RLP genes potentially involved in different stages of pathogen infection. Additionally, we observed the potential involvement of kiwifruit LRR-RLPs in abiotic stress responses, with upstream transcription factors possibly regulating their expression. Furthermore, protein interaction network analysis unveiled the participation of kiwifruit LRR-RLP genes in the regulatory network of abiotic stress responses. These findings highlight the crucial roles of LRR-RLPs in mediating both biotic and abiotic stress responses in kiwifruit, offering valuable insights for the breeding of stress-resistant kiwifruit varieties.

The Ylistrum japonicum is a commercially valuable scallop known for its long-distance swimming abilities. Despite its economic importance, genetic and genomic research on this species is limited. This study presents the first complete mitochondrial genome of Y. japonicum. The mitochondrial genome is 19,475 bp long and encompasses 13 protein-coding genes, 3 ribosomal RNA genes, and 23 transfer RNA genes. By selecting 15 Pectinidae species and additional outgroup taxa, we have constructed phylogenetic trees to elucidate the evolutionary placement of Ylistrum within the family Pectinidae. Our analysis reveals that Ylistrum is a basal lineage to the Pectininae clade, distinct from its previously assigned tribe, Amusiini. This study offers critical insights into the genetic makeup and evolutionary history of Y. japonicum, enhancing our knowledge of this economically vital species.

Thorough and precise gene structure annotations are essential for maximizing the benefits of genomic data and unveiling valuable genetic insights. The cucumber genome was first released in 2009 and updated in 2019. To increase the accuracy of predicted gene models, 64 published RNA-seq data and 9 new strand-specific RNA-seq data from multiple tissues were used for manual comparison with the gene models. The updated annotation file (version 2.0) contains increased 24,145 predicted genes from previous version (24317 genes) with a higher BUSCO value of 96.9%. 6231 and 1490 transcripts were adjusted and newly added, respectively, accounting for 31.99% of the overall gene tally. These newly added and adjust genes were renamed (CsaV3.1_XGXXXXX), while genes remaining unaltered preserve their original des ignations. A random selection of 21 modified/added genes were validated using RT-PCR analyses. Additionally, tissue-specific patterns of gene expression were examined using newly obtained transcriptome data with the revised gene prediction model. This improved annotation of cucumber genome will provide essential and accurate resources for studies in cucumber.

Capulus danieli, a distinct member of Capulidae, exhibits a unique ecological behaviour by attaching and drilling onto the pecten, distinguishing itself from other gastropods. Herein, we present the first characterisation of the mitochondrial genome of Capulus danieli. The results showed that the genome spans 15600 base pairs, with an A + T content of 71.12% and a G + C content of 28.88%. Within this genetic makeup, we identified 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Phylogenetic analysis revealed a close evolutionary relationship between Capulus danieli and members of the Ficoidea superfamily. The divergence time estimation suggested that Capulus danieli diverged approximately 52.29 million years ago. These findings significantly contribute to our understanding of the evolutionary history and genetic architecture of Capulus danieli, shedding light on its unique ecological adaptations to the marine environment.

To address the response of small RNAs (sRNAs) biogenesis to drought stress, we identified sRNAs biogenesis genes, including 11 encoding argonauts (AGO), 8 encoding dicer-like proteins (DCL), and 9 encoding RNA-dependent RNA polymerases (RDR) in the peach genome. Notably, the largest numbers of sRNAs biogenesis genes are located to chromosome 1. The PAZ, PIWI, and MID domains were identified in PpAGOs, while the ribonuclease IIIa and IIIb domains were characterized in PpDCLs. The RDRP domain was recognized in PpRDRs. Orthologous similarity and collinearity analyses between Arabidopsis and peach revealed 5, 1, and 2 collinear blocks in AGOs, DCLs, and RDRs, respectively. Moreover, 41, 40, and 42 cis-acting elements were located in the promoters of PpAGOs, PpDCLs, and PpRDRs, respectively, with the majority related to drought stress response. Analysis of RNA sequencing (RNA-seq) data revealed that sRNAs biogenesis genes were involved in drought stress response in different tissues. Furthermore, the expression of candidate genes was verified in two peach cultivars, Beijing 2-7 (BJ2-7) and Sinai (SN), which are drought tolerance and sensitive, respectively. Interestingly, the expression of PpAGO2b, PpDCL2b, PpDCL4, and PpRDR4 genes was induced in ‘BJ2-7’ but inhibited in ‘SN’ under drought stress. Overall, this study provides insight into the roles of sRNA biogenesis genes in response to drought stress in peach.

Energy from biogas is one of the most promising renewable energy sources. Significant, for technical, economic, and environmental reasons, is the construction of agricultural microgas plants next to households specialising in cattle rearing. The availability of free substrate makes it possible not only to produce one's electricity and heat but also to produce a more environmentally and human-friendly manure. The operation of biogas plants is, unfortunately, associated with technical problems. This paper presents the results of a study of the quality of electricity generated in an agricultural biogas plant built at a farm specialising in dairy cows, where the owner reported a problem with the voltage in the power supply network. Therefore, selected power quality parameters occurring at the connection point of the biogas plant were analysed in detail. As the authors' research showed, in the case in question, the agricultural biogas plant was connected via a cable with too small a conductor cross-section, which resulted in significant voltage exceedances, making it impossible to switch on the second of the biogas plant's generators. Replacement of the feed-in cable has enabled both generators to run, but significant fluctuations in the feed-in voltage values were still noticeable. Analysis of the subsequent measurements revealed the need for changes in the design of the digester itself. The proposal for change is to replace the two current digester mixers (each rated at 9 kW) with more mixers of lower power. The new mixers would operate in an alternating system, where only one would always be activated.

As the global demand for renewable energy continues to rise, biogas technology has emerged as a promising solution for sustainable energy generation. This review article presents the advantages of biogas technologies and extensively discusses the main principles of biogas production in the methane fermentation process. In this respect, the main parameters of the process, which require monitoring and are at the same time decisive for its course and efficiency are described, the principles of substrate selection are discussed and the necessity and advantages of the use of organic waste according to the model of a circular economy and the concept of sustainable development, are indicated. The part on biogas production is summarised with an explanation of the necessity to treat and purify biogas, taking into account the share of methane extracted. A special place in this paper is devoted to the design, construction, functioning and operation of biogas plants, based on both scientific and practical aspects. In conclusion of this chapter, the economic aspects and profitability of operating biogas plants are discussed, taking into account, in a theoretical balance sheet – in addition to investment and operating costs and the availability and cost of raw materials – the possibilities of producing and using electricity and heat, as well as environmental and social benefits. The article concludes with a discussion of opportunities and barriers to the development of biogas plants, pointing to: financial issues, access to feedstock, political regulations, public awareness and the geopolitical situation as key factors issues related to biogas plants – in different regions of the world.

Biogas production from organic waste is a promising renewable energy source, but achieving optimal production and digester stability can be challenging. This study investigated the impact of the Evogen microbial additive on biogas production and digester quality through microbial abundance and physicochemical parameter analysis. Two biogas plants, BG01 and BG02, were examined using 16S rRNA profiling to assess microbial abundance. Simultaneously, physicochemical parameters, including FOS/TAC ratio, total solids, volatile solids, biogas production, and VFA profile, were measured to evaluate digester performance. Results revealed distinct microbial community shifts in Evogen-treated digesters. Increased abundance of methanogenic archaea and hydrolytic bacteria indicated improved anaerobic digestion. Evogen supplementation also positively affected digester performance, with higher FOS/TAC ratios indicating enhanced acidification and methanogenesis. Reductions in total solids and volatile solids demonstrated improved organic matter degradation. Significantly higher biogas production was observed in Evogen-treated digesters, highlighting its potential as a microbial additive. Furthermore, VFA profiling demonstrated improved process stability and reduced substrate inhibition in Evogen-treated digesters. In summary, Evogen microbial additive positively influenced microbial dynamics, improving biogas production and digester quality. These findings contribute to optimizing biogas production systems and understanding the complex microbial interactions within anaerobic digesters.

Additives for anaerobic digestion (AD) can play a significant role in optimising the process by increasing biogas production, stabilising the system and improving digestate quality. The role of additives largely boils down to: (i) enhancing direct interspecies electron transfer (DIET) between microbial communities, resulting in improved syntrophic interactions and methane production rates (e.g. biochar, magnetite and carbon nanotubes), (ii) adsorption of toxic substances that may inhibit microbial activity (e.g. activated carbon, zeolites), (iii) improving microbial activity and increasing process stability (e.g. cobalt, nickel, iron, selenium), (iv) maintaining optimal pH levels for microbial activity (e.g. magnesium oxide), (v) reducing inhibition (the aforementioned adsorbents and conductive substances), (vi) accelerating the decomposition of complex organic materials into simpler compounds that are more easily digested by microorganisms, thereby increasing the rate of hydrolysis (enzymes, including cellulases, proteases and lipases). Through the aforementioned action, additives can significantly affect AD performance. The function of these materials varies, from enhancing microbial activity to maintaining optimal conditions and protecting the system from inhibitors. The choice of additives should be carefully tailored to the specific needs and conditions of the digester to maximise benefits and ensure sustainability. In light of these considerations, this paper characterizes the most commonly used additives and their combinations based on a comprehensive review of recent scientific publications, including a report on the results of conducted studies. The publication features chapters that describe: carbon-based conductive materials, metal oxide nanomaterials, trace metal and biological additives, including enzymes and microorganisms. It concludes with a chapter summarising reports on various additives and discussing their indications for functional systems with determined properties. A notable advantage of this work is the updated literature data, clear summaries, and a substantive description of the performance of the additives discussed.

Biological pre-treatment holds much promise as an innovative and sustainable strategy to improve biogas production from crop residues. In the present study, a novel hot spring cellulolytic microbial consortium (HSCMC) was used to pre-treat crop residues, such as maize stover, wheat straw and soybean straw to improve biogas production by anaerobic digestion (AD). Results indicated that the ash and volatile solids in maize stover were significantly reduced by maximum values of 21.3 % and 69.2 % respectively, after 7 days of pre-treatment over non-treated residues. The maximum decline of 83.9 % in total solid content was reported in wheat straw. The total reducing sugar content was increased by maximum yields of 60.9 %, 96.3 % and 84.7 % in maize stover, wheat straw and soybean straw, respectively. Furthermore, the HSCMC consortium enhanced the cumulative methane yield of wheat straw, maize stover and soybean straw by 50.6 %, 50.2 % and 56.6 %, respectively. Increased methane yield from pre-treated crop residues implies that this method could be an economically viable option for large-scale valorization of the AD process.

Wheat straw (WS) is a promising substrate for biogas production by anaerobic digestion (AD) due to its high carbohydrate content. An estimated 0.603 million t yr-1 of WS are generated from wheat production systems in South Africa. This is equivalent to an energy potential of 11 PJ. Despite, WS is still undervalued as a bioenergy resource in South Africa due to its structural complexity and low nitrogen content. WS disposal methods, such as use in livestock bedding, burning and burying into the soil, inter alia, are not sustainable and may contribute to global warming and climate change. Commercialization of the AD of WS needs to be further developed and promoted. Pre-treatment (i.e. physical, chemical, biological and hybrid methods) and anaerobic co-digestion (AcoD) are novel strategies that can support the conversion of WS into biogas and other value-added products. Current and future research should focus on optimizing pre-treatment and AcoD conditions towards industrialization of WS into valuable products. This paper focuses on the potential use of WS for biogas production by AD in South Africa. The aim is to create information that will promote research and development, and encourage policy makers and stakeholder participation and investment in WS biogas technology.

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness consistency recovery method is pro-posed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from the images. After image-level processing, a combined underwater acousto-optic feature association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for the visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for performance against the OKVIS framework. The system achieves better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

The present study analyzed the effect of temperature, pH, retention time, pretreatment and mixing ratio on the anerobic digestion process. The parameters during the anaerobic co digestion of cow manure and food waste were then optimized using the Taguchi experimental design method. ANOVA is carried out to find the significant parameters which influence the biogas production. Experimental tests were carried out at laboratory scale reactors kept at different temperatures (28 °C, 35 °C, and 50 °C). The specific methanogenic Performance (SMP) during anerobic digestion at higher temperatures was characterized with the analysis of acetate, propionate, butyrate, hydrogen, glucose, and formate. The improvement of biogas production with different pre-treatment i.e., ultrasonic, autoclave, and microwave techniques also analysed. The results showed that reactor that maintained at 35 °C showed the highest biogas production. While the reactor that maintained at lower temperature (28 °C) produced the lower volume of biogas. As the retention time increases, the amount of biogas produced increases. Methanogenic activities of microorganisms reduced at higher temperature conditions (65 °C). Biogas production increased by 28.1%, 20.23%, and 13.27 % when the of substrates treated with ultrasonic, autoclave, and microwave, respectively, compared to the untreated substrate. The optimized condition for the highest biogas production during anaerobic co digestion of food waste and cow manure is temperature of 35 °C, pH of 7 and mixing ratio (CM: FW=1.5:0.5). ANOVA showed that temperature is the most important input parameter affecting biogas production, followed by mixing ratio.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, we propose an innovative enhancement algorithm that integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is intro-duced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct convolutional neural network. Finally, the Fourier’s high and low-frequency information of the original image are utilized to fuse different features. To demon-strate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach surpasses other mainstream methods, achieving superior PSNR and structural sim-ilarity index measure (SSIM) metrics and leading to a detection performance improvement of over ten percent.

This study assesses the biomethane (CH4) generation and greenhouse gas (GHG) emissions resulting from municipal solid waste landfilling in Phnom Penh, Cambodia, with a focus on the impact of fugitive CH4 emissions and operation processes in four landfilling scenarios: simple dumping (S1), improved management with leachate treatment (S2), engineered landfill with flaring (S3), and engineered landfill with energy recovery (S4). The study also considered the environmental benefits of carbon sequestration and landfill gas utilization. The LandGEM and IPCC FOD models were used to calculate CH4 generation over the period of 2009-2022, and it was found that approximately 18 and 21 M kg/year of CH4 are released, respectively. The energy potential from CH4 recovery was 51–61 GWh/year. Overall GHG emissions in S2 were the highest, amounting to 409–509 M kg CO2-eq/year, while S1 had lower emissions at 397–496 M kg CO2-eq/year. Flaring captured CH4 in S3 could reduce GHG emissions by at least 55%, and using captured CH4 for electricity production in S4 could mitigate at least 83% of GHG emissions. Electricity recovery (S4) could avoid significant amounts of GHG emissions (˗104.5 kg CO2-eq/tMSW). The study suggests that landfill gas-to-energy could significantly reduce GHG emissions.

Due to global climate change, cold temperatures have significantly impacted rice production, resulting in reduced yield and quality. In this study, we investigated two traits related to cold tolerance (CT) of 1992 diverse rice accessions at the seedling stage. Geng accessions exhibited higher levels of CT compared to xian accessions, with the GJ-tmp subgroup displaying the strongest CT. However, extreme CT accessions were also identified within the xian subspecies. Through GWAS analysis based on survival rate (SR) and leaf score of cold tolerance (SCT), a total of 30 QTLs associated with CT at the seedling stage were identified, among which four QTLs (qSR3.1a, qSR4.1a, qSR11.1x and qSR12.1a) were found to be important. Furthermore, five candidate genes (LOC_Os03g44760, LOC_Os04g06900, LOC_Os04g07260, LOC_Os11g40610 and LOC_Os12g10710) along with their favorable haplotypes were identified through gene function annotation and haplotype analysis. Pyramiding multiple favorable haplotypes resulted in a significant improvement in CT performance. Subsequently, three selected accessions (CX534, B236, and IRIS_313-8565), carrying different superior alleles for CT, were selected and recommended for molecular breeding for CT using marker-assisted selection (MAS). The findings from this study provide valuable resources for enhancing rice's ability for CT while laying a foundation for future cloning of novel genes involved in conferring CT.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

The objective of this work is to analyze the improvement of heat transfer in trapezoidal fin heat exchangers using numerical simulation. Heat exchangers are essential components frequently used in contemporary electronic gadgets to effectively disperse heat produced by high-power components. Nevertheless, as electronic devices continue to advance and become more powerful, the heat transfer capacity of heat exchangers is facing more requirements, therefore requiring enhanced heat dissipation efficiency. Afterwards, various fin angles were adjusted, and the heat transmission efficiency was compared. Modifying the angles can significantly improve the effi-ciency of convective heat dissipation. Moreover, it has been found that employing an optimized trapezoidal fin structure can further enhance the efficiency of heat transfer. The results indicate that trapezoidal fin heat exchangers provide superior heat dissipation efficiency compared to conventional heat exchangers. This is important for enhancing the thermal dissipation capabili-ties of electrical gadgets, hence guaranteeing steady functionality and extending their longevity. Subsequent study might explore supplementary methods to boost heat transmission in different heat exchanger configurations in order to meet the growing requirements for heat dissipation.

Cytokine and AMH profiling were done in prepubertal Murrah buffalo heifers post-progesterone supplementation and co-synchronization protocol. Cytokines: IFNγ, IL6, IL1, IL13, TNFα, and TGFβ, and, AMH, Progesterone and Estrogen hormone estimations were done. Ovarian follicular pattern and fertility outcome were recorded. Eighteen prepubertal heifers, 15-17 months of age, 250-300 kg BWt were randomly divided into 3 groups, Group1: n=6, supplemented with altrenogest 0.044mg/kg/day/heifer orally for 14 days along with Co-synch programme; Group2: n=6, administered CIDR for 14 days and co-synch programme, Group3: n=6, received co-synch programme. Ultrasonography was done to ovarian follicle status on respective days of sampling. Pregnancy diagnosis was done around 45 and 60 post-AI. Estradiol 17-β level remained constant in altrenogest group, CIDR implanted and Control groups during the Progesterone treatment and Co-synch treatment. Estradiol levels during Co-synch were significantly less on day 9 of Co-synch (14.41 ± 1.97 pg/mL) than day 0 and on day7 in pre pubertal buffalo heifers in altrenogest group. However, no significance was observed in other groups. Progesterone levels in buffalo heifers subjected to synchronisation protocols varied significantly (P< 0.05) on day 7 of P4 treatment between altrenogest supplemented and CIDR implanted groups, as well as, Co-synch groups. Supplementation of altrenogest showed a significant (P= 0.043) increase in Progesterone levels by day 14 of altrenogest treatment. Progesterone varied significantly in all Groups on day 9 of co synch protocol [ Group 1 (P= 0.02), Group 2 (P= 0.041) and Group 3 (P= 0.007)]. Cytokine IFN-γ showed high correlation with progesterone, indicating the role of IFN-γ in puberty in buffalo heifers (r= 0.626, P< 0.01). AMH had a significant positive correlation supplemented with altrenogest with IFN-γ (r= 0.673, P< 0.01) and TGF-β (r= 0.463, P< 0.01), whereas, with TNF-α it was a negatively correlated (r= -0.34, P< 0.05). Based on Karl Pearson correlation coefficients, IL-13 and TGF-β could be considered as markers for puberty in buffalo heifers.

Proper Post-Activation Performance Enhancement helps to improve athletic performance. This study aimed to investigate the effects of two different velocity loss (VL), 10%VL and 20%VL, on post-activation performance enhancement (PAPE) in 20m sprint performance among sprint athletes. Twenty-four male sprint athletes (100m sprint time: 10.96±0.15 s) were recruited. A randomized crossover experimental design was used for traditional group (TG), 10%VL, and 20%VL interventions. Sprint tests were conducted at the 4th, 8th, 12th, and 16th minutes after the intervention. Two-way repeated measures ANOVA revealed an interaction effect between group and time on 20m sprint performance (F=2.817, p=0.037, partial η²=0.585). Simple effect analyses showed significant differences compared to baseline at the 4th minute for the 20%VL group (P&lt;0.05). Cohen's d values indicated improvement in 10m sprint times at the 8th minute of the rest interval for all three intervention groups (TG: ES=-0.270, 10%VL: ES=-0.038, 20%VL: ES=-0.279). Improvement in 20m sprint times was observed at the 4th minute for the 20%VL group (ES=-0.296) and at the 16th minute for the 10%VL group (ES=-0.276). Compared to traditional PAPE schemes based on 1RM, PAPE schemes based on velocity loss (20%VL) can better induce PAPE effects in sprint athletes.

Enhancement of orthodontic tooth movement (OTM) through local administration of biofunctional molecules has become increasingly significant, particularly for adult patients seeking aesthetic and functional improvements. This comprehensive systematic review analyzes the efficacy of various biofunctional molecules in modulating OTM, focusing on the method of administration and its feasibility, especially considering the potential for topical application. A search across multiple databases yielded 36 original articles of experimental human and animal OTM models, which examined biofunctional molecules capable of interfering with the biochemical reactions that cause tooth movement during orthodontic therapy, accelerating the OTM rate through their influence on the bone metabolism (Calcitriol, Prostaglandins, Recombinant human Relaxin, RANKL and RANKL expression plasmid, growth factors, PTH, osteocalcin, vitamin C and E, biocompatible reduced graphene oxide, exogenous thyroxine, sclerostin protein, a specific EP4 agonist (ONO-AE1-329), carrageenan and herbal extracts). The results indicated a variable efficacy in accelerating OTM, with Calcitriol, Prostaglandins (PGE1 and PGE2), RANKL, growth factors and PTH, among others, showing promising outcomes. PGE1, PGE2 and Calcitriol experiments had statistically significant outcomes in both human and animal studies, and, while other molecules underwent only animal testing, they could be validated in the future for human use. Notably, only one of the animal studies explored topical administration, which also suggests a future research direction. This review concluded that while certain biofunctional molecules demonstrated potential for OTM enhancement, the evidence is not definitive. The development of suitable topical formulations for human use could offer a patient-friendly alternative to injections, emphasizing comfort and cost-effectiveness. Future research should focus on overcoming current methodological limitations and advancing translational research to confirm these biomolecules' efficacy and safety in clinical orthodontic practice.

Studying the multi-function bacterial implications on the natural phytochemicals could be among the best solutions for producing clean-label functional juices. This study aimed to develop a high-quality mulberry juice with enhanced antioxidant capacity through fermentation. The juice was fermented using mono- and co-cultures of Lactobacillus casei (LC), L. plantarum (LP), L. paracasei (LPC), L. acidophilus (LA), and L. helveticus (LH) at different time intervals (24, 48, 72, and 96 h). The total phenolic content (TPC), total flavonoid content (TFC), flavonols, anthocyanins, and proan-thocyanidins were determined, along with the antioxidant activity assays and FTIR analysis. The results revealed significant changes in molecular interactions and functional group presence post-fermentation, indicating microbial incorporation's effect on the juice's functional profile. These changes were attributed to the formation of new compounds during fermentation, such as organic acids, alcohols, glycerol, and phenols, as evidenced by the FTIR spectra. Besides, LC-LA and LC-LPC co-culture treatments yield the most substantial stability in TPC, TFC, and TF after 48 h compared to 24 h and 72 h. After 48 h, the co-cultures of LPC-LH (1265.5 GAE/L) and LPC-LA (1145 GAE/L) were increased the TPC after 48 h than the single cultures. Conclusively, LC-LPC and LP-LPC co-culture fermentation was recommended as a cost-effective method to enhance the an-tioxidant potential and the overall phytochemical capacity of mulberry products. The findings of this study provide valuable insights into the potential of fermented mulberry juice as a functional food with improved health benefits.

There is a need to find methods to intensify the anaerobic digestion process. One possibility is the use of pre-treatment techniques. Many laboratory tests confirm their effectiveness, but in most cases, there is no verification work carried out on industrial plants. The aim of the research carried out under laboratory conditions and on a large scale was to determine the technological and energy efficiency of the use of hydrodynamic cavitation in the pre-treatment of a waste mixture from dairy farms. It has been shown that hydrodynamic cavitation significantly increases the concentration of organic compounds in the dissolved phase. In the most effective variants, the increase in the content of these indicators was over 90% for both COD and TOC. The degree of solubilisation achieved was 49±2.6% for COD and almost 52±4.4% for TOC. Under laboratory conditions, the highest effects of anaerobic digestion were achieved after 10 minutes of pre-treatment. The amount of biogas was on average 367±18 mL/gCOD and the amount of methane 233±13 mL/gCOD. Further large-scale optimisation trials showed that after 8 minutes of hydrodynamic cavitation, the biogas yield was 327±8 L/kgCOD with a CH4 content of 62.9±1.9%. With this variant, the net energy yield was 66.4±2.6 kWh/day, a value that was 13.9% higher than the original variant with 10 minutes of disintegration and 3.1% higher than the variant without pre-treatment.

The marine region of the Zhongjieshan Islands is an essential component and location for enhancement and release in Zhoushan Fishing Ground. The scientific assessment of the target species' ecological carrying capacity serves as the foundation for augmentation and release efforts. Based on data from the 2021-2022 survey of biological resources in the waters of Zhongjieshan islands, the Ecopath model was built to analyze the trophic structure, energy flow, and overall characteristics of the ecosystem, as well as the ecological carrying capacity of the main enhancement and releasing species, Larimichthys crocea and Sepiella maindroni. The ecosystem's functional groups had trophic levels ranging from 1 to 3.48, with an average energy transfer efficiency of 8.11% and total flow of 2125.905 t·km-2. The ratio of total primary output to total respiration is 1.713. Zhongjieshan Islands' ecosystem was currently at an embryonic stage. The ecological carrying capacity of Larimichthys crocea and Sepiella maindroni was calculated to be 0.2325 t·km-2 and 0.1617 t·km-2, separately or individually. Larimichthys crocea and Sepiella maindroni showed enormous potential for development and release. The findings offered essential data for ecological studies on the waters of Zhongjieshan Islands, as well as a reference for enhancement and release efforts in this area.

Recent advancements in semiconductor photonic and electronic devices such as Uni-travelling-carrier photodiodes (UTC-PDs), have played a significant role in the development of Terahertz communication technology. However, a major challenge that still persists is the limited output power from a single UTC-PD required for practical transmission distances. To enhance the output power, we propose and demonstrate a novel Terahertz wave power combining technique using a photomixer device that comprises of two arrayed UTC-PDs monolithically integrated with a microstrip line-based 2 × 1 Wilkinson power combiner and a rectangular patch antenna on a Silicon Carbide (SiC) substrate at 300 GHz. At photocurrents of 8 mA and 10 mA from the two UTC-PDs, the device experimentally exhibited a 7.3 dB increase in power relative to the power obtained with 8 mA UTC-PD and a 4.4 dB increase in power relative to the power obtained with 10 mA UTC-PD, when both UTC-PDs were irradiated. This implies that power can be enhanced by a factor of N2 if the photocurrent is multiplied by a factor of N. This technology presents a holistic and cutting edge platform for further power multiplication by further increasing the number of arrayed UTC-PDs.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

This study addresses the critical challenge of Speech Enhancement (SE) in noisy environments, where the deployment of Deep Neural Networksolutions on microcontrollers is hindered by their extensive computational demands. Focusing on this gap, our research introduces a novel SE method optimized for MCUs, employing a 2-layer GRU model that capitalizes on perceptual speech properties and innovative training methodologies. By incorporating self-reference signals and a dual strategy of compression and recovery based on the Mel scale, we develop an efficient model tailored for low-latency applications. Our GRU-2L-128 model demonstrates a significant reduction in size and computational requirements, achieving a 14.2× decrease in model size and a 409.1× reduction in operations compared to traditional DNN methods like DCCRN, without compromising performance. This advancement offers a promising solution for enhancing speech intelligibility in resource-constrained devices, marking a pivotal step in SE research and application.

The decline of fossil fuels, the increase of greenhouse gases and the global demand for energy are driving the search for alternative energy sources. Anaerobic digestion is a promising technology because it can convert organic biomass into biogas. As the climate warms, there is an increase in biomass of plant origin in water bodies, and ecosystems are unable to clean themselves. This work aims to show that macroalgae and aquatic plants can be an excellent raw material for biogas production. By mixing them with co-substrates such as cattle manure, higher biogas and methane yields can be obtained. When Cladophora glomerata and Zostera marina macroalgae are mixed with cattle manure, the biogas yield is up to 458.8 mL/gVS and 397.9 mL/gVS, respectively. Methane concentration remained high and reached 62.4%–64.1%. The obtained research results show the high energy value of biogas and the energy potential of biomass. Macroalgae increased the energy potential of biogas to 22.9 MJ/m3, and the energy potential of biomass reached 2.40 MJ/Kg. Due to its high energy value, biogas produced from the considered substrates can be an excellent alter-native to fossil fuels. Integrating aquatic macroalgae into anaerobic digestion is a promising approach for a waste-free marine and freshwater system.

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment but has also posed significant challenges to network security, especially the physical layer security challenges,which is crucial for the reliable transmission of messages interconnected devices and sensors. Focus on the enhancing the physical layer security in the IIoT communication, this paper proposed a mechanism of Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate device is known, while eavesdropper on the random distribution of device locations, forming the SSOP model related to device locations from the perspective of insecure regions (ISR) and secure regions (SR), and deduce the closed-form expression for its upper bound. Subsequently under the constraint of SSOP conditions, establish an optimization model with the objective of system throughput maximization based on Spatial Secrecy Outage Probability(SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in the MATLAB, we verified the accuracy of the definition and derived upper bound of SSOP. The experimental results also show that SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. These performance are crucial for understanding and optimizing the security performance of IIoT communication systems.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

Seaports are critical hubs in global trade, facing increasing pressure to accelerate green transi-tions while navigating evolving economic and environmental landscapes. This research analyses the intricate relationship between port organizational ecosystem resilience and the successful implementation of green transitions, highlighting the challenges and opportunities in moving towards a more sustainable future. The research employed a combined focus group interview and participatory observation methodology to gather insights from experts. Analysis revealed that while technological advancements are a primary driver for greening port operations, a lack of managerial capacity is a significant impediment to the successful integration of these innova-tions. It was identified several challenges, including outdated infrastructure, cybersecurity threats, and the need for ongoing investments in new technologies, along with the need to foster a sustainable behaviour culture. Experts emphasized the importance of developing managerial competencies and continuous learning. The research also highlights the role of strong stake-holder partnerships, including collaboration with government agencies, industry associations, and environmental organizations. This research provides valuable insights for policymakers, port managers, and other stakeholders seeking to navigate the challenges for ensuring a sus-tainable future for the maritime sport organizational ecosystems and maritime industry.

The world is facing a serious energy crisis and environmental pollution problems due to a sharp increase in the world population. Bioenergy is an eminent solution to these problems. Anaerobic digestion is a green energy technology used worldwide for the conversion of organic waste to biogas. It is reported that organic wastes are hard to digest and need some technical improvement in the anaerobic digestion process to improve biogas yield. Iron-based additives, due to their electron acceptance and donation capabilities, have been emphasized as being exceptional in improving anaerobic digestion process efficiency amongst all other enhancement options. This study reviews the major available types of iron-based additives, their characteristics, and their preparation methods. The preferred iron-based additive that has a significant effect on the enhancement of biogas yield is also discussed. The use of iron-based additives in the anaerobic digestion process with varying dosages and their impact on the biogas generation rate is also being studied. Substrates, operating parameters, and types of anaerobic digesters used in recent studies while researching the effects of iron-based additives are also part of this review. Lastly, this study also confirms that iron-based additives have a significant effect on the reduction rate of the volatile suspended solids, methane content, biogas yield, and volatile fatty acids.

The real implementation of biogas reforming at industrial scale to obtain interesting products, like hydrogen or syngas, is a developing research field where multidisciplinary teams are continuously adding improvements and innovative technologies. Thus, these works can contribute to the proliferation of green technologies where circular economy and sustainability are present. To assess the sustainability of these processes, there are different tools like life cycle assessment (LCA), which implies a complete procedure where even small details count to consider a certain technology sustainable or not. The aim of this work was to review works where LCA is applied to different aspects of biogas reforming, especially focusing on the role of catalysts, which are essential to improve the efficiency of a certain process, but also present a strong influence on its environmental impact. As a conclusion, catalysts have an influence on LCA through the improvement on the catalytic performance and the impact of their production, whereas other aspects related to biogas or methane reforming could equally influence on their durability or reusability, with the subsequent effect on LCA. Further research about this subject is required in a continuously changing technology with plenty of possibilities.

The aim of the study was to examine the biogas and methane yield of maize silage, a wastes in agricultural point of view and compare it to the biogas productivity of commonly used mixture of maize silage and mixed fodder with water. The experimental study was carried out into 2 parts. Firstly, the batch experiment was conducted in Mesophilic conditions (35°C), at five different hydraulic retention times (HRT): 6 DAT, 11 DAT 20 DAT 29 DAT and 41 DAT. The results revealed that maize silage was generated the highest biogas yield of 537 mL /241 mL at the HRT of 6days /41 days. Mixed fodder produced the highest biogas yield of 421 mL at the HRT of 6th days and the lowest one was 252 mL at the HRT of 20th days. Finally, the methane, carbon dioxide and hydrogen sulphide of biogas produced from maize silage and mixed fodder were analyzed using a gas chromatography. The results shown that biogas from maize silage precedes the gas less than the mixed fodder. In the field level experiment proved that from mixed fodder produced gas which would be enough for 7 member’s family used 3 months for the purpose of their cooking.

In the ground-based infrared optical remote sensing system, the target signal is very weak due to the dynamic strong light background and the movement of small and small targets. In order to improve the limit detection capability, the background suppression and target enhancement methods are required to be more suitable for this scene. To solve this problem, we first analyze the image features in the current scene, and propose a more perfect point target and noise model. Then, we propose a new WY distribution function based on the Fermi Dirac distribution function, and propose an anisotropic filtering method based on this function, and further suppress the back-ground through the difference results of two steps. Then, based on the distribution function, we further designed an energy concentration accumulation strategy in 9 scale directions, through which the SNR of the target is effectively improved and the suppression ability of the background is enhanced. Through quantitative and qualitative experimental analysis, compared with the same type of algorithms, the proposed method has better robustness against extremely weak targets and dynamic backgrounds.

The abrupt increase of the electric vehicles in Bangladesh needs huge amount of power. As a result, alternative energy sources are emphasized due to limited fossil fuels in order to develop a sustainable energy sector with environment friendly resources. Bangladesh has an enormous potential in the field of renewable resources like biogas and biomass. This paper presents 20 kW electric vehicle charging station (EVCS) utilizing biogas resources where maximum energy requirement is 100 kWh. In this paper, the biogas based EVCS is designed using MATLAB Simulink and HOMER software. Daily 15-20 electric vehicles can be recharged their batteries using the proposed charging station. The proposed system offers lower cost of energy compared to the grid electricity. Moreover, the proposed charging station shows 68.75% reduction in CO₂ emission than grid based charging station. In addition, the proposed EVCS will save monthly $ 16.25 and $ 27.50 respectively for easy bike and auto rickshaw type electric vehicles in Bangladesh.

The rapid development and integration of Autonomous Mobile Robots (AMRs) have 1 revolutionized industries by enhancing automation capabilities. A critical challenge in this evolution 2 is achieving effective navigation and obstacle avoidance, which are essential for deploying AMRs 3 seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation 4 and obstacle avoidance advancements through the application of reinforcement learning, specifically 5 focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness 6 of Q-learning in managing dynamic obstacles within a factory environment. The AMR successfully 7 avoided obstacles in 36 out of 50 test runs, achieving a 72% success rate, and maintained an average 8 distance of 12 cm from each obstacle, underscoring the algorithm’s precision in maintaining safe nav- 9 igation paths while dynamically adapting to environmental changes. The continuous monitoring by 10 ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making 11 process and efficiently navigate through the environment. This paper also provides a comprehensive 12 examination of conventional methods, tracing their historical development and assessing their role in 13 addressing real-world challenges. The results highlight the significant improvements brought by rein- 14 forcement learning, particularly when integrated with sensor fusion and motor control technologies, 15 to enhance navigation and dynamic object collision avoidance. The study involves developing an 16 AMR prototype, refining algorithms, and assessing system performance in controlled environments. 17 By automating material transportation and addressing operational constraints in manufacturing 18 settings that predominantly rely on manual labor, this paper advances the practical deployment of 19 AMRs. Additionally, it discusses ethical considerations, potential limitations, and the importance 20 of real-world validation, offering valuable insights for the future development and integration of 21 autonomous mobile robotics in dynamic industrial environments.

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air-conditioning systems (HVACS). This is done using a specific range of controllers, which have more than thirty loops of proportional, integral and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen, oxygen, and maintaining the desired temperature of the battery storage room within the recommended limits (i.e. 25°C ±1°C tolerance) without compromising quality as set out in the user requirement specification in Appendix-A. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database amongst others. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and ensure that the HVACS extract the gas content to the outside environment. The results of the system show that the network control panel effectively operates as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, the effective operation of HVACS enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead acid Batteries (LABs).

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

This study investigated whether biogas reactor performance, including microbial community development, in response to a change in substrate composition is influenced by initial inoculum source. Test reactors were first started with two different inocula and operated with the same grass-manure mixture for more than 120 days. These reactors initially showed great differences depending on inoculum source, but eventually showed similar performance and overall microbial community structure. At the start of the present experiment, the substrate was complemented with milled feed wheat, added all at once or divided into two portions. The starting hypothesis was that process performance depends on initial inoculum source and microbial diversity, and thus that reactor performance is influenced by the feeding regime. In response to the substrate change, all reactors showed increases and decreases in volumetric and specific methane production, respectively. However, specific methane yield and development of the microbial community showed differences related to initial inoculum source, confirming the hypothesis. The different feeding strategies had however only minor effects on process performance and overall community structure, but still induced differences in the cellulose-degrading community and in cellulose degradation.

To achieve higher crop yields and maintain environmental conservation, it becomes imperative to adopt novel agricultural methods that improve both the quantity and quality of produce. The above study focused on investigating the effectiveness of integrated use of biogas slurry (BS) and inorganic nutrigation on spinach growth and nutrient uptake through drip irrigation. Under field conditions and using a split plot design, spinach was cultivated with seven different treatments: biogas slurry nutrigation (BSN) (T1), integrated inorganic + organic nutrigation: 40% Recommended dose of fertilizer (RDF) +BSN (T2), 60% RDF +BSN (T3), 80% RDF +BSN(T4), 100% RDF(T5), slurry broadcasting (SB) (T6), and control(T7). The results showed that spinach grown with T4 80% RDF +BSN exhibited optimum plant height and leaf count compared to spinach under other treatments and was at par with T5 100% RDF for crop parameters. T5 treated plants demonstrated the longest roots, followed by T4 treated plants. The biomass produced by T4 was at par with T5 in the first (T4= 4.60 tonha-1, T5 =4.67 tonha-1) and second harvesting(T4= 6.69 tonha-1, T5 =6.89 tonha-1).In terms of macronutrient content in spinach leaves, significant differences were found only for nitrogen (N),potassium (K), while the phosphorus contents were not significantly influenced. Incorporating biogas slurry into the soil modifies microbial enzyme activities, specifically dehydrogenase and phosphatase. Normally, alkaline phosphatase shows greater activity than acidic phosphatase, but the addition of biogas slurry equalized the enzymatic activity of both, establishing a harmonized enzymatic profile. Fertilizing spinach with integrated Biogas slurry nutrigation + inorganic nutrigation not only improves growth and development to a similar extent as inorganic fertilizer but also enhances the nutrient content of the spinach, contributes to environmental preservation, and reduces production costs.

Additives can improve the efficiency of anaerobic digestion by increasing biogas production, reducing air pollution, and preventing total ammoniacal nitrogen (TAN) inhibition. Biological or chemical supplementation can also improve the economic efficiency of anaerobic digestion. However, the effects of specific additives on biogas production can vary depending on the type of supplement used. This research utilizes the additives on an industrial scale and monitors the optimization of the anaerobic digestion operating parameters after their addition. The various AD additives were examined in a sufficient cycle of operation for three biogas plants located in northern Greece. In this manner, the effectiveness was investigated in multiple initial feeds and unstable operating situations caused by the seasonality of specific feedstocks. The existing operation state in the three biogas plants was recorded before and after adding the supplements. In all three cases, lower concentration levels were observed in many of the examined trace elements. Introducing additives proved to be a stabilization factor in AD performance and inhibition mediator.

In agriculture, soil-borne fungal pathogens, especially Fusarium oxysporum strains, are posing a serious threat to efforts to achieve global food security. In the search for safer agrochemicals, silica nanoparticles (SiO2NPs) have recently been proposed as a new tool to alleviate pathogen damage including Fusarium wilt. Hollow mesoporous silica nanoparticles (HMSNs), a unique class of SiO2NPs, have been widely accepted as a desired pesticide carrier. However, their role in enhancing the disease resistance and the specific mechanism remain unknown. In this study, three sizes of HMSNs (19, 96 and 406 nm as HMSNs–19, HMSNs–96 and HMSNs–406, respectively) were synthesized and characterized to determine their effects on seed germination, seedling growth, and Fusarium oxysporum f. sp. phaseolus (FOP) suppression in cowpea roots by foliar spray using phenotypes, fresh biomass and disease progression as indicators. The results revealed that three HMSNs exhibited no adverse impacts on seed germination and tended to improve plant growth. Also, they exert their FOP suppression with a size- and concentration-dependent manner. HMSNs–406 possessed the best control effect at a concentration of 1000 mg/L showing an upto 40.00% decline in the disease severity. The Si(OH)4 control was also effective on FOP suppression at a lower concentration of 100 mg/L, whereas its higher concentrations exhibited obviously adverse impacts on FOP control, seed germination and plant growth. Moreover, we conformed that HMSNs posed their Fusarium wilt suppression in cowpea plant by activating SA (salicylic acid)-dependent SAR (systemic acquired resistance) responses rather than directly suppressing FOP. A higher level of SA content and elevated expression of its maker genes of PR-1 and PR-5 in HMSNs–406 treated cowpea roots provided substantial evidences of this mode of action. Other resistance-related genes, as well as defense-responsive enzymes, were also involved in the HMSNs-activated SAR pathway. Overall, for the first time, our results extended a new role of HMSNs as a potent elicitor to serve as a versatile alternative for plant disease protection of low cost, highly efficiency and sustainability.

In the DC distribution networks, DC bus voltage is maintained by the grid-connected converter, the controllability and reliability of the grid-connected converter significantly affects the bus voltage characteristic. To address the problem of limited stability and frequent oscillations, this paper proposes a state feedback control method for the AC/DC converter. Conventional AC/DC converter adopts the voltage-current double-closed-loop control structure with the proportional integral (PI) controllers, which is the equivalent of the typical type II control system, but the typical type II control system cannot fully settle the stability and immunity problems. In contrast, the state feedback control strategy not only achieves the control objectives of the traditional double-closed-loop control, but also reduce the AC/DC converter system model to a typical I-type system, which improves the stability, thus enhances the oscillation suppression capability of the bus voltage. By selecting multiple state variables and designing the converter pole configuration range, the proposed single-loop state feedback control method manages to optimize both the dynamic and steady-state performances of the grid-connected AC/DC converter. Finally, the effectiveness of the proposed single-loop state feedback control strategy has been verified by both the simulation and experiment results.

Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detail layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared to state of the art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

: Sulfate presence is of great importance for anaerobic digestion as its addition can control the anaerobic microbial community. In this study, the effect of sulfuric acid addition on the perfor-mance of an UASB reactor fed with hydrolyzed starch was investigated. TOC, Fe and SO4-2 re-moval as well as the methane production were monitored under various COD/ SO4-2 ratios, HRTs and OLRs. When HRT was 16 hours and OLR equal to 2 g COD/LUASB-d the methane production was 0.24 L CH4/LUASB-day. After the sulfuric acid addition, TOC removal reached 95%. At HRT, OLR, and COD/ SO4-2 ratio equal to 16 hours, 2 g COD/ LUASB-d and 3.72 respectively, the methane production was 0.267 L CH4/LUASB-d. When the OLR was increased to 5.94 g COD/LUASB-d and COD/SO4-2 ratio was equal to 12.5, the methane production was 3 times higher (0.84 L CH4/LUASB-d) with a methane content in the produced biogas greater than 70%. 16s rRNA gene analysis demonstrated that after the sulfate addition, SRB were enriched and methane was mainly produced by Methanosaeta sp. This study indicates that appropriate sulfate concentrations in the feed in combination with the presence of iron in the UASB reactor lead to enhanced me-thane yields.

Agricultural product like cassava produces huge amounts of waste when processed to consumable goods. The waste generated is generally considered to contribute largely to environmental pollution. This study therefore investigates the waste management practice that is adopted by cassava processors in Ogun State, Nigeria. Five (5) Local Government Areas (LGAs) dominant in processing cassava were selected for the study on the basis of spatial location distribution; landmass and population. The survey involved the use of structured questionnaires administered to cassava processors of the selected LGAs. The Statistical Package for Social Sciences (SPSS) software application and descriptive statistics were used for data analysis. Results of the analysis show that majority (70%) of the cassava processors are females. Cassava peel constitutes 10% of the waste produced, of which 91% are heaped at refuse dump in most communities. Results also reveal that 86.3% of cassava residues are used for animal feeds. Other findings show that the peels when dried are used as bio-fuel for cooking and there is a significant potential for biogas production. From the data captured from respondent during the study, most processors are willing to pay for an improved waste management system. The study therefore suggests proper waste management of cassava waste to minimize environmental pollution.

An energy production system consisting solar collector, biogas dry reforming reactor and solid oxide fuel cell (SOFC) was assumed to be installed in Kolkata, India. This study aims to understand the impact of climate condition on the performance of solar collectors with different lengths of parabolic trough solar collector (dx) and mass flow rate of heat transfer fluid (m). In addition, this study has evaluated the amount of H2 produced by biogas dry reforming (GH2), the amount of power generated by SOFC (PSOFC) and the maximum possible households (N) whose electricity demand could be met by the energy system proposed, considering the performance of solar collector with the different dx and m. As a result, the optimum dx was found to be 4 m. This study revealed that temperature of heat transfer fluid (Tfb) decreased with the increase in m. Tfb in March, April and May was higher than that in other months, while Tfb from June to December was the lowest. GH2, PSOFC and N in March, April and May were higher than that in other months irrespective of m. The optimum m was 0.030 kg/s.

This case study focuses on exploring the potential of biogas as a direct substitute for conventional fossil fuels in industrial applications. Instead of being limited to its common uses as a vehicle fuel or for power generation, this research aims to investigate how biogas can be integrated into industrial processes as an advanced and sustainable energy solution. By conducting a detailed analysis of the financial implications associated with the utilization of landfill gas and gas derived from biomass, a comprehensive understanding of their practical applications in the industrial sector can be obtained. This research goes beyond a superficial examination and delves into the intricate aspects of incorporating biogas as a viable alternative fuel source. When we say that utilizing biogas is technically feasible, it means that the necessary infrastructure and technologies are available to support its integration into industrial processes. This implies that there are existing methods and systems in place that can efficiently harness and utilize biogas for various industrial applications. These technologies ensure a seamless transition from conventional fossil fuels to renewable energy sources, leading to a more sustainable and environmentally friendly industrial landscape. Moreover, the statement that utilizing biogas is economically feasible emphasizes that the financial viability of employing biogas in industrial processes is advantageous. This research explores the potential cost savings and benefits associated with substituting conventional fuels with biogas. By carefully examining the economic aspects, such as initial investments, operational costs, and potential revenue streams, a comprehensive understanding of the financial impact of adopting biogas solutions in the industrial sector can be gained. By expanding and broadening the understanding of the technical and economic feasibility of biogas integration, this research aims to provide valuable insights into the potential of advanced biogas solutions for industrial applications. Indeed, to make biogas a viable and sustainable option as a raw material, it is necessary to establish farming subsidies and financial support specifically tailored for biogas production, similar to the existing support provided for food production. These subsidies and financial incentives play a crucial role in promoting and encouraging the growth of the biogas industry. By establishing such support systems, farmers and other stakeholders in the agricultural sector can be incentivized to participate in the production of biogas. This can involve the utilization of agricultural waste, organic residues, or dedicated energy crops for biogas generation. The financial assistance can help offset the initial investment costs, provide incentives for production, and ensure the economic viability of biogas projects. Moreover, when considering large-scale landfills or situations where industrial demands for energy are relatively low, landfill gas becomes a viable and practical option. Landfill gas, which is generated by the decomposition of organic waste in landfills, can be harnessed and utilized for various industrial purposes. This not only helps in managing waste but also provides a valuable source of renewable energy. In summary, while farming subsidies and financial support are crucial for promoting biogas production, landfill gas remains a viable option for large landfills or situations with minor industrial demands. The combination of these two approaches offers a comprehensive outlook on utilizing biogas as a renewable energy source for industrial applications.

The paper presents a synthesis of deep eutectic solvents (DESs) based on choline chloride (ChCl) as hydrogen bond acceptor and phenol (Ph), glycol ethylene (EG), and levulinic acid (Lev) as hydrogen bond donors in 1:2 molar ratio. DESs were successfully used as absorption solvents for removal of dimethyl disulfide from (DMDS) from model biogas steam. Several parameters affecting the absorption capacity and absorption rate has been optimized including kind of DES, temperature, the volume of absorbent, model biogas flow rate, and initial concentration of DMDS. Furthermore, reusability and regeneration of DESs by means of adsorption and nitrogen barbotage followed by the mechanism of absorptive desulfurization by means of density functional theory (DFT) as well as FT-IR analysis were investigated. Experimental results indicate that the most promising DES for biogas purification is ChCl:Ph, due to high absorption capacity, relatively long absorption rate, and easy regeneration. The research on the absorption mechanism revealed that van der Waal interaction is the main driving force for DMDS removal from model biogas.

The kinetics of biogas production from biomass depends on several factors such as: carbon to nitrogen ratio (C/N), reactor temperature (T), and retention time (RT). The purpose of this study was to obtain a new model for predicting biogas production. Spent Mushroom Compost (SMC) was used to produce biogas in a batch type reactor. The experiments were carried out with different C/N ratios (12.1, 20, 30 and 40) and in both mesophilic (35°C) and thermophilic (55°C) temperatures. The results showed that the Maximum biogas production at 35°C, C/N=20 was equal to 41.9 mL/gVS and 55°C, C/N=30 was equal to 51.6 mL/gVS. By using experimental data, a new kinetic model was proposed to predict biogas production. Comparing the values of the results indicate that the total values of RMSE for Logistics, Gampartz and new kinetic models was 0.1906, 0.1830 and 0.1617, respectively. Therefore, the process of anaerobic digestion of biomass can be assumed to be just a chemical reaction, and the new kinetic model is an appropriate alternative to microbial growth models.

Due to rapid urbanization, the quantity of wastewater treatment plants (WWTP) has increased, and with it the amount of waste generated by them. Sustainable management of this waste can lead to the creation of energy-rich biogas through the fermentation process. This review presents recent advances in the anaerobic digestion process resulting in greater biogas production. Disintegration techniques for enhancing waste activated sludge fermentation can be generally partitioned into biological, physical and chemical, each of which are covered in this review. These disintegration techniques were compared mainly in terms of their biogas yield. It was found that ultrasonic and microwave disintegration provides the highest biogas yield (>500%); however, they are also the most energy demanding (>10,000 kJ kg-1 total solids).

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.

In this study organic bulk heterojunction (BHJ) solar cell along with the spiro OMeTAD as a hole transport layer (HTL) and the PDINO as an electron transport layer (ETL) was simulated through the Solar Capacitance Simulator, one dimensional (SCAPS-1D) software to examine the performance of this type of polymer thin film organic solar cells. As an active layer the blend of polymer donor PBDB-T and non-fullerene acceptor ITIC-OE was used. Numerical simulation was performed by varying the thickness of HTL and active layer. Firstly, the HTL layer thickness was optimized to 50 nm, after that the active layer thickness varied up to 80 nm. These results of simulation demonstrated that the HTL thickness has rather little impact on efficiency while in the case of active layer thickness improves efficiency significantly. The temperature effect on the performance of solar cells was considered, by the simulation performed for temperature from 300 K up to 400 K and the efficiency of solar cell decreases with increasing temperature. Generally, polymer films usually are full of traps and defects, the density of defects (Nt) value was also introduced to the simulation, and it was confirmed that with the increasing of defect density (Nt), the efficiency of solar cell decreases. After thickness, temperature and defect density optimization, the reflection coating was also applied on it. It turned out that by introducing the reflection coating on the back side of solar cell, the efficiency increases by 2.5 %. Additionally, the positive effect of HTL and ETL doping on efficiency of this type of solar cells was demonstrated.

ABSTRACT Introduction: Traditional penile venous surgery and enhancement have been controversial. Based on De Novo penile fibrovascular assembly, we report on penile venous stripping (PVS) and factual penile girth enhancement (FPGE). Methods: From 2013 to 2023, 18 men underwent PVS and FPGE, all confirmed with veno-occlusive dysfunction (VOD). PVS involves the stripping of erection-related veins. FPGE was performed along the tunica albuginea bilaterally; then, the two tunic defects were repaired with a 70.0x30.0 mm venous stripe and covered with either autologous venous walls (AVW, n=10) or Surgiform (SF, n=8). The penile girth was measured, and radio-opaque contrast was used. The abridged 5-item version of the International Index of Erectile Function (IIEF-5) score and the Erection Hardness Scale (EHS) were used yearly during follow-up via the Internet. Results: The follow-up period was 5.3±1.5 years. Radiopacity was consistently enhanced. Although there was indifference between AVW and SF, there was a significant improvement (both P＜0.01) in IIEF-5 and EHS scores (9.7±2.8 vs. 20.8±2.3; 1.7±0.6 vs. 3.2±0.2, respectively). Both the diameters of the glans and penile shaft increased (28.0±2.3mm and 28.3±2.1mm vs 35.3±2.2mm and 36.3±2.1mm respectively). The satisfaction rate was 81.3% (13/16). Conclusions: This novel combination of PVS and FPGE is promising, although a larger sample size is needed for further validation.

The decentralized treatment of wastes is studied for an urban scenario considering a high-density population of 2500 inhab./km2. Food and garden waste were assumed to be treated in a co-digestion configuration using several mid-size digesters. In contrast biogas and digestate valorization were carried out in a centralized manner. Electricity and thermal energy were produced from this configuration, accounting for 1.3% of residential electricity demand and 3.2% of thermal demand when using double-turbocharged engines. However, the location of the treatment plants is a factor that may raise social discomfort. Rejection of locating the plant in specific sites close to residential neighbors, nuisance due to possible odor and gaseous emissions, and house market distortions are just a few of a long list of problematic aspects that may threaten the decentralized alternative. These factors are of great relevance and must be given a practical solution if the circular economic model is to be implemented by considering the insertion of waste streams into the production system and generating local energy sources and raw materials.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

The low water solubility of any pharmacoactive molecules limits their pharmacological potential but the solubility parameter cannot compromise, so different approaches are employed to enhance their bioavailability. Pharmaceutically active molecules with low solubility convey a higher risk of failure for drug innovation and development. Pharmacokinetics, pharmacodynamics, and several other parameters, such as drug distribution, protein binding and absorption, are majorly affected by their solubility. Among all pharmaceutical dosage forms, the oral dosage forms cover more than 50 %, and the drug molecule should be water-soluble. For good therapeutic activity by the drug molecule on the target site, solubility and bioavailability are crucial factors. The pharmaceutical industry screening programs identified that around 40% of new chemical entities (N.C.E.s) face various difficulties at the formulation and development step. These pharmaceuticals are attributed to their less solubility and bioavailability. The bioavailability and drug solubility enhancement are significant challenges in the area of pharmaceutical formulations. According to the Classification of Biopharmaceutics, Class II and IV drugs (APIs) exhibits poor solubility, lower bioavailability, and less dissolution. Various technologies are discussed in this article to improve the solubility of the poorly water-soluble drug, for example, complexation of active molecules, utilization of emulsion formation, micelles, microemulsions, cosolvents, polymeric micelles preparation, particle size reduction technologies, pharmaceutical salts, prodrugs, solid-state alternation technique, soft gel technology, drug nanocrystals, solid dispersion methods, crystal engineering techniques and nanomorph technology. This review mainly describes several other advanced methodologies for solubility and bioavailability enhancement, such as crystal engineering, micronization, solid dispersions, nano sizing, use of cyclodextrins, solid lipid nanoparticles, colloidal drug delivery systems and drug conjugates, by some appropriate research reports.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

Making sustainable business models, especially in manufacturing, is a key approach for organizations to adopt in order to create more sustainable processes. New technologies are needed to implement new policies that can minimize the economic, environmental, and social consequences of industrial activity. Sustainable maintenance is decisive to guarantee the availability, reliability, and safety of assets, surpassing traditional maintenance policies and embracing the principles of the circular economy. The main objective of Maintenance 4.0 is to increase the lifespan and availability of the equipment by preventing unplanned downtime, reducing planned shutdowns, and improving safety. This research focuses on the integration of advanced Industry 4.0 tools with conventional monitoring and control techniques in process industries. The article suggests that while complex neural networks might provide good predictions, simpler solutions can generalize well and have lower prediction errors because they can capture the underlying relationships between variables in a specific asset better. The combination of maintenance function, Industry 4.0 tools, and advanced statistical solutions to obtain sustainable processes can facilitate organizational change and aid decision-making in equipment management. These solutions seek to reduce interruptions and expenses through the unnecessary utilization of labor and spare parts. All these considerations improve communication and data processing, resulting in improved maintenance of a hydrogen compressor in the petrochemical industry.

The article presents comparative results from 5 categories of animals (4 samples originating from pigs, 2 from cows, 1 from chicken, 1 from ostriches, 1 from horses, samples quantified with A, B, C, D, E, F, G, H, I). These samples were collected from large, medium and small (family) farms. The results were validated by the biogas production generated by the respective samples, using the Automatic Methane Potential Test System (AMPTS II) bio-processor. The largest amounts of biogas (2,464.5 Nml, 2,262.0 Nml) were recorded for the samples from pig farms (Sample B and Sample D), whereas the lowest values were obtained in the case of Sample A (414.1 Nml) and Sample C (558.3 Nml). The heavy metals analyzed in the article are Zn, Cu, Fe, Mn, Ca, K, As. The results corroborate that the C/N ratio can also vary to values lower than 20-30, without affecting the biogas production process, and the heavy metal content must be known especially for the full management of the wastes. All final data presented in the article are based on repeated, triplicate experiments, with less as 2% deviation between data.

Chitosan's favourable characteristics, such as biocompatibility, biodegradability, and bioactivity, have made it a popular biomaterial and the subject of numerous studies over the last years. This study investigates the enhancement of mechanical properties of chitosan fibres through wet impregnation using tripolyphosphate (TPP) as cross-linker. A 1% w/v solution of TPP was prepared and five separate chitosan fibre specimens were immersed individually in TPP solution for varying times (1, 2, 4, 6, and 8 hours). The specimens were then characterized using FTIR, SEM, and dynamometry to evaluate their physical, chemical, and mechanical properties. The FTIR results indicated successful interaction between the protonated amine groups of chitosan and TPP ions, with the crosslinking reaction reaching completion after 2 hours of immersion. SEM images showed that the surface of the fibres became rougher after the crosslinking reaction, suggesting the formation of new chemical bonds. Mechanical testing revealed a significant improvement in tensile strength for the treated samples compared to untreated ones, indicating successful strengthening through crosslinking. The fibres demonstrate significant potential for use in wound healing dressings.

Scintillators play a key role in the detection chain of several applications which rely on the use of ionizing radiation, and it is often mandatory to extract and detect the generated scintillation light as efficiently as possible. In positron emission tomography (PET), for example, both energy resolution and coincidence resolving time, two of the key detection parameters, depend strongly on the total amount of light which reaches the photodetector surface as well as its spatial and temporal distribution. Typical inorganic scintillators do however feature a high index of refraction, which impacts light extraction efficiency in a negative way. Furthermore, several applications such as preclinical PET rely on pixelated scintillators with small pitch. In this case, applying reflectors on the crystal pixel surface, as done conventionally, can have a dramatic impact of the packing fraction and thus the overall system sensitivity. This paper presents a study on light extraction techniques, as well as combinations thereof, for two of the most used inorganic scintillators (LYSO and BGO). Novel approaches, employing distributed Bragg reflectors (DBRs), metal coatings, and a modified Photonic Crystal (PhC) structure, are described in detail and compared with commonly used techniques. The nanostructure of the PhC is surrounded by a hybrid organic/inorganic silica sol-gel buffer layer which ensures robustness while maintaining its performance unchanged. We observed in particular a maximum light gain of about 41% on light extraction and 21% on energy resolution for BGO, a scintillator which has gained interest in the recent past due to its prompt Cherenkov component and lower cost.

In this manuscript we report new effects of resonance detuning on various dynamical parameters of a generic 3-wave system. Namely, for suitably chosen values of detuning the variation range of amplitudes can be significantly wider than for exact resonance. Moreover, the range of energy variation is not symmetric with respect to the sign of the detuning. Finally, the period of the energy oscillation exhibits non-monotonic dependency on the magnitude of detuning. These results have important theoretical implications where nonlinear resonance analysis is involved, such as geophysics, plasma physics, fluid dynamics. Numerous practical applications are envisageable e.g. in energy harvesting systems.

The goal of germplasm enhancement is to introgress traits from wild crop relatives into cultivated material and eventually cultivars. It seeks to restore genetic diversity that has been lost over time or to augment cultivated material with novel alleles that improve parents in breeding programs. This paper discusses potato germplasm enhancement efforts in the past, focusing on effective examples such as disease resistance and processing quality. In addition, it outlines new strategies for enhancement efforts, shifting the focus from evaluating phenotypes to tracking and manipulating specific DNA sequences. In the genomics era, germplasm enhancement will increasingly be focused on identifying and introgressing alleles rather than traits. Alleles will come from a broad pool of genetic resources that includes wild species relatives of potato, landraces, cultivated potato itself, and distantly related species. Genomics tools will greatly increase the efficiency of introgressing multigenic traits, and will make it possible to identify rare alleles and utilize recessive alleles.

Quantitative phase microscopy (QPM), a technique combining phase imaging and microscopy, enables visualization of the 3-D topography in reflective samples as well as the inner structure or refractive index distribution of transparent and translucent samples. However, as in conventional optical microscopy, QPM provides either a large field of view (FOV) or a high resolution but not both. Many approaches such as oblique illumination, structured illumination and speckle illumination have been proposed to improve the spatial resolution of phase microscopy by restricting other degrees of freedom (mostly time). Therefore, the space bandwidth product (SBP) of QPM becomes enlarged. This paper aims to provide an up-to-date review on the resolution enhancement approaches of QPM, discussing the pros and cons of each technique as well as the confusion on resolution definition claim on QPM and other coherent microscopy.

The Bacillus megaterium LVN01 species native to Colombia has demonstrated the ability to metabolize different co-products or industrial waste (such as fique juice, cane molasses, residual glycerol) and accumulate polyhydroxybutyrate (PHB), giving it potential in the bioplastics industry. In this research, the potential of liquid digestate as a carbon source for the production of PHA in fermentation processes with this bacterial strain was evaluated. Favorably, it was found that B. megaterium utilizes the nutrients from this residual substrate to multiply appropriately and efficiently synthesize poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV). Bench scale aerobic batch fermentation, under the operational conditions of this research [Volume: 3L; Temperature: 30.8°C; Agitation: 400 rpm; pH: 7.0 ± 0.2; Disolved Oxygen: 100% saturation; Antifoam 10% (v/v)], generated maximum values of DCW (0.56 g cell L-1) at 60 h, while the maximum PHB yield (360 mg PHB L-1) occurred at 16 h, which is very favorable for sustainable degradable bioplastics production. Additionally, GC-MS and NMR analyses confirmed that the PHBV copolymer synthesized by B. megaterium is made up of the monomers 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV). Furthermore, the thermal properties determined by TGA (Tonset=283.1 ºC, Tendset= 296.98 ºC. Td =290.114 °C) and DSC (Tm= ºC155.7 °C, ΔHf= 19.80 J g-1, Xcr = 18.17%), indicate that it is a thermally stable biopolymer with low percentages of crystallinity, providing flexibility that facilitates molding, adaptation, and application in various industrial sectors.

This study investigates the efficiency of a biogas-powered cooling system through the utilization of energy and exergy calculations. Biogas, which can be generated and stored in small-scale plants as needed, serves as a viable fuel source for absorption cooling systems. The present research focuses on the biogas consumption of a triple-effect absorption cooling system, specifically designed to supply a fixed cooling load of 100 kW under varying operational conditions. The study highlights the COP (Coefficient of Performance) and ECOP (Exergetic Coefficient of Performance) values of the system, along with the exergy destruction rates of its individual components, at the optimal temperatures of operation. Furthermore, to determine the necessary biogas consumption, the study explores the establishment of dedicated farms for various animal species, ensuring an adequate number of animals for biogas production. The findings reveal a COP of 1.78 and an ECOP of 35.4% at the optimized operating temperatures. The minimum mass flow rate of biogas is determined to be 0.0034 kg/s, facilitating the operation of the boiler with a methane content of 65%. The study concludes that a total of 290 head of cattle is required to generate the annual biogas consumption necessary for the cooling system. Also, number of the cattle is enough to establish 284 biogas plants in Bursa province in Türkiye.

Wild artisanal cultures, such as SCOBY-based membranes (Symbiotic Culture of Bacteria and Yeasts) and water kefir grains (WKG), are complex consortia of microorganisms comprising yeasts, lactic and acetic acid bacteria, with a large strains’ diversity and abundance. The fermented products (FPs) obtained by the microbiome’s contribution can be included into functional products due to their metabiotics (pre-, pro-, post-, and paraprobiotics) as a result of the complex and synergistic association as well as due to the metabolic functionality. In this study, both consortia of SCOBY and WKG were involved in the co-fermentation of a new formulated substrate, that was further analysed aiming at increasing of the postbiotic composition of the FPs. Plackett-Burman (PB) and Response Surface Methodology (RSM) techniques were used for the experimental designs to select and optimize the most important parameters that influence the freeze-dried starter cultures of SCOBY and WKG activity as a multiple inoculum. The tea concentration (1-3%), sugar concentration (5-10%), raisins concentration (3-6%), SCOBY lyophilized culture concentration (0.2-0.5%), WKG lyophilized culture concentration (0.2-0.5%), and fermentation time (5-7 days), were considered the independent variables for mathematical analysis and fermentation conditions’ optimization. The pH, total acidity (TA), antioxidant activity (DPPH), and antimicrobial activity against Escherichia coli ATCC 25922, Bacillus subtilis MIUG B1, Staphylococcus aureus ATCC 25923, and Aspergillus niger MIUG M5 were evaluated as responses. The rich postbiotic bioactive composition of the FP obtained in optimized biotechnological conditions highlighted the usefulness of the artisanal co-cultures, through their symbiotic metabolic interactions for the improvement of bioactive potential.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

Sometimes when diabetic retinopathy (DR) is found and treated quickly, vision loss can indeed be spared. This study deploys a deep learning (DL) model that can discover all 5 stages of DR more accurately than other methods. The proposed methodology shows two cases scenarios: case 1 with image enhancement using CLAHE and ESRGAN, and case 2 without image enhancement. Augmentation techniques are then employed to produce a balanced dataset with the identical criteria for both scenarios. The generated model using DenseNet-121 on the APTOS dataset outperformed other approaches for locating the 5 stages of DR, with an accuracy of 98.7 percent for case 1 and 81.2 percent for case 2. Using CLAHE and ESRGAN was shown to improve a model's performance and ability to learn.

Distributed fiber optic vibration sensors based on Φ-OTDR using Rayleigh backscattering of highly coherent light source pulses are very popular type in the area of sensors due to many attractive features, mainly due to its distributed manner of sensing, i.e. they can operate as thousands of local sensors simultaneously. The paper summarizes and describes several important modifications of the original sensor scheme to enhance its key performance parameters and to extend its application potential. A multi-fiber sensor form was proposed. Single- and two-fiber sensors were designed, constructed and tested both in the laboratory and in a real environment. Variants of two-fiber sensor utilizing optical switches for the sensor flexibility enhancement are proposed and their applications are described.

The inaccurate estimates of linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrades speech enhancement performance. The existing methods proposed a tuning of the biased Kalman gain particularly in stationary noise condition. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then construct a whitening filter (with its coefficients computed from the estimated noise) and employed to the noisy speech, yielding a pre-whitened speech, from where the speech LPC parameters are computed. Then construct KF with the estimated parameters, where the robustness metric offsets the bias in Kalman gain during speech absence to that of the sensitivity metric during speech presence to achieve better noise reduction. Where the noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on NOIZEUS corpus demonstrates that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of a fault(s) in the system. A subsection with variable boundaries is referred to as island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of islanding boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage a dynamic voltage sensitivity factor (DVSF) is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak busbars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB softwares.

This paper aims to study the performance of solar collectors with various sizes under different weather conditions of different Japanese cities, i.e. Kofu city, Nagoya city and Yamagata city. The heat generated from the solar collector was used to conduct a biogas dry reforming reactor in order to produce H2 to feed a solid oxide fuel cell (SOFC). This study has revealed that the output temperature of solar collector, Tfb in April and July was higher than that in January and October irrespective of city. The optimum length of absorber (dx) of the collector was 4 m irrespective of city. It was clarified that Tfb in Yamagata city in January and October, i.e. winter and autumn is lower than that in Kofu city and Nagoya city especially, which is influenced by the tendency of a solar intensity (I) strongly, not velocity of surrounding air (ua). On the other hand, Tfb is almost the same in April and July, i.e. spring and summer, irrespective of city. The amount of produced H2 from the biogas dry reforming reactor and the power generated by SOFC using H2 in spring and summer were higher compared to the other seasons irrespective of city. This study has revealed that the highest available households number per month was 4.7 according to the investigation in this study.

Ferric oxide containing waterworks sludge can be used to reduce formation of hydrogen sulfide during anaerobic digestion. The ferric compound is reduced biochemically in the digester and forms insoluble pyrite in digester sludge. Often virgin ferric chloride is used to solve the hydrogen sulfide problem. Since 2013, Sydvatten AB has supplied a growing number of digestion plants in Sweden with ferric containing dewatered waterworks sludge from the drinking water treatment plant Ringsjöverket to limit the formation of hydrogen sulfide. At the waterworks, ferric chloride is added to enhance coagulation of organic matter from the source water. The sludge formed in this process is dewatered and landfilled, but also recycled in the biogas production in order to reduce the hydrogen sulfide concentration. In this study, the use of sludge, for hydrogen sulfide removal in digesters, was technically and economically evaluated from cases studies from 13 full-scale digesters in Sweden. Compared with use of fresh ferric chloride, the operational costs are reduced up to 50 % by using sludge. The quality of the sludge is high and its content on metals is low or very low, especially when compared with requirements from different certification standards on biosolid reuse applied in Sweden. An addition of waterworks sludge containing iron to a digester for removal of dissolved hydrogen sulfide is a technically and economic good alternative when producing biogas. It is also one step closer to a circular economy, when replacing use of virgin chemicals with the by-product waterworks sludge which saves energy, material and reduces the carbon foot-print of the waterworks.

Salinity, resulting from climate change and excessive mineral fertilization, burdens farmers and negatively impacts soil and water ecosystems in the Northeastern Nile Delta. Organic and biological approaches are crucial for addressing these issues. This study examined the effects of individual and combined inoculations with cyanobacteria, yeast, and Arbuscular Mycorrhizal Fungi (AMF), with or without K-humate, on enhancing salinity tolerance, increasing yield, and improving crop quality of wheat and faba bean. The aim was to reduce NPK mineral fertilizer application rates to 25% of recommended doses. An economic evaluation was also conducted. In a preliminary laboratory experiment, the interactive effects of these microbiomes on the production of plant antioxidant enzymes and soil enzymes were investigated under salinity stress. Results showed that co-inoculation, especially with K-humate, yielded superior outcomes compared to individual inoculations. The laboratory experiment findings were confirmed by a field trial conducted in saline-alkaline soil in the Northeastern Nile Delta region. The study found that the integrated inoculation of cyanobacteria, yeast, and mycorrhizae with K-humate, along with 25% mineral NPK fertilization enhanced microbial activity, including AMF colonization, spore density, and total bacteria, cyanobacteria, and yeast counts in the plant rhizosphere. It also increased soil enzyme activity, improving soil fertility compared to individual or mineral control treatments. This integration resulted in wheat yields of 2.77 tons/ha and faba bean yields of 4.75 tons/ha. Crop quality improved, with higher levels of nitrogen, potassium, phosphorus, and protein in wheat grains and faba bean seeds. Microbiomes inoculation increased potassium uptake over sodium, enhancing the potassium/sodium ratio and salt stress tolerance. This approach reduces reliance on costly mineral fertilizers by up to 75%, enabling economically viable cultivation in marginal lands. These sustainable practices optimize resource utilization, maximize economic efficiency, and preserve natural resources.

Dearth of information on extent of genetic variability in cassava limits the genetic improvement of cassava in Sierra Leone. This study aimed at assessing the genetic variability and relationships within 103 cassava genotypes using agro-morphological and culinary markers. A field trial was conducted in the School of Agriculture and Food Sciences research site. The experiment was laid in an augmented randomized block design. Morphological classification based on qualitative and quantitative traits categorized the germplasm into three different groups. A total of ten principal components (PCs) in the qualitative and six PCs in the quantitative trait sets accounted for 66.74% and 67.27% of the total genetic variation, respectively. Traits with significantly strong and positive correlations included presence of fruit (PFRT) and presence of seed (PSE)(r = 0.86***),root yield per plant (RYPP) and number of storage root (NSR) (r = 0.76***), RYPP and number of commercial root (NCR) (r = 0.68***), length of leaf lobe (LLL) and width of leaf lobe (WLL) (r = 0.66***),and between NSR and NCR (0.84***). Cooking time, cooking percentage, texture, mealiness, taste and aroma varied widely among the accessions. Findings established that the cassava germplasm possess useful genetic variability that could be exploited through selection for short term release programme, genetic conservation of valuable germplasm and genetic improvement of the crop.

Whilst Model Reference Adaptive Control theory provides mathematical techniques for achieving the performance of the system without undue dependence upon dynamic systems models, its applicability may not be suitable for systems that are crucial to safety due to suboptimal transient and error convergence performance. In this article, our approach enhances the performance of MRAC by utilizing a Generalized Dynamic Inversion (GDI). Two control actions are permitted under the GDI control law. One control is the particular part that is responsible for enforcing the reference system-constrained behaviors. Another control action is carried out by the control law’s auxiliary component and is incorporated into the standard MRAC control law. This augmented part is referred to as a null control vector, and it is used to enhance the performance of MRAC. The classic MRAC control law, when supplemented with a null vector term consisting of an adaptive gain that changes in response to tracking errors, forces the dynamical system to behave similarly to the reference model. This null vector, in particular, restricts the oscillation intensity of the tracking errors, which is employed to generate the adaptive parameters. It is demonstrated that this critical element of our approach enhances transient performance and ensures that error dynamics vanish asymptotically. Extensive simulations have been conducted to prove the efficacy of the proposed method.

This paper proposes to combat active eavesdropping by intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS and the allowable maximum SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

In this paper, the power quality of interconnected microgrids is managed using a Model Predictive Control (MPC) methodology which manipulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the microgrids working modes: grid-connected, islanded and interconnected. The results and simulations are also applied to the transition between the different working modes. In order to show the potential of the control algorithm, a comparison study is carried out with classical Proportional-Integral Pulse Width Modulation (PI-PWM) based controllers. The proposed control algorithm not only improves the transient response in comparison with classical methods but also shows an optimal behavior in all the working modes, minimizing the harmonics content in current and voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems

The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a cuckoo optimization algorithm. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary cuckoo optimization algorithm. The results procured after simulation by the cuckoo optimization algorithm are compared with Bacterial foraging algorithm and ant colony optimization based image enhancement and this approach is found to be improved.

Multi-junction solar cells comprised of stacked III-V semiconductor junctions represent the highest-efficiency photovoltaic technology, with recent demonstrations exceeding 47% efficiency . Optimizing the design and thickness of each junction is critical for maximizing performance . This work utilizes Silvaco TCAD tools to systematically optimize a 5-junction cell based on AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge similar to recent record cells . The junction thicknesses are varied using a predictive profiler to sample the parameter space . For each combination, the spectral absorption and I-V characteristics are simulated to determine the efficiency . Statistical analysis identifies the optimal thickness set that maximizes performance to 26% under 1 sun illumination .

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This study investigates the efficacy of an integrated rehabilitation program that combines vestibular and knee-focused therapies to improve proprioceptive function, balance, and motor control in patients suffering from both Benign Paroxysmal Positional Vertigo (BPPV) and knee joint conditions. The study involved 59 participants diagnosed with both vestibular dysfunction and knee impairments, who were enrolled in a comprehensive rehabilitation program. Vestibular interventions included specific maneuvers such as the Epley maneuver and Brandt-Daroff exercises, aimed at repositioning otoliths and reducing vertigo symptoms, while balance retraining exercises were implemented to enhance stability and prevent falls. Additionally, TECAR therapy was used for its deep tissue heating and regenerative benefits, and specific proprioceptive exercises targeting the knee joint were conducted to improve joint position sense and reduce the risk of falls. The intervention resulted in significant improvements in proprioceptive accuracy, as evidenced by enhanced joint position sense measurements and increased scores on the Dynamic Gait Index (DGI). These findings suggest that the dual-targeted rehabilitation approach not only addresses the individual impairments associated with vestibular dysfunction and knee pathology but also effectively improves overall functional outcomes by tackling the complex interplay between these conditions. The study further highlights the importance of a multidisciplinary approach in rehabilitation, where addressing vestibular and musculoskeletal dysfunctions simultaneously can lead to better patient outcomes. The integrated rehabilitation strategy presented in this study offers a comprehensive approach to managing complex clinical cases, ultimately contributing to improved quality of life and functional independence in patients affected by concurrent vestibular and knee joint issues.

Anchovies are amid the largest fish catch worldwide. The anchovy fillet industry generates a huge amount of biowaste (e.g. fish heads, bones, tails) that can be used for the extraction of several potentially valuable bioproducts including omega-3 lipids. Following the extraction of valued fish oil rich in omega-3, vitamin D₃ and zeaxanthin from anchovy fillet leftovers using biobased limonene in a fully circular process, the solid residue was used as starting substrate for the production of biogas by anaerobic digestion. The results first reported in this study demonstrate good potential energy recovery of the anchovy sludge of about 280 mL_CH4∙g_VS^-1. Due to unbalanced C/N ratio typical of marine biowaste, co-digestion with a carbon rich substrate is recommended.

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.

Self-enhancement (SE) is often overlooked as a fundamental cognitive ability mediated via the Pre-Frontal Cortex (PFC). Here we present research that establishes the relationship between the PFC, SE, and the potential evolved beneficial mechanisms. Specifically, we believe there is now enough evidence to speculate that SE exists to provide significant benefits and should be considered a normal aspect of the self. Whatever the metabolic or social cost, the upside of SE is great enough that it is a core and fundamental psychological construct. Furthermore, though entirely theoretical, we suggest that a critical reason the PFC has evolved so significantly in Homo sapien is to, in part, sustain SE. We therefore elaborate as to its proximate and ultimate mechanisms.

This study analyzed the anaerobic co-digestion of Inoculum (I), Rubber Plant Effluent (RPE) and Gliricidia leaves (GLS) at different mixing ratios for the simultaneous production of methane and organic fertilizer. The results were analyzed for volatile fatty acids, pH, C/N ratio, and methane production. The organic quality of the substrate mixture before and after anaerobic digestion was analyzed for organic carbon, Kjeldahl nitrogen, potassium, and phosphorus content. The study concluded that the inoculum, Rubber Plant Effluent (RPE), and Gliricidia leaves (GLS) mixed in the ratio of 10:2:2 showed higher methane production than other experiments conducted at different mixing ratios. The nitrogen, Potassium and phosphorus content of the substrates are increased after anaerobic digestion. However, the organic carbon content in the substrate decreased in all experiments. This research recommends the planting of rubber trees and Gliricidia maculata to promote economic development in Mauritania. Keywords: Biogas production; Gliricidia leaves; Methane; Rubber Plant Effluent; Organic Manure

With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, K-Nearest Neighbor (KNN), Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques.

The microgrid is a small-scale, autonomous decentralized power plant with its own distributed generation, storage capacity and multiple loads, with the capacity to function in grid interconnected and an island mode. The decentralized control of microgrids with parallel operated voltage source converters (VSCs) is proposed in this paper to improve power quality using a machine learning approach. The DNN based MPPT controller is proposed and its best performance is presented. The SRF-PLL is utilized for AC side synchronization in VSC control. The proposed microgrid involves two PV arrays fed to two voltage source converters along with their independent controls, connected in parallel through LC filters and line coupling transformers and serves the loads at PCC. The proposed model is simulated using MATLAB/Simulink. The dq-framed inner loop control is employed to individually regulate the real and reactive power at the point of common coupling. Furthermore, the proposed model is analyzed and compared by employing a mathematical model of AC system dynamics, inner loop control, output voltage quality, AC harmonic spectrum analysis, and total harmonic distortion (THD) in both grid interconnected and island mode. In island mode, AC harmonic spectrum and THD are accomplished within the permissible range.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

Patch antennas are compact, less complex, planar structures and therefore, widely used in small satellite missions for telecommand, data link, and intersatellite link, particularly in S- band and X- band. Improved performance of these patch antennas in terms of gain and compactness will di-rectly affect the communication efficiency of small satellite missions. Especially the coming IoT (Internet of Things) constellations require high gain and efficient antenna arrays. An optimization of single patch antenna elements is an important cornerstone for the missions. Therefore, the ef-fects of various antenna enhancement techniques, such as slotted ground plane, resistor and ca-pacitor integration, parasitic patch elements, are analyzed. These techniques were applied on a rectangular patch antenna with parameter variation to identify the optimal performances with respect to bandwidth, operating frequency, gain, polarization, and power flow. Finally, the techniques were combined to obtain an optimized antenna in terms of gain and compactness. The results were compared to a slotted reference antenna. For the scenario of a 2.4 GHz patch antenna, a gain optimization of 27 % (from 7.09 to 8.14 dBi) or size reduction of 52 % (from 96.04 to 46.2 cm²) could be achieved. Overall, our study revealed an effective way to increase the patch antenna performance, which can directly contribute to more efficient communication links and design of antenna arrays.

Along with the increasing industrialization of contries and population growth, the global. With the thougth of overcoming climate change and renducing greenhouse gas emissions in the atmosphere, interest in alternative sloar, wind, water, geothermal, tidal and ocean wave energy has significantly increased. Hydropower is one of the most used inexhaustible sources of energy on a global scale. The efficiency of water turbines can be increased by optimizing the blade cascade and the casing. For this purpose, research is conducted on specialized laboratory benches. The purpose is to determine the influencing parameters which affecting the nature of the power and cavitaion curves This paper presents the results of numerical and laboratoty studies of axial hydrokinetic turbines. A detailed analysis of the influence of the geometric parameters of the blades on the operating curves is made. A mathematica model for synthesis of the blade cascade is derived. An assessment of water energy soures has been made and sites of high energy potential have been pointed. The present material can be used for development of a methodology for calculation of efficient axial hydrokinetic turbines.