This research is aimed at determining the characteristics of the current level of entrepreneurial potential of the Three Seas Initiative (3SI) countries, the ability to overcome the consequences of extraordinary events, such as COVID-19 and prospects for the return to an accelerated development once the destabiliser of the economic system, the coronavirus pandemic, has ceased. Eurostat, World Bank and the World Economic Forum data for 2015-2019 were used for the purpose of the research. The research was divided into three stages, i.e. assessment of economic development on the basis of a synthetic ratio of economic anchor development, for which a relative benchmark method based on spatial median (so-called L1 median or Weber point) was used, identification of conditions for the development of entrepreneurial capacity and statistical analysis showing the correlation between economic anchor measures and selected factors of the 3SI countries economic development. Our study found that the entrepreneurial capacity of the 3SI countries in 2015-2019 was determined by nine characteristics, belonging to six areas, i.e. local economy, demographic situation, social situation, trade exchange, innovation and tourism economy. The entrepreneurial potential of the 3SI countries was spatially diversified, and its development was determined, among others, by the entrepreneurial activity of residents (entrepreneurship index) and the conditions for running a business.

Synthetic biology uses engineering principles to redesign organisms and construct biological systems with specific functions, which is an important way to develop drugs, vaccines, biofuels, etc. In the process of transforming synthetic organisms, researchers need to conduct large-scale experiments for a long time, and many of these processes are manual and repetitive tasks, which results in the problems such as low throughput, low efficiency, and slow iteration. In addition, there are many experimental instruments and equipments required in the synthetic biology experiment process. The communication protocols between different instruments and equipments are not uniform, which seriously restricts the automation upgrade process of synthetic biology. To solve these problems, this paper put forward an integration technology based on the Object Linking and Embedding for Process Control Unified Architecture (OPC UA) to realize the interconnection of the synthetic biology automated casting platform and related instruments needed in synthetic biology process. The stability of the system is further designed and improved by using the concept of Triple Modular Redundancy. This helps the development of an efficient, stable and scalable automated casting platform for synthetic biology, which greatly improves the efficiency of synthetic biology experiments.

Open Inertia Modelling (OpInMod) is a modelling framework designed to create unit commitment and economic inertia dispatch optimisation problems. Present energy system modelling generators do not consider power system inertia in unit commitment and economic dispatch modelling to assess future energy system pathways. However, maintaining sufficient power system inertia in power systems is the foundation for power frequency controllability. The work at hand describes the functionality and approaches of open source tool OpInMod. The softwares universal design approach increases reuse potential. OpInMod is distributed with a set of examples to test and understand OpInMod

Meloidogyne nematodes, commonly known as root-knot nematodes, pose a considerable threat to crop yields, resulting in significant economic losses due to their intricate biology and limited control methods. In line with the European Union's focus on promoting organic farming and soil health to encourage sustainable agricultural practices, this study explores the efficacy of two characterized bacterial communities obtained from compost, namely SynCom1 and SynCom2, against Meloidogyne javanica in tomato plants. Through pot experiments, it is demonstrated that both bacterial communities effectively suppress nematode reproduction and root infections while simultaneously enhancing various growth parameters in tomatoes. These findings underscore the potential of synthetic bacterial communities as promising tools for organic or integrated pest management, thereby supporting sustainable agricultural practices and contributing to improved crop yields.

This study develops a structure for mapping native vegetation in a transition area between Brazilian Cerrado and Atlantic Forest from integrated spatial information from the Sentinel-1 and Sentinel-2 satellites. Most studies use this data separately, however, the integrated use of optical sensors with Synthetic Aperture Radar (SAR) sensors can improve the methodology for classifying land use and land cover (LULC). The fusion of Sentinel-1 and Sentinel-2, for example, can increase the accuracy of mapping algorithms, using complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified, using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings in which the SAR and optical data were evaluated separately (C1 and C2, respectively) and in an integrated manner (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlandsin C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the use of SAR and optical data becomes an alternative to optimize LULC mapping in tropical regions, due to the limited influence of atmosphere on images in the microwave range.

Plastics are very versatile materials that have contributed to the development of society since the 19th century, however, their mismanagement has led to an accumulation of waste in almost every ecosystem affecting the fauna of the planet. In this way recently, studies have shown that some insects might be able to adapt, consuming a wide range of hydrocarbon base polymers. In this work it was studied the adaptive capacity of Zophobas morio larvae when feeding on different synthetic polymers derived from petroleum. Four 30-day larval feeding treatments were carried out with synthetic polymers as expanded polystyrene (PS), low density polyethylene (PBD), polyisoprene (PI) and butyl rubber (BT), in addition a positive control of organic diet was included. Intestinal bacteria were isolated from the treatments and identified by Sanger sequencing. To analyze the chemical composition and physical form of the frass produced, fourier transform infrared spectroscopy (FITR) and images of the feces surface were taken with scanning electron microscopy (SEM) respectively. Zophobas morio larvae were able to consume 54% of PS in 30 days, equivalent to 3.2 mg/d/larva. Nine bacterial strains associated with the decomposition of synthetic polymers were identified in the intestine of the larvae. As for the physicochemical analysis of the feces, FTIR spectra showed the scission of bands corresponding to functional groups of the synthetic polymers in the comparison of the plastic diet treatments versus the feces of antibiotic-treated and plastic-fed larvae, while the comparison of spectra of the plastic and control treatments also identified differences in the absorption peaks. SEM imaging demonstrated that superworm feces differed in dependence on the substrate consumed. The findings demonstrate that Zophobas morio larvae possess a gut biological complex that allows them to feed and survive by consuming various petroleum-derived polymers.

The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

Biometric fingerprint identification hinges on the reliability of its sensors, however, calibrating and standardizing these sensors poses significant challenges, particularly in regard to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like shaped phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels and in the contactless fingerprint sensor setting, 3D printing and silicone filling provides the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

Indole is a versatile pharmacophore widely distributed in bioactive natural products. This privileged scaffold has been found in a variety of molecules isolated from marine organisms such as algae and sponges. Among these, indole alkaloids represent one of the biggest, most promising family of compounds, having shown a wide range of pharmacological properties, including anti-inflammatory, antiviral and anticancer activities. The aim of this review is to show the current scenario of marine indole alkaloid derivatives, covering not only the most common chemical structures, but also new synthetic routes developed during the last years as well as promising therapeutic applications.

The general synthesis of boron-containing cyclic compounds (boracycles) necessitates toxic organotin precursors or highly reactive boron halides. Here we report the synthesis of seven- and five-membered boracycles utilizing arylboronic acid pinacol esters (ArBpins) as stable boron sources. Grignard reagents generated from 2,2′-dibromodibenzyl or 2,2′-dibromobiphenyl reacted with ArBpins, where Ar = 9-anthryl (Anth), 2,4,6-trimethylphenyl (Mes), 2,4,6-triisopropylphenyl (Tip), to give 10,11-dihydro-5H-dibenzo[b,f]borepins or dibenzoborole derivatives. This Bpin-based method was successfully applied to a one-shot double boracycle formation, providing a dihydrodibenzoborepin–anthracene–dihydrodibenzoborepin triad molecule in a good yield. The dihydrodibenzoborepin bearing the Anth group was directly converted to the unsaturated borepin by NBS/AIBN. All products were characterized by NMR, HRMS, and in some cases, single-crystal X-ray diffraction analysis. Additionally, the photophysical properties of the products are also reported.

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

The microbial contamination in food packaging have been a major concern that paved the way for the search for natural based new anti-microbial agents, such as modified α-mangostin. In the present work, twelve synthetic analogs were obtained via semi-synthetic modification of α-mangostin by Ritter reaction, reduction by palladium-carbon (Pd-C), alkylation, and acetylation. The evaluation of the anti-microbial potential of the synthetic analogs showed higher therapeutic value than the parent molecule. The anti-microbial studies proved that I E showed higher antibacterial activity whereas I I showed most significant antifungal activity. Due to their microbial properties, modified α-mangostin can be utilized as active anti-microbial agents in food packaging.

Genetic switches can be utilized for many purposes in synthetic biology including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although genetic switches have mainly been developed in prokaryotes to date, eukaryotic genetic switches are increasingly being reported as both rational and irrational engineering technologies mature. In this review, we describe genetic switches in yeast based on synthetic transcription factors and/or synthetic promoters. We also discuss directed evolution technologies for the rapid and robust construction of yeast genetic switches.

Natural cytokinins are a promising group of cytoprotective and anti-tumor agents. In this research, we synthesized a set of novel cytokinin analogs with alkyl substituents at various positions of the aromatic moiety, as well as with oxygen-containing groups, and tested their antiproliferative activity in MDA-MB-231, A-375, and U-87 MG cell lines, and cytoprotective properties in H2O2 and CoCl2 models. Two amino-linked compounds were anti-proliferative with the ability to act synergistically with doxorubicin, and two carbamate-based ones were cytoprotective. According to the molecular docking data, anti-proliferation was based on the A2AR and APRT inhibition and cytoprotection on the CDK2 inhibition. The obtained results are promising for the development of novel anti-cancer therapeutics.

Lipids have not traditionally been considered likely candidates for catalyzing reactions in biological systems. However, there is significant evidence that aggregates of amphiphilic compounds are capable of catalyzing reactions in synthetic organic chemistry. Here we demonstrate the potential for the hydrophobic region of a lipid bilayer to provide an environment suitable for catalysis by means of a lipozyme, or lipid aggregate capable of speeding up a chemical reaction. By bringing organic molecules into the non-polar or hydrophobic region of a lipid bilayer, reactions can be catalyzed by collections of small, nonpolar or amphiphilic molecules. We demonstrate this concept by the ester hydrolysis of calcein-AM to produce a fluorescent product, which is a widely used assay for esterase activity in cells. The reaction was first carried out in a two phase octanol-water system with the organic phase containing the cationic amphiphiles cetyltrimethylammonium bromide (CTAB) or octadecylamine. The octanol was then replaced with DOPC added to the water in the form of vesicles, where the reaction was also found to be carried out. The reaction was monitored using quantitative fluorescence which revealed catalytic turnover numbers on a scale of 10−4s−1 for each system, which is comparable to some slow enzymes. The reaction product was characterized by 1H-NMR measurements which were consistent with ester hydrolysis. The implications of thinking about lipid aggregates as catalytic entities are discussed in the context of biochemistry, pharmacology and synthetic biology.

Ichthyosis is a genetic skin disorder marked by hyperkeratosis, pruritus, and scaling. This case study explores a potential association between ichthyosis and scalp paresthesia, a symptom not commonly linked with this dermatological condition. In a longstanding history of ichthyosis patients, we detail the therapeutic use of fluocinolone, a synthetic corticosteroid, in managing scalp paresthesia. The effectiveness of fluocinolone in reducing paresthesia episodes underscores its potential as a critical component in the symptomatic treatment of this complex interplay between skin and neurological symptoms. This case prompts a broader discussion on the need for increased awareness and education among healthcare providers regarding the diverse manifestations of ichthyosis. Enhanced understanding and recognition of these associations can lead to improved patient counseling, tailored management strategies, and, ultimately, better quality of life for individuals affected by ichthyosis.

Natural host defensins, also sometimes termed antimicrobial peptides, are evolutionarily conserved. They have been studied as antimicrobials, but some pharmaceutical properties, undesirable for clinical use, led to the development of synthetic molecules with constructed peptide arrangements and/or peptides not found in nature. The leading development currently is synthetic small molecule nonpeptide mimetics, whose physical properties capture the characteristics of the natural molecules, and share their biological attributes. We studied brilacidin, an arylamide of this type, for its activity in vitro against fungi (40 clinical isolates, 20 species) that the World Health Organization has highlighted as problem human pathogens. We find antifungal activity at low concentrations for many pathogens, which indicates further screening for activity, particularly in vivo, is justified to evaluate this compound, and other mimetics, as attractive leads for development of effective antifungal agents.

To investigate the effects of organic fertilizer combined with reduced chemical fertilizer on radish yield, quality, and soil environment in high mountainous areas (elevation: 1800 m), and to mitigate the environmental pollution caused by excessive use of chemical fertilizers. The field trial was conducted to study the effects of different fertilizer treatments. (Control:local fertilizer application rate: 375 kg·hm² reduction in chemical fertilizer + 4500 kg·hm² organic fertilizer; T1:12% reduction in chemical fertilizer + 4500 kg·hm² organic fertilizer; T2:20% reduction in chemical fertilizer + 4500 kg·hm² of organic fertilizer; T3:28% reduction in chemical fertilizer + 4500 kg·hm² organic fertilizer). Four different fertilizer ratios were studied to assess their impacts on radish quality, yield, and soil environment. The findings showed that T2 significantly increased radish yield by 12.92%. Moreover, it led to significant improvements in the content of vitamin C, soluble sugars, soluble solids, soluble proteins, titratable acidity, and glucosinolates, which increased by 10.62%, 2.15%, 50.00%, 26.90%, 43.90%, and 44.57%, respectively. Treatment T2 also significantly increased soil nutrient content, with a 7.69% and 14.29% increase in total nitrogen and total phosphorus content compared to the control (Control) treatment, and a significant reduction of 21.64% in soil bulk density . T2 treatment significantly enhanced soil urease activity, sucrase activity, alkaline phosphatase activity, and catalase activity, showing an improvement of 28.97%, 55.92%, 52.08%, and 17.47% compared to the control, respectively (p<0.05). Moreover, treatment T2 resulted in a relative abundance increase of 12.25% and 10.43% in Proteobacteria and Actinobacteria respectively. Thus, the combined application of T2:20% reduction in chemical fertilizer + 4500 kg·hm² of organic fertilizer improved the quality and yield of radish while optimizing the soil environment. T2 effectively promoted radish growth, enhanced radish yield and quality, and improved the soil environment.

In this paper we develop and explore a methodical pipeline for swimmer localisation in outdoor environments. The developed framework is intended to be used for enhancing swimmer safety. A main issue we deal with by the proposed approach is the lack of real world training data in such outdoor environments. Natural lighting changes, dynamic water textures and possibly barely visible swimming persons are key elements to approach. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for the use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets are used to train a YOLO architecture for the possible future application in real-time detection. The trained framework is then tested and evaluated on outdoor environment imagery acquired by a safety drone to investigate and confirm the usefulness for outdoor swimmer localisation.

The relationship between the structure characteristics and performances of coal based hydrogenation isomeric base oil (CTL) and metallocene catalyzed coal based polyalphaolefin (mPAO) base oil was clarified here. CTL and mPAO were compared with typical petroleum based and natural gas based commercial API Class III and IV base oils. Pressurized differential scanning calorimetry (PDSC), Rotary Bomb Oxidation Test (RBOT) and four-balls friction tester were used to evaluate the oxidation stability and lubrication performance of base oils under different working conditions. The sensitivity of different base oils to typical antioxidants and extreme pressure antiwear agents was compared. Specially, the composition and structure of CTL base oil are obviously different from GTL and mineral base oil. The coal-based CTL and mPAO base oils exhibit commendable viscosity-temperature properties, characteristics, coupled with low-temperature fluidity, fire safety, and minimal evaporation loss. The lubricating properties, oxidation stability and the sensitivity to extreme pressure antiwear agents of CTL are close to those of similar base oils. But the sensitivity of CTL to typical antioxidants is relatively poor. Besides, compared with commercial PAO base oil, mPAO has lower isomerization degree and fewer isomerization types. The oxidation stability and sensitivity to typical antioxidants of mPAO base oil are comparable with those of commercial PAO base oil, while its lubrication performance and sensitivity to typical extreme pressure antiwear agents are significantly better than those of commercial PAO base oil.

In the last decades synthetic jet actuators have gained much interest among the flow control techniques due to their short response time, high jet velocity and absence of traditional piping, that matches the requirements of reduced size and low weight. A synthetic jet is generated by the diaphragm oscillation (generally driven by a piezoelectric element) in a relatively small cavity, producing periodic cavity pressure variations associated with cavity volume changes. The pressured air exhausts through an orifice, converting diaphragm electrodynamic energy into jet kinetic energy. This review paper faces the development of various lumped-element models (LEMs) as practical tools to design and manufacture the actuators. LEMs can quickly predict device performances such as the frequency response in terms of diaphragm displacement, cavity pressure and jet velocity, as well as the efficiency of energy conversion of input Joule power into useful kinetic power of air jet. The actuator performance is analyzed also by varying typical geometric parameters such as cavity height and orifice diameter and length, through a suited dimensionless form of the governing equations. A comprehensive and detailed physical modeling aimed to evaluate the device efficiency is introduced, shedding light on the different stages involved in the process. Overall, the influence of the coupling degree of the two oscillators, the diaphragm and the Helmholtz’s one, on the device performance is discussed throughout the paper.

Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be non-biodegradable, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed towards developing efficient enzymatic recycling technologies that could overcome the limitations of mechanical and chemical methods. These enzymes are versatile molecules obtained from microorganisms living in various environments, including soil, compost, surface seawater, and extreme habitats such as hot springs, hydrothermal vents, deep-sea regions, and Antarctic seawater. Among various plastics, PET and polylactic acid (PLA) have been the primary focus of enzymatic depolymerization research, greatly enhancing our knowledge of enzymes that degrade these specific polymers. They often display unique catalytic properties that reflect their particular ecological niches. This review explores recent advancements in marine-derived enzymes that can depolymerize synthetic plastic polymers, emphasizing their structural and functional features that influence the efficiency of these catalysts in biorecycling processes. Current status and future perspectives of enzymatic plastic depolymerization are also discussed, with a focus on the underexplored marine enzymatic resources.

Inspired by recent work of Fink, Reeves, Palma and Farr [1] on innovation in language, gastronomy, and technology, I study how new symbol discovery manifests itself in terms of additional "word" vocabulary being available from dictionaries generated from a finite number of symbols. Several distinct dictionary generation models are investigated using numerical simulation, with emphasis on the scaling of knowledge as dictionary generators and parameters are varied, and the role of which order the symbols are discovered in.

This paper is aimed at studying the temporal evolution of the surface displacements occurred over the past few years in the ocean-reclaimed platforms of the Shanghai megacity (China), which are mainly ascribable to consolidation processes of large dredger fills and alluvial deposits. With respect to previous analyses carried out over the same area, this work provides a joint multi-platform differential interferometry synthetic aperture radar (DInSAR) analysis, based on the application of the advanced Small BAseline Subset (SBAS) algorithm. This led us to retrieve long-term deformation time-series that are helpful for a better understanding of the on-going deformation phenomena. To this aim, we have exploited two sequences of SAR data collected by the ASAR/ENVISAT and by the COSMO-SkyMed (CSK) sensors, respectively, spanning the whole time period from 2007 to 2016. Unfortunately, the large time gap (of about three years) existing between the available ASAR/ENVISAT and CSK datasets gave rise to additional difficulties for their combination. Nevertheless, this problem has been faced by benefiting from the knowledge of a time-dependent model describing the temporal evolution of the expected deformations affecting the Shanghai ocean-reclaimed platforms.

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which require a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in that deep learning-based SAR ship detectors struggle due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed as Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher-student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch, but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and learning comprehensive diversity information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach.

GR4J (Genie Rural a 4 parame`tres Journalier) is a rainfall-runoff model widely used to modelling rainfall into discharge. In this model, the input data used are daily rainfall and evapotranspiration data, calibrated using daily observed discharge data. This model seeks the optimum parameter value that produces the minimum deviation or error. The Nash-Sutcliffe Coefficient and Relative Volume Error (RVE) are used to estimate the errors that occur. One of the modelling parameters, namely X4 (peak time), has a value that does not match the observed hydrograph peak time (Tp). For this reason, it is necessary to adjust the modelling equation so that the unit hydrograph of the model appropriately in terms of value and shape with observed unit hydrograph. This study examines ten watersheds in Java Island so that it is expected to represent catchment conditions in tropical areas and developing countries. A relationship was obtained between the difference peak time from modelling (X4) and observed Tp. Adjustments Formula can be made to the general GR4J modelling equation for unit hydrograph in Java Island watersheds.

Abstract: With a resurgence in ITRT (Immediate Tooth Replacement Therapy) as a method of preserving both hard and thus soft tissues for improved aesthetic outcomes, this multi-centre, retrospective study will look at two novel products and their effect on these outcomes. A new synthetic particulate graft material along with a novel Implant design was utilized in the study and a 1 year follow up assessment, analysed for hard and soft tissue regeneration.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully-polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a Channel Fusion Module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. The article proposes a module called Cross-Domain Adversarial Learning Module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, the paper introduces the Anti-Interference Head (AIH) which decouples the detection head to reduce the conflict of classification and localization problems. This improves the anti-interference and generalization ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarization dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves Precision by 4.9%, Recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalization capability in the field of target detection.

The article explores the concept of 'synthetic man' (SM) in the context of 'synthetic biology' (SB), analysing the potential, challenges, and ethical implications of these achievements. Through dialogue with ChatGPT 4, an in-depth understanding of the evolution, contributions of SM, ChatGPT's contribution, and concerns associated with SM is sought, providing insight into how this technology could affect our society. Our study uses a structured dialogue framework with ChatGPT that addresses essential SM questions regarding SM history, contributions, challenges, and concerns. ChatGPT responses are analysed to extract relevant information and identify trends and insights. The ChatGPT dialogue reveals that MS is still a theoretical and speculative concept with significant advances in gene therapy and genetic engineering. The potential contributions of SM in addressing global challenges such as health, environment and education are identified. Ethical and social concerns such as health risks and psychological impact are also highlighted. Differences in ChatGPT responses were observed between different sessions or even within the same session, which highlighted the limitations in the replicability of the research and sometimes the need for clarification of the questions. The article is distinguished by its unique approach of using an advanced language model to explore a complex and speculative topic. The LLM Dialogue Framework on Technology (LLM-DFonTech) framework proposal for structured dialogue with ChatGPT on various technological topics related to the original contribution provides an adaptable tool for interdisciplinary research in emerging technologies. This methodology opens up new possibilities for analyzing and developing trends in technology and society.

Permafrost thaw is an important aspect of Earth’s carbon cycles. Initial estimates suggest that permafrost thaw could contribute anywhere between 20 to 500 Gt of CO2-eq by 2100. These estimates are all fundamentally centered around one number: active layer thickness (ALT). The deeper the ALT, the more emissions. Unfortunately, ALT is a highly spatially heterogeneous number and determined by numerous thermal, soil hydrology, and geomorphological effects. The remotely sensed active layer thickness (ReSALT) algorithm was introduced in 2010 to provide scientists with a way to model ALT heterogeneously at meter-scale resolution. However, upon inspection, this work shows that ReSALT’s modeling approach is self-inconsistent when using a variable soil porosity model. This work then introduces SCReSALT (Self-Consistent ReSALT) to solve that problem. Experimental comparisons to a past study in Utqiagvik (formerly Barrow), Alaska show significant improvement and suggest that SCReSALT should become a standard tool in permafrost thaw modeling. Code to reproduce all results can be found at https://github. com/jmathur25/permafrost-prediction.

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in bio-technology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affin-ities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and en-abling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation, and in ad-dressing ethical concerns related to AI-driven protein design. This review provides a compre-hensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

Drug-target binding is an essential parameter in drug discovery and design to ensure drug efficacy and specificity. In 2022, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined: $Kd = f(molecules, envPara)$. Technically, GIBAC represents a set of in silico approaches for structural and biophysical data generation towards a paradigm shift in precise drug discovery and design, providing a comprehensive framework for intermolecular binding affinity calculation with adequate accuracy, precision and efficiency. For the first time, this study reports a prototype of GIBAC (semaGIBAC), i.e., a one-dimensional semaglutide-GLP-1R-based mini static GIBAC, based on an experimental complex structure of semaglutide and GLP-1R. Semaglutide is a potent GLP-1 receptor agonist used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. In 2021, a structural modification involving a Val27-Arg28 exchange was manually introduced to enhance semaglutide-GLP-1R binding affinity. This study employs a comprehensive structural and biophysical analysis aimed at thoroughly exploring the sequence space of semaglutide-GLP-1R to design analogues with improved binding affinity, leading to the identification of a promising semaglutide analogue, which binds to GLP-1R with an affinity that is more than two orders of magnitude (113.3 times) higher than native semaglutide. To sum up, this article puts forward a promising structural biophysical approach for developing GLP-1 receptor agonists with enhanced efficacy, and with a GIBAC prototype (semaGIBAC), this article argues again that the time is now ripe for the construction of a real GIBAC to be listed on the agenda of the drug discovery and design community.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.

Plant-based insecticides are considered among the most economical and eco-friendly chemicals for the protection of field plants and even stored grains. The sweet melon (Cucumis melo L.) insect pest causes more than 90% damage to most melon leaves, vines, and balls at its growth stage. The study looked at how effective the plant extract from Africa Basil and the synthetic insecticide Cymbush were at killing insects on sweet melon (Cucumis melo L.). The study explored four different concentrations (2.5, 50, 75, and 100%) and a synthetic insecticide, Cymbush, against insect pests of sweet melon and compared the effects of distilled water, which is used as a control in a Randomized Complete Block Design (RCBD). The toxicity percentage levels of the African basil extracts against field pests of sweet melon were 25, 50, 75, and 100%. Maximum plant protection of the crop was achieved by the synthetic insecticide Cymbush (3.0 pest point), followed by Africa basil extract B100 (3.3 pest point), whereas weak and minimum insect pest protection were observed in the control (5.53 pest point). In conclusion, these results indicate the utility of African basil plant extracts against field insect pests of sweet melon as potential botanicals to control field insect pests and mitigate the hazardous use of synthetic insecticides on humans, animals, and the environment

Polarimetric Synthetic Aperture Radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep Learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional Neural Networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued nature of PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN named Shallow to Deep Feature Fusion Network (SDF2Net) is proposed for PolSAR image classification. To validate the performance of the proposed method, classification results are compared against multiple state-of-the-art approaches using the Airborne Synthetic Aperture Radar (AIRSAR) datasets of Flevoland, San Francisco, and ESAR Oberpfaffenhofen dataset. The results indicate that the proposed approach demonstrates improvements in OA, with 1.3% and 0.8% enhancement for the AIRSAR datasets and 0.5% improvement for the ESAR dataset. The analyses conducted on the Flevoland data underscore the effectiveness of the SDF2Net model, revealing a promising OA of 96.01% even with only 1% sampling ratio.

Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant scattering information of objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. Although significant progress has been achieved in related research, there are still issues such as insufficient utilization of polarimetric information and features. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features inputted into the deep learning network are the power values of polarimetric channels, where these channels are treated equivalently and contain complete polarimetric information. In terms of experimental comparative study, besides polarimetric power combinations, three different data input schemes were employed as data for the input end of the convolutional neural network (CNN), taking into account existing research. The neural networks utilizes the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of different input schemes. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.

Active flow control is a promising technology for reducing noise, emissions, and power consumption in various applications. To better understand the performance of synthetic jet actuators, a computational model that couples structural mechanics with electrostatics, pressure acoustics, and fluid dynamics has been needed. The model presented here has been validated against experimental data and then used to investigate the fluid behavior inside and outside the synthetic jet actuator cavity, impacts of thermoviscous losses on capturing the acoustic response of the actuator, and viability of different modeling methods of diaphragms in computational simulations. The results capture the feedback from the fluid onto the diaphragm and highlight the need for careful acoustic modeling.

We developed a protein to rapidly and accurately diagnose Chagas disease, a life-threatening illness identified by the WHO as a critical worldwide public health risk. Complicating the current health situation are serological test limitations that contribute to most infected persons being unaware of their condition and therefore untreated. To improve diagnostic testing, we developed an immunological mimic of the etiological agent, Trypanosoma cruzi, by combining ten pathogen specific epitopes within the beta-barrel protein structure of Thermal Green Protein. The resulting multi-epitope protein, DxCruziV3, displayed high specificity and sensitivity as the antibody capture reagent in an ELISA platform with an analytical sensitivity that exceeds WHO recommendations. Within an immunochromatographic platform, DxCruziV3 showed excellent performance for the point of application diagnosis in a region endemic for multiple diseases, the municipality of Barcelos in the state of Amazonas, Brazil. In total, 167 individuals were rapidly tested using whole blood from a finger stick. As recommended by the Brazilian Ministry of Health, venous blood samples were laboratory tested by conventional assays for comparison. Test results suggest utilizing DxCruziV3 in different assay platforms can confidently diagnosis chronic infections by T. cruzi. Rapid and more accurate results will benefit everyone but will have the most noticeable impact in resource-limited rural areas where the disease is endemic.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

The heavy transition metal tantalum (Ta) offers a wide range of applications, especially in the field of electronics, e.g. Ta capacitors with a comparatively long life-time. Therefore, electronic scrap containing these components is a potentially valuable secondary source of this metal. However, the recovery of Ta, as other critical raw materials, that are lost in pyrometallurgical slags can be a challenging process. As a way of overcome this, the "engineered artificial minerals" EnAM approach is proposed by first searching for suitable host minerals and characterizing them. A favorable prerequisite is the enrichment of the desired element(s) in compounds that have particularly suitable properties for this purpose, such as morphology, crystal size, surface or magnetic properties. Subsequently, the slag system is adapted (e.g. chemism, solidification curve, cooling and holding times) to optimize this compound. This paper presents the results of mineralogical characterization of Ta-containing iron (Fe)-rich synthetic slags in reducing environment under different cooling rates, and describes and discusses the incorporation of Ta into the different compounds detected by powder X-ray diffraction (PXRD) and electron beam microanalysis (EPMA). Additionally, the speciation of Fe and Ta was accessible through X-Ray absorption near edge structure (XANES) spectroscopy. EPMA also provides a first semi-quantitative assessment of the Ta distribution in these individual compounds. According to this, depending on the cooling rate the enrichment factor is highest in tantalite/perovskite-type oxides (FexTayO6, CaxFeyTazO3) with up to 60 wt.% Ta and ‘tantalomagnetite’ (FeII(FeIII(2-5/3xTax)O4) with a maximum of ~ 30 wt.% Ta (only fast cooling). This is followed by a perovskite-like silicon containing oxide (XYO3) with 12-15 wt.-% Ta (only slow cooling), and a hedenbergite-like compound (XYZ2O6) with varying content of 0.3 - 7 wt.-%. The Ta-concentration in pure Fe, Fe(1-x)O, hercynitic spinel and hematite is negligible. Since a larger fraction of potentially amorphous hedenbergitic matrix is formed at faster cooling, in this case the main Ta is enriched in this compound despite the significantly lower Ta content.

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

This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light 22 Scattering (DLS) analysis demonstrated its ability to form aggregates with an average size of 391 nm, extending to low micrometric sizes. Furthermore, cellular biological assays revealed its ability to enhance fibroblast cell growth, highlighting its potential for tissue regenerative applications. Circular dichroism (CD) spectroscopy showed the ability of the synthetic amino acid to bind serum albumins (using bovine serum albumin (BSA) as a model), and CD deconvolution provided insights into the changes in secondary structures of BSA upon interaction with the amino acid ligand. Additionally, molecular docking using HDOCK software elucidated the most likely binding mode of the ligand inside the BSA structure. We also performed in silico oligomerization of the synthetic compound in order to obtain a model of aggregate to investigate computationally. More in detail, in the dimer formation achieved by molecular self-docking, two distinct poses were observed, corresponding to the lowest and comparable energies, with one pose exhibiting a quasi-coplanar arrangement characterized by a close alignment of two aromatic rings from the synthetic amino acids within the dimer, suggesting the presence of π-π stacking interactions. In contrast, the second pose displayed a non-coplanar configuration, with the aromatic rings oriented in a staggered arrangement, indicating distinct modes of interaction. Both poses were further utilized in the self-docking procedure. Notably, iterative molecular docking of amino acid structures resulted in the formation of higher-order aggregates, with a model of a 512-mer aggregate obtained through self-docking procedures. This model of aggregate presented a cavity capable of hosting therapeutic cargoes and biomolecules, rendering it a potential scaffold for cell adhesion and growth in tissue regenerative applications. Overall, our findings highlight the potential of this synthetic amino acid for tissue regenerative therapeutics and provide valuable insights into its molecular interactions and aggregation behavior.

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

Advances in genetic engineering have placed synthetic biology at a prime position to develop new products, materials, and services that could contribute to the 2030 UN Sustainable Development goals. These include novel materials for water purification, new bio-based products to replace toxic industrial chemicals, and engineered organisms for bioremediation. Supporting the development of synthetic biology initiatives in developing countries is needed to ensure these benefits are open to all.

Digital twin technology, a cutting-edge approach that creates dynamic digital replicas of physical systems, is increasingly integral to various industrial applications. However, the effective deployment of digital twins often faces challenges associated with data availability, quality, and interoperability, which can impede the development and operational efficacy of these virtual counterparts. This paper presents a systematic review of the application of artificial intelligence (AI) in enhancing and managing digital twin technologies across multiple domains such as industry, healthcare, urban planning, business, education, technology, and others. It specifically examines how machine learning models, particularly neural networks and deep generative models, are being employed to optimize the creation, maintenance, and functionality of digital twins. The review explores the role of artificial intelligence in overcoming data-related limitations by providing robust, scalable data solutions for digital twins. Additionally, it addresses critical considerations of real-time data processing and system interoperability within these applications. This survey not only identifies the prevailing challenges and opportunities within this emerging field but also highlights potential future research directions that could further the integration of AI with digital twin technology. Through a detailed exploration of the intersection between AI and digital twins, this paper aims to contribute significantly to the knowledge base and encourage further innovations in this interdisciplinary area.

Nervous necrosis virus (NNV) has spread throughout the world, affecting more than 120 freshwater and marine fish species. While vaccination effectively prevents disease outbreaks, the difficulty of producing sufficient viruses using cell lines continues to be a significant disadvantage for producing inactivated vaccines. This study, therefore, explored the application of synthetic peptides as potential vaccine candidates for the prevention of NNV in Asian seabass (Lates calcarifer). Using the epitope prediction tool and molecular docking, three predicted immunogenic B cell epitopes (30-32 aa) derived from NNV coat protein were selected and synthesised, corresponding to amino acid positions 5 to 34 (P1), 133 to 162 (P2) and 181 to 212 (P3). All the predicted peptides interact with Asian sea bass’s MHC class II by docking. The antigenicity of these peptides was determined through ELISA and all peptides were able to react with NNV-specific antibodies. Subsequently, the immunogenicity of these synthetic peptides was investigated by immunisation of Asian seabass with individual peptides (30 μg/fish) and a peptide cocktail (P1+P2+P3, 10 μg each/fish) by intraperitoneal injection, followed by a booster dose at day 28 post-primary immunisation. There was a subset of immunised fish that were able to induce upregulation of CD4 in the head kidney and spleen. Importantly, antibodies derived from fish immunised with synthetic peptides reacted with whole NNV virions. Taken together, these findings indicate that synthetic linear peptides based on predicted B cell epitopes exhibited both antigenic and immunogenic properties, suggesting that they could be potential vaccine candidates for the prevention of NNV in fish.

Synthetic biology (SynBio) is an interdisciplinary field that has developed rapidly in the last two decades. It involves the design and construction of new biological systems and processes from standardized biological components, networks and synthetic pathways. The goal of Synbio is to create logical forms of cellular control. Biological systems and their parts can be re-designed to carry out completely new functions. SynBio is poised to greatly impact human health, environment, biofuels and chemical production with huge economic benefits. SynBio presents opportunities for the highly agro-based African economies to overcome setbacks that threaten food security: The setbacks are brought about by climate change, land degradation, over-reliance on food imports, global competition, and water and energy security issues among others. With appropriate regulatory frameworks and systems in place, the benefits of harnessing SynBio to boost development in African economies by far potentially outweigh the risks. Countries that are already using GMOs such as South Africa and Kenya should find the application of SynBio seamless, as it would be a matter of expanding the already existing regulations and policies for GMO use.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime’s understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open- accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

Quorum sensing (QS) is a sophisticated bacterial cell-to-cell communication mechanism that allows bacteria to sense population density through the secretion and detection of diffusible small molecular signals. This process leads to the coordinated expression of specific genes at the transcriptional level. Over time, continuous research has elucidated the genetic elements and regulatory principles of QS. Recently, synthetic biology has leveraged these insights to construct genetic circuits incorporating QS components, enabling both intra-species and inter-species artificial communication. These QS-based genetic circuits hold significant potential for applications in biotechnology and biomedicine. This paper reviews several well-characterized microbial QS systems and their functional roles, while also introducing the application of QS-based genetic circuits in cellular communication across species. The paper further discusses the role of QS in the development of biological computing tools, population density regulation, and metabolic flux control, offering a forward-looking perspective on future advancements. For intra-species communication, the focus is on the use of QS systems in constructing biological computing tools, including toggle switches, biosensors, and logic gates in synthetic biology. These tools, designed on the QS mechanism, can more precisely coordinate cellular behavior by integrating biological control circuits to achieve spatial, temporal, and population-level regulation. In the context of inter-species communication, the introduction of QS systems plays a pivotal role in population density control and metabolic flow regulation. By recombining metabolic networks, QS enables the redistribution of metabolic flux in desired pathways, facilitating the regulation of population density and the co-culture of mixed strains. Moreover, combining QS with oscillator models has shown great potential in synchronizing microbial communities. In summary, in-depth research into QS mechanisms and their applications not only lays a solid foundation for understanding microbial ecological competition and dynamic balance, but also offers promising avenues for regulating pathogenic bacteria and developing innovative disease control strategies.

In (Venetz et al., 2019), authors rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Then, they reduced the native Caulobacter crescentus native Caulobacter NA1000 genome sequence real genome ( 4042929 bp ) to the 785,701-bp reduced synthetic genome. Here we demonstrate the existence of a palindromic-like mirror structure that exists in real genomes and disappears totally in the synthetic genome. This biomathematic meta-organization is based on characteristic proportions of Fibonacci numbers between DNA single strand nucleotides proportions TC / AG on the one hand and TG / AC on the other hand. In both cases, we suggest that this meta-structure enhances the three-dimensional cohesion of the two DNA strands of the genome. We then generalize this study to the different synthetic genomes and synthetic cells published by the Craig Venter Institute on Mycoplasma Mycoides JCVI-syn1.0 (in 2010), JCVI-syn3.0 (in 2016) and JCVI-syn3A (in 2019). Finally, in the discussion section, we extend this study to synthetic genomes of E-Coli and Yeast chromosome XII.

Natural fibers have been widely used as a reinforcing material for the construction of drones. The main goal of this review is to identify a biodegradable natural fiber material that can be built for Unmanned Aerial Vehicles (UAVs). The review shows that natural fibers such as kenaf and PALF can be used as an alternative composite material, particularly for multifunctional applications where their smaller weight and less expensive characteristics make them incredibly realistic. This review also discusses the mechanical properties, advantages and disadvantages, and fabrication of the composites.

In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, a high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper.

The advancement of computational drug discovery and design necessitates continuous innovation to enhance the accuracy and scope of predictive models for early-stage drug research and development. This article introduces a novel workflow for in silico generation of structural and intermolecular binding affinity data with reasonable accuracy, combining two computational tools: Modeller and Prodigy. By leveraging synthetic structural and biophysical data, this approach addresses the limitations of existing experimental datasets, generating extensive, high-quantity binding affinity data with reasonable accuracy for biomolecular binding pairs, which broadens the horizon of computational biomolecule design and discovery by enabling extensive exploration of the sequence space of biomolecular binding pairs, and narrows the gap between experimental binding affinity data and its unexplored territories. Overall, this article presents a methodological advance to enhance the accuracy and scope of computational biomolecule discovery and design, paves the way for the development of preclinical candidates with improved efficacy and specificity, and holds transformative potential for further advancements in artificial intelligence-enabled biomolecule discovery and design in the future.

Cryptococcus neoformans is a human multidrug-resistant yeast with high mortality rates in immunocompromised patients. Recently, the synthetic peptide Mo-CBP3-PepII emerged as a potent anticryptococcal molecule with an MIC50 at low concentration. Here, the mechanisms of action of Mo-CBP3-PepII were deeply analyzed to provide new information about how it led C. neoformans cells to death. Light and fluorescence microscopies, analysis of enzymatic activities, and proteomic analysis were employed to understand the effect of Mo-CBP3-PepII on C. neoformans cells. Light and fluorescence microscopies revealed Mo-CBP3-PepII induced the accumulation of anion superoxide and hydrogen peroxide in C. neoformans cells. In addition to a reduction in the activity of superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT) in the cells treated with Mo-CBP3-PepII. In the presence of Ascorbic acid (AsA), no ROS were detected and Mo-CBP3-PepII lost the inhibitory activity against C. neoformans. Yet, Mo-CBP3-PepII inhibited the activity of lactate dehydrogenase (LDH), ergosterol biosynthesis, and induced the decoupling of cytochrome c from the mitochondrial membrane. Proteomic analysis revealed a reduction in the abundance of proteins related to energetic metabolism, DNA and RNA metabolism, pathogenicity, protein metabolism, cytoskeleton, and cell wall organization and division. Our findings indicated that Mo-CBP3-PepII might have multiple mechanisms of action against C. neoformans cells, mitigating the development of resistance and thus being a potent molecule to be employed in the production of new drugs against C. neoformans infections.

The article features the structure and functions of plant synthetic promoters frequently exercised to precisely regulate complex regulatory routes. The composition of plant native promoters together with interacting proteins is presented to provide a better understanding of tasks associated with synthetic promoter development. The production of synthetic promoters is conferred on relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. Moreover, the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep learning methodologies is presented. A particularly interesting group of synthetic promoters are bidirectional forms that enable the putative expression of up to 6–8 genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and random integration of multiple transgenes. These complications are commonly observed during transgenic crop development through traditional, multistep transformation by genetic constructs containing a single gene. Another path to solving problems associated with the low complexity and homology of already tested DNA fragments is through orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article.

Minimal plasmids play an essential role in many intermediate steps in molecular biology. They can for example be used to assemble building blocks in synthetic biology or be used as intermediate cloning plasmids that are ideal for PCR-based mutagenesis methods. A small backbone also opens up for additional unique restriction enzyme cloning sites. Here we describe the generation of pICOz, a 1185 bp fully functional high-copy cloning plasmid with an extended multiple cloning site (MCS). To our knowledge, this is the smallest high-copy cloning vector ever described.

Possibilities of utilization of two frequencies synthetic aperture radar (SAR) operating simultaneously in P-band and S or L-band are considered. Advantages of such system are shown. They are caused by improvement of data interpretation and by decrease of ionosphere influence. Tasks of surface and subsurface sensing by the complex SAR were under investigation for vegetation, soil covers are given. As an example, the investigation results of coniferous forest areas backscattering by the spaceborne P-band SAR together with L-band SAR in the September-February period are presented. Studies have shown that the joint use of the results in L and P bands offer unique opportunities to explore the effects of changes in the backscattering properties of forest surfaces.

Using the phenomenon of transpiration, plants are able to raise soil water to a height of about 100 meters. According to the Cohesion - Tension theory, water moves through the capillaries of plant along a gradient of negative water potential in a single unified flow along a single route from roots to leaves, entering on its way into living cells for their nutrition and evaporating into the atmosphere in significant amounts in the leaves. The paper shows that in a case of artificial transpiration devices (ATD), the flow of rising water can be divided into two streams - the transpiration stream and the passive water stream - and as a result, such devices become capable of raising water to any given height. As proof, the working variant of two-stream ATD is proposed. The development of this ATD was carried out in two stages, discussed in details in the paper. At the first, experimental stage, a physical model was designed and manufactured, in which the flow of rising water was divided into two streams moving along two independent routes. The first route is used exclusively for the purpose of transpiration of water - transporting it into an artificial evaporator for complete evaporation into the atmosphere. The second route is used exclusively to transport passive water using the energy obtained in the first route during the process of transpiration. During many hours of testing, the physical model demonstrated a stable ability to bring masses of passive water, maintaining normal atmospheric pressure in them, to a small height, even in the extreme case not exceeding 10 meters. At the second, analytical stage, this two-stream model was developed into a two-stream multi-stage model, capable of raising water to any given height by choosing the number of stages. The number of stages can be very large, and portions of water can be delivered to any given height, remaining under normal atmospheric pressure. The conclusion part of the paper discusses the prospects of creating on the basis of the developed two-stream ATDs large systems that can, using the phenomenon of transpiration, become a sources of clean energy, similar to how it is realized in the mass of plants.

Neonatal rats that receive sucrose during a critical postnatal period (CP, days 12 to 28) develop hypertension by the time they reach adulthood. Changes during this period could contribute to inflammation and be associated with variations in the vascular smooth muscle (VSMC) phenotype. We studied changes in inflammatory pathways that could underlie the expressions of the secretory phenotype in the VSMC in the thoracic aorta of rats that received sucrose during CP. We analyzed histological changes in the aorta and expressions of the COX-2, TLR4, iNOS, eNOS, MMP-2, -9, β-, α-actin, and TNF-α, IL-6 and IL-1β expressions, and fatty acids by gas chromatography. The aortic wall presented disorganization, decellularization, wavy elastic fibers and increase in the lumen area. The COX-2, TLR4 expressions, TNF-α and IL-6 were increased, but iNOS, MMP-2 and -9 tended to increase and eNOS and arachidonic acid tended to decrease, con increase in oleic acid in CP in comparison with control group. There is a transient hypertension at the end of the CP that is accompanied by an inflammation and a change in the phenotype of VSMC to the secretory phenotype. The inflammatory changes could act as epigenetic signals to determine the hypertension development during adulthood.

Organisms from the genus Streptomyces feature actinobacteria with complex developmental cycle and great ability to produce a variety of natural products, which is possible due to complicated crosstalk between primary and secondary metabolism. These soil bacteria produce more than 2/3 of antibiotics used in medicine, and a large variety of bioactive compounds for industry and agricultural use. Although Streptomyces spp. have been studied for decades, the engineering of these bacteria remains challenging, and available genetic tools rather limited. Recent advancements in genetic manipulation of Streptomyces involving proposal of CRISPR/Cas9-based workflows as well as synthetic components (e.g. promoters, ribosome-binding sites, terminators, reporter genes) allowed to facilitate the turnaround time of strain engineering, but still has strain-specific limitations. However, a new perspective offered by synthetic biology to exploit the potential of existing and novel pathways in primary and secondary metabolism allows combining of different biosynthetic steps originating from diverse bacteria using a limited toolbox. Manipulation of interplay between primary and secondary metabolism proposes strategies to overcome strain-specific difficulties in engineering, leveraging insights in Streptomyces-specific physiological features. In this review, developments of these approaches for Streptomyces engineering are discussed and an overview of the synthetic biology developments is provided.

As synthetic/artificial life forms become more abundant and sophisticated, an increasing number of bizarre creatures - xenobots, robots, soft A-life entities, genetically engineered organisms, etc. - are invading our society. Therefore, we need to bring order to all this, to establish what is living and what is not. Here, I intend to classify all these non-natural entities and clarify their status with reference to their consideration or not as living beings, leaving the door open to an uncertain future in which perhaps we can see how "the artificial" and "the natural" merge to originate something new. To order all this "new biodiversity" and to also give entry to viruses (which are excluded of the three-domains tree of life), I propose the creation of a new domain, Lithbea (from the name: life-in-the-border entities), in which all these new human-made entities as well as the viruses will be included. Within this domain there would be two kingdoms, Virus and Humade (contraction of human-made), based on their origin, natural or human-made. A brief description of each component of Lithbea is included and the implications for society and biology of this “new biodiversity” is briefly discussed.

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

Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data with 3.52 billion tokens of rich textbook data, Intellecta is crafted to foster advanced reasoning and comprehensive educational narrative generation. Leveraging the Mixtral-8x7B-Instruct-v0.1 model, the dataset facilitates the generation of complex thought processes and detailed, textbook-style explanations, thus enabling language models to engage in both critical thinking and profound educational discourse. This hybrid dataset stands as a testament to the potential of synthetic data in pushing the boundaries of AI, offering a repository that is not only vast and varied but also refined to align with ethical standards and intellectual rigor.

During early embryonic development, fertilized one-cell embryos develop into preimplantation blastocysts and subsequently establish three germ layers through gastrulation during post-implantation development. In recent years, stem cells have emerged as a powerful tool to study embryogenesis and gastrulation without the need for eggs, allowing the generation of embryo-like structures known as synthetic embryos or embryoids. These in vitro models closely resemble early embryos in terms of morphology and gene expression and provide a faithful recapitulation of early pre- and post-implantation embryonic development. Synthetic embryos can be generated through a combinatorial culture of three blastocyst-derived stem cell types, such as embryonic stem cells, trophoblast stem cells, and extraembryonic endoderm cells, or totipotent-like stem cells alone. This review provides an overview of the progress and various approaches in studying in vitro embryogenesis and gastrulation using stem cells. Furthermore, recent findings and breakthroughs in synthetic embryos and gastruloids are outlined. Despite ethical considerations, synthetic embryo models hold promise for understanding mammalian (including human) embryonic development and have potential implications for regenerative medicine and developmental research.

Mucus is a critical part of the human body’s immune system which traps and carries away various particulates such as anthropogenic pollutants, pollen, viruses etc. Various synthetic hydrogels have been developed to mimic mucus, using different polymers as their backbones. Common to these simulants is a three-dimensional gel network which is physically crosslinked and is capable of loosely entrapping water within. Two of the challenges in mimicking mucus using synthetic hydrogels include the need to mimic the rheological properties of the mucus and its ability to capture particulates (its adhesion mechanism). In this paper, we review the existing mucus simulants and discuss their rheological, adhesive and tribological properties. We show that most, but not all, simulants indeed mimic the rheological properties of the mucus but only one mimics the ability of mucus to capture particulates.

While rapidly becoming a main thread in the development of new therapies, the rise of synthetic biology is also tied to concerns about the potential impact on ecosystems. That is particularly relevant in the of deployment in natural habitats, including the human microbiome. These concerns have boosted the analysis of diverse strategies of containment, from engineered cell death to xenobiology to the creation of Xeno nucleic acids. However, little attention has been paid to the potential containment implicit in nonlinear ecological interactions and the lessons provided by the population dynamics models used in community ecology. If we consider synthetic strains as some class of "species" embedded within an ecological context, it is possible to show that some network interaction patterns and their associated nonlinear responses can offer a reliable source of containment. Here we present and discuss some simple examples of these "ecological firewalls" that could help provide a self-regulating biocontainment. Our firewall designs can help to ensure that engineered organisms have a limited spread while, when required, preventing their extinction. The basic synthetic designs and their dynamical behaviour are presented, each one inspired in a given ecological class of interaction. Their possible applications are discussed and the broader connection with invasion ecology outlined.

Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

Additive manufacturing (AM) has revolutionized the field of biopolymers by providing a range of opportunities to create complex and intricate structures. Here we provide an overview of developments in biopolymer AM, first in the context of how natural systems have impacted the synthetic processes of AM, and secondly in the context of how synthetically derived structures can be used for biomedical applications. In particular, we explore the range of biopolymers developed for AM, AM techniques for biopolymers, and potential biocompatible medical applications with a focus on four categories of tissue in the human body: skin, bone, heart, and nerve. Despite the current challenges of the technology, exciting results in tissue regeneration, synthetic replacements for natural grafts, and complex dynamic organoids have already come to pass - with many more possible in the future.

Anatomical airways parameters as length, diameter and angles have a strong effect on the flow dynamics. Aiming to explore the effect of variations of the bifurcation angle (BA) and carina rounding radius (CRR) of lower human airways on the respiratory processes, numerical simulations of airflow during inhalation and exhalation were performed using a synthetic bifurcation models. Geometries for the airways models were parameterized based on a set of different BA's and several CRR's. A range of Reynolds numbers (\Reyn) relevant to the human breathing process were selected to analyzed the airflow behaviour. The numerical results show a significant influence of BA and the CRR on the development of the airflow within the airways, and therefore affecting some relevant features of the flow, namely the deformation of velocity profiles, alterations of pressure drop, the flow patterns, and finally enhance or attenuation of wall shear stresses (WSS) appearing during the regular respiratory process. Numerical results show that increases in the bifurcation angle value are accompanied by pressure increases of about 20\%, especially in the regions close to the bifurcation. Similarly, increases in the BA value lead to a reduction in peak shear stresses of up to 70\%. For the ranges of angles and radii explored, an increase in pressure of about 20\% and a reduction in wall shear stress of more than 400\% were obtained by increasing the carina rounding radius. Analysis of the coherent structures and secondary flow patterns also revealed a direct relationship between the location of the vortical structures, the local maxima of the velocity profiles and the local vorticity minima. This relationship was observed for all branches analysed, for both the inhalation and exhalation processes of the respiratory cycle.

Increasing environmental concern and consumer demand for natural, sustainable and eco-friendly products have prompted the replacement of synthetic surfactants with their natural plant-based alternatives. Saponins are the plant based natural surfactants characterized by their foam forming properties in aqueous solution. Their natural origin makes them eco-friendly, bio-degradable and non-toxic. Further, they possess better physicochemical properties than the syn-thetic ones. They are also reported to exhibit a lot of useful biological activities such as anti-cancer, antifungal, anti-inflammatory, antimicrobial, antioxidant and cholesterol-lowering properties. Because of their excellent surface activity, biological activities and wide distribution in nature, saponin rich plants deserve deeper insight as a sustainable source of natural surfactants as they possess the potential to replace toxic synthetic surfactants abundant today. This review article is intended to provide a brief overview on the saponins with a special notion on their surface-active properties. It encourages further studies on development of commercial formulations based on saponins for the complete replacement of the synthetic counter parts, making better use of plants sources thereby contributing to global agenda of green environment.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations are designed, and the classification accuracy of various approaches is evaluated using the classic convolutional neural networks(CNNs) AlexNet and VGG16. The findings reveal that the commonly employed 6-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete 9-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other 7 schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

The conducted investigation encompassed the comprehensive integration of mechanical, envi-ronmental, chemical, and microstructural evaluations of a composite amalgamating sandy soil and a synthetic polymer at two distinct concentrations (2.5% and %) across multiple curing in-tervals (0, 1, 2, 4, 7, 15, 30, and 45 days). The studied soil originates from an environmentally sig-nificant protected area in Brazil. The implementation of mechanisms for soil improvement in the region must adhere to technical criteria without causing environmental harm. Direct shear testing was conducted, permeability was assessed, and microstructure analysis and XRD and XRF/EDX studies of both the soil and composites were conducted. It was observed that longer curing times yielded improved results in shear stress, friction angle, and cohesive intercept, with SP_5% exhibiting the highest values compared to the soil and SP_2.5%. Adding the polymeric so-lution to the soil contributed to cementation and cohesion gains in the substrate. Through mi-crostructural characterization, the polymer's role as a cementing agent for the grains is evident, forming a film on the grains and binding them together. Based on the analyses and studies con-ducted in the research, it can be concluded that there is technical feasibility for applying the pol-ymeric solution at both dosages in geotechnical projects.

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As DMs are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of DMs, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of DMs and provide guidelines for future researchers and practitioners across diverse disciplines.

Improvements that can be attained in seasonal climate predictions in various parts of Africa using the multimodel supersensemble scheme are presented in this study. The synthetic superensemble (SSE) used follows the approach originally developed at Florida State University (FSU). The technique takes more advantage of the skill in the climate forecast data sets from atmosphere-ocean general circulation models running at many centres worldwide including the WMO global producing centers (GPCs). The module used in this work drew data sets from the Four versions of FSU coupled model system, seven models from the DEMETER project which is the forerun to the current European Ensembles Forecast System, the NCAR Model, and the Predictive Ocean Atmosphere Model for Australia (POAMA), all making a set of 13 individual models. An archive consisting of monthly simulations of precipitation was available over all the 5 regions of Africa, namely Eastern, Central, Northern, Southern, and Western Africa. The results showed that the SSE forecast for precipitation carries a higher skill compared to each of the member models and the ensemble mean. Relative to the ensemble mean (EM), the SSE provides an improvement of 18% in simulation of season cycle of precipitation climatology. In Eastern Africa, during December-February season, a north-south gradient of precipitation prevails between Tropical East Africa and the sector of the region towards Southern Africa. This regional scale climate pattern is a direct influence of the Intertropical Convergence Zone (ITZC) across the African continent during this time of the year. The SSE emerges with superior skill scores such as lowest root mean square error above the EM and the member models, for example in the prediction of spatial location and precipitation magnitudes that characterize the see-saw precipitation pattern in Eastern Africa. In all parts of Africa, and especially Eastern Africa where seasonal precipitation variability is a frequent cause huge human suffering in due to droughts and famine, the multimodel superensemble and its subsequent improvements will always provide a forecast that out weighs the best Atmosphere-Ocean Climate Model.

The research results presented in the article were carried out during the realization of the project, the aim of which is to develop a method of drying cellulose insulation in power transformers with the use of synthetic ester. This method uses a very high water absorption of the ester. During the drying of transformers, the ester is systematically contaminated with mineral oil, which gradually loses its ability to absorb water. Information on the oil concentration in the mixture is needed in two cases: at the stage of making a decision on the treatment of the mixture and during its treatment. The article presents the results of investigations of two methods: 1) based on the measurement of the mixture density, and 2) based on the measurement of the capacitance of the capacitor immersed in the mixture. The conducted research shows that the method of measuring the density of the mixture gives an uncertainty of 2.6 p. %, while the method of measuring the capacitance of a capacitor gives an uncertainty of 2.2 p. %. A significant advantage of the method of measuring the capacitance is the possibility of using it online to control the ester treatment process.

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions in-situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for the Lake Orajärvi in 2011-12. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarisation (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarised σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

Credit rationing, especially prevalent for smaller firms, impedes economic growth. A Central Bank aligned “not for profit” managed business to business “Stable-Coin” (“Synthetic Central Bank Digital Currency”) providing trade debt liquidity can lower small firm credit rationing. It raises growth by reducing monetary transmission imperfections consequent upon asymmetric information, commercial bank underwriting restrictions, market power dynamics and regulatory distortion. A simple framework is developed to contextualise small firm credit rationing and associated monetary transmission imperfections with broader credit flows into both the real and monetary sectors. Evidence is presented regarding US monetary transmission efficacy to firms, paving the way to proposing a trade debt business to business focused stable-coin to help close credit rationing gaps. Designed to minimise “moral hazard”, the proposed stable-coin provides an additional monetary transmission channel that supplies interest free trade debt funding to firms to support economic activity and raise growth. In addition to helping close the small firm financing gap, the stable-coin provides policy makers with an additional contra-cyclical monetary transmission instrument to minimise real economic disruption consequent upon financial system crises and liquidity events.

Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can therefore play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in a poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This paper presents three specialized algorithms or retrackers to retrieve water levels from SAR altimeter data over inland water bodies dedicated to minimizing land contamination from the waveforms. The performances of the proposed waveform portion selection method with three retrackers, namely, the threshold retracker, Offset Centre of Gravity (OCOG) retracker and 2-step physical-based retracker, are compared. Time series of water levels are retrieved for water bodies in the Ebro River basin (Spain). The results show good agreement with in situ measurements from the Ebro Reservoir (width is approximately 1.8 km) and Ribarroja Reservoir (width is approximately 400 m) with un-biased root-mean-square errors (RMSEs) of approximately 0.28 m and 0.16 m, respectively. The performances of all three retrackers are also compared with the European Space Agency’s ocean retracker in the Sentinel-3 Level-2 product.

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

Klebsiella pneumoniae is a multidrug-resistant opportunistic human pathogen related to various infections. As such, synthetic peptides have emerged as potential alternative molecules. Mo-CBP3-PepI has presented great activity against K. pneumoniae by presenting an MIC50 at a very low concentration (31.25 µg mL-1). Here, fluorescence microscopy and proteomic analysis revealed the alteration in cell membrane permeability, ROS overproduction, and protein profile of K. pneumoniae cells treated with Mo-CBP3-PepI. Mo-CBP3-PepI led to ROS overaccumulation and membrane pore formation in K. pneumoniae cells. Furthermore, the proteomic analysis highlighted changes in essential metabolic pathways. For example, after treatment of K. pneumoniae cells with Mo-CBP3-PepI, it was seen a reduction in the abundance of protein related to DNA and protein metabolism, cytoskeleton and cell wall organization, redox metabolism, regulation factors, ribosomal proteins, and resistance to antibiotics. These reductions lead to the inhibition of DNA repair, inhibition of cell wall turnover, protein turnover, and ROS accumulation leading to cell death. Our findings indicated that Mo-CBP3-PepI might have mechanisms of action against K. pneumoniae cells, mitigating the development of resistance and thus being a potent molecule to be employed in producing new drugs against K. pneumoniae infections.

The Earth’s synthetic gravitational and density models can be used to validate numerical procedures applied for a global (or large-scale regional) gravimetric forward and inverse modelling. Since the Earth’s lithospheric structure is better constrained by tomographic surveys than a deep mantle, most of existing 3-D density models describe only a lithospheric density structure, while 1-D density models are typically used to describe a deep mantle density structure below the lithosphere-asthenosphere boundary. The accuracy of currently available lithospheric density models is examined in this study. The error analysis is established to assess the accuracy of modelling the sub-lithospheric mantle geoid, while focusing on the largest errors (according to our estimates) that are attributed to lithospheric thickness and lithospheric mantle density uncertainties. Since a forward modelling of the sub-lithospheric mantle geoid comprises also numerical procedures of adding and subtracting gravitational contributions of similar density structures, the error propagation is derived for actual rather than random errors (that are described by the Gauss’ error propagation law). Possible systematic errors then either lessen or sum up after applying particular corrections to a geoidal geometry that are attributed to individual lithospheric density structures (such as sediments) or density interfaces (such as a Moho density contrast). The analysis indicates that errors in modelling of the sub-lithospheric mantle geoid attributed to lithospheric thickness and lithospheric mantle density uncertainties could reach several hundreds of meters, particularly at locations with the largest lithospheric thickness under cratonic formations. This numerical finding is important for a calibration and further development of synthetic density models of which mass equals the Earth’s total mass (excluding the atmosphere). Consequently, the (long-to-medium wavelength) gravitational field generated by a synthetic density model should closely agree with the Earth’s gravitational field.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

A perfectly balanced wine can be said to create a symphony in the mouth. To achieve the sublime, both in wine and music, requires imagination and skilled orchestration of artistic craftmanship. For wine, inventiveness starts in the vineyard. Similar to a composer of music, the grapegrower produces grapes through a multitude of specifications to achieve a quality result. Different Vitis vinifera grape varieties allow the creation of wine of different genres. Akin to a conductor of music, the winemaker decides what genre to create and considers resources required to realise the grape’s potential. A primary consideration is the yeast: inoculate the grape juice or leave it ‘wild’; which specific or combined Saccharomyces strain(s) should be used; or proceed with a non-Saccharomyces species? Whilst the various Saccharomyces and non-Saccharomyces yeasts perform their role during fermentation, the performance is not over until the ‘fat lady’ (S. cerevisiae) has sung (i.e. the grape sugar has been fermented to specified dryness and alcoholic fermentation is complete). Is the wine harmonious or discordant? Will the consumer demand an encore and make a repeat purchase? Understanding consumer needs lets winemakers orchestrate different symphonies (i.e. wine styles) using single- or multi-species ferments. Some consumers will choose the sounds of a philharmonic orchestra comprising a great range of diverse instrumentalists (as is the case with wine created from spontaneous fermentation); some will prefer to listen to a smaller ensemble (analogous to wine produced by a selected group of non-Saccharomyces and Saccharomyces yeast); and others will favour the well-known and reliable superstar soprano (i.e. S. cerevisiae). But what if a digital music synthesiser ‒ such as a synthetic yeast ‒ becomes available that can produce any music genre with the purest of sounds by the touch of a few buttons? Will synthesisers spoil the character of the music and lead to the loss of the much-lauded romantic mystique? Or will music synthesisers support composers and conductors to create novel compositions and even higher quality performances that will thrill audiences? This article explores these and other relevant questions in the context of winemaking and the role that yeast and its genomics play in the betterment of wine quality.

Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.

This study describes the development of an optical-based surface plasmon resonance (SPR) aptasensor for the detection of cocaine. The aptasensor was prepared by first attaching gold nanoparticles to a clean SPR chip surface, followed by the addition of an aptamer to create a modified surface. This surface was characterized using Contact Angle and Atomic Force Microscopy, revealing surface roughness values of 0.28 nm and 28.12 nm for the blank and modified surfaces, respectively. The detection of cocaine was carried out in the concentration range of 1 ng/mL to 1000 ng/mL, with a detection time of approximately 8 minutes and a cocaine limit of detection (LOD) of 0.43 ng/mL. Repeatability studies were conducted, and the stability of the signal response was examined at a concentration of 200 ng/mL. Adsorption isotherm models, including Scatchard, Langmuir, and Freundlich, were calculated to assess the surface homogeneity of the SPR aptasensor chip, with the results indicating compatibility with the Langmuir isotherm model.

The possibility of abrupt transitions threatens to poise ecosystems into irreversibly degraded states. Recently, it has been proposed the use of engineered microbiomes in endangered ecosystems to prevent them to cross tipping points and avoid collapse. Potential targets for such interventions include some of the most prominent life-support systems in the biosphere: drylands and coral reefs. Since engineering can require the introduction of microorganisms not present in resident communities, how can we weight the potential outcomes? One way is to use general models of species interactions where the "synthetic" strain is incorporated into a standard multispecies model. Here we follow this approach by modelling a resource-consumer community where one of the species is a modified one that acts by preserving some key resource. We show how the indirect effect of damping the decay of shared resources results in biodiversity increase, and last but not less, the successful incorporation of the synthetic within the ecological network. Further extensions and implications for future restoration and terraformation strategies are discussed.

Non-homologous end-joining (NHEJ) is a major DNA repair pathway in mammalian cells that recognizes, processes and fixes DNA damages throughout the cell cycle, and is specifically important for homeostasis of post-mitotic neurons and developing lymphocytes. Neuronal apoptosis increases in the mice lacking NHEJ factors Ku70 and Ku80. Inactivation of other NHEJ genes, either Xrcc4 or Lig4, leads to massive neuronal apoptosis in the central nervous system (CNS) that correlates with embryonic lethality in mice. Inactivation of either Paxx, Mri or Dna-pkcs NHEJ gene results in normal CNS development due to compensatory effects of Xlf. Combined inactivation of Xlf/Paxx, Xlf/Mri and Xlf/Dna-pkcs, however, results in late embryonic lethality and high levels of apoptosis in CNS. To determine the impact of NHEJ factors on early stages of neurodevelopment, we isolated neural stem and progenitor cells from mouse embryos and investigated proliferation, self-renewal and differentiation capacity of these cells lacking either Xlf, Paxx, Dna-pkcs, Xlf/Paxx or Xlf/Dna-pkcs. We found that XLF, DNA-PKcs and PAXX maintain the neural stem and progenitor cell populations and neurodevelopment in mammals, which is particularly evident in the double knockout models.

The Wave Mode (WM) is a unique imaging mode of spaceborne synthetic aperture radar (SAR), which is dedicatedly designed for global ocean surface wave observations. Since it was firstly achieved in the ERS-1/SAR satellite starting from 1991, the WM data over global oceans have been continuously acquired by the ESA’s SAR instruments onboard the satellites of ERS-2 (1997 – 2011), ENVISAT (2002 – 2012), Sentinel-1A(2014--)/1B(2016--) and the Chinese GaoFen-3 SAR (2016--), for nearly 30 years. Therefore, spaceborne SAR WM data have been a ‘big data’, which however, has not been widely exploited for global ocean wave measurements. In this study, we demonstrate its application on global ocean wave measurements based on the ten-year Advanced SAR (ASAR) WM data.

Nowadays, we can observe a worldwide trend towards the development of synthetic biomaterials. Numerous researches have been carried out in order to better understand the cellular behavior involved in the inflammation and bone healing processes related to living tissues. The aim of this study is to evaluate the tissue behavior of two different types of biomaterials: synthetic nano-hydroxyapatite / beta-tricalcium phosphate and hydroxyapatite in non-critical bone defects in rat calvaria. Twenty four rats underwent surgery in which two 3 mm defects in each cavity were performed. The rats were divided into 2 groups: Group 1, using xenogen hydroxyapatite (Bio oss ™); Group 2, using synthetic nano-hydroxyapatite / beta-tricalcium phosphate (Blue Bone ™). Sixty days after the surgery, the calvaria bone defect was filled with biomaterial, the animals were sacrificed and marked with Masson's trichrome and PAS staining, and immunohistochemistry (TNF-α, MMP-9) and electron microscopy analyzes were performed. Histomorphometric analysis indicates a greater presence in the protein matrix in Group 2, in addition to higher levels of TNF-α, MMP-9. The ultrastructural analysis shows an association of material with fibroblasts in the tissue regeneration stage. Paired statistical data indicates that Blue Bone ™ can improve bone formation / remodeling when compared to biomaterials of xenogenous origin.

ABSTRACT Surfactants are a broad category of tensio-active biomolecules with multifunctional properties applications in diverse industrial sectors and processes. Surfactants are produced synthetically and biologically. The biologically derived surfactants (biosurfactants) are produced from microorganisms with Pseudomonas aeruginosa, Bacillus subtilis Candida albicans and Acinetobacter calcoaceticus as dominant species. Rhamnolipids, sophorolipids, mannosylerithritol lipids, surfactin, and emulsan are well known in terms of their biotechnological applications. Biosurfactants can compete with the synthetic surfactants in terms of performance with established advantages over the synthetic ones including eco-friendliness, biodegradability, low toxicity, and stability over a wide variability of environmental factors. However, at present, the synthetic surfactants are a preferred option in different industrial applications, because of their availability in commercial quantities, unlike the biosurfactants. Usage of synthetic surfactants introduce new species of recalcitrant pollutants to the environment and lead to undesired results where a wrong selection of surfactants is made. Substituting synthetic surfactants with biosurfactants resolves these drawbacks, thus, interest has been intensified in biosurfactant applications in a wide range of industries hitherto considered as experimental fields. This review, therefore, intends to offer an overview of diverse applications where biosurfactants have found useful, with emphases in petroleum biotechnology, environmental remediation and in the agriculture sector. Application of biosurfactant in these settings would lead to industrial growth and environmental sustainability.

Continuous glucose monitoring devices allow diabetes condition management. However, when limited data is available, one option is to increase their size by generating synthetic samples. From a homemade wearable prototype was created a real dataset with 18 instances and 53 attributes that capture characteristics of capillary and venous blood glucose, oxygen concentration, pulse rate, skin temperature, and 24 modules and 24 phases related to bio-impedance. The objective of this article is to generate synthetic datasets, and also it investigates the ideal features subset and optimal model for non-invasive diabetes prediction. Gaussian-Copulas (GC), conditional generative adversarial networks (CG), variational autoencoders, and Copula-GAN techniques' were used to generate five synthetic datasets. Experiments show that GC1 and GC2 datasets follow min/max boundaries and are not copies of the original data. Multilayer perceptron regressor outperformed (train and test) with 2.17, 2.51 in MAE; 9.29, 13.59 in MSE; 3.05, 3.69 in RMSE, and 0.95, 0.92 in R2 in GC1, and 2.64, 3.02 in MAE; 11.43, 15.11 in MSE; 3.38, 3.89 in RMSE, and 0.94, 0.92 in R2 in GC2 with eight features. Future work is necessary to explore autoencoder and generative architectures, datasets with diverse characteristics, and the effect of the number of features.

Abstract: Yeast – especially Saccharomyces cerevisiae – have long been a preferred workhorse for the production of numerous recombinant proteins and other metabolites. S. cerevisiae is a noteworthy aroma compound producer, and has also been exploited to produce foreign bioflavour compounds. In the past few years, important strides have been made in unlocking the key elements in the biochemical pathways involved in the production of many aroma compounds. The expression of these biochemical pathways in yeast often involves the manipulation of the host strain to direct the flux towards certain precursors needed for the production of the given aroma compound. This review highlights recent advances in the bioengineering of yeast – including S. cerevisiae – to produce aroma compounds and bioflavours. To capitalise on recent advances in synthetic yeast genomics, this review presents yeast as a significant producer of bioflavours in a fresh context and proposes new directions for combining engineering and biology principles to improve the yield of targeted aroma compounds.

This paper presents a concise exploration of the synthetic biology currently to artificially 1 create life, juxtaposed against natural life’s emergence over Earth’s 4.5-billion-year history. It explores 2 the complexities of life’s origins, sustaining factors, and the consequences of synthetic life creation, 3 addressing key scientific milestones such as the Urey-Miller experiment and Jacques Monod’s pro- 4 posals. The narrative navigates through the roles of energy, Maxwell’s demons, bifurcations, and 5 genetic information in the emergence and evolution of complex biological systems. Despite significant 6 strides in genetic engineering, the challenge of creating life from scratch persists, necessitating a deep 7 understanding of diverse scientific disciplines. While synthetic biology holds promise for medicine 8 and biotechnology, the creation of truly autonomous synthetic life forms raises ethical concerns. The 9 paper also stresses the imperative for careful ethical evaluation of the societal and environmental 10 implications arising from these advancements.

Naturally-derived synthetic leather is a widely used and high-quality type of faux leather or synthetic leather. Many consumers prefer authentic leather goods due to their exceptional qualities, including high water vapor permeability and superior wearing comfort. Consequently, the exploration of artificial leather made from natural materials with exceptional qualities as an alternative to genuine leather has garnered significant interest and is now a highly studied subject. Synthetic leather, sometimes known as imitation leather, has gained popularity recently as a highly suitable alternative to genuine leather. Continuous advances in this area of research increase the range of uses for synthetic leather using natural fibers. Thus, this research aims to explore synthetic leather materials using orange peel waste, which can be applied to the fashion industry. This research applies several types of orange peel waste, such as Pomelo, Sweet orange, and Sunkist orange, to different compositions and strengthening materials, such as bandages and dacron. The resulting bio-composite materials' morphology, mechanics, and adhesive properties were evaluated following ASTM D 2209-00 and ASTM D 4533-04 standards. The results indicate that specimen 1A (Pomelo using bandage as reinforcement) has the most significant effects, with a tensile test value of approximately 472 N/cm2, an elongation of roughly 67.28 % was the highest value on sweet orange using bandage as reinforcement, and a tear resistance of around 14.28 N/cm2 for Pomelo with bandage as reinforcement. Furthermore, the test results also show that composition 2B still does not meet the standards of SNI 1294:2009 and ISO 3376 for tensile strength and ISO 3377-1 for tear strength. Additionally, statistical analysis showed no appreciable changes in the samples analyzed. Furthermore, statistical evaluations showed no significant alterations in the studied samples. The results of this experimental investigation can be explored further and used as a basic framework for the creation of bio-composites from orange peel waste for future fashion.

Cryptococcus neoformans is a multidrug-resistant human pathogenic yeast responsible for infections in immunocompromised patients. Here, Itraconazole (ITR), a commercial antifungal drug with low effectiveness against C. neoformans, was combined with different synthetic peptides Mo-CBP3-PepII, RcAlb-PepII, RcAlb-PepIII, PepGAT, and PepKAA. The mechanisms of action responsible for the synergistic effect were evaluated for the best combinations by Fluorescence Microscopy (FM). The synthetic peptides enhanced the activity of ITR by 10-fold against C. neoformans. Our results demonstrated that the combinations could induce pore formation in the membrane and overaccumulation of ROS on C. neoformans cells. Our findings indicate that our peptides successfully potentialize the activity of ITR by reducing it by 10-fold to reach antifungal activity against C. neoformans. Therefore, synthetic peptides are potential molecules to act as co-adjuvant agents in treating Cryptococcal infections.