A proposed animal welfare assessment protocol for semi-arid rangeland-based cow-calf systems in Namibia combined 40 measures from a protocol developed for beef cattle in New Zealand with additional Namibia-specific measures. Preliminary validation of the protocol had been undertaken with five herds in one semi-commercial village. The aim of the current study was to apply this protocol and compare animal welfare across three cow-calf production systems in Namibia. A total of 2529 beef cows were evaluated during pregnancy testing in the yards of 17 commercial, 20 semi-commercial and 18 communal (total: 55) herds followed by an assessment of farm resources and a questionnaire-guided interview. Non-parametric tests were used to evaluate the difference in the welfare scores between the production systems. The results indicated a discrepancy of animal welfare between the three farm types, with a marked separation of commercial farms from semi-commercial, and communal village farms in the least. The differences in these production systems was mainly driven by economic gains through access to better beef export market for commercial farms and semi-commercial villages, as well as by the differences in the available grazing land, facility designs/quality and traditional customs in the village systems. The results indicate an advantage of commercialisation over communalisation.

Bacterial infections are world-wide problem and resistant bacteria is increasing globally that lead to treatments failure. Thus, it is important to find new techniques that are effective for the treatment antibiotic-resistant microorganisms. Green medicine used for the medication of different bacterial infection. Therefore , the study aimed to evaluate the immunomodulatory activity of essential fatty acid semi carbazide (EFASC) compounds extracted from flaxseed to eradicate resistance pathogens . Crud extracts of Linum usitatissimum was extracted by hexane for extraction of EFASC. The results of LD₅₀ appear that a live suspension of E.coli was 0.49×10⁷ ,while HK E.coli was 10⁸ , 125 mg /ml were the optimum dose in stimulate immune response of EFASC which gave maximum dose of total WBC , Neutrophil and Monocyte count. The immunomodulatory activity appear that E.coli + EFASC released moderate levels of IL-4 . HK E.coli release higher IL-4 lead to increase the hypersensitivity and hyper responses of immune system. HK + EFASC immunogen revealed moderate increased in IL-12 mean level 0.99 pg /ml, HK bacteria recorded 1.49 pg/ml, while the mean of EFASC revealed no significant different compare with control. An effective protection was observed in immunized groups with HK E.coli and EFASC challenged with 100 of LD50 of a live E.coli.

Membranes based on sulfonated synditoactic polystyrene were thoroughly characterized by contrast variation SANS over a wide Q-range in dry and hydrated states. The film samples were prepared by solid-state sulfonation that allowed a uniform sulfonation of only the amorphous phase while preserving the crystallinity of the membrane. The samples were loaded with different guest molecules in either the amorphous (fullerenes) or the crystalline (toluene) regions, in order to vary the neutron contrast or to reproduce the conditions enabling an increased resistance of the membranes to chemical decomposition. The use of uni-axially deformed film samples and contrast variation with different H2O/D2O mixtures allowed for the identification and characterization of different structural levels with sizes between nm and μm, which form and evolve in the membrane morphology in dry and hydrated states and produce scattering features on different detection sectors and at different detection distances after the sample, depending on their size and orientation.

Bamboo agroforestry is currently being promoted as a viable land use option to reduce dependence on natural forest for wood fuels in Ghana. To align the design and introduction of bamboo agroforestry in conformity with farmers’ needs, perceptions, skills and local cultural practices, information on its acceptability and adoption potential among farmers is necessary. It is therefore the objective of this study to (1) describe bamboo ethnobotany and (2) assess socioeconomic factors that affect the acceptability and adoption of bamboo and its integration into farming practices. Accordingly, information has been collected from 200 farmers in the dry semi-deciduous forest zone of Ghana. The study identified the socioeconomic risks and uncertainties as well as biophysical factors that are likely to influence the potential adoption of bamboo agroforestry in the study region. Gender, age, farmers’ known uses of bamboo, the practice of leaving trees on farmlands, farmers’ networking and access to extension services, land availability and ownership by farmers were identified as suitable predictor variables for the adoption of bamboo agroforestry. It is envisaged that bamboo agroforestry is a good bet in the DSFZ though there is the need to explore domestic energy (fuelwood) provision and substitution potential in order to have a broader picture of the technology.

The primary objective of this current study is to conduct a comprehensive review of existing literature that discusses research on post and core restorations, covering aspects such as their composition, manufacturing methods, and clinical effectiveness. The methodology employed in this review encompasses the implementation of a well-defined search strategy, the establishment of criteria for inclusion and exclusion, and the selection of relevant studies to summarize their findings. To gather relevant literature published between 1993 and 2023, the research team conducted separate searches on PubMed, Scopus, and Embase databases. In total, 165 titles were initially retrieved from these electronic databases. By applying the predefined exclusion criteria, the researchers identified 73 articles that specifically address the conventional and computer-aided design/computer-aided manufacturing (CAD/CAM) technologies employed in post and core restorations. These restorations are frequently utilized procedures for repairing teeth that have undergone endodontic treatment and subsequently lost dental structure. The development of computerized technology for the creation of customized posts and cores has emerged as a straightforward and efficient alternative to traditional methods. The review encompasses a synthesis of papers discussing the various techniques and materials employed in constructing post and cores using CAD/CAM technology. Notably, different strategies for restoring endodontically treated teeth through CAD/CAM post and cores have been documented, revealing both direct and indirect approaches. The most mentioned materials for these restorations include zirconia, composite resin, and hybrid ceramics. While the available literature on CAD/CAM post and core procedures is somewhat limited, the review underscores the need for further research to explore the long-term outcomes and efficacy of this treatment approach.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

Stipa tenacissima (S. tenacissima), or esparto, is a perennial tussock grass that coexists with Pinus halepensis (P. halepensis) in semi-arid mediterranean woodlands. This research was carried out to explore diurnal transpiration at leaf level in esparto grass under different levels of pine-esparto competition and in contrasting environmental conditions of soil water. The measurements were spanned from the summer 2020 to spring 2021. The relationship between transpiration and competence was conducted in open and closed P. halepensis stands and taken into account the type of leave (green, senescent) and the maturity of the esparto grass. We observed a higher control of transpiration in green leaves, and correlations between transpiration and soil water were noted exclusively in this type of leaf. Our results substantiated a significant impact of pine competitors on transpiration of esparto grass, particularly during seasons characterized by high water-demand scenarios: the summer drought period and the commencement of the growing and flowering period (spring). Furthermore, our findings confirmed a greater response to transpiration in mature bushes compared to young ones under severe water stress, denoting higher adaptation to drought as esparto ages. Our results may have important implications for the dynamics and management of these semi-arid mixed woodlands, as well as the planning of reforestation programs aimed at restoring esparto grass steppes.

Immunohistochemistry is a powerful technique, widely used in biomedical research and clinics, that allows to determine the expression levels of some proteins of interest in tissue samples by means of the intensity of color due to the expression of biomarkers using specific antibodies. As such, immunohistochemical images are complex and problematic to be quantified. Recently we proposed a novel method including a first separation stage based on nonnegative matrix factorization (NMF) that achieved good results. However, that method was highly dependent on sparseness and non-negativity parameter choice and on algorithm initialization. Furthermore, the previously proposed method needed a reference image as starting point for the NMF algorithm. In the present work, we propose a new, simpler and robust method for the automated unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest). The new proposed method represents a simpler approach that, in one side, avoids the use of NMF for the separation stage, and in the other side, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest by means of principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing the finding of color vectors by considering the peaks of density of points. The method also develops a new scoring stage by avoiding, again, reference images, making the procedure more robust and less dependent on the parameters. Experiments for the semi-quantitative scoring of images in five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

The asymmetric synthesis of polyunsaturated triene C18:3 n-3 and C18:3 n-6 methoxylated ether lipids (MEL) of the 1-O-alkyl-sn-glycerol type is described as possible structural candidates for a triene C18:3 MEL of an unknown identity found in a mixture of shark and dogfish liver oil. Their C18:3 hydrocarbon chains constitute an all-cis methylene skipped n-3 or n-6 triene framework, along with a methoxyl group at the 2'-position and R-configuration of the resulting stereogenic centre. The methoxylated polyenes are attached by an ether linkage to the pro-S hydroxymethyl group of the glycerol backbone. The syntheses were based on the polyacetylene approach that involves a semi-hydrogenation of the resulting triynes. Both syntheses were started from our previously described enantio- and diastereomerically pure isopropylidene-protected glyceryl glycidyl ether, a double-C3 building block that was designed as a head group synthon for synthesis of various types of MELs.

Multibeam echo-sounders provide ideal data for semi-automated seabed feature extraction and accurate morphometric measurements. However, these methods have not been tested in morphologically highly complex environments. In this study, bathymetric and raw backscatter data were initially used to manually map the complex reef morphology found in the semi-enclosed Gera Gulf, in the northern Aegean Sea (Greece). A large number of reefs (more than 7000) were detected, making manual mapping extremely time-consuming. Benthic Terrain Modeller (BTM) was selected as a semi-automated method to map the reefs. The BTM did not function properly in this irregular relief and the results were discouraging. Trying to improve the BTM functionality, both bathymetry and slope were modified (enhanced); this time, the BTM outcome was unexpectedly improved, producing accurate results that appeared to exceed the accuracy of manual mapping. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the Goodness-of-Fit (GOF), while Precision, Recall, and F1 Score were also calculated. GOF values suggest more than 50% overlap between the resulting polygons while, Precision, Recall and F1 score resulted in values higher than 0.78 suggesting good detection accuracy for the semi-automated method. It becomes apparent that BTM provided more efficient results in comparison to the time-consuming manual mapping. This study asserts that semi-automated mapping stands as an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making.

Wheat yield plays an important role in the global economy. Changing climate conditions and increased drought negatively affect grain yield. Currently, breeding programs are aimed at improving wheat productivity. To accelerate the selection process of hybrids and parental forms for crosses, it is essential to pay attention to plants' phenotypic and genetic potential. We studied the genes of the "green revolution" group Rht and coarse-grained TaGW and TaGS to increase yields by selecting optimal parameters for wheat. We focused on short-stemmed and coarse-grained characteristics in a dry climate and conducted a correlation analysis between the genetic potential of wheat varieties and hybrids and their phenotypic manifestation in drought conditions. Rht-D1b significantly reduced the height of plants by 51.67% and reduced the coleoptile length by 30.45%. At the same time, TaGW8-B1a increased the grain width by 13.37%. However, in the presence of Rht-D1b and Rht-B1b in the genome, the 1000-kernal weight was 35.06 g (p<0.05), the Rht-B1b gene enhanced it by 9% (p<0.05), while both Rht-D1a and Rht-B1a alleles gave a 1000-kernal weight of 37.96 g (p<0.05). The Rht-D1b and Rht-B1b alleles gave 18.5 seeds per ear, while the Rht-D1a and Rht-B1a alleles gave the value of grains per ear -24.1 pcs. The data obtained indicated the relationship of phenotypic and genetic parameters in wheat between the coarse-grained and short-stemmed genes, which makes it possible to select based on the genetic parameters of varieties and hybrids, thereby speeding up the selection process.

Minimum housing standards aim to safeguard housing rights and enhance residential conditions. Governments develop housing policies, including provision, preservation, and support for welfare programs, guided by the status of households below these standards. Growing nations commonly utilize this approach to decrease the proportion of households not meeting these criteria. In South Korea, the ratio of households below these standards was 4.5% in 2021, down from 16.6% in 2006, as indicated by the initial Korea Housing Survey. However, this downward trend has stalled over the past decade. With the 2004 and 2011 revisions, the standards have been effective for 12 years, yet no longer mirror current housing realities due to ongoing improvements. This study aimed to propose enhancements to Korea’s minimum housing standards. Through analyzing laws, prior research, present household statuses, international cases, and expert insights, recommendations emerged. Categorizing households by size, we developed precise standards covering area, facility, and location aspects. These new standards led to an 8.4% non-compliance rate in 2021. This research’s findings anticipate aiding the revision of minimum housing standards, formulating pragmatic policies for enhancing residential conditions in line with present situations.

The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows considerable increase of the brain surface. In premature childrens, these alterations take place outside the womb that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic GUI platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians, for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches, pre-processing tools, and is friendly designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach’s effectiveness.

Recently, a paper was written to reformulate and strengthen the theory of semi-structured complex numbers H (a new number set invented to enable division by zero). Whilst the paper had profound results, the application of this number set in many areas of engineering and science has not been fully explored. Consequently, the aim of this paper was to establish the number set H as a useful mathematical tool by providing an amalgam of results arising from its application in several areas of engineering and science. In the process, this paper makes four major contributions: these are (1) determining the product of the gradients of a horizontal and vertical line; (2) developing new Euler formulas relating exponential, trigonometric and hyperbolic functions in the 3D semi-structured complex number Euclidean space; (3) establishing semi-structured complex numbers as a better extension of the complex number set than quaternions; (4) resolving singularities that may arise in engineering and science equations (because of division by zero) to develop reasonable conclusions in the absence of experimental data. These results and their applications provide a firm foundation to advance the number set H as a useful mathematical tool.

In this paper, in order to provide proper parameters for the preparation of semi-solid billets, the semi-solid annealing of hot-rolled 2A14 Al alloy was investigated. The microstructure was characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) with X-ray energy dispersive spectrometer (EDS) and electron backscattered diffraction (EBSD), scanning transmission electron microscope (STEM). XRD results show that with the increase of temperature, equilibrium θ-Al2Cu gradually dissolved in the matrix. The EDS results of SEM and STEM show that there were coarse θ-Al2Cu phase, ultra-fine precipitate Al(MnFeSi) or (Mn, Fe)Al6 phase and atomic clusters in the microstructure. EBSD results show that the recrystallization mechanism was dominated by continuous static recrystallization (CSRX), the homogeneous nucleation occurred when the sample was heated to near solidus temperature, and CSRX happened in the semi-solid temperature. In the process of recrystallization, the micro-texture changed from preferred orientation to random orientation. A variety of experimental results show that static recrystallization (SRX) occurred at semi-solid temperature due to the blocking effect of atomic clusters on dislocation slip and the Zener drag effect of fine precipitates on low angle grain boundaries (LAGBs) disappeared with melting at semi-solid temperature.

The study aimed to develop standards for a welfare assessment protocol, by validating potential categorisation thresholds for assessment of beef farms in various beef cow-calf production systems in Namibia. Forty measures combined from a New Zealand-based protocol plus Namibia-specific measures, were applied on 55 beef farms (17 commercial farms, 20 semi-commercial and 18 communal village farms) during pregnancy testing, and a questionnaire guided interview. The categorised measures on a 3-point welfare score of 0: good 1: marginal and 2: poor/unacceptable welfare were subsequently compared with derivation of thresholds based upon the poorest 15% and best 50% of herds for each measure. Overall combined thresholds of continuous measures across the 3 farm types, showed 10/22 measures that posed welfare compromise across Namibia, where commercial farms had 4/22 measures and semi-commercial and communal village farms had 12/22 and 11/22 respectively with high thresholds. Most measures-imposed thresholds were retained because of significant importance to welfare of animals and preventiveness of the traits, while leniency was given to adjust good feeding and mortality measures to signify periods of drought. Handling measures (fearful, falling/lying) and abrasions thresholds were adjusted to reflect the temporary stress caused by infrequent cattle handling, and faulty yard designs/design and possible cattle breed influence on handling. Hence, the country needs prioritised investigation of underlying contributing factors and remediation to reduce the high thresholds.

To determine the photon emission or absorption probability for a diatomic system in the context of the semiclassical approximation it is necessary to calculate the characteristic canonical oscillatory integral which has one or more saddle points. Integrals like that appear in a whole range of physical problems, e.g. the atom-atom and atom-surface scattering and various optical phenomena. A uniform approximation of the integral, based on the stationary phase method is proposed, where the integral with several saddle points is replaced by a sum of integrals each having only one or at most two real saddle points and is easily soluble. In this way we formally reduce the codimension in canonical integrals of "elementary catastrophes" with codimensions greater than 1. The validity of the proposed method was tested on examples of integrals with three saddle points ("cusp" catastrophe) and four saddle points ("swallow-tail" catastrophe).

The aim of this article is to extend the local as well as the semi-local convergence analysis of multi-point iterative methods using center Lipschitz conditions in combination with our idea, of the restricted convergence region. It turns out that this way a finer convergence analysis for these methods is obtained than in earlier works and without additional hypotheses. Numerical examples favoring our technique over earlier ones completes this article.

To reduce solar power production invariance, it is critical to study varying patterns of power production in the concerned region. Analyzing the patterns of past power production trends can help simulate power production scenarios for future. The current study area is around Amsterdam, located in Netherlands. PVoutput.org website is used to mine 6 months of solar power production data for 120 stations around Amsterdam city. FME Workbench software is used to actively fetch the data from the mentioned website and manage in a MySQL database. Solar attenuation maps created using ArcGIS, helped to graphically visualize the variations in solar power production at different times and locations. Further, spatial autocorrelation is checked between the stations using semi-variograms in geostatistical tool of ArcMap. This feature allows to check whether the stations located close to each other are more correlated to each other rather than stations which are far apart. The statistical data analysis of power production can aid solar power production companies to better interpolate and predict solar power in advance for the concerned study region.

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still signicant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certied and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certication is costly and time consuming. The study specically analyses Selective Laser Melting (SLM) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as `faulty' or `acceptable'. A fully supervised approach identied faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certication data. The results show that semi-supervised learning is a promising approach for the automatic certication of AM builds that can be implemented at a fraction of the cost currently required.

Carbon nanotubes (CNTs) are effective supports for nano metals and together they represent hybrids that combine unique properties of both. A microwave induced reaction was used to deposit nano palladium on carboxylated and octadecylamine functionalized multiwall CNTs, which were used to carry out C-C coupling reactions in DMF and toluene. These hybrids showed excellent catalytic activity with yield as high as 99.8% while its enhancement with respect to commercially available Pd/C catalyst reached as high as 109%, and the reactions times were significantly lower. Polarity of the functionalized form was found to be a significant factor with the polar carboxylated CNT showing better activity in DMF while the relatively nonpolar octadecyl amine was better in toluene. The results suggest the possibility of tailor making functionalized CNT when used as catalyst supports

Background: Ovarian clear cell carcinoma (OCCC) is a type of ovarian cancer with a poor prognosis if detected in the progressive stage since there is less effective chemotherapy. Recent advancements in molecular-targeted drugs have not substantially affected OCCC treatment. Therefore, we explored the potential of targeting MDM2 in OCCC cells. Methods: We used TOV-21G and KOC7c cells as the ARID1A mutant-type, and RMG-I and ES2 cells as the ARID1A wild-type. Then, we performed small interfering library screening, Western blotting, real-time polymerase chain reaction analysis, cell proliferation assay, cell cycle analysis, time-lapse cell proliferation assessment, and DNA damage assessment. Next, to generate murine intraperitoneal tumors, 7.5 × 106 TOV-21G cells in 200 μL of phosphate-buffered saline were injected subcutaneously into the intraperitoneum in 5–6-week-old athymic nude mice. Results: Using various cell lines with ARID1A mutations or without mutation, the results showed that the interference of MDM2 effectively reduced cell proliferation in ARID1A-mutant cells but not in ARID1A wild-type cells. Additionally, interference with ARID1A against ARID1A wild strains reproduced susceptibility to MDM2 interference. In vivo experiments demonstrated that nutlin-3, an MDM2 inhibitor, significantly suppressed tumor growth in the ovarian cancer mouse model. Conclusion: These findings suggest that targeting MDM2 may be a viable strategy for the treat-ment of ARID1A-mutated OCCC, offering a new therapeutic approach for this challenging type of cancer.

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.

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.

Multicellularity is one of the major evolutionary transitions, and its rise provided the ingredients for the emergence of a biosphere inhabited by complex organisms. Over the last decades, the potential for bioengineering multicellular systems has been instrumental in interrogating nature and exploring novel paths to regeneration and disease, as well as cognition and behaviour. Here, we provide a list of open problems that encapsulate many of the ongoing and future challenges in the field, and we suggest conceptual approaches that may facilitate progress.

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.

Synthetic cathinones, derived from cathinone found in the plant Catha edulis, represent the second-largest and most frequently seized group of new psychoactive substances. They are considered as β-keto analogs of amphetamine, sharing pharmacological effects with amphetamine and cocaine. This review describes the neurotoxic properties of synthetic cathinones, encompassing their capacity to induce neuroinflammation, dysregulate neurotransmitter systems, and alter in monoamine transporters and receptors. Additionally, it discusses the rewarding and abuse potential of synthetic cathinones drawing from findings obtained through various preclinical animal models, contextualized with other classical psychostimulants. The review also offers an overview of current abuse trends of synthetic cathinones on the illicit drug market, specifying the aspects covered, and underscores the risks they pose to public health.

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.

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications, such as the military, where the dynamic grasp of enemy targets can help improve the early warning capability of naval defence, and the civilian detection of illegal fishing vessels can help improve the level of maritime management. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposes an attention mechanism for multiscale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), is proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our model is more lightweight, only 18.43M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy AP50 of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 4.9% and 5% higher than that of YOLOv7, respectively.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

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.

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.

Disinfection byproducts (DBPs) are formed whenever disinfectant ingredients are in contact with organic matter. Due to widespread increase in production and use of disinfectants and antiseptics, the presence of their components in coastal environments is increasing. However, very limited scientific literature is available in regard to the occurrence of DBPs and related compounds in the coastal zone and particularly in seawater. In this context, the aim of this preliminary compilation is to outline the main up-to-date findings and raise awareness on their increasing occurrence in coastal environments. Sampling and analysis of marine samples in the USA, Spain, China, Taiwan, Malaysia, Korea, Kuwait and Hong-Kong revealed the presence of several categories of DBPs and related compounds in coastal environments, with the highest levels being detected in urban areas, probably related to the nearby wastewater treatment plants. The investigations considered in this preliminary compilation, report the detection of DBPs and related compounds as well as the formation of toxic brominated and iodinated DBPs in high levels due to chlorination of wastewater, especially after use of seawater for flushing in toilets. New DBP groups have also been detected, namely halogenated phenolic DBPs and halopyrroles.

The objective of this study was to compare the effect of different botanical powders and synthetic chemical in controlling Sitophilus zeamais in stored maize. A laboratory experiment was done to assess three botanicals, Zingiber officinale, Moringa oleifera, Xylopia aethiopica, in controlling the S. zeamais. The results of this study demonstrated the active potentials of these plant products as plant-derived insecticides against maize weevil. The synthetic chemical used showed both higher weevil mortality and higher grain loss than the botanicals (P< 0.05). The Botanicals also showed significance difference in the mortality rate (P < 0.05) as Z. officinale recorded the highest mortality rate (5.66%) while X. aethiopica recorded the lowest mortality rate (3.66%). The control attained the highest grain damage (7.33%) while amongst the botanicals, the highest was found in Z. officinale (7.33%) and lowest was on M. oliefera (5.33%). The exit holes made by the weevils at the end of the experiment were highest (2.66) in M. oliefera whereas lowest was recorded on X. aethiopica (1.00). Z. officinale, X. aethiopica and M. oleifera were efficacious against S. zeamais instead of synthetic chemical insecticides that have environmental health hazards and they can be used in integrated pest management by farmers and foods merchants. Therefore, since these botanicals have no any adverse effects on the seeds and safe to the environment, they are recommended for future usage in storage grains to control of S. zeamais.

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.

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.

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.

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.

Increasingly, architects are looking towards nature to design more sustainable, efficient cities to reduce the environmental impact of urban life. At the moment, plants are incorporated into urban design for conservation or aesthetic reasons. Here, I argue plants can be rationally designed into synthetic systems based on chemical and other functional traits to increase the stability of urban infrastructure, protect native biodiversity, and promote human health while meeting key UN Sustainable Development Goals.

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community.

Due to the advantages such as low power consumption and higher concealment, deceptive jamming against synthetic aperture radar (SAR) receives extensive attention during the past decades. However, the large scene deception jamming is still a challenge because of the huge computing burden. In this paper, we propose a new large scene deceptive jamming algorithm. First, the time-delay and frequency-shift (TDFS) algorithm is introduced to improve the jamming processing speed. The system function of jammer (JSF) for a fake scatter is simplified to the multiplication of the scattering coefficient, a time-delay term in range dimension and a frequency-shift term in azimuth dimension. Then, in order to solve the problem that the effective region of the TDFS algorithm is limited, the scene deceptive jamming template is divided into several blocks according to the SAR parameters and imaging quality control factor. The JSF of each block is calculated by the TDFS algorithm and added together to achieve the large scene jamming. Finally, the correction algorithm in squint mode is derived. The simplification and parallel block processing could improve the calculation efficiency significantly. The simulation results verified the validity of the algorithm.

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.

Ship speeds extracted from AIS data vary with ice conditions. We extrapolated this variation with SAR data to a chart of expected icegoing speed. The study is for the Gulf of Bothnia in March 2013 and for ships with ice class 1A Super that are able to navigate without icbreaker assistance. The speed was normalized to 0-10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap, from hours to two days, we calculated a set of local SAR statistics over several scales. We used random tree regression to estimate the speed. The accuracy was quantified by mean squared error (MSE), and the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. MSE varied from 0.4 to 2.7 units² for daily routes. 65 % of the estimates deviated less than one unit and 82 % less than 1.5 units from the AIS speeds. The estimated daily mean speeds were close to the observations. Largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when ice chart thickness was included as one predictor.

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

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.

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.

Synthetic fuels play an important role in the defossilization of future aviation transport. To reduce the ecological impact of remote airports due to long range transportation of kerosene, a decentralized on-site-production of synthetic paraffinic kerosene is applicable, preferably as near-drop-in fuel or alternatively as blend. One possible solution for such a production of synthetic kerosene is the Power-to-Liquid process. The basic development of a simplified plant layout addressing the specific challenges of a decentralized kerosene production which differ from most current approaches for infrastructural well-connected regions is described. The decisive influence of the Fischer-Tropsch synthesis on the PtL process is shown by means of a steady-state reactor model which was developed in Python and serves as basis for further development of a modular environment able to represent entire process chains. The reactor model is based on reaction kinetics according current literature. The effects of adjustments of the main operation parameters on the reactor behavior are evaluated and the impacts on up- and downstream processes are described. The results prove the governing influence of the Fischer-Tropsch reactor on the PtL process and show its flexibility regarding the desired product fraction output, which makes it an appropriate solution for a decentralized kerosene production.

This study aimed to analyze the population dynamics of the mite Varroa destructor in honey bee (Apis mellifera) colonies in a temperate semi-arid climate in Mexico. Ten colonies homogeneous in population, food stores, and levels of mite infestation, were used. The mite infestation rate in brood and adult bees, total number of mites, daily mite fall, brood and adult bee population, and food stores, were determined periodically for 10 months. There was a significant effect (P &lt;0.05) of sampling period on the population of V. destructor in adult bees, brood, total mite population, and daily fallen mites. The total mite population increased 26% on average per colony. The increase in brood amount reduced the mite infestation rate in adult bees, and the opposite occurred when the brood decreased. Monitoring V. destructor populations by recording fallen mites is more reliable than determining mite infestation rates in bees, as mite fall has a dynamic pattern similar to that of the total mite population. The best period to apply an acaricide treatment in the region of study is between November and December because most mites were in the phoretic phase, since there was less brood in the colonies compared to other times.

The next generation network requires not only ultra high data rate, global coverage and connectivity, but also reducing network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For a RIS-aided multiple-input and single-output (MISO) system, in this paper, to achieve the channel state information (CSI), we propose a principle component analysis (PCA) based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL) based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results verify our proposed channel estimation scheme based on PCA and beam tracking scheme based on DNN for the semi-active RIS-aided MISO systems can obtain approximate performances with much lower computational complexity.

This paper investigates the use of Physics-Informed Neural Networks (PINNs) in active learning cycles. We defined two scenarios: one semi-supervised and the other fully supervised. PINNs emphasize the integration of physical laws into neural networks to improve the predictive performance of vanilla neural networks and to enhance the efficiency of traditional methods for solving partial differential equations (PDEs). Key contributions include adapting existing computational frameworks to enable the use of Graph Neural Networks for solving problems that require the calculation of gradients on unstructured triangle meshes, a query strategy focusing on the physical loss, and a comparative analysis of this strategy against random sampling across both defined scenarios. This work establishes a foundation for future research aimed at expanding the application of Physics-Informed Graph Neural Networks (PIGNN) using active learning and addressing real-world problems in fluid dynamics and electrodynamics.

Facial acne is a prevalent dermatological condition regularly observed in the general population. This study introduces a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrates improvement in performance, offering a novel direction for future acne analysis.

How hands-on gardening impacts behaviors including healthy eating and physical activity during early childhood can be of critical importance for preventing early onsets of obesity. This research investigates how participating in hands-on gardening impacts preschoolers’ (3-5 years old) physical activity (measured by accelerometers) in childcare centers in the Semi-Arid Climate Zone. The research was conducted in eight licensed childcare centers located in West Texas with 149 children (n=149). Four childcare centers in the experimental group received hands-on garden interventions; the other four in the control group did not. In both experimental (intervention) and control (non-intervention) centers, children wore Actigraph GT3X+ accelerometers continuously for 5 days before and for 5 days after intervention (a total of 10 days). Results show that the duration of sedentary behavior of children in the experimental (intervention) group significantly decreased compared to children in the control (non-intervention) group. The finding suggests that the positive effects of childcare hands-on gardening on physical activity extend to semi-arid climate zones where gardening is challenging due to high temperatures and lack of annual rainfall. The research emphasizes the critical need to incorporate hands-on gardening in childcare centers as an obesity prevention strategy nationally in the U.S. and beyond.

Towards better understanding of factors controlling carbon (C) exchange between inland waters and atmosphere, we addressed inorganic carbon cycle in semi-arid lakes of Central Eurasia, subjected to strong impact of on-going climate change. For this, we assessed the hydrochemical vari-ability and quantified its control on formation of authigenic carbonate minerals, occurring within the upper layer of sediments in 43 semiarid lakes located in the southwest of Western Siberia (Central Eurasia). Based on measurements of pH, total dissolved solids (TDS), cationic and anionic composition, dissolved organic and inorganic C, as well as textural and mineralogical characterization of bottom sediments using X-ray diffraction and scanning electron microscopy, we demonstrate that lake water pH and TDS are primarily controlled by both lithological and climatic context of the lake watershed. We have not revealed any direct relationships between lake morphology and water chemistry. The most common authigenic carbonates scavenging atmospheric CO2 in the form of insoluble minerals in lake sediments were calcite, aragonite, Mg-calcite, dolomite and hydro-magnesite. The calcite was the most common component, aragonite mainly appears in lakes with sediments enriched in gastropod shells or artemia cysts, while hydromagnesite was most common in lakes with high Mg/Ca molar ratios, as well at high DIC concentrations. The relationships between mineral formation and water chemistry established in this study can be generalized to a wide suite of arid and semi-arid lakes in order to characterize current status of inorganic C cycle and predict its possible modification under on-going climate warming such as water temperature rise and change of hydrological connectivity, primary productivity and nutrient regime.

Keywords: glycerin; Ni-based catalyst; lanthanum; semi-industrial scale; hydrogen production; biorefinery; monitoring.

The positional deviation of the Automatic Emergency Call System (AECS) under collision conditions brings difficulties for post-rescue operations. Currently, there is a lack of analysis on the system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on the data deviation of the simulated GNSS system. We also propose environmental limits applicable to comprehensive AECS tests, which can objec-tively evaluate the reliability of AECS and provide data support for AECS bench test. Through the satellite signal-based semi-physical simulation, AECS is subjected to bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibra-tion and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the AECS test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature dif-ference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of satellite signal simulation, nec-essary environmental conditions for AECS test are obtained, including the temperature consistent with the maximum operating temperature of the vector generator and the vibration Power Spectral Density (PSD) lower than 1.2gRMS.

The paper proves a theorem about the structure of the distribution function on which the extremum of fractionally linear functional is reached in the presence of an uncountable number of linear constraints. The problem of finding an extremal distribution function arises when determining the optimal control strategy in a class of Markov homogeneous randomized control strategies. The structure of extremal functions is described by a finite number of parameters, hence the problem is greatly simplified since it is reduced to the search for an extremum of some function.

Semi-structured complex numbers H was a number set developed to enable division by zero in ordinary algebraic equations. Its utility has been shown in mathematics and engineering. However, very little has been done to show its usefulness in computer science. Consequently, the aim of this paper was to show the utility of semi-structured complex numbers in computer science by developing a division by zero calculator. First two computer programs were written, one for a standard (STD) calculator and the other for a division by zero (DBZ) calculator. The programs were fed 20000 randomly generated arithmetic equations of varying lengths and the space and time complexity associated with processing these equations were measured and compared to determine the efficiency of each calculator. In the process, three major contributions were made: (1) A representation for semi-structured complex numbers that enables it to be easily used by a computer was developed; (2) It was demonstrated that the DBZ calculator outperforms the STD calculator in terms of efficiency; and, (3) It was shown that the number set H reduced the amount of error handling required to run a computer program. These results provide a firm foundation to advance the number set as a useful tool in computer science.

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Let $G$ be a group with identity $e$ and $R$ be a commutative $G$-graded ring with nonzero unity $1$. Graded semi-primary and graded $1$-absorbing primary ideals have been investigated and examined by several authors as generalizations of graded primary ideals. However, these three concepts are different. In this article, we characterize graded rings over which every graded semi-primary ideal is graded $1$-absorbing primary and graded rings over which every graded $1$-absorbing primary ideal is graded primary.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

Recently, there has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used across India for the improvement of local water security. The Central Groundwater Board of India is en-couraging the construction of such interventions for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at basin scales to maximise their effectiveness. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling exercises. A bespoke version of the GWAVA model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the estimated average annual simulated streamflow at the outlets of the two sub-catchments and the basin whereas tanks and check dams have a more significant effect. In-terventions generally were found to increase evaporation losses across the catchment. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

Increased attention has been paid to the influence of coal mining subsidence on ecological environment. Restoration of ecosystem in damaged mining area is critical for restoring disturbed environment. The comparing of plant communities and microbial communities in the artificial restoration and natural restoration areas provides an effective method for evaluating the restoration effects. However, such studies are limited in coal mining subsidence restoration areas. Subsidence area in Shendong mining area, located in the semi-arid region of Western China, was restored from 2003 with 5 ecological restoration plant species. In July 2017, the comparison and analysis of plant and microbial communities were conducted at the artificial restoration areas (AR) and the natural remediation areas (NR). The results showed that the artificial ecological restoration in Shendong mining area has achieved some success, but it has not recovered to a similar ecosystem before the destruction. A higher plant species, coverage and bacterial community diversity were observed in AR. However, these features have lower similarity compared with those in NR sites. Potential soil factors, such as pH, moisture content, total carbon content, organic matter, nitrogen and bulk density, have a greater impact on soil bacterial community structure and diversity. In the ecological restoration of the mining area, attention should be paid to the restoration of soil properties in the mining area. This study can provide theoretical guidance for more scientific ecological restoration in the damaged mining area.

The ecological background condition of the semi-arid steppe region (SASR) is extremely fragile. It is recognized that the development of coal and electricity power is a kind of strong human interference behavior for regional landscape ecology. Landscape ecological classification (LEC) is the premise of landscape ecology research of the mining area. The current research on the SASR and grassland LEC of coal-power base is relatively less, but still remains uncertainty concerning how to stratify and classify urban mining landscapes into units of ecological significance at spatial scales appropriate for management. This study is based on hierarchy theory, scale theory, landscape process, the patch-corridor-matrix model, the network, the theory of multiple planning integration and the principle of remote sensing. According to the comprehensive principle, principles of the combining of structure and function, principle of the combining human-ominated and natural landscape, principle of emphasis, and principle of combining qualitative analysis with quantitative research of LEC in large-scale coal-power base(LSCPB). On the basis of occurrence method land classification, fully consider the ecological attributes of the land, integration pattern, processes and function theory of the landscape ecology, the LEC system of the LSCPB in the SASR has been constructed by using top-down decomposition classification method. Empirical research of the Victory and Mindong No.1 mining areas of Shenhua Group shows that the classification system constructed in this paper can meet the requirements of LEC and fully reflect the status of landscape ecology of LSCPB in SASR. This study can provide theoretical guidance for the landscape ecology of LSCPB, while also supporting a theoretical reference for the LEC research.

The objective of this research is to develop an experimental-theoretical analysis about the influence of the cooling medium and the geometry of the welding bead profile in fatigue life and associated parameters with structural integrity of welded joints. A welded joint with cruciform geometry is considered using SMAW, plates in structural steel ASTM A36 HR of 8 mm of thickness and E6013 electrode input. A three-dimensional computational model of the cruciform joint was created using the finite element method. For this model, the surface undulation of the cord and differentiation in the mechanical properties of the fusion zone were considered, the heat-affected zone (HAZ) and base material, respectively. In addition, an initial residual stress field which was established experimentally was considered. The results were a set of analytical expressions for the weld magnification factor Mk. It was found that values for the latter decrease markedly in function of the intensity of the cooling medium used in the post welding cooling phase, mainly due to the effect of the residual compressive stresses. The obtained models of behavior of the weld magnification factor are compared with the results from other researchers with some small differences, mainly due to the inclusion of the cooling effect of the post weld and the variation of the leg of the weld bead. The obtained analytical equations in the present research for Mk can be used in management models of life and structural integrity for this type of welded joint.

Semi-solid feedstock of AZ80 magnesium alloy modified by minor rare-earth Y element (0, 0.2, 0.4, 0.8 wt.%) were fabricated by strain induces melting activated (SIMA) in the form of extrusion and partial remelting. The effect of Y addition on the microstructure evolution of extruded and isothermal treated alloy was observed by optical microscope (OM), scanning electron microscope (SEM), X-ray diffraction (XRD) and quantitative analysis. The results show that the Y addition can refine the microstructure and make the β-Mg17Al12 phases agglomerating. During the subsequent isothermal treatment at 570℃, the average solid grain size, shape factor and liquid fractions increased with prolonged soaking time. Smaller spheroidal solid grains and the larger shape factor were obtained due to Y addition. The coalescence and Ostwald ripening of solid grains operated the coarsening process simultaneously. The coarsening rate constants of AZ80M1 (0.2 wt.% Y addition) of 164.22 μm3s-1 was approximately four times less than the un-modified AZ80 alloy of 689.44 μm3s-1. In contrast, the desirable semi-solid structure featured by fine, well globular solid grains and appropriate liquid fractions and shape factor was achieved in AZ80M1 alloy treated at 570℃ for 20-30 min.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

This paper presents a simple and streamlined compensation technique for improving the quality of Synthetic Aperture Radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing 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.

Metabolic engineering in plants offers powerful strategies to enhance crop yield and quality, addressing global challenges in agriculture and food security. This study explores cutting-edge approaches in manipulating primary and secondary metabolic pathways, utilizing advanced genetic modification tools to improve key traits in crops. We examine successful applications that have enhanced photosynthetic efficiency, nutrient use, stress tolerance, and the production of valuable metabolites. The study also delves into emerging trends, such as synthetic biology approaches and multi-gene trait stacking, which are revolutionizing the field. By integrating omics technologies and computational modeling, researchers are optimizing metabolic engineering designs with unprecedented precision. As the field rapidly evolves, we consider the regulatory and biosafety aspects of genetically modified crops, providing insights into the future of sustainable agriculture and crop improvement.

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.

The study of ecosystems, both natural and artificial, has historically been mediated by population dynamics theories. In this framework, quantifying population numbers and related variables (associated with metabolism or biological-environmental interactions) plays a central role in measuring and predicting system-level properties. As we move towards advanced technological engineering of cells and organisms, the possibility of bioengineering ecosystems (from the gut microbiome to wildlands) opens several questions that will require quantitative models to find answers. Here, we present a comprehensive survey of quantitative modelling approaches for managing three kinds of synthetic ecosystems, sharing the presence of engineered strains. These include test tube examples of ecosystems hosting a relatively low number of interacting species, mesoscale closed ecosystems (or ecospheres), and macro-scale, engineered ecosystems. The potential outcomes of synthetic ecosystem designs and their limits will be relevant to different disciplines, including biomedical engineering, astrobiology, space exploration and fighting climate change impacts on endangered ecosystems. We propose a space of possible ecosystems that captures this broad range of scenarios and a tentative road map for open problems and further exploration.

Public-private partnership (PPP) is a prominent tool for sustainable infrastructure development. However, the positive contributions of PPPs towards attainment of sustainable, climate resilience and zero carbon infrastructure projects are hampered by poor financial risk management. This problem is more prevalent in developing countries like Ghana where private investment inflow has plummeted due to COVID-19 recession and poor project performance. Thus, this study aims at assessing the key financial risk management strategies in ensuring sustainable PPP infrastructure projects in Ghana. The study utilised primary data from PPP practitioners in Ghana solicited through survey questionnaires. Factor analysis, mean scores and fuzzy synthetic analysis are the data analysis techniques for this study. The results revealed sustainable and green funding models, effective cost reduction initiatives, competent team with committed leadership, and emerging technologies and regulations constitute the key strategies to manage financial risks of sustainable PPP infrastructure projects. Although, future studies must expand the scope of data gathering, the findings of the study enrichen the theoretical understanding of financial risks in sustainable investments in PPP infrastructures. Relevant remedies that will aid the development of practical financial risk management guidelines are provided in this study for PPP practitioners.

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.

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.

This work presents the possibility of using a synthetic membrane for pretracing mucosal penetration studies. The permeability of two types of membranes, porcine oral mucosa and a synthetic Nuclepore membrane, to water was compared. Moreover, the water permeability of membranes modified with waterproofing formulations was measured to study their ability to protect against the penetration of viruses, toxins, etc. A very good correlation was obtained between the transmucosal water loss (TMWL) values of the artificial membrane and the mucosa. These results support the possible use of this synthetic membrane in the screening of the water permeability of formulations. In addition, studies of the permeation of different actives, drugs and biocides through the two membranes were carried out, and these results were compared with their skin permeation data. The synthetic membrane does not seem to discern between compounds in terms of permeability, which could be due to the lack of lipids in the composition of this synthetic membrane. Therefore, Nuclepore alone cannot be used as a model for the release of actives. Additionally, the permeation of caffeine through intact or modified membranes incorporating waterproofing formulations was also studied. In this case, similar permeation profiles through the synthetic membrane and mucosa were found. The results from these assays should lend support to the use of this synthetic membrane when screening formulations to be applied in oral penetration studies.

Lodging is one of the important factors affecting the high and stable yield of wheat worldwide. Solid-stemmed wheat has higher stem strength and lodging resistance than hollow-stemmed wheat. There are many solid stemmed varieties, landraces and old varieties of durum wheat. However, the transfer of solid stem genes from durum wheat is suppressed by the suppressor gene located on chromosome 3D in common wheat, and only hollow-stemmed lines have been created. However, synthetic hexaploid wheat can serve as a bridge to transfer solid stem genes from tetraploid wheat to common wheat. In this study, the F1, F2, and F2:3 generations of the cross between solid-stemmed Syn-SAU-119 and semisolid-stemmed Syn-SAU-117 were developed. A single dominant gene, tentatively designated Su-TdDof, was identified in synthetic hexaploid wheat Syn-SAU-117 by genetic analysis, which suppresses stem solidity. Using bulked segregant RNA-seq (BSR-seq) analysis, Su-TdDof was mapped to chromosome 7DS and flanked by markers KASP-669 and KASP-1055 within a 4.53 cM genetic interval corresponding to 3.86 Mb and 2.29Mb physical region in the Chinese Spring (IWGSC RefSeq v1.1) and Ae. tauschii (AL8/78 v4.0) genome, respectively, in which three genes related to solid stem development were annotated. Su-TdDof differed from a previously reported solid stem suppressor gene based on its origin and position. Su-TdDof would provide a valuable example for research on the suppression phenomenon. The flanking markers developed in this study would be useful for screening Ae. tauschii accessions with no suppressor gene (Su-TdDof) to develop more synthetic hexaploid wheat lines for wheat lodging resistance breeding and further cloning the suppressor gene Su-TdDof.

Most research findings in Ghana and all over the globe lay much emphasis on the technical knowledge of plastics; especially giving preference to the biodegradable over the synthetic/Single-Use plastics. Questionnaires were administered to about 200 respondents in the Ho Municipality out of which 163 responses were filled and returned. Results showed that of all the variables tested only Legislative Framework has a meaningful impact on the behaviour of Ho indigenes. Awareness level: (Synthetic Plastics and Biodegradable Plastics) as well as Attitudinal Change do not influence people’s behaviour when using plastics. Findings from this study are further discussed and recommendations were proffered to instil eco-friendly behaviour among people.

The influence of long-term chemical fertilization in paddy soils is based on the interaction between labile carbon and phosphorus fractions and how this influences soil organic carbon (SOC). Four soil depths (0–30 cm) were analyzed in this study. Easily oxidized organic carbon components, such as permanganate oxidized carbon (POXC) and dissolved organic carbon (DOC), and other physicochemical soil factors were evaluated. The correlation and principal component analyses were used to examine the relationship between soil depth and the parameter dataset. The results showed that Fe-P concentrations were greater in the 0–5 cm soil layer. DOC, inorganic phosphate fraction, and other soil physiochemical characteristics interacted more strongly with SOC in the 0–5 cm soil layer, compared to that in the 10–15 cm layer, influencing soil acidity. An increase in DOC in the 0–5 cm soil layer had a considerable effect on lowering SOC, consistent with P positively correlated with POXC, but negatively with SOC and water-soluble carbon (WSC). The changes in SOC could be attributed to the relationship between DOC and inorganic phosphate fractions (as Fe-P) under a specific soil pH condition. An increase in soil DOC could be caused by changes in the P fraction and pH. The DOC:Avai. P ratio could serve as a compromise for the C and P dynamic indicators. The soil depth interval is a critical element that influences these interactions. Agricultural policy and decision-making may be influenced by the P from chemical fertilization practices, considering the yields and environmental effects.

Synthetic cannabinoids are a series of synthetic substances that mimic the effects of natural cannabinoids and produce a much stronger toxicity than natural cannabinoids, which have become the most abused family of new psychoactive substances. A solid-phase extractive-liquid chromatography-triple quadrupole/linear ion trap mass spectrometry method was developed to determine 11 synthetic cannabinoids in rat urine. The factors affecting recovery were optimized, and Oasis HLB was selected to extract synthetic cannabinoids simultaneously. The results showed that the linear correlation coefficients of the synthetic cannabinoids ranged from 0.993 to 0.999, and the limit of quantitation ranged from 0.01 to 0.1 ng/mL, and the spiked recoveries ranged from 69.90% to 118.39%. This method has the advantages of good purification ability, simple operation, and good reproducibility, and can be used for the high sensitivity analysis of various synthetic cannabinoids in urine.

With the aim of profitable conversion of carbon dioxide (CO2) in an efficient, economical and sustainable manner, we developed a CuBr/ionic liquid (1-butyl-3-methylimidazolium acetate) catalytic system which could efficiently catalyze the three-component reactions of propargylic alcohols, 2-aminoethanols, and CO2 to produce 2-oxazolidinones and α-hydroxy ketones. Remarkably, this catalytic system employed lower metal loading (0.0125-0.5 mol%) but exhibited the highest turnover number (2960) ever reported, demonstrating its excellent activity and sustainability. Moreover, our catalytic system could efficiently work under 1 atm of CO2 pressure and recycle among the metal-catalyzed systems.

Biological systems often need to operate in complex environments where conditions can rapidly change. This is possible due to their inherent ability to sense changes and adapt their behavior in response. Here, we detail recent advances in the creation of synthetic genetic parts and circuits whose behaviors can be dynamically tuned through a variety of intra- and extra-cellular signals. We show how this capability lays the foundation for implementing control engineering schemes in living cells and allows for the creation of biological systems that are able to self-adapt, ensuring their functionality is maintained in the face of varying environmental and physiological conditions. We end by discussing some of the broader implications of this technology for the safe deployment of synthetic biology.

Abstract: The biological process of photosynthesis was critical in catalyzing the oxygenation of Earth’s atmosphere 2.5 billion years ago, changing the course of development of life on Earth. Recently, the fields of applied and synthetic photosynthesis have utilized the light-driven protein-pigment supercomplexes central to photosynthesis for the photocatalytic production of fuel and other various valuable products. The reaction center Photosystem I is of particular interest in applied photosynthesis due to its high stability post-purification, non-geopolitical limitation, and its ability to generate the greatest reducing power found in Nature. These remarkable properties have been harnessed for the photocatalytic production of a number of valuable products in the applied photosynthesis research field. These primarily include photocurrents and molecular hydrogen as fuels. The use of artificial reaction centers to generate substrates and reducing equivalents to drive non-photoactive enzymes for valuable product generation has been a long-standing area of interest of the synthetic photosynthesis research field. In this review, we cover advances in these areas and further speculate synthetic and applied photosynthesis as photocatalysts for the generation of valuable products.

Sentinel-1 satellite system continuously observes European countries in a relatively high revisit frequency of 6 days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for processing is an original data provided by ESA, for interferometry (InSAR) this level is a Single Look Complex (SLC) data. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nation-wide Sentinel-1 data stored in burst units that have been preprocessed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g. Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nation-wide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provide a signal for early warning against a potential dangerous displacement, e.g. a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nation-wide PS processing using data over Czechia has been performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de novo biological design. Here, we show how sequencing is supporting the creation of a new wave of biological parts and systems, as well as providing the vast data sets needed for the machine learning of design rules for predictive bioengineering. However, we believe this is only the tip of the iceberg and end by providing an outlook on recent advances that will likely broaden the role of sequencing in synthetic biology and its deployment in real-world environments.

Cortical actomyosin flows, among other mechanisms, scale up spontaneous symmetry breaking and thus play pivotal roles in cell differentiation, division, and motility. According to many model systems, myosin motor-induced local contractions of initially isotropic actomyosin cortices are nucleation points for generating cortical flows. However, the positive feedback mechanisms by which spontaneous contractions can be amplified towards large-scale directed flows remain mostly speculative. To investigate such a process on spherical surfaces, we reconstituted and confined initially isotropic minimal actomyosin cortices to the interfaces of emulsion droplets. The presence of ATP leads to myosin-induced local contractions that self-organize and amplify into directed, large-scale actomyosin flows. By combining our experiments with theory, we found that the feedback mechanism leading to a coordinated, directional motion of actomyosin clusters can be described as asymmetric cluster vibrations, caused by intrinsic non-isotropic ATP consumption, in conjunction with spatial confinement. By tracking individual actomyosin clusters, we identified fingerprints of vibrational states as the basis of directed motions. These vibrations may represent a generic key driver of directed actomyosin flows under spatial confinement in vitro and in living systems.

What is the potential for synthetic biology as a way of engineering, on a large scale, complex ecosystems? Can it be used to change endangered ecological communities and rescue them to prevent their collapse? What are the best strategies for such ecological engineering paths to succeed? Is it possible to create stable, diverse synthetic ecosystems capable of persisting in closed environments? Can synthetic communities be created to thrive on planets different from ours? These and other questions pervade major future developments within synthetic biology. The goal of engineering ecosystems is plagued with all kinds of technological, scientific and ethic problems. In this paper we consider the requirements for Terraformation, i. e. for changing a given environment to make it hospitable to some given class of life forms. Although the standard use of this term involved strategies for planetary terraformation, it has been recently suggested that this approach could be applied to a very different context: ecological communities within our own planet. As discussed here, this includes multiple scales, from the gut microbiome to the entire biosphere.

Background Tropical and subtropical crops such as coffee, cacao, and papaya are valuable commodities and its consumption is a seemingly indispensable part of the daily lives of billions of people across the world. Conventional breeding in these crops is lengthy and yields are threatened by runaway global warming. In this review we propose the application of chromosome engineering and synthetic biology principles in order to enhance synthesis of key metabolites, and transmission of wild traits for resistance to stress and disease. Conclusions It is hoped that the adoption of such technological approaches may enhance the resilience of agricultural communities, lead to economic growth and secure the availability of key resources for generations to come.

used traditional microbial starters revealed that effective fermentation requires three microbial strains with complementary metabolic activities: filamentous fungi (Rhizopus oryzae), yeast (Saccharomyces cerevisiae), and lactic acid bacteria (Lactobacillus plantarum). Relative to natural communities, modulation of the ratio of these three microorganisms led to significant differences not only in terms of ethanol and organic acid production, but also with the profile of volatile compounds. However, inoculation of an equal ratio of spores/cells of the three aforementioned microbial strains led to a flavor profile and ethanol yield similar to that obtained with natural communities. Compartmentalization of metabolic tasks through the use of a biofilm cultivation device allowed further improvement of the entire fermentation process, notably by increasing the amount of key components of the aroma profile of the fermented beverage (i.e., mainly phenylethyl alcohol, isobutyl alcohol, isoamyl alcohol, and 2-methyl-butanol) and reducing the amount of off-flavor compound. This study represents an initial step toward understanding interkingdom microbial interactions with a strong potential for application in the food biotechnology.

According to current forecasts, global heating is likely to exceed 2,8°C by the end of the century. This makes substantial adaptation measures necessary to secure a broad basis for livelihoods provision and the conservation of biodiversity. While the implementation of top-down and technocratic adaptation efforts is the majority, related adaptation shortcomings of socio-economic and ecological nature are becoming more and more apparent. Community-based adaptation (CBA) with its participatory, inclusive and needs-based bottom-up approach offers a promising and powerful alternative. This article uses a semi-systematic literature review approach to screen the current literature and identify core issues of CBA. Linking communality, locality, multidimensionality, power imbalances, transformative potential, localisation, the triad of adaptation metrics and adaptation with nature to corresponding potential actions for practical implementations provides a more holistic conceptualisation and broadens the horizons for further learnings, research and improved application.

The dynamic beam equations provide an analytical model and a generic solution for continuous systems. The constants for amplitudes and frequencies are given by the boundary conditions, which are here considered looped, in order to represent a closed element, by topological closure. This type of model is applied to the representation of a semicircular canal. This approach addresses the deformability of this anatomical element, both in Lagrangian solid parts (bone, membranes, intermediate materials) and in fluids (Eulerian pressure waves). The model is proposed for both physiological representation and pathology modeling. The latter is represented by a passive or retroactive singularity. An original analytical approach is thus developed for each segment, representing physiological modes – nodes and bellies - and possible disturbances. The damping entropy is also the subject of a special segmented treatment, to take account of fluid-solid interactions, providing a coherent model. The result is a simplified but robust model that both reproduces the vibro-acoustic modes of the semicircular canal and anticipates the effects of a singular pathology, such as a third window or neuritis. Mathematically, this model opens the way to structural analytical models of the inner ear, and to possible dynamic couplings between equilibration and acoustics.

Soil water content (SWC) and soil organic carbon (SOC) are two important factors to consider when revegetation in degraded land of arid and semiarid areas. Knowing the response of SWC and SOC to vegetation restoration as well as their coupling interaction is important for sustainability of vegetation restoration. In this study, three common plantations of the same recovery years were selected on China’s Loess Plateau, which included Pinus sylvestris, Pinus tabuliformis and Populus simonii, to evaluate the response of SWC and SOC to vegetation restoration as well as their interactions, and then revealed the influencing factors of SWC and SOC variations. Results showed that compared to the grassland, three plantations all exacerbated the deficit of deep soil water, but Populus simonii increased the SOC, while the rest all decreased the SOC. And tradeoff of Populus simonii had the lowest root mean squared error (RMSD). Therefore, Populus simonii was identified as representing a suitable revegetation technique for this region. There was strong interaction between SWC and SOC for shallow soil layers. Land use type (LUT) has an important effect on deep SOC variations, while SOC and soil texture for deep SWC variations. The results were helpful to the sustainable development of artificial forests in this region as well as other similar arid and semiarid areas.

The objective of this research was to study the dynamics of moisture and nutrition at the root zone and to quantify the variables linked to efficient water management in avocado orchards of mid and high-lands of Michoacán, México, in typical Andisols of low fertility with (A)C(w2) and C(w2) climates, respectively. Using the FDR (Frequency Domain Reflectometry) technology, the dynamics of soil moisture and salinity were recorded daily at four depths: 0-15, 15-30, 30-45, and 45-60 cm. The soil moisture balance model was used to determine actual evapotranspiration (ETr), crop coefficient (Kc), and water use efficiency (WUE). Results indicated that ETr is 66-73% of the total water at 0-30 cm depth; the fertilizer absorption is limited to 0-30 cm, being negligible at deeper layers. The water use efficiency is 4.4 to 5.6 kg of fruit m-3 of applied water, and the ratio of the water use index (ETr/irrigation sheet applied, ISA) is 0.65-0.70; while the annual avocado Kc is 0.72 in Cutzato and 0.78 in Tiamba. We concluded that avocado has a low water requirement, it does not cause leaching of nutrients and/or water, the absorption of water and nutrients mostly occurs at 30 cm depth, and from a hydrological point of view, the crop is environmentally friendly.

This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the inference functional were devised. Furthermore, numerical experiments are illustrated. The complex proofs in our paper are original results which emphasizes the credibility of the class of Rényi Generalized Entropies as measures of information, and that the field is open to extend an enhanced methodology regarding queuing networks with heavy tails.

In this paper, we present demographic, clinical, anamnestic, cognitive, and functional data, as well as haematological, haematochemical, immunological and genetic parameters of an exceptional individual: Antonino Turturici, a semi-supercentenarian who held the title of the oldest living Italian centenarian from December 28, 2020, to September 23, 2021. The purpose of this study is to offer fresh insights into extreme phenotypes, with a particular focus on immune-inflammatory parameters. To the best of our knowledge, this study represents the first phenotypic investigation of a semi-supercentenarian, illustrating both indicators of age-related immune phenotype (ARIP) recognized as significant gauges of biological ageing and INFLA-score, a metric designed to assess the cumulative impact of inflammatory markers. The aim of this study was, indeed, to advance our understanding of the role of immune-inflammatory responses in achieving extreme longevity. The results bolster the idea that the immune system can play a role in promoting extreme longevity. However, this does not exclude the involvement of other body systems or organs in achieving extreme longevity.

In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the Language Learning Model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model's accuracy. Interestingly, there's an optimal limit to how much synthetic self-explanation data can bene-fit the system. Exceeding this limit doesn't further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.

Sesquiterpene lactones (STLs) are a large group of terpenoids most commonly found in plants of the Asteraceae family, e. g. in chicory plants, displaying a wide range of interesting biological activities. However, further studies on the biological potential of chicory derived STLs and analogues are challenging as only four of these molecules are commercially available (as analytical standards), and to date there are no published or patented simple extraction-purification processes capable of large scale STLs isolation. In this work we describe a novel three-step large scale extraction and purification method for the simultaneous purification of 11,13-dihydrolactucin (DHLc) and lactucin (Lc) starting from a chicory genotype rich in these STLs and in the corresponding glycosyl and oxalyl conjugated forms. After a small-scale screening on 100 mg of freeze-dried chicory root powder, the best results were achieved with a 17 h water maceration at 30°C. With these conditions we managed to increase the content of DHLc and Lc, at the same time favouring the hydrolysis of their conjugated forms. On a larger scale, the extraction of 750 g of freeze-dried chicory root powder, followed by a liquid-liquid extraction step and a reversed-phase chromatography, allowed the recovery of 642.3 ± 76.3 mg of DHLc and 175.3 ± 32.9 mg of Lc. The two pure STLs were subsequently used in the context of semi-synthesis to generate analogues for biological evaluation as antibacterial agents. In addition, other described chicory STLs that are not commercially available were also synthesized or extracted to serve as analytical standards for the study. In particular, lactucin-oxalate and 11,13-dihydrolactucin-oxalate were synthesized in two steps starting from Lc and DHLc, respectively. On the other hand, 11β,13-dihydrolactucin-glycoside was obtained after a MeOH/H₂O (70/30) extraction, followed by a liquid-liquid extraction step and a reversed-phase chromatography. Together, this work will help facilitate the evaluation of the biological potential of chicory derived STLs and their semisynthetic analogues.

This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT) – Indonesia. The study was conducted in Central Sumba District of ENT Province. Classification and regression tree (CART) for land cover classification has been analyzed using machine learning techniques using Google Earth Engine. A survey with Focus Group Discussion (FGD) and followed with in-depth interviews were conducted for primary data collection involving a total of 871 respondents. The Socio-economic data analyzed statistics descriptively and non-parametric tests. The study showed that: 1). There has been a substantial land use change during the devo-lution era that has both positive and negative implications for food security and envi-ronmental services. 2). There has been population pressure in the fertile or agricultur-al land as the direct impact of the development of city infrastructures; and 3). Nation-al intervention through the Food Estate program has fostered and shaped land use change and land management in Central Sumba District. The study highlights the importance that the devolution spirit should help to well manage the limited ara-ble/agricultural land in dominated semi-arid to ensure food security and environment services.

Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labelled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student-teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both unlabeled data with the pseudo boxes and labelled data as ground truth for training. It learns representations of furniture items by combining labelled and unlabeled data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labelled data, respectively. Our experiment affirms the efficiency of the proposed approach as it outperforms the fully supervised counterpart using only 10% of the labels.