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Breast cancer is the most common type of cancer worldwide. Diagnosing breast cancer relies on clinical examination, imaging and biopsy. A core-needle biopsy enables a morphological and biochemical characterization of the cancer and is considered the gold standard for breast cancer diagnosis. A histopathological examination uses high-resolution microscopes with outstanding contrast in the 2D plane, but the spatial resolution in the third, Z-direction, is reduced. In the present paper, we propose two high-resolution table-top systems for phase-contrast X-ray tomography of soft-tissue samples. The first system implements a classical Talbot–Lau interferometer and allows to perform ex-vivo imaging of human breast samples with a voxel size of 5.57 μm. The second system with a comparable voxel size relies on a Sigray MAAST X-ray source with structured anode. For the first time, we demonstrate the applicability of the latter to perform X-ray imaging of human breast specimens with ductal carcinoma in-situ. We assessed image quality of both setups and compared it to histology. We showed that both setups made it possible to target internal features of breast specimens with better resolution and contrast than previously achieved, demonstrating that grating-based phase-contrast X-ray CT could be a complementary tool for clinical histopathology. [ABSTRACT FROM AUTHOR]

This paper studies the causal relationship between house prices and the access to bank lending in Kangnam, the hottest submarket in Seoul and four ‘cold’ markets which have shown relatively modest price increases. In response to the rapid escalation of house prices in Seoul, primarily in Kangnam in recent years, the Korean government implemented a number of policies to stabilize house prices. In particular, it introduced more strict limits on loan-to-value ratio and debt-to-income ratio as part of the mortgage loan qualification process in order to restrict the availability of bank lending for the housing market. The short-run influence of the bank lending on the apartment prices is clearly present in ‘cold’ markets, while it is not in Kangnam, the ‘hot’ market, even though the long-run influence is stronger in Kangnam than in the other markets. This result holds for the entire sample period (1999–2006) as well as for the subperiods before and after the introduction of lending restrictions in August, 2005. It also holds for Kangnam and Kangbuk for an extended period of 1988 to 2006. Our results suggest that in the short run the lending restriction may cause a disruption in untargeted housing markets while it has little influence on the apartment prices in the targeted market. We also find that banks have adjusted the bank lending in response to changes in the house prices in Kangnam as well as in the other markets. [ABSTRACT FROM AUTHOR]

As an important way to inactivate tumor suppressor genes (TSGs) during cancer development, promoter hypermethylation can be used to define novel TSGs and identify biomarkers for cancer diagnosis. SLC19A3 (solute carrier family 19, member 3) was found to be such a biomarker. SLC19A3 expression was downregulated in gastric cancer cell lines (71%, 5/7) and restored after pharmacological demethylation. Notably, hypermethylation of SLC19A3 promoter was detected in gastric cancer cell lines (57%, 4/7), primary gastric carcinoma tissues (51%, 52/101) and precancerous lesion (intestinal metaplasia) tissues (32%, 8/25). Exogenous SLC19A3 expression caused growth inhibition of gastric cancer cells. In summary, SLC19A3 was epigenetically downregulated in gastric cancer. Methylation of SLC19A3 promoter could be a novel biomarker for early gastric cancer development. Copyright © 2009 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable ‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart, which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%, corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts. [ABSTRACT FROM AUTHOR]

Indicators of soil fertility are needed for the effective management of organic farming systems. Sustainable management hinges upon our gaining an improved understanding of C and N dynamics. The influence of cropping systems and amendments applied in the Lakeland Wisconsin Integrated Cropping Systems Trial on total hydrolyzable organic N (THN) fractions and particulate organic matter (POM) was investigated after a decade in a conventional cash grain system (Conv) of continuous maize amended with inorganic fertilizer, an organic cash-grain system (Org-CG) that relied on legume N, and an organic animal-based system (Org-AN) that included alfalfa and manure additions. Maize yields had consistently ranked Org-CG < Conv < Org-AN. The THN and amino acid-N (AA-N) contents were ranked Org-AN > Org-CG > Conv. Amino sugar-N (AS-N) contents, which reflect microbially derived N, did not differ among systems and concentrations were quite high (346.5 mg AS-N/kg soil in the 0–50 cm depth). This, and soil variability were attributed to the sites’ history of manure application. The amount (1.3 g POM-C/kg soil) and proportion (≈7.5% of total SOC) of POM-C were quite low and did not differ among systems. Failure to accumulate SOC or POM in these soils, even under organic management, is attributed to rapid C decay and/or limited root growth. An N rate study was added the fall before samples were taken and N addition did increase yield in the Conv and Org-CG systems despite evidence of soil N surplus. This suggests that either amino N is unavailable to plants or that root N acquisition is limited by other constraints. Low POM-C contents accompanied by high AS-N and AA-N levels reveal an imbalance in these soils which are likely to be C limited. Based on this, we conclude excess N has prevented use of organic practices from enhancing soil quality at this site. [ABSTRACT FROM AUTHOR]

The nucleolus is a key organelle that coordinates the synthesis and assembly of ribosomal subunits and forms in the nucleus around the repeated ribosomal gene clusters. Because the production of ribosomes is a major metabolic activity, the function of the nucleolus is tightly linked to cell growth and proliferation, and recent data suggest that the nucleolus also plays an important role in cell-cycle regulation, senescence and stress responses. Here, using mass-spectrometry-based organellar proteomics and stable isotope labelling, we perform a quantitative analysis of the proteome of human nucleoli. In vivo fluorescent imaging techniques are directly compared to endogenous protein changes measured by proteomics. We characterize the flux of 489 endogenous nucleolar proteins in response to three different metabolic inhibitors that each affect nucleolar morphology. Proteins that are stably associated, such as RNA polymerase I subunits and small nuclear ribonucleoprotein particle complexes, exit from or accumulate in the nucleolus with similar kinetics, whereas protein components of the large and small ribosomal subunits leave the nucleolus with markedly different kinetics. The data establish a quantitative proteomic approach for the temporal characterization of protein flux through cellular organelles and demonstrate that the nucleolar proteome changes significantly over time in response to changes in cellular growth conditions. [ABSTRACT FROM AUTHOR]

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework. [ABSTRACT FROM AUTHOR]

This method of estimating 3D end milling force coefficients was experimentally verified for a wide range of cutting conditions, and gave significantly better predictions of cutting forces than any other methods. The estimated value of the runout offset also agreed well with the measured value. [ABSTRACT FROM AUTHOR]

The performance of 2D material-based field-effect transistors (2D FETs) is significantly influenced by the vertical extension, or depth, of electrostatically doped side Schottky contacts, which is determined through etching. This study employs TCAD modeling to compare back-gated FETs with varying source/drain contact depths and channel lengths. Results indicate that deeper side contacts hinder electric field crowding at the metal/channel interface, resulting in wider Schottky barriers, diminished carrier tunneling, and reduced on-state current. In contrast, introducing a low-k dielectric beneath the source and drain yields the opposite effect. Therefore, in the development of industry-compatible 2D FETs, the depth and design of side contacts must be carefully optimized, as they are critical factors in achieving low-contact resistance devices. [ABSTRACT FROM AUTHOR]

The working performance of the discrete functional surface is affected by the surface form. Both the surface form and the geometric function should be considered in tolerance design. However, the tolerance of different parts has different influence on the geometric function and surface form. This fact makes the tolerance optimization method focusing only on the geometric function requirements unreasonable for the discrete functional surface. In this regard, the tolerance balancing optimization with multiple constraints on the form and function is proposed. The crucial tolerances that significantly affect the surface form and geometric function of the discrete functional surface are extracted, which reduces the number of tolerances to be optimized. The tolerance optimization model with the penalty function considering the multiple constraints on the form and function is established. The tolerance contribution is incorporated into the nonlinear inertial weight particle swarm algorithm. By balancing the influence of tolerance on geometric function and surface form, and the extent of the tolerance optimization, the part tolerances that meet the requirements of geometric function and surface form are solved and the tolerance balancing optimization is achieved. [ABSTRACT FROM AUTHOR]

Fabricating nanocrystalline bright copper coatings is crucial for protection and decoration. A novel brightener organic anion acid blue 1 (AB1) was used to obtain nanocrystalline bright copper coatings. The suppressing effect of AB1 was verified and analyzed by electrochemical tests. Further insights into the effect mechanism of AB1 were investigated through density functional theory (DFT) calculations, molecular dynamics (MD) simulations and in situ Raman spectra. The phenomena of adsorption of AB1 on copper surface discovered by MD simulation and in situ Raman spectra, coupled with coordination interaction between − SO3− of AB1 and free Cu2+, synergistically inhibit the growth of copper coatings, resulting that compact and flat copper plating coatings were formed. The electroplating condition and copper coating morphology were studied, respectively. Bright flat copper coatings can be obtained with 100 to 200 mg/L AB1 under 5 A/dm2. Besides, mass spectrum (MS) was used to evaluate the stability of AB1. The phase structure under the influence of AB1 concentration was studied using XRD. With the increase of AB1 concentration, the (111) surface is preferentially obtained. The average grain size of optimized copper coating, calculated according to Scherrer's formula, is about 11 nm, indicating the acquisition of nanocrystalline copper plating coating. Finally, copper coatings obtained are of good corrosion resistance owing to low roughness and small grain size. [ABSTRACT FROM AUTHOR]

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a 2-layer convolutional neural network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9% at a 1% false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation. [ABSTRACT FROM AUTHOR]

Organic–inorganic lead halide perovskite (OLHP) semiconductors have garnered significant attention for their versatile applications in solar cells, LEDs, photodetectors, and transistors, owing to their remarkable optoelectronic properties and facile solution-based fabrication. Understanding the fundamental physicochemical properties of OLHPs remains challenging due to their soft lattice structures and dynamic structural vibrations with molecular motions. It often results in extraordinary charge transporting and energy transfer, which can be found in many devices. Raman spectroscopy emerges as a vital tool for probing these structural properties due to its high resolution and non-destructive nature. This paper reviews Raman spectral analysis for OLHPs, highlighting the complexities in interpreting low and high-frequency vibrational modes associated with PbX₆ octahedra and organic/molecular cations, respectively. We identify the challenges posed by laser-induced degradation and phase transitions during resonance Raman measurements. Our study proposes improved Raman measurement strategies, emphasizing non-resonance conditions and careful selection of laser wavelengths to mitigate artifacts. These strategies aim to enhance the accuracy of Raman spectra and deepen our understanding of the structural dynamics of OLHPs, which is crucial for advancing their application in next-generation optoelectronic devices. [ABSTRACT FROM AUTHOR]

Deformable light-emitting devices, capable of maintaining consistent light emission even under mechanical deformations, represent a cornerstone for next-generation human-centric electronics. Quantum dot light-emitting diodes (QLEDs), leveraging the electroluminescence (EL) of colloidal quantum dots (QDs), show exceptional promise in this domain. Their superior advantages, such as excellent color purity, high luminous efficiency, slim form factor, and facile fabrication on various soft substrates, position them as prime candidates for deformable EL devices. This review explores recent advancements in deformable QLEDs, with a particular focus on material engineering and fabrication strategies. We begin by introducing various types of QDs and the operational principles of QLEDs, along with summarizing performance enhancements in reported deformable devices. Next, we categorize device structures based on the direction of light emission. We then discuss representative methods for patterning QD thin films on flexible substrates to fabricate full-color QLEDs. Additionally, we highlight fabrication strategies for deformable QLEDs with unconventional form factors, including flexible, foldable, fiber-type, and stretchable devices, and their potential applications. We conclude this review with a brief outlook on the future of this technology. [ABSTRACT FROM AUTHOR]

Based on the perspective of reputation and resource-based theory, we take sharing economy industry as an example and explore the reputation factors that affect the survival of startups. The dataset of 501 Chinese car-sharing platform startups and the logit regression model were used to test the variable relationships and hypotheses. The results show that the reputation gained from third-party organizations (business group affiliation, supplier) is more critical to the survival of startups than the resource advantages released by them (venture capital, technology innovation). The reputation crisis of startups is a fatal blow to the survival of startups. [ABSTRACT FROM AUTHOR]

As AI-generated text continues to evolve, distinguishing it from human-authored content has become increasingly difficult. This study examined whether non-expert readers could reliably differentiate between AI-generated poems and those written by well-known human poets. We conducted two experiments with non-expert poetry readers and found that participants performed below chance levels in identifying AI-generated poems (46.6% accuracy, χ2(1, N = 16,340) = 75.13, p < 0.0001). Notably, participants were more likely to judge AI-generated poems as human-authored than actual human-authored poems (χ2(2, N = 16,340) = 247.04, p < 0.0001). We found that AI-generated poems were rated more favorably in qualities such as rhythm and beauty, and that this contributed to their mistaken identification as human-authored. Our findings suggest that participants employed shared yet flawed heuristics to differentiate AI from human poetry: the simplicity of AI-generated poems may be easier for non-experts to understand, leading them to prefer AI-generated poetry and misinterpret the complexity of human poems as incoherence generated by AI. [ABSTRACT FROM AUTHOR]

Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like developmental stage or health remain underexplored or manually annotated, with limited focus on automating these features. As a proof-of-concept, we developed a computer vision model utilizing the YOLOv5 algorithm to accurately detect monarch butterfly caterpillars in photographs and classify them into their five developmental stages (instars). The training data were obtained from the iNaturalist portal, and the photographs were first classified and annotated by experts to allow supervised training of models. Our best trained model demonstrates excellent performance on object detection, achieving a mean average precision score of 95% across all five instars. In terms of classification, the YOLOv5l version yielded the best performance, reaching 87% instar classification accuracy for all classes in the test set. Our approach and model show promise in developing detection and classification models for developmental stages for insects, a resource that can be used for large-scale mechanistic studies. These photos hold valuable untapped information, and we've released our annotated collection as an open dataset to support replication and expansion of our methods. [ABSTRACT FROM AUTHOR]

In the framework of sustainable ecology, financial sustainability takes on greater significance. As a result, this study examines the impact of financial development (bank adequacy) on both renewable energy and carbon emissions for Gulf Cooperation Council (GCC) countries from 2005 to 2020, using institutional quality (government stability and corruption control) as a moderating factor while controlling for FDI, population growth, and urbanization. However, for the investigation, the quantile-on-quantile regression technique was used, while the fixed effect OLS and Driscoll Kraay OLS techniques were used as robustness checks. The first model outcome reveals that all the variables have a positively significant connection with renewable energy. This implies that higher bank capital adequacy-augmented liquid assets, heightened asset returns, and enhanced investment viability. prospects may encourage physical asset outlays of companies operating in greening. But for the second model, financial development, FDI, and government stability have a positive relationship with carbon emissions, which confirms the presence of the pollutant haven hypothesis for the understudied countries while controlling corruption, population, and urbanization to decrease ecological degradation. This outcome implies that excess-investment clean energy enterprises intermediary effect of liquidity of banks for the understudy countries. Moreover, this study adds to the current literature by comparing the financial development (capital adequacy of banks) in GCC countries that are leaders in climate finance and by highlighting the role that bank capital adequacy plays in strengthening environmental laws to promote investment in renewable energy. [ABSTRACT FROM AUTHOR]

In the current economic downturn, consumers are showing increased price sensitivity. With mobile technology enabling anytime connection, understanding daily price sensitivity fluctuations is crucial. This study analyzes transaction data from two companies, covering both offline and online scenarios. Additionally, an online experiment was conducted to further reveal the causal relationship between time-of-day and consumers' price sensitivity. The studies indicate that price sensitivity is highest in the morning, decreasing by approximately 0.5% per hour. These findings are interpreted using the circadian rhythms theory. This research deepens our understanding of how price sensitivity changes over time, offering invaluable insights for businesses to tailor their pricing and promotional strategies throughout the day. [ABSTRACT FROM AUTHOR]

An analytical expression is obtained for the shape of a coherent kinoform X-ray lens surface, which is different from a parabola. A lens with a focal length of 1 m for monochromatic radiation with a photon energy of 12 keV is calculated. Two refractive lens materials—Rose's alloy and silver—are selected. With a lens diameter of 200 μm, the diameter of the central diffraction maximum at the focus, calculated using the Huygens–Fresnel principle, is about 1 μm. The fraction of the light flux power incident on the lens in the central diffraction maximum of the focal spot is 14.6% for Rose's alloy and 16.1% for silver. The capabilities of the femtosecond two-photon nanolithography developed at the Lebedev Physical Institute and Moscow Institute of Physics and Technology for producing efficient kinoform lenses for synchrotron radiation X-ray microscopy are demonstrated. [ABSTRACT FROM AUTHOR]

Primordial odontogenic tumour is a recently categorised rare benign mixed epithelial and mesenchymal odontogenic tumour which occurs most frequently in first two decades of life. It is composed of cellular myxoid connective tissue lined by cuboidal to columnar odontogenic epithelium resembling inner enamel epithelium in early stages of tooth development. Here, we are presenting a unique case of bilateral primordial odontogenic tumour of mandible in a paediatric patient. [ABSTRACT FROM AUTHOR]

Municipal solid waste incineration tailings were used as lightweight aggregate (MSWIT-LA) in the preparation of specified density concrete to study the effects on compressive strength, axial compressive strength, flexural strength, microhardness, total number of pores, pore area, and pore spacing. The results showed that the internal curing and morphological effects induced by an appropriate quantity of MSWIT-LA improved the compressive response of specified density concrete specimens, whereas an excessive quantity of MSWIT-LA significantly reduced their mechanical properties. An analysis of pore structure indicated that the addition of MSWIT-LA increased the total quantity of pores and promoted cement hydration, resulting in a denser microstructure than that of ordinary concrete. The results of a principal component analysis showed that the mechanical response of specified density concrete prepared with 25% MSWIT-LA was superior to that of an equivalent ordinary concrete. It was therefore concluded that MSWIT-LA can be feasibly applied to achieve excellent specified density concrete properties while utilising municipal solid waste incineration tailings to protect the environment and alleviate shortages of sand and gravel resources. [ABSTRACT FROM AUTHOR]

In the era of the internet, people are increasingly interested in highly personalized customization, which has been leading the global industry to transform from "Enterprise centered Mass Manufacturing" to "User centered Mass Customization". In recent decades, ontology-based semantic models have been used for semantic association description and syntax consistency alignment of product structures. However, the description of industrial terminology and general concepts has difficulty achieving accurate end-to-end matching of personalized user customization requirements and manufacturing capabilities. This requires detailed descriptions of process standards, component/assembly/part specifications, etc. Therefore, we propose the Abstraction-Instance-Capability (AIC) model based on a knowledge graph, which is a multigrained and multiview industrial knowledge model. By modeling industrial knowledge at a deeper level from various perspectives and by considering the expression of the relationships between knowledge, an industrial knowledge graph was constructed to support personalized customization. This graph can be used for personalized customization plan generation and other related operations, such as Design BOM (Bill of Materials) modification and verification. First, a meta-object facility (MOF)-based meta-model is defined for AIC model design. Second, the proposed model is used to define a multiview structure for modeling knowledge from various perspectives. Such a design realizes efficient industrial knowledge retrieval from various aspects, such as manufacturing resource retrieval, and production capacity analysis. Finally, in this paper we validate operability and introduce two case studies to demonstrate that the model has good performance in the recommendation of components (BOM modification) and the verification of personalized customization schemes (BOM verification). [ABSTRACT FROM AUTHOR]

High-order diffraction (HOD) from optical microstructures is undesirable in many applications because of the accompanying ghosting patterns and loss of efficiency. In contrast to suppressing HOD with subwavelength structures that challenge the fabrication of large-scale devices, managing HOD is less developed due to the lack of an efficient method for independently manipulating HOD. Here, we report independent manipulation of HODs, which are unexploited for subdiffraction-limit focusing in diffractive lenses, through an analytical formula that correlates the diffraction order and the width of each zone. The large spatial frequencies offered by the HODs enable our lenses to reduce the lateral focal size down to 0.44 λ even without any subwavelength feature (indispensable in most high-NA diffractive lenses), facilitating large-scale manufacture. Experimentally, we demonstrate high-order lens-based confocal imaging with a center-to-center dry resolution of 190 nm, the highest among visible-light confocal microscopies, and laser-ablation lithography with achieved direct-writing resolution of 400 nm (0.385 λ). The authors introduce the independent manipulation of high-order diffraction with a high-order diffractive lens for sub-diffraction limit focusing in confocal microscopy and laser ablation lithography. [ABSTRACT FROM AUTHOR]

Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates. [ABSTRACT FROM AUTHOR]

Land consolidation projects are fundamental tools that enable the reorganization of agricultural space to enhance agricultural productivity and improve quality of life in rural areas. However, the high costs associated with such projects necessitate ongoing refinement of their technical aspects, including cost reduction and shortened implementation time while maintaining the required accuracy parameters. This study aimed to assess the accuracy of digital orthomosaic creation obtained using UAVs from the perspective of the implementation of land consolidation projects. The research area is located in southern Poland (Przeginia village), and the data used for the study were obtained during the ongoing land consolidation project. The processing of the resulting images was performed with Structure from Motion algorithms using 103 adjustment points with known coordinates. An analysis performed using a set of 87 control points showed an average error in the position of points on a surface of 0.08 m in relation to control results carried out using the GNSS RTK technique. The observed maximum error value was 0.29 m. The analysis of the causes of the high value of observed errors indicates that they were the result of an incorrectly planned, too low number of control points and their uneven distribution across the study area. [ABSTRACT FROM AUTHOR]

Climate change and natural resource depletion have reinvigorated the relevance of technical advancements in energy efficiency, which has been emphasized by the Paris Climate Accord that includes a framework for carbon neutrality. In that effort, the governments have been allocating a considerable amount of budget shares to research and development relating to energy efficiency and green energy. Thus, this study primarily examines the impact and effectiveness of research and development on energy efficiency and energy sources (renewables and fossil fuels) on CO2 emissions in the European region using a novel Method of Moments Quantile Regression (MMQR). Our data cover annual observations of 9 EU countries, including Austria, Denmark, Finland, France, Germany, Netherlands, Norway, Spain, and Sweden, based on the period 1990–2021. Our empirical findings confirm that the U-shaped Environmental Kuznets Curve occurs across all quantiles at 1% significance levels. The findings reveal that energy consumption increases CO2 emissions across the lower to higher quantiles. We discover that (i) R&D on renewable energy cuts CO2 emissions just at the median and higher quantiles, (ii) R&D on Fossil Fuels decreases CO2 emissions just at the lower quantile, and (iii) R&D on Energy efficiency lowers CO2 emissions at both the median and higher quantiles. Therefore, empirical findings suggest that policymakers should maintain their incentives for energy efficiency R&D and renewable R&D to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

In the present experimental investigation, novel Himalayacalamus falconeri fiber reinforced polylactic acid biocomposites were developed via direct injection molding. Standard test procedures were used to evaluate the mechanical, thermal, microstructural, and water absorption properties of the developed biocomposites as a function of fiber concentration (5–20%) and alkali treatment (5% w/v NaOH solution). It was observed that the tensile, flexural, and impact strength of all developed biocomposites were gradually enhanced with the addition of fiber concentration up to 15 wt.% and thereafter start decreasing with increasing fiber concentration up to 20%. Alkali-treated biocomposite with 15 wt.% fiber content (PLA/THF-15) exhibited the highest tensile strength (44.59 MPa ± 1.55 MPa) and flexural strength (75.68 MPa ± 0.88 MPa). Untreated biocomposite (PLA/UHF-15) showed a maximum impact strength of 41.61 J/m. Meanwhile, the fractured surfaces from mechanical testing were examined using a scanning electron microscope to identify the causes of failure in the developed biocomposites. Alkali-treated biocomposite with 20 wt.% fiber content (PLA/THF-20) exhibited the highest hardness value of 90.66 HD, while untreated biocomposite with 20 wt.% fiber content (PL/UHF-20) exhibited the maximum water absorption rate (2.60%) and soil degradation rate (2.18%). The Vicat softening temperature (VST) and heat deflection temperature (HDT) were found to be 56.7 °C for PL/THF-20 and 57.55 °C for PLA/THF-15, respectively. It can be concluded from this present investigation that short Himalayacalamus falconeri fiber can be used as reinforcement in PLA-based matrix to make entirely biodegradable green composites that can replace petroleum-based synthetic polymer composites in lightweight and non-structural applications. [ABSTRACT FROM AUTHOR]

Financial literacy is vital for individuals to understand their economic life cycles and make suitable financial decisions. Due to the increasing complexities, the available financial systems lack the capacity to better educate individuals to facilitate informed financial decision-making. In this study, we capitalize on the configuration principles for creating customized products and services and develop a personalized financial configurator, aiming to better educate individuals for their pension-related decision-making. The personalized financial configurator is developed based on a three-tier distributed system architecture and can generate diverse personalized outputs, e.g., future financial asset changes, future investment potentials for retirement, and recommendations. The outputs together with recommendations facilitate individuals to make informed pension-related decisions, e.g., suitable retirement ages and suitable investment strategies. Hence, the results demonstrated that the personalized financial configurator can better educate individuals by providing personalized data and information, thus facilitating their informed pension-related decision-making. [ABSTRACT FROM AUTHOR]

In this paper, a new repairable k-out-of-n system model is proposed, in which the deviation cost of the system is taken into account. Firstly, the standard k-out-of-n system is replaced at planned time or at the failure time. Next, replacement policies are studied at planned time or periodic time when k is a constant. Thirdly, replacement policies are studied at planned time or periodic time when k is a random number, respectively. Finally, replacement policies are planned at the completion of random working time. The expected cost rate functions and its optimum replacement policies are derived analytically for each model. Meanwhile, optimum number of units is designed. The analytical solution of replacement policies for each model is discussed. Numerical examples are given to demonstrate our results. [ABSTRACT FROM AUTHOR]

Smart construction is gaining attention as a solution to improve the construction industry's productivity and safety problems. Although there have been many actions to activate the utilization of smart construction technologies, their effectiveness remains unclear. Diagnosing the current status of the smart construction industry is fundamental, eventually establishing a diagnosis-prescription-improvement virtuous cycle. However, previous attempts for such diagnosis were limited to the confined scope (e.g., major contractors only) and the subjective judgment of technological capacity, primarily caused by the lack of comprehensive background data. Therefore, this study aims to investigate the current status of smart construction in Korea. Specifically, the scope of smart construction technologies was defined. This study then investigated diverse aspects of smart construction, including market trends, technological maturities, research and developments, and site applications. Based on the results, the smart construction technologies were classified into three categories: 1) similar to the global trends; 2) lacking; and 3) leading in the domestic markets. The directions for future studies were also suggested to develop a performance evaluation indicator for smart construction to understand trends and capacities in time. The study results will provide policymakers and practitioners with background information to establish strategies for advancing smart construction in Korea. [ABSTRACT FROM AUTHOR]

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection. [ABSTRACT FROM AUTHOR]

This study examines the factors that influence the blue economy in EUROMED, aiming at promoting economic growth in line with the UN Sustainable Development Goals. EUROMED was chosen because all of its nations have fisheries and marine tourism, which are the two key indicators of the blue economy. Blue economy contributes to sustainable development in the fisheries and marine tourism sectors. A commitment to sustainability has sped up national and regional blue economy policy development. The study uses secondary qualitative data and literature review to analyse the synergies and conflicts between EUROMED blue economy strategies and the UN Sustainable Development Goals. The findings suggest that GDP growth, aqua production, open trade, CO2 emissions, and inflation rate influence the blue economy, and that ecotourism that considers education, society, and the environment may generate sustainable fisheries and marine tourism. Only 21 countries gave the ARDL test between 2000 and 2019. This study analyses synergies and conflicts between EUROMED blue economy strategies and UN Sustainable Development Goals. Location-based contextual development of blue economies that suit all players' requirements is essential to maintain sustainability objectives. Ensure resilience against future environmental and political shocks, preserve the ecological underpinning for vibrant blue economies, and create capacity at all levels to promote effective and fair governance. This study tries to make good use of ocean conservation and aquaculture within the context of the blue economy. This is the first EUROMED blue economy study and contributes to the theoretical and methodological development of blue economy research. [ABSTRACT FROM AUTHOR]

Low limit of detection (LOD), high sensitivity and selectivity are essential performance criteria of a sensor. As the naturally occurring uranyl ion (U6+) is toxic, a sensor with suitable performance parameters for measuring U6+ in drinking water is of interest. In this work, four aspects of the detection of U6+ are presented. First, a composite synthesized from aniline, N-phenylglycine and graphene oxide (GO) was used for the first time to detect the uranyl ion. Second, the ligand 2-(5-bromo-2-pyridylazo)-5-(diethylamino) phenol (BromoPADAP) was used as a ligand, attached at the composite surface, functionalized with diamine (Design I) and also as a complexing agent in the electrolyte (Design II), and a comparative study for both the cases was conducted. The parameters for comparison were the sensitivity, LOD and interference from several test ions. It was found that for Design I, the LOD was 50 ppt, while for Design II the LOD was 8 ppb. Next, the interference phenomena were explained by the conventional peak reduction (PR) technique and the barrier width (BW) technique. The BW technique was used for the first time to scale the interference of test ions toward the target analyte Cd2+, as in our previous works. PR technique requires a higher number of tests, while the BW technique employs a couple of tests, thus minimizing the effort. In this work, the BW technique supported the PR technique results in predicting the interference behavior. Finally, the BW technique was used to explain the difference in Design I and II results. [ABSTRACT FROM AUTHOR]

Highlights: The review provides a comprehensive summary of performance limits of the single two-dimensional transition metal dichalcogenide (2D-TMD) transistor. The review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. The review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices. Two-dimensional (2D) transition metal dichalcogenides (TMDs) allow for atomic-scale manipulation, challenging the conventional limitations of semiconductor materials. This capability may overcome the short-channel effect, sparking significant advancements in electronic devices that utilize 2D TMDs. Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance. This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor. It delves into the impacts of miniaturization, including the reduction of channel length, gate length, source/drain contact length, and dielectric thickness on transistor operation and performance. In addition, this review provides a detailed analysis of performance parameters such as source/drain contact resistance, subthreshold swing, hysteresis loop, carrier mobility, on/off ratio, and the development of p-type and single logic transistors. This review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. It also emphasizes the role of 2D TMD-based transistors as memory devices, focusing on enhancing memory operation speed, endurance, data retention, and extinction ratio, as well as reducing energy consumption in memory devices functioning as artificial synapses. This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices. This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications. It underscores the anticipated challenges, opportunities, and potential solutions in navigating the dimension and performance boundaries of 2D transistors. [ABSTRACT FROM AUTHOR]

Population growth and increasing food demand have enhanced non-renewable energy consumption, emitting pollutants, and decreased soil fertility. The solution to this dilemma is the deployment of renewable energy. On the other hand, global warming and climate change pose a severe threat to the agricultural sector and endanger food security, especially in developing countries. Therefore, this study examines the relationship between climate change, consumption of renewable and non-renewable energy, and agricultural development in the thirteen Middle East and North African countries. Historical precipitation and temperature are included in the research model as climatic factors, and agricultural value-added is considered a proxy for agricultural development. This research has applied a Method of Moments Quantile regression using data from 2000 to 2018. The findings revealed a significant and negative relationship between temperature and value-added of agriculture in 0.3–0.9 quantiles. In addition, precipitation improves agricultural development in all quantiles except 0.1. Also, renewable energy usage increases agricultural value-added in upper and mid-quantiles, while the consumption of non-renewable energy reduces agriculture value-added in lower and mid-quantiles. The causality test demonstrates the existence of two-way causality between temperature, precipitation, non-renewable energy, and agricultural development, and a one-way causality link from consumption of renewable energy to agriculture value-added. According to the obtained outcomes, it is suggested that the governments should direct more resources to the development of renewable energy, especially in the agricultural sector. [ABSTRACT FROM AUTHOR]

Alginate, a naturally sourced bio-polymer derived mainly from brown seaweed, has gained incredible attention due to its versatile properties and wide range of applications. This review outlines alginate regarding sources, extraction methods, classifications, and critical characteristics. The critical development of alginate, focusing on its application in 3D bioprinting in fields such as vascular tissue formation, bone printing, and cartilage printing, has been discussed. Recent trends regarding the application of alginate in different sectors, from cosmetic textiles to wastewater treatment, wound dressing, and preparation of intelligent materials, have also been probed. The techniques used, benefits, and limitations form the discussion of each application. In addition, future alginate perspectives are discussed by considering current research with potential innovations that can expand its utility. Particular attention is devoted to the promising role of alginate in these emerging technologies that hold promise for radical changes in many industries due to its biocompatibility, biodegradability, and unique physicochemical properties. This review seeks to lay down basic knowledge on alginate by synthesizing the state of the art and, in this way, brings out the gaps that might inspire further studies and development in this dynamic field.Article Highlights: The physicochemical and biological properties of alginate are described, along with the extraction process. It reports the application of alginates in 3D bioprinting, cosmetic textiles, smart materials with techniques and limitations involved. Future perspectives of alginate are mentioned. [ABSTRACT FROM AUTHOR]

The increased incidence of hypertension associated with obstructive sleep apnea (OSA) presents significant physical, psychological, and economic challenges. Peroxisome proliferator-activated receptor gamma (PPARγ) plays a role in both OSA and hypertension, yet the therapeutic potential of PPARγ agonists and antagonists for OSA-related hypertension remains unexplored. Therefore, we constructed a chronic intermittent hypoxia (CIH)-induced hypertension rat model that mimics the pathogenesis of OSA-related hypertension in humans. The model involved administering PPARγ agonist rosiglitazone (RSG), PPARγ antagonist GW9662, or normal saline, followed by regular monitoring of blood pressure and thoracic aorta analysis using staining and electron microscopy. Intriguingly, our results indicated that both RSG and GW9662 appeared to potently counteract CIH-induced hypertension. In silico study suggested that GW9662's antihypertensive effect might mediated through angiotensin II receptor type 1 (AGTR1). Our findings provide insights into the mechanisms of OSA-related hypertension and propose novel therapeutic targets. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) pose a significant burden on global health. Developing effective diagnostic, therapeutic, and prognostic indicators for CVDs is critical. This narrative review explores the role of select non-coding RNAs (ncRNAs) and provides an in-depth exploration of the roles of miRNAs, lncRNAs, and circRNAs in different aspects of CVDs, offering insights into their mechanisms and potential clinical implications. The review also sheds light on the diverse functions of ncRNAs, including their modulation of gene expression, epigenetic modifications, and signaling pathways. It comprehensively analyzes the interplay between ncRNAs and cardiovascular health, paving the way for potential novel interventions. Finally, the review provides insights into the methodologies used to investigate ncRNA-mediated gene regulation in CVDs, as well as the implications and challenges associated with translating ncRNA research into clinical applications. Considering the broader implications, this research opens avenues for interdisciplinary collaborations, enhancing our understanding of CVDs across scientific disciplines. [ABSTRACT FROM AUTHOR]

This paper examines how humans judge the capabilities of artificial intelligence (AI) to evaluate human attributes, specifically focusing on two key dimensions of human social evaluation: morality and competence. Furthermore, it investigates the impact of exposure to advanced Large Language Models on these perceptions. In three studies (combined N = 200), we tested the hypothesis that people will find it less plausible that AI is capable of judging the morality conveyed by a behavior compared to judging its competence. Participants estimated the plausibility of AI origin for a set of written impressions of positive and negative behaviors related to morality and competence. Studies 1 and 3 supported our hypothesis that people would be more inclined to attribute AI origin to competence-related impressions compared to morality-related ones. In Study 2, we found this effect only for impressions of positive behaviors. Additional exploratory analyses clarified that the differentiation between the AI origin of competence and morality judgments persisted throughout the first half year after the public launch of popular AI chatbot (i.e., ChatGPT) and could not be explained by participants' general attitudes toward AI, or the actual source of the impressions (i.e., AI or human). These findings suggest an enduring belief that AI is less adept at assessing the morality compared to the competence of human behavior, even as AI capabilities continued to advance. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030. [ABSTRACT FROM AUTHOR]

The aim of the article is to analyse the factors influencing entrepreneurs' decisions about investing in renewable energy. It outlines a number of different factors that may affect the process of transforming entrepreneurs into business prosumers, who thus want to limit the effects of rising energy prices. The article defends the thesis that in addition to the economic, technological and psychological dimensions, legal and political stability, access to reliable information and the level of trust in a given society are equally important. Based on the quantitative research results, the article indicates which elements are particularly important for entrepreneurs when making decisions about investing in renewable energy and which institutions are indicated by Polish entrepreneurs as responsible for implementing energy transition. The article also indicates that information about the possibility of receiving funding from the European Union and the government, the government's energy policy and technological possibilities is important for entrepreneurs' decisions about investing in renewable energy in Poland. It is always difficult to implement sustainable development goals without an atmosphere of trust and predictable legal stability in which entrepreneurs can run their businesses. [ABSTRACT FROM AUTHOR]

In this manuscript, we demonstrate the ability to use a reel-to-reel processing technology for conductive surface functionalization of textile yarns using oxidative chemical vapor deposition in a continuous manner. We designed and built a vacuum deposition system, which allows the winding of yarns into the oCVD reactor by unreeling from the outside atmosphere, where the yarn is pre-treated with oxidant solution. Iron(III)chloride (FeCl3) and 3,4-ethylenedioxythiophene were used as the oxidant and monomer, respectively, to deposit thin films of poly(3,4-ethylenedioxythiophene) (PEDOT) thin films on the synthetic PET yarn surfaces. FTIR and XPS analyses were carried out to verify the chemical structure of as-deposited PEDOT films. Effects of temperature, oxidant concentration, and winding speed on the electrical conductivities of the yarns after oCVD were studied. All yarns exhibited non-zero conductivity values independent of the deposition conditions studied. Very high conductivity uniformities were observed along the longitudinal direction of the yarns even at the highest studied winding speed of 24 cm/min. [ABSTRACT FROM AUTHOR]

The [4π+2π]-cycloaddition of N-phenylmaleimide to 2-tropylcyclohexanone was performed to prepare previously unknown 9-(2-oxocyclohexyl)-4-phenyl-4-azatetra-cyclo[5.3.2.02,6.08,10]dodec-11-ene-3,5-dione in 92% yield. The structure of the azacyclic compound was established by modern methods, including 1D and 2D NMR spectroscopy and X-ray diffraction analysis. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT IoT ) is built on a foundation of wireless sensor devices that connect humans and physical objects to the Internet and enable them to interact with one another to improve the living conditions of citizens. Wireless Sensor Networks ( WSNs ) are widely utilized in systems based on I o T to collect the data required by intelligent environments. However, I o T - enabled W S N s encounter a variety of difficulties such as poor network lifespan, limited throughput, and long communication delays, due to the massive non-homogenous data streaming from numerous sensor devices. Therefore, a multi-objective intelligent clustering routing schema for I o T - enabled W S N s utilizing deep reinforcement learning is proposed in this paper to overcome these shortcomings. The proposed schema partitions the entire network into various unequal clusters based on the present data load existing in sensor nodes, effectively preventing the network from dying prematurely. In addition, an unequal clustering mechanism is utilized to balance inter-cluster and intra-cluster energy consumption among cluster heads. The simulation findings demonstrate the effectiveness of the proposed schema in terms of energy efficiency, delivered packets, end-to-end delay, alive nodes, energy balancing, and network lifespan compared with the other two state-of-the-art existing schemes. [ABSTRACT FROM AUTHOR]