This study presents a Historic Building Information Modeling (HBIM)-based approach for the digital restoration and documentation of lost wooden architectural heritage. The approach was applied to Building 1-2 of Hyeumwonji, the site of a temporary Goryeo Dynasty palace in Paju, South Korea. To reconstruct this lost structure, we combined historical and archaeological analyses to estimate the original design and generated blueprints that guided the HBIM-based 3D model of the building. We collected LiDAR point cloud data from the site, aligned them with the HBIM model, and visualized the integrated result using Unreal Engine 5. The outcome was a comprehensive virtual restoration comprising 13,814 individual building elements. This case study demonstrates that, even with minimal physical remains, wooden heritage sites can be digitally restored by leveraging HBIM and historical reasoning. It also highlights the strengths of HBIM in version tracking, incorporation of historical updates, and systematic documentation throughout the restoration process. Compared to traditional 2D CAD-based restoration methods, the HBIM approach offers significant advantages in terms of updatability, data integration, and long-term preservation of restoration data. Overall, the project illustrates how combining rigorous historical analysis with advanced digital modeling can revive lost heritage architecture in virtual form, providing a rich resource for research and conservation.
Abstract A cost-effective and large-scale method for synthesizing ZnCo2O4 nanoflowers with surface oxygen vacancies as electrode materials for supercapacitors is presented. The existence of oxygen vacancies on the surface of the ZnCo2O4 nanoflowers has been confirmed through X-ray photoelectron spectroscopy (XPS). The energy bands and density of states (DOS) of ZnCo2O4 are examined using density functional theory, revealing that treatment with NaBH4 reduces the band gap of ZnCo2O4 while increasing the DOS near the Fermi level compared to pristine ZnCo2O4. Furthermore, the specific capacitance of reduced ZnCo2O4 is nearly double that of its unmodified counterpart. This straightforward and practical approach significantly enhances both conductivity and specific capacitance in metal oxides, making it applicable to other similar materials.
Materials of engineering and construction. Mechanics of materials
Brown rice (Oryza nivara S.D.Sharma & Shastry) is a rice variety that belongs to the Graminae family. Brown rice contains vitamins A, B, C, Zn and B complex. Vitamin B1 is one type of vitamin that is not stable. Its stability is influenced by pH, temperature and processing. The purpose of this study was to determine the comparison of vitamin B1 levels in brown rice and cooked brown rice. The study began with a qualitative test of vitamin B1 using 10% Pb acetate and 6 N NaOH if a yellow color and brown precipitate formed after heating, the sample was positive for vitamin B1. Determination of vitamin B1 levels in brown rice and cooked brown rice by alkalimetric method using NaOH as a titer that has been standardized in advance with potassium biftalat 0.1 N. Data analysis using the Mann Whitney test is an alternative to the Independent T-test if the t-test requirements are not met. The Mann Whitney test is used to determine whether or not there is a difference between two independent samples. The results of the qualitative test of vitamin B1 in brown rice and cooked brown rice showed that the samples were positive for vitamin B1. The quantitative test results of vitamin B1 levels in brown rice and cooked brown rice obtained an average of 12.40 mg / kg and 4.96 mg / kg. Statistical test results, the significance value (p) = 0.043, where p < 0.05 means there is a significant difference in vitamin B1 levels in brown rice and cooked brown rice. The conclusion of this study is that vitamin B1 levels in brown rice are higher than vitamin B1 levels in cooked brown rice.
Pharmacy and materia medica, Nutrition. Foods and food supply
Introduction: Adult exposure to endocrine-disrupting chemicals (EDCs) may reduce muscle mass and strength; however, few studies considered EDC mixtures and their potential mechanisms. Objectives: We aimed to explore associations of EDC mixtures with adult muscle mass and strength, the modifying effects of diet and exercise, as well as the potential metabolic perturbations through plasma metabolome. Methods: We included 127 adults from a panel study that repeated measures across 3 seasons. We measured 110 EDCs spanning 12 groups in plasma and urine, along with the plasma metabolome. Bayesian kernel machine regression (BKMR), Bayesian weighted quantile sum regression, and quantile-based g-computation were employed to assess the mixture effects and relative contributions. Key EDCs were defined as those with weights exceeding the group average in at least two models. Stratified analyses were employed to investigate the modifying effects of diet and exercise. A meet-in-the-middle (MITM) approach was applied to characterize the underlying mechanisms. Results: BKMR results revealed significant negative associations between 7 EDC groups and both appendicular skeletal muscle mass (ASM) and hand-grip strength (HGS), namely per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, organophosphate pesticides, bisphenols, neonicotinoids, atrazine, and parabens. Three multi-exposure models identified 22 and 17 key EDCs linked to decreased ASM and HGS, respectively. Mixtures of these key EDCs were associated with decreases in both ASM and HGS, with significantly attenuated effects observed among participants with healthy diets or regular exercise. MITM approach identified overlapping pathways linking key EDC mixtures to ASM, including arachidonic acid, linoleic acid, and alpha-linolenic acid metabolism. Key EDC Mixtures were negatively associated with glycocyamine, which was positively associated with ASM. Conclusions: Adult exposure to EDC mixtures was linked to reduced ASM and HGS, whereas healthy diets and regular exercise mitigated such impairment. Downregulated glycocyamine and altered fatty acid metabolism were potential mechanisms underlying the decreased ASM.
This research unravels the stationary or transitionary dilemma of hybrid technologies in transitions processes. A system dynamics technology interaction framework is built and simulated based on Technological Innovation System and Lotka-Volterra to investigate the inter-technology relationship impacts and modes that hybrid technologies establish with incumbent and emerging technologies. This is conducted for the case of conventional, hybrid and battery electric vehicles under various scenarios . Results reveal that, by acting as an exploration-hybrid solution, hybrid technologies maintain a transitionary role by supporting mainly the technological development side of emerging technology. On the contrary, by acting as an exploitation-hybrid solution, they hardly (or never) sustain an inhibitive role against both the technological and market development sides of incumbent technology. While hybrid technologies may play a stationary role on the market development side in transitions processes, simulation results show that maintaining all inter-technology relationship modes as business-as-usual (i.e., baseline scenario) but instead simultaneously strengthening the various socio-technical dimensions of emerging technology and destabilising the various socio-technical dimensions of incumbent technology (i.e., sociotechnical scenario) is a more promising pathway in both short term (e.g., an accelerated uptake of emerging technology and decline of incumbent technology) and long term (e.g., highest emission reduction). Findings, additionally, reinforce the existence of both spillover and try-harder versions of 'sailing-ship effect', which are either seriously doubted in the literature or partially validated using raw bibliometric and patents data.
Adolescents' mobile technology use is often regulated through rigid control mechanisms that fail to account for their autonomy and natural usage patterns. Drawing on Taoist philosophy, particularly Wu Wei, Yin-Yang, and Zi Ran, this position paper proposes Tao-Technology, a self-organizing, adaptive regulatory framework. Integrating insights from Reflective Informatics and Information Ecologies, we explore how mobile technology can dynamically adjust to context while fostering self-reflection and meaning-making. This approach shifts from external restrictions to dynamic co-adaptative regulation, ensuring technology governance remains flexible yet structured, supporting adolescents in cultivating a balanced and intentional relationship with digital technology.
Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples.
Mafalda Reis Pereira, Mafalda Reis Pereira, Filipe Neves dos Santos
et al.
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine – SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants’ defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.
Kulwa Mwita Mang'ana, Daniel Wilson Ndyetabula, Silver John Hokororo
Small and Medium-Sized Enterprises in agriculture sector, contribute significantly to economic change in developing countries by addressing a wide range of unemployment, nutrition, income poverty, and food security issues. Despite their critical role and contribution to economic growth, they have received a great deal of criticism for their poor performance. Most of the challenges confronting these agro-enterprises, however, are the result of poor financial management practices. Previous research studies have indicated generally that financial management practices have an impact on the performance and success for small businesses, yet scholarly research shows there is limited empirical evidence on which financial management practices have an influence on the agri-SMEs performance, which is why it was critical to examine this phenomenon. A total of 427 SMEs in Tanzania's agricultural sector were surveyed and examined. The developed hypotheses were evaluated using Structural Equation Modeling (SEM) with Smart PLS 4 to determine the effect of implementing financial management practices on the performance of agri-SME. Findings from the empirical study shows that working capital management practices and financing management practices have significant positive influence on both financial and organizational performance of the surveyed agro enterprises, while the accounting, financial reporting practices and capital budgeting management practices have insignificant influence on the performance agri-SMEs performance. Based on the findings, the study recommends that the government and regulatory authorities such as the Small Industries Development Organization (SIDO) must continue to emphasize their policies for improved agri-SME performance and sustainability while directly or indirectly encourage managers (venture owners) to consider working capital and financing practices as core to their financial management strategies.
History of scholarship and learning. The humanities, Social sciences (General)
Workers separate from jobs, search for jobs, accept jobs, and fund consumption with their wages. Firms recruit workers to fill vacancies. Search frictions prevent firms from instantly hiring available workers. Unemployment persists. These features are described by the Diamond-Mortensen-Pissarides modeling framework. In this class of models, how unemployment responds to productivity changes depends on resources that can be allocated to job creation. Yet, this characterization has been made when matching is parameterized by a Cobb-Douglas technology. For a canonical DMP model, I (1) demonstrate that a unique steady-state equilibrium will exist as long as the initial vacancy yields a positive surplus; (2) characterize responses of unemployment to productivity changes for a general matching technology; and (3) show how a matching technology that is not Cobb-Douglas implies unemployment responds more to productivity changes, which is independent of resources available for job creation, a feature that will be of interest to business-cycle researchers.
Abstract Background It was been agreed that significantly elevated progesterone level on the hCG trigger day have detrimental effect on clinical outcomes in IVF/ICSI cycles. However, few studies explored whether slightly elevated progesterone level also same impact on clinical outcomes. Methods We retrospectively studies the effect of slightly elevated progesterone level on outcomes of IVF/ICSI in GnRH-ant cycles. Propensity score matching was used to confounding variables. The women were divided into two groups according to the progesterone level: Group 1: < 1.0 ng/ml; Group 2: 1.0 ng/ml–1.5 ng/ml. Then compare the clinical pregnancy rate (CPR) between the two groups. Result A total of 847 IVF/ICSI cycles were included in the present study. The average CPR per transfer cycle was 51.7%. CPR of group 1 was 55.22%, significantly higher than that of group 2 (40.66%, P = 0.013). Progesterone level on the day of hCG injection was further evaluated at threshold increments of 0.1 ng/ml, and the CPR was decreased dramatically once the progesterone level higher than 1.4 ng/ml. Conclusion The slight elevation progesterone level on the hCG trigger day may have a negative effect on the clinical pregnancy in GnRH-ant cycles. In the case of progesterone > 1.4 ng/ml on the hCG injection day, freeze-all strategy was recommended. Summary The present retrospective study aimed to evaluate the effect of slightly elevated progesterone (1.0 ng/ml ~ 1.5 ng/ml) on outcomes of IVF/ICSI in GnRH-ant cycles. Slightly elevated progesterone level leaded to significant lower clinical pregnancy rate (CPR) that that of group with normal progesterone level (40.66% vs. 55.22%, P = 0.013). The CPR was decreased dramatically once the progesterone level higher than 1.4 ng/ml. So slightly elevated progesterone level on the trigger day may have a negative effect on the clinical pregnancy in GnRH-ant cycles. In the case of progesterone > 1.4 ng/ml on the hCG injection day, freeze-all strategy was recommended.
pH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>c</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>, <i>RMSEC</i>, <i>RMSEP</i>, and <i>RPD</i> of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage.
Rohit K Ramakrishnan, Aravinth Balaji Ravichandran, Arpita Mishra
et al.
Quantum information processing has conceptually changed the way we process and transmit information. Quantum physics, which explains the strange behaviour of matter at the microscopic dimensions, has matured into a quantum technology that can harness this strange behaviour for technological applications with far-reaching consequences, which uses quantum bits (qubits) for information processing. Experiments suggest that photons are the most successful candidates for realising qubits, which indicates that integrated photonic platforms will play a crucial role in realising quantum technology. This paper surveys the various photonic platforms based on different materials for quantum information processing. The future of this technology depends on the successful materials that can be used to universally realise quantum devices, similar to silicon, which shaped the industry towards the end of the last century. Though a prediction is implausible at this point, we provide an overview of the current status of research on the platforms based on various materials.
Alexander Glavackij, Dimitri Percia David, Alain Mermoud
et al.
Because of the considerable heterogeneity and complexity of the technological landscape, building accurate models to forecast is a challenging endeavor. Due to their high prevalence in many complex systems, S-curves are a popular forecasting approach in previous work. However, their forecasting performance has not been directly compared to other technology forecasting approaches. Additionally, recent developments in time series forecasting that claim to improve forecasting accuracy are yet to be applied to technological development data. This work addresses both research gaps by comparing the forecasting performance of S-curves to a baseline and by developing an autencoder approach that employs recent advances in machine learning and time series forecasting. S-curves forecasts largely exhibit a mean average percentage error (MAPE) comparable to a simple ARIMA baseline. However, for a minority of emerging technologies, the MAPE increases by two magnitudes. Our autoencoder approach improves the MAPE by 13.5% on average over the second-best result. It forecasts established technologies with the same accuracy as the other approaches. However, it is especially strong at forecasting emerging technologies with a mean MAPE 18% lower than the next best result. Our results imply that a simple ARIMA model is preferable over the S-curve for technology forecasting. Practitioners looking for more accurate forecasts should opt for the presented autoencoder approach.
The exponentially multiplying population of the world demands increasing freshwater resources. Thelimited resources comprising less than 3% of the earth’s water resources are getting polluted at an alarming rate. To deal with this situation, seawater reverse osmosis is being carried out at large scales across the globe. The concentrate generated in return is two times more concentrated in terms of total dissolved solids when compared to the feed. The adverse effects of the concentrate stream on the marine ecosystem and further pollution of water cause an immediate need to treat the concentrate. In this review, the harm caused by the direct discharge of concentrate stream has been discussed and therefore volume minimization using treatment methods has been addressed. The treatment methods are mainly classified into four types; membrane-based, thermal-based, electricity-based, and chemical-based methods. Integrated methods, which have been mainly tested on a pilot scale for zero liquid discharge, have also been discussed. The treatment methods that are probable for seawater concentrate treatment falling under the above categories for other concentrate sources have also been attended to. Finally, the disposal methods employed for the discharge of the leftover concentrate have been addressed. Thermal methods are well established but require a lot of energy compared to other methods whereas chemical methods can be economic due to the profit obtained from recovered chemicals, but they are mostly employed for pretreatment. Electricity-based and membrane-based methods are emerging technologies. It was also found that seawater reverse osmosis concentrate is usually discharged directly and therefore integrated methods based on zero liquid discharge are to be implemented. To compensate for the intensive research required for zero liquid discharge to become a reality, innovative and environmentally-friendly disposal methods are available to cut the resultant footprint.
Environmental effects of industries and plants, Science (General)
Understanding Quantum Technologies 2025 is the 8th update of a free open science ebook that provides a 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections, quantum computing energetics and a new subsection of the effects of the Lieb-Robinson limit), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors scientific and engineering approaches and roadmaps), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs and manufacturing process, raw materials), unconventional computing (potential alternatives to quantum and classical computing), quantum computing algorithms, software development tools, resource estimate and benchmark tools, use case and case studies analysis methodologies, application use cases per market, quantum communications and cryptography (including QKD, PQC and QPU interconnect technologies), quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an update to the 2024, 2023, 2022, and 2021 editions published respectively in October 2024, 2023, 2022 and 2021. An update log is provided at the end of the book.
In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the “deep” paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.
En la presente investigación se realiza un estudio para la resolución de ecuaciones diferenciales de una aplicación al modelado en un caso concreto del sector Biofarmacéutico, que fue necesario en su momento realizarlo por la importancia de la aplicación de este medicamento en una enfermedad que se convirtió en una pandemia a nivel mundial. En este problema se describe el impacto de la cidovudina (acidotimidina o AZT) sobre la supervivencia de quienes desarrollan el síndrome de inmunodeficiencia adquirida (SIDA) por infección con el Virus de la Inmunodeficiencia Humana. Para la solución de este modelo se cuenta con una ecuación diferencial ordinaria de primer orden con valores iniciales sobre la cual se aplica el método de separación de variables para obtener la solución real de forma analítica. Se aplican tres métodos numéricos (Euler, Euler Mejorado y Runge Kuta 4) usando el asistente matemático MATLAB para calcular las soluciones aproximadas. Finalmente se muestran los resultados de los métodos, los errores absolutos y relativos de cada uno y la comparación con la solución analítica, con sus respectivas tablas y gráficas.