We examine a linear q-difference differential equation which is singular in complex time t at the origin. Its coefficients are polynomial in time and bounded holomorphic on horizontal strips in one complex space variable. The equation under study represents a q-analog of a singular partial differential equation, recently investigated by the author, which comprises Fuchsian operators and entails a forcing term that combines polynomial and logarithmic type functions in time. A sectorial holomorphic solution to the equation is constructed as a double complete Laplace transform in both time t and its complex logarithm log t and Fourier inverse integral in space. For a particular choice of the forcing term, this solution turns out to solve some specific nonlinear q-difference differential equation with polynomial coefficients in some positive rational power of t. Asymptotic expansions of the solution relatively to time t are investigated. A Gevrey type expansion is exhibited in a logarithmic scale. Furthermore, a formal asymptotic expansion in power scale is displayed, revealing a new fine structure involving remainders with both Gevrey and q-Gevrey type growth.

Abstract: A two-year study of pearl millet-groundnut cropping systems across two fertilizer levels was conducted at the INRAN/TARNA research station in Maradi, Niger in the Sahelian zone of West Africa. The objective of this study was to identify the best cropping system with and without fertilizer application to optimize pearl millet and groundnut yields, land use efficiency, and economics as measured by value-to-cost ratio. The experiment was conducted with three replications, and ten treatments combination of five pearl millet and groundnut cropping systems (pearl millet and groundnut sole crops, and three pearl millet – groundnut intercrops), and two fertilizer levels. Sole cropping produced the highest pearl millet grain by over 300 kg ha-1 and groundnut pod yields by over 500 kg ha-1, but all the intercropping systems had a LER above 1.0 with land use efficiency increased by 19 to 41%, also intercropping increased diversity, and reduced risk. Fertilizer application increased yields in all cropping systems by 200 to 600 kg ha-1. Government subsidy increased the value-to-cost ratio by 0.5 to 2, 5 units, and was required for economic response for pearl millet sole and intercrops in 2022. The groundnut sole cropping system had the greatest economic response to fertilizer application. Based on the risky environment and multiple end uses needed by producers, the intercrop system M-G: 1:3:1 with fertilizer application is usually the best option to optimize pearl millet and groundnut production.

To further understand the role of phosphorylation in ISGF3- and STAT2/IRF9-mediated constitutive and long-term IFN-I-stimulated transcriptional responses, we performed RNA-Seq and ChIP-Seq, in combination with phosphorylation inhibition and anti-viral experiments. First, we identified a group of ISRE-containing ISGs that were commonly regulated in IFNα treated WT and STAT1-KO cells. Thus, in 2fTGH and Huh7.5 WT cells IFNα-inducible transcription and anti-viral activity relied on the recruitment of the ISGF3 components STAT1, STAT2 and IRF9 in a phosphorylation- and time-dependent manner. Likewise, in ST2-U3C and Huh-STAT1KO cells lacking STAT1, ISG expression correlated with DNA-binding of phosphorylated STAT2/IRF9. This pointed to a dominant role of classical ISGF3 and STAT2/IRF9, and not U-ISGF3 or U-STAT2/IRF9, in the regulation of early and prolonged ISG expression and viral protection, in WT and STAT1-KO cells. In addition, comparative experiments in U3C (STAT1-KO) cells overexpressing all ISGF3 components (ST1-ST2-IRF9-U3C), revealed a threshold-dependent role of U-ISFG3, and potentially U-STAT2/IRF9, in the regulation of constitutive and possibly long-term IFNα-treated ISG expression and anti-viral activity.

As a service business models have been ranked by management consultants the management innovation of 2020, and yet are scarcely theorized. Therein, value stipulations in customer contracts lead to the shift from product to service. However, there is no available research about the nature, formula and decisional process about these value stipulations. The motive for this research is to fill in this gap. The goal is to provide first insight about value stipulations in as a service business models: their formula and decisional methodology. The article is a multiple theory building case study on leading platform as a service providers, which aims to contruct the paradigm of the value decision methodology to invest in this technology by purchasing it from dedicated vendors. The methodology works to identify the management instruments involved in the decisional activities to invest in digital technology, where each management instrument is assumed to match an activity at seller and buyer; the sequence of the activities using the Critical Path Method gives the decisional process. Dependent variables are the elements of the as a service business contracts: the customer offering (product-service solution) and the value created, captured and delivered by the solution. Independent variables are given by empirical data analysis. Findings show a relationship between dependent variable value in the as a service business model contracts and independent variables governance objective, strategy, as a service business model type, activity management techniques (roadmap, program, project, operations, lean start-up), capital allocation techniques (equity, debt, options, venture capital typology). If innovation is exploitative, then the customer offering is known and value is determinable via roadmap – project management techniques (program, project, an operations) and capital allocation techniques for precictable environment, If innovation is radical, then the customer offering is not known and value is a target used to architect the solution via lean start-up innovation techniques funded by venture capital. The contribution is a new theoretical construct and deep insight into the practice of digital transformation considering all inherent issues.

In the bioprinting process, controlling the motion of bioprinters involves a computer-aided design (CAD) model, converting that model into g-code, and transmitting the motion commands to the bioprinters. The g-code file contains information about the motion of the axes and can be generated using various software. However, a challenge arises when using g-code between different bioprinters. Therefore, the objective of this study was to propose a reliable method for analyzing the differences in 3D bioprinting devices by parameterizing a crucial variable for extrusion bioprinting: the E-value. Four bioprinters from three different manufacturers were used to investigate the extrusion behavior when subjected to the same E-value.

The recent years have been marked by rapid growth in the field of "ESG finance," leading to the emergence and expansion of ESG ratings and rating agencies. This study seeks to examine how financial investors respond to updates in ESG ratings provided by two prominent ESG rating agencies: MSCI and Refinitiv. Specifically, the goal is to determine whether any potential positive or negative alterations in a company's sustainability ratings directly affect its market value. The chosen methodology for this investigation is the Event Study, a model employed to assess the influence of economic events on corporate valuations by analyzing their Cumulated Average Abnormal Returns (CAARs). Over 840 instances of rating updates (events) were analyzed using a sample of 75 companies across various industries, all listed on major stock exchanges. The findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not have a significant impact on companies' market capitalization. Moreover, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. Potential explanations are offered and discussed in relation to existing literature on the subject.

We review the main features of the non-local gauge, named the contour gauge. The contour gauge belongs to the axial type of gauges and extends the local gauge used in the most of approaches. The geometry of gluon fields and the path-dependent formalism are the essential tools for the description of non-local gauges. The principle feature of the contour gauge is that there are no the residual gauges which are left in the finite domain of space. In the review, we present the useful correspondence between the contour gauge conception and the Hamiltonian (Lagrangian) formalism. The Hamiltonian formalism is turned out to be a very convenient framework for the understanding of contour gauges. The comprehensive comparison analysis of the local and non-local gauges advocates the advantage of the contour gauge use. As an example of practical worth, we consider the Drell-Yan process and discuss the gauge invariance of the corresponding hadron tensor. We show that the appropriate use the contour gauge leads to the existence of extra diagram contributions. These additional contributions, first, restore the gauge invariance of the hadron tensor and, second, give the important terms for the observable quantities. We also demonstrate the significant role of the additional diagrams to form the relevant contour in the Wilson path-ordered exponential. Ultimately, it leads to the spurious singularity fixing. Moreover, in the present review, we discuss in detail the problem of spin and orbital angular momentum separation. We show that in SU(3) gauge theories the gluon decomposition on the physical and pure gauge components has a strong mathematical evidence provided the contour gauge conception has been used. In addition, we prove that the contour gauge possesses the special kind of residual gauge that manifests at the boundary of space. Besides, the boundary field configurations can be associated with the pure gauge fields.

As one of the important wastes, waste PET pollutes human living and natural environment seriously. PET is an important solid waste that needs to be recycled at present. The complete degradation of PET was realized at low temperature. The lipophilic hydrophobic membrane was formed on the surface of stainless steel mesh (SSM) by a simple dip coating method, and the oil-water separation material was successfully prepared. Due to the load of degradation products, the surface roughness of SSM increased from 19.09 μm increased to 62.33 μm. The surface changed from hydrophilic to hydrophobic, and the water contact angle increased to 123o. The oil-water separation flux of modified SSM is 9825 L/(m2·h) and the separation efficiency is 98.99%. The modified SSM has good reuse performance. This hydrophobic modification method can also be used to modify other porous substrates, such as activated carbon, filter paper, foam, and other materials. In this study, the porous substrate modified by the degradation product of waste PET was used to prepare oil-water separation materials, which not only solved the problem of white pollution, but also reduced the dependence on non renewable resources in the conventional preparation methods of oil-water separation materials. The research provided new raw materials and methods for the industrial production of oil-water separation materials, and had important application prospects.

This study explores the correlation between payment statistics and sector-wise Gross Value Added (GVA) in the Indian economy from 2011 to 2022, assessing the importance of payment and settlement systems. Through regression analysis, the impact of payment statistics on GVA across diverse sectors is assessed. The results indicate that, during the specified period, payment statistics exhibit a significant influence on sector-wise GVA. These findings underscore the pivotal role of payment and settlement systems in the Indian financial landscape.

In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

The purpose of the article is to measure, using the VAIC and MVAIC models, the impact of intellectual capital and its elements on the financial performance of Omani industrial companies listed on the Muscat Stock Exchange (MSE) from 2017 to 2021. Regression analysis using the Gretl software was applied to the study of panel data on the activities of 34 industrial companies in Oman, obtained from the official MSE website and from published financial statements. The analysis of literature sources was carried out using bibliometric analysis, the results of which were visualized using the VOSviewer software. To analyze the relationships between financial performance and its determinants (intellectual capital variables and control variables), four types of PERF models were developed, based on which sixteen models were directly examined. Four dependent variables (Earnings before interest, Taxes, Depreciation and Amortization, Return on Assets, Return on Equity, Assets Turnover Ratio) and eight independent variables (Value Added Intellectual Coefficient, Modified Value Added Intellectual Coefficient, Capital Employed Efficiency, Human Capital Efficiency, Structural Capital Efficiency, Relational Capital Efficiency, Size, and Leverage) were used to form these models. The Empirical Results section has discussed descriptive statistics and correlations, selection of estimate panel data parameter based on the F-statistics, Breusch-Pagan test, and Hausman test, assumption test results (Normality, Autocorrelation, Heteroscedasticity), and panel regression models. Analysis of the impact of VAIC, MVAIC and its elements on the financial performance of Omani industrial companies has yielded mixed results. VAIC and MVAIC in general have a significant positive effect only on ATO. For individual elements of VAIC and MVAIC, the following most significant associations with financial performance measures were found: CEE negatively affects ROE; SCE has a positive effect on l_EBITDA and ATO; RCE negatively affects ATO; l_SIZE has a positive effect on l_EBITDA and ROA; and LEV has a positive effect on ROA.

This study evaluated five annual and eleven perennial Indian pasture legumes species for their nutritive value, dry matter, mineral contents and in vitro fermentation parameters, aiming to boost their potential as animal feed. Legume species significantly differed (p<0.05) in organic matter, crud protein (CP), ether extracts, fibres, and protein fractions. Clitoria ternatea (CT) a perennial had higher (p<0.05) buffer soluble protein (477), while neutral detergent soluble protein was highest in annually grown Lablab purpureus (420 g/kg CP). Atylosia scarabaeoides (AS) had higher NSC (392 g/kg DM) than SC (367 g/kg DM). Rapidly degradable fraction (51.7 g/kg tCHO) was lower (p<0.05) than other fractions of carbohydrate. Total digestible nutrients, digestible energy and metabolisable energy varied and values were higher for Desmenthus virgatus and lowest for Stylosanthas seabrana (SSe). The NE values for lactation (NEL), maintenance (NEM) and gain (NEG) were also higher for DV (6.6, 7.95 and 4.28) and lowest for SSe (3.54, 4.24 and 0.58 kJ/g DM), respectively. dry matter intake, digestible DM (DDM) and relative feed value of legumes differed (p<0.05) with mean values of 2.22% body weight, 592 g/kg DM and 102%, respectively. Annual grasses like Dolichos biflorus, Macroptilium atropurpureum, Rhynchosia minima (RM) were found better with micro minerals compared to other perennials. In vitro dry matter degradability, partition factor, short chain fatty acids and microbial protein production of legumes varied (P<0.05) and mean values were 578 g/kg DM, 5.61 mg DDM/mL, 2.42 mm/g and 352 mg/g, respectively. Gas and CH4 production (mL/g and mL/g DDM) varied (p<0.05) being lowest gas from AS (55.5) and highest from CT-blue (141), while CH4 production was lower (p<0.05) from CT-white and AS (8.24 and 9.14) and higher from Arachis glabrata (AG) and Arachis hagenbackii (AH) (15.2 and 15.1). Methane in total gas was low for DV, RM and CT-w (8.99, 9.72 and 9.51%) and loss of DE and ME as CH4 varied (P<0.05) amongst the legumes with mean values of 4.62 and 7.86%, respectively. Based on these findings, each legume offers unique benefits, allowing for tailored combinations of annual and perennial legumes to optimize rumen feed efficiency.

This study examined the effect of the media usage environment, particularly TV and mobile devices, on advertisement acceptance, lifestyle, and psychological satisfaction. Hypothesis pathways and comparative structural mediating effects were analyzed. Media and Consumer Research public data were used for empirical analysis. A total of 6,640 people who participated in sports activities were included in the study. The results demonstrated that TV and mobile device usage environments had similar effects on advertisement acceptance. While TV did not influence the pursuit of personal life, mobile device use environments affected the pursuit of personal life. Mobile device use environments had a greater effect on individual psychological satisfaction than TV. Adoption had a mediating effect on the influence of media usage environments on the pursuit of personal life. The pursuit of a personal life had a significant mediating effect on the influence of media use environments on psychological stability. This study revealed no differences in the effects of new and traditional media on advertisement acceptance, while new media had a greater influence on lifestyle and psychological satisfaction.

The present study took into account the influence of local cuisine in the restaurants awarded Michelin stars in Phuket Old Town, influenced by Chinese cultural diffusion to other cultures contributing to a unique culture in the Malay Peninsula. It sought to investigate components of local gastronomic tourism and perceived value towards the Michelin Guide’s assessment criteria of local cuisine in Phuket Old Town, delving into the relationship between these two factors. In particular, a structural equation model (SEM) was adopted to explore such a relationship, with an exploratory factor analysis (EFA) and a confirmatory factory analysis (CFA) to assess a measurement model of latent variables and accuracy and reliability of the structure. The study was conducted in the context of Phuket Old Town, Phuket Province, Thailand, renowned as a City of Gastronomy. The results demonstrated that the phenomenon of local cuisine in Phuket Old Town embodied the perceived value of local cuisine, origins, history, traditions, rituals, and unique lifestyles; particularly, the components of local gastronomic tourism reflected the significance of food products in the food industry, together with the organization of local cuisine activities and festivals. This finding contributed to an understanding of the reasons behind the perceived value among consumers traveling to this town to gain experiences of local cuisine in the restaurants awarded with Michelin stars. It could serve as a policy for improving food establishments’ quality and standards based on the specified criteria to foster sustainable gastronomic tourism in Thailand.

A The guinea pig value chain in Jauja, Peru, has been developed through the application of productive projects, institutional strengthening and implementation of the principles of responsible investment in agriculture and food systems (CFS-RAI). The actors in this chain have been showing economic improvements, representativeness and participation, characteristics that make them the key human capital for development. To improve the performance of the actors in the sustainable rural development of the territory of Jauja, Peru, the research carried out an evaluation of the competencies and capacities for project management in 46 actors linked to the business programme on the CFS-RAI Principles, representing 1,094 people in the guinea pig value chain in Jauja, using the Working With People (WWP) model and empirical instruments based on the Octagon method, International Project Management Association (IPMA) competencies, performance evaluation carried out by experts, attitudinal evaluation and the Business Model Canvas. The results have helped to structure the actors of the value chain, who show an intermediate mastery of project management competencies (2.73/5), institutional capacities in development (2.89/7) and a growing organizational management performance (26.2/100). In addition, trust and proactivity have been generated among the actors in the chain, with an interest in continuing with the implementation of the CFS-RAI Principles in order to achieve sustainability in their productive projects with commercial innovation. The results have allowed us to generate the design of an innovative programme for the development of competencies, which balances the three dimensions of IPMA competencies (perspective, person and practice) in connection with the CFS-RAI Principles. Both processes complement the strengthening of human capital in agrifood value chains through sustainable project management and contributing to sustainable development in the region.

There is a limited data on variation of microbial hygiene load in dairy products during different seasons. In this study, a longitudinal design with a simple random sampling method was used to collect data in the dry season (January to April) and wet season (June to August). A total of 448 milk and cottage samples were collected from 3 regions. Enumeration of total aerobic mesophilic bacteria (APC), total coliforms (TCC), and Escherichia coli (EC) was performed according to standard methods. Independent t-test were employed to assess the significant variation at (p<0.05) between the two seasons. The cumulative result of APC of 7.61 log cfu/ml and g and TCC of 3.50 log cfu/ml in the dry season were significantly higher than wet season of 7.15 log cfu/ml and 2.49 log cfu/ml, respectively. Whereas generic E. coli count (EC) was significantly higher in the wet season (0.70 log cfu/ml and g) than that in the dry season (0.40 log cfu/ml and g). The results of hygienic indicator microbial load were significantly varied with season. Hence, a sustainable awareness creation on hygienic milk production and handling that comprehend seasonal influence should be implemented to improve the safety of milk.

: The primary use of the potato is direct consumption. The classification of potatoes into consumption type is made on the basis of consumption evaluation of hydrothermally treated tubers. Nutrient deficiency in the soil, including Mg, contributes to the inhibition of potato growth and development and negatively affects the cooking characteristics of the tubers. Magnesium and biostimulants are responsible for plant nitrogen management. Thus, it is necessary to control the content of harmful nitrates and toxic nitrites in tubers. In potato production, it is also important to maintain appropriate conditions during storage. The purpose of this study was to determine the effect of mineral fertilization with magnesium and a biostimulant preparation during cultivation of edible potato and long-term storage on the traits determining utility-consumption type and the content of harmful nitrogen compounds in tubers. The study was conducted using the early potato variety Satina. In a three-year (2015-2017) three-factor experiment: evaluation date (immediately after harvest and after six months of storage), soil fertilization with mineral magnesium (0, 30, 60, 90 kg MgO ha-1), application of an amino acid biostimulant (0, 1.5, 3.0 l ha-1), five consumption traits of tubers after cooking determining the utility-consumption type of potato were determined. In addition, the consumer safety of the potato was determined based on the nitrate and nitrite content of the tubers. The tendency to overcook tubers significantly depended on all the factors used during potato cultivation. In contrast, the texture and structure of tuber flesh after cooking depended only on mineral fertilization with magnesium. The flesh of the tubers after cooking was most tender (1.0 pt) and firm (1.4 pt) after the application of 60 and 90 kg MgO ha-1, respectively. The moisture level of the flesh after cooking was significantly influenced by the interaction of magnesium fertilization with the biostimulant preparation. The factors of the experiment did not modify the utility-consumption type (B/A), while there was a change in the type in relation to that specified by the grower - general utility (B). The applied cultivation technology and long-term storage did not deteriorate the consumer safety of tubers (&lt;200 mg kg-1) in terms of NO3 (max. 112.1 mg kg-1) and NO2 (max. 1.08 mg kg-1) contents. Organoleptic traits: mealiness, moisture and flesh structure were significantly positively related to nitrate and nitrite content of tubers immediately after harvesting and to nitrite concentration after long-term storage.

Conducting computational stress-strain analysis using finite element methods (FEM) is a common approach when dealing with the complex geometries of atherosclerosis, which is a leading cause of global mortality and complex cardiovascular disease. The considerable expense linked to FEM analysis encourages the substitution of FEM with a considerably faster data-driven machine learning (ML) approach. This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial wall. We first proposed a U-Net based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches to further improve the prediction accuracy of field maps. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

U-Net, based on a deep convolutional neural network (CNN), has been clinically used to au-to-segment normal organs and potentially target volumes. However, CNNs with local geometric dependencies may limit the accuracy of segmentation. Additionally, the performance of CNNs can vary depending on the selection of network hyper-parameters, which was mitigated by the proposition of nnU-Net. We chose a vision transformer architecture called VT U-Net, which features a self-attention excluding the convolution layer, to overcome the limitations of CNNs by utilizing global geometric information of images. The VT U-Net v.2 became more powerful thanks to the adaptive hyper-parameter optimizer embedded in nnU-Net. However, despite leveraging the benefits of nnU-Net, VT U-Net v.2 still had additional network hyper-parameters that needed to be optimally chosen. Accordingly, among various hyper-parameters, this study attempted to find the optimal combination of the patch size and the embedded dimension regarding the transformer. From the 4-fold cross-validation, the modified VT U-Net v.2 showed the highest average performance for planning target volume (PTV) segmentation among the investigated networks. Though nnU-Net was based on convolution layers, the adaptive hyper-parameter optimizers turned out to enhance the performance. It was also confirmed that network hyper-parameters affected the segmentation accuracy of vision transformers.

Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often could not accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently could capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

Using a wide range of organic substrates in the methane fermentation process enables efficient biogas production. Nonetheless, in many cases, the efficiency of electricity generation in biogas plant cogeneration systems is much lower than expected, close to the calorific value of the applied feedstock. This paper analyses energy conversion efficiency in a 1 MWel agricultural biogas plant fed with corn silage or vegetable waste and pig slurry as a feedstock dilution agent, depending on the season and availability. Biomass conversion studies were carried out for 12 months, during which substrate samples were taken once a month. The total primary energy in substrates was estimated in laboratory conditions by measuring the heat of combustion in a ballistic bomb calorimeter (17,760 MWh·year-1), and in the case of pig slurry, biochemical methane potential (BMP, (201.88±3.21 m3·Mg VS-1). Further, the substrates were analysed in terms of their chemical composition — from protein, sugar and fat content to mineral matter determination, among other things. The results obtained during the study were averaged. Based on such things as the amount of biogas produced at the plant, the amount of chemical (secondary) energy contained in methane as a product of biomass conversion (10,633 MWh·year-1) was calculated. Considering the results obtained from the analyses, as well as the calculated values of the relevant parameters, biomass conversion efficiency was determined as a ratio of chemical energy in methane to (primary) energy in substrates, which was 59.87%, as well as electricity production efficiency, as a ratio of electricity produced (4,913 MWh·year-1) to primary energy, with a 35% cogeneration system efficiency. Full energy conversion efficiency, related to electricity production, reached a low value of 27.66%. This article provides an insightful, unique analysis of energy conversion in an active biogas plant as an open thermodynamic system.

Long-term storage may reduce the nutritional quality of brown rice, so the present study was aimed to evaluate the nutritive values and application in pig diets. In Exp.1, 18 Landrace × Yorkshire (L × Y) barrows with initial body weight (IBW) of 25.48 ± 3.21 kg were randomly assigned to three treatments, which included corn diet, one-year stored brown rice (BR1) diet and six-year stored brown rice (BR6) diet, to determine the digestible energy (DE) and metabolisable energy (ME) value of stored brown rice. In Exp. 2, 24 barrows (L × Y; IBW: 22.16 ± 2.42 kg) fixed with ileal T-cannula were randomly allotted to four dietary treatments, including corn diet, two stored brown rice diets and nitrogen-free diet, to evaluate the amino acid (AA) digestibility of stored brown rice. In Exp. 3 and 4, 108 crossbred weaned piglets (L × Y; IBW: 9.16 ± 0.89 kg) and 90 crossbred growing pigs (L × Y; IBW: 48.28 ± 3.51 kg) were allotted to 3 treatment diets, including control diet and two stored brown rice diets, respectively, to investigate the application of stored brown rice in weaned piglet and growing-finishing pig diets. Results showed that: 1) The DE and ME of corn and stored brown rice showed no significant differences (P > 0.05), while the apparent ileal digestibility (AID) of arginine, histidine, aspartic acid, and the standardized ileal digestibility (SID) of arginine and histidine were higher in stored brown rice compared to corn (P < 0.05). 2) Compared with the control diet, the stored brown rice diets showed no significant effects on growth performance, nutrient apparent total tract digestibility (ATTD) and serum biochemical indices (P > 0.05), but decreased digestive enzymes activities in duodenum, jejunum and ileum of piglets (P < 0.05). 3) Compared with the control diet, the stored brown rice diets showed no significant effects on growth performance, carcass traits, meat quality, fatty acids profiles in the longissimus dorsi muscle of growing-finishing pigs (P > 0.05). In conclusion, the brown rice stored for 6 years under good conditions has no obvious changes in available energy and nutrient values. Although it may reduce digestive enzyme activity in small intestine of the piglets, the stored brown rice showed no obvious adverse effect on growth performance and meat quality, and can be effectively used in pig diet.

As the largest accretionary orogen in the world, the Central Asian Orogenic Belt (CAOB) has continuous juvenile crustal growth in the Phanerozoic. The northern margin of the North China Craton(NCC) and its adjacent area is the eastern segment of the CAOB, and is a key area to study the geological evolution of Paleo-Asian Ocean (PAO). In the Permian, the west of the northern margin of NCC belongs to a post-collision extensional environment, and the east belongs to a subduction stage. But, As a connecting area, the Permian evolution of PAO in the middle of northern margin of NCC has not been systematically studied. In order to fill the blank and understand the temporal and spatial continuous evolution process of PAO, this paper focused on the Permian granitic rocks in the Chifeng area, zircon U-Pb dating and the geochemical analysis of the whole rock main and trace elements were conducted in order to build a granite chronological framework, discuss the genesis and tectonic background of the granite rocks as well as the magma-tectonic evolution history in the Chifeng area. Zircon U-Pb dating results of 8 samples are 269±1 Ma, 268±3 Ma, 260±4 Ma, 260±1 Ma, 260±1 Ma, 255±2 Ma, 254±2 Ma, 256±1Ma, which showed that the Permian granitic rocks had undergone three stages of emplacement: (1) The monzogranite and syenite (294-284 Ma); (2) The monzogranite (269-260Ma) and (3) The monzogranite and syenite (256-254Ma). The Middle Permian magmatism (269-260Ma) was represented by the monzogranite assemblages with different grain sizes. The geochemical characteristics showed that they were high-potassium calc-alkaline-potassium dossonite series of granites formed in compressional environment, indicating there was a collision between the Xing’an-Mongolian Orogenic Belt (XMOB) and the North China Craton (NCC). During the Late Permian-Early Triassic (256-248Ma), the granites in the Chifeng area are dominated by the A-type and I-type granites of high-potassium calc-alkaline series formed under an extensional environments, which constituted typical "bimodal" rock assemblage combined with the coeval basic rocks, suggesting the study area was in an extensional environment where the subducting slab is fragmented during the collision between the XMOB and the NCC. According to emplacement time and occurrence location, the plutons are interpreted to have been generated by the subduction-collision of the Paleo-Asian oceanic crust beneath the NCC. This study provides strong evidence for Permian tectonic evolution and the characterization of the eventual closure of the Paleo-Asian Ocean in the Chifeng City at the northern margin of the NCC.

Collaborative robots have experienced low acceptance in applications, especially in industry. 1 This fact has attracted the attention of researchers and practitioners, who point to different causes 2 for this low acceptance. One of the main reasons is the difficulty in converging on suitable methods 3 for modeling collaborative interactions between robots and their surrounding context during the 4 requirements phase. These interactions must be elicited and modeled during requirements modeling 5 to maximize value creation through collaboration and must be formally verified, taking into account 6 the risks of human-robot interaction. However, such modeling is often not present in collaborative 7 robot design, and the choice of an appropriate approach remains an open problem. In this paper, 8 this problem is addressed from a requirements engineering perspective, a goal-oriented model and 9 a service-based approach supported by an additional verification method based on Petri nets is 10 proposed. A case study based on collaborative robots used in a hospital environment is presented.

The time evolution of the continuous measure symmetry for the system built of the three bodies interacting via the potential U(r)~1/r is reported. Gravitational and electrostatic interactions between the point bodies were addressed. In the case of the pure gravitational interaction the three-body-system deviated from its initial symmetrical location, described by the Lagrange equilateral triangle, comes to collapse, accompanied by the growth of the continuous measure of symmetry. When three point bodies interact via the Coulomb repulsive interaction, the time evolution of CMS is quite different. CMS calculated for all of studied initial configurations of the point charges and all of their charge-to-mass ratios always comes to its asymptotic value with time, evidencing the stabilization of the shape of the triangle, constituted by the interacting bodies.

The development of the chicken value chain in The Gambia has immense potentials to lead to the modernisation of agriculture and creating employment for young people and women. Chicken can be raised throughout the year in most parts of The Gambia. With increasing populations, environmental degradation and loss of rangelands, chicken is easier to producer than other livestock, such as cattle and small ruminants. The development of the chicken value chain in The Gambia is also mainly constrained at commercialisation. Commercial farmers in The Gambia are not able to compete with cheap import. Critical inputs such as day-old chicks and feed are imported. Most farms are operating below 40% capacity and only less than 4% utilisation of the available processing capacity. At full capacity and with some needed investments along the value chain, The Gambia has the capacity to meet the demand for chicken and eggs. 88% of the value chain actors believe that the imposition of tariffs on chicken imports will lead to the upward transformation of the chicken value chain being able to employ at least 10% of the Gambian population.

This study examines how the design of museum archaeological boxes and the overall museum image affect behavioral intention. This research used partial least squares-structural equation modeling, indicating a relationship between aesthetics, symbolism, affective museum image, perceived value, and behavioral intention. This study found that symbolism predicted behavioral intention. Moreover, aesthetics, symbolism, and affective museum image affected perceived value, in turn triggering behavioral intention. This indicates theoretical and practical implications for museum products.

Triangular resonators re-shaped with the Sierpinski geometry and U-shaped resonators have been designed, linking them with single-pole-double-through (SPDT) RF MEMS switches to provide frequency tuning for potential applications in the K-Band. Prototypes of band-stop narrowband filters working around 20 GHz and 26 GHz, interesting for RADAR and satellite communications, have been studied in coplanar waveguide (CPW) configuration, and the tuning was obtained by switching between two paths of the devices loaded with different resonators. As a result, dual-band operation or fine-tuning can be obtained depending on the choice of the resonator, acting as a building block. The studied filters belong to the more general group of devices inspired by the metamaterial design.

The overexploitation of forest resources in the charcoal production basin of the city of Lubum-bashi (DR Congo) is reducing the resilience of miombo woodlands and threatening the survival of the riparian as well as urban human populations that depend on it. We assessed the socio-economic value and availability of plant-based non-timber forest products NTFPs in the rural area of Lubumbashi through ethnobotanical (100 respondents) and socio-economic (90 respondents) interviews, supplemented with floristic inventories, in two village areas selected on the basis of the level of forest degradation. The results show that 60 woody species, including 46 in the degraded forest (Maksem) and 53 in the intact forest (Mwawa), belonging to 22 families are used as sources of NTFPs in both villages. Among these species, 25 are considered priority species. NTFPs are collected for various purposes, including handcrafting, hut building, and traditional medicine. Moreover, the ethnobotanical lists reveal a similarity of almost 75%, indicating that both local communities surveyed use the same species for collecting plant-based NTFPs, despite differences in the level of degradation of the miombo woodlands in the two corresponding study areas. However, the plant-based NTFPs that are collected from miombo woodlands and traded in the urban markets have significant economic value, which ranges from 0.5 to 14.58 USD per kg depending on the species and uses. NTFPs used for handicraft purposes have a higher economic value than those used for other purposes. However, the sustainability of this activity is threatened due to unsustainable harvesting practices that include stem slashing, root digging, and bark peeling of woody species. Consequently, there is a low availability of plant-based NTFPs, particularly in the village area where forest degradation is more advanced. It is imperative that policies for monitoring and regulation of harvesting, and promoting sustainable management of communities’ plant-based NTFPs priority, be undertaken to maintain their resilience.

In this study, we investigate the existence of positive solutions within a system of Riemann-Liouville fractional differential equations that incorporate the (r1,r2,r3)-Laplacian operator while being subject to three-point boundary conditions. These equations incorporate various fractional derivatives and are influenced by parameters represented as (ψ1,ψ2,ψ3). Our approach involves employing techniques such as cone expansion and compression of the functional type, in conjunction with the Leggett-Williams fixed point theorem, to establish the existence of positive solutions. To emphasize the practical significance of our findings in the realm of fractional differential equations, we provide two illustrative examples.

To explore the forage quality of wild oat populations under different growth environments, the basic nutrient composition and forage quality of five wild oat populations in Israel were compared. Ash, crude protein, and soluble sugar content of oats were significantly (P<0.05) related to some forage quality and possible environment of population origin. Among them, the environmental factors of origin had significant effects on the indicators of ash, crude protein, and soluble sugars of oat straw, but less on the contents of crude fat, total phosphorus, and total potassium. Meanwhile, the annual rainfall and the number of days of rainfall in the country of origin greatly influenced the cellulose content. Finally, we investigated the similarity of feeding values of different populations using PCA analysis. The results showed that, in conclusion, the higher variability of wild oat populations may be a new possibility brought by feeding oats.

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting system and the protection of cultivated land resources. This study take Guangxi, China as an example and construct a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) The total value of cultivated land resources in Guangxi reach 19,729.2 billion yuan, and the average cultivated land value is 5.3511 million yuan/hm2, of which the ratio of economic, social and ecological values is about 6:1:2; (2) The value of cultivated land has a certain distribution pattern in the global space, and the average economic and social value show the spatial distribution patterns of "low in the west and high in the east" and "low in the northwest and high in the southeast" respectively."(3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with "high-high" and "low-low" clustering. (4) Economic development level is significantly negatively correlated with the ecological value of cultivated land, while has a significant positive correlation with social value; (5) Natural conditions, economic development and agricultural development approaches are the key factors affecting the value of cultivated land resources.

This study addresses the challenge of objectively quantifying the value of specific views, known to provide psychological and physical benefits, from city center apartments. Unlike subjective evaluations, objective measurement is hindered by limited research and methodology. The research focuses on quantifying visibility by projecting rays from measurement points and determining whether they intersect with unobstructed views, such as the sky and natural elements. Two metrics are proposed to evaluate visibility: the number of view rays and the ratio of view rays to windows. The study analyzed 17 types of residential units in 12 complexes in northern Seoul, South Korea, with the aim of isolating the effect of views on housing prices from other factors. Correlation coefficients were used to assess the relationship between view and housing prices. The results indicated that valuations based on average visibility are more favorable than those based on maximum visibility. The amount of visibility showed a stronger correlation with apartment prices than the number of floors, suggesting the need to measure views across the entire space. The study's methodology can be applied to analyze views in different buildings and urban areas.

The introduction of digitization has changed all spheres of business on a global level, including geospatial data. In the Republic of Serbia, the process on this topic should begin with the introduction of the terms ePlan and eSpace. The general goal of the paper implies the construction and implementation of the ePlan as a future part of the eSpace for digital management of geospatial data, by creating co-creating value. For this purpose, the authors conducted research in which representatives of local self-governments and holders of public authority participated, by means of structured online research. The focus was on the digitization of urban and planning documents and the establishment a central database of spatial and planning documents in electronic format, and its further distribution through the one system. In that way, easy access to digital plan data expands the user community, and enables communication with different stakeholder groups. Ac-cording to the results of the research, the authors point out that it is necessary to form a new model for managing geospatial data through a eSpace system. This is achieved by shifting the focus from urban and spatial planning to other databases and their registers that are related to geospatial data. The goal of the paper indicates that it is necessary to raise the awareness of society, to introduce the concept of value co-creation, because conditions are created for the implementation of all measures aimed at digitization and management of electronic services in sustainable project society, at all countries.

A wide panel of microtubule-associated proteins and kinases is involved in coordinated regula-tion of the microtubule cytoskeleton and may thus represent valuable molecular markers contrib-uting to major cellular pathways deregulated in cancer. We previously identified a panel of 17 microtubule-related (MT-Rel) genes that are differentially expressed in breast tumors showing resistance to taxane-based chemotherapy. In the present study, we evaluated the expression, prognostic value and functional impact of these genes in breast cancer. We show that 14 MT-Rel genes (KIF4A, ASPM, KIF20A, KIF14, TPX2, KIF18B, KIFC1, AURKB, KIF2C, GTSE1, KIF15, KIF11, RACGAP1, STMN1) are up-regulated in breast tumors compared with adjacent normal tissue. Six of them (KIF4A, ASPM, KIF20A, KIF14, TPX2, KIF18B) are overexpressed by more than 10-fold in tumor samples and four of them (KIF11, AURKB, TPX2 and KIFC1) are essential for cell survival. Overexpression of all 14 genes, and underexpression of 3 other MT-Rel genes (MAST4, MAPT and MTUS1) are associated with poor breast cancer patient survival. A Systems Biology approach highlighted three major functional networks connecting the 17 MT-Rel genes and partners, which are centered on spindle assembly, chromosome segregation and cytokinesis. Together our studies identified mitotic Aurora kinases and their substrates as major targets for therapeutic approaches against breast cancer.

AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512x512 pixels were extracted using translation lesion boxes that encompass lesions in different positions and capture different contexts of perilesional skin. In total, 16488 translation augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.

This study analyzes the influence of CEO types on corporate governance, focusing on de facto (substantial) CEOs. We examine how substantial CEOs impact ESG activities (Hypothesis 1) and corporate value (Hypothesis 2). Data were collected from KIS-VALUE and DART (Electronic Disclosure System) of the Financial Supervisory Service, defining substantial CEOs as highest remuneration recipients exceeding the representative director of the company pay. Results sup-port Hypothesis 1, showing companies with substantial CEOs are more engaged in ESG activities, potentially to improve public image while concealing self-serving behaviors. Hypothesis 2 is validated, indicating lower corporate value in companies with substantial CEOs due to prioritizing personal interests over long-term profit maximization. Despite limitations in exploring governance relationships beyond remuneration data, the study offers key contributions. It expands research on corporate governance and ESG activities by identifying substantial CEOs through objective remuneration data. It highlights the importance of an independent board for transparent governance and positive corporate value. Lastly, empirical evidence shows the negative impact of misdirected ESG activities on corporate value. By using remuneration as an indicator, this study illuminates substantial CEOs' influence on corporate value and ESG activities, providing insights for future research in corporate governance and ESG.

Supermarkets in Port Harcourt (PH) city, Nigeria, predominantly rely on diesel electricity generation due to grid instability, leading to high cost of electricity prices. Although solar photovoltaic (PV) systems have been proposed as an alternative, these supermarkets have yet to adopt them, mainly due to high investment costs and a lack of awareness of the long-term financial and environmental benefits. This paper examines the technical and economic practicality of a PV system for these supermarkets using the PVsyst software and a spreadsheet model. Solar resources showed that PH has a daily average solar radiation and temperature of 4.21 kWh/m2/day and 25.73℃, respectively. Market Square, the supermarket with the highest peak power demand of 59.8 kW, and a 561 kWh/day load profile, was chosen as a case study. A proposed PV system with a power capacity of 232 kW, battery storage capacity of 34,021 Ah, a charge controller size of 100 A/560V, and an inverter with a power rating of 60V/75 kW has been designed to meet the load demand. The economic analysis showed a $266,936 life cycle cost, $0.12 per kWh levelized cost of electricity (LCOE), 4-year simple payback time, and a 20.5% internal rate of return (IRR). The PV system is feasible due to its positive net present value (NPV) of $165,322 and carbon savings of 582 tCO2/year.

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

This article explores the important role of traditional shipyards in the global maritime industry, covering aspects of construction, repair, and maintenance. With the advent of faster manufacturing techniques, traditional shipyards face important challenges such as planning errors, coordination problems, delivery delays, and underutilization of technology, which results in high costs, reduced productivity, and prolonged projects. The application of Manufacturing Cycle Efficiency (MCE) emerged as an important solution to significantly increase production efficiency. MCE empowers shipyards to deal effectively with waste, bottlenecks, and disruptions, thereby increasing performance, competitiveness, and profitability. Using a comprehensive approach that uses both qualitative and quantitative methods, including field surveys, and in-depth interviews in the traditional shipyards industry, this research identifies Non-Value-Added (NVA) processes, conducts process mapping, and calculates MCE. The findings reported in this article underscore the significant wastage in the production process, indicating an urgent need for improvement, given the current average MCE value of 67.08%, indicating considerable room for improvement. This article provides innovative perspectives on optimizing the traditional shipyards industry through production cycle efficiencies while offering actionable recommendations. Key focus areas include integrating management systems, adopting advanced technologies, and implementing sustainable strategies to improve MCE, especially by reducing non-value-added time wastage, such as inspection and storage. By implementing strategies that optimize production, minimize waste, and overcome the challenges of global competition, this research contributes to improving MCE. In conclusion, this study is an invaluable guide for industry stakeholders, enabling them to enhance their competitiveness and adapt effectively to a dynamic business environment.

In this paper we study a special kind of finite mixture model. The sample drawn from the model consisits of three parts. The first two parts are drawn from specified distributions f1 and f2, while the third one is drawn from the mixture. A problem of interest is whether the two distributions f1 and f2 are the same. To test this hypothesis, we first define the regular location and scale family of distributions and assume that f1 and f2 are regular denisty functions. Then the hypothesis transforms to the equalities of the location and scale parameters, respectively. To utilize the information in the sample, we use Bayes’ theorem to obtain the posterior distribution and give the sampling method. Then we propose the posterior p-value to test the hypothesis. The simulation studies show that our posterior p-value largely improves the power in both normal and logistic cases while nicely controls the Type-I error. A real halibut dataset is used to illustrate the validity of our method.

This paper contextualises the common queries of "why is crypto crashing?" and "why is crypto down?", the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency. The study examines blockchain technologies and their pivotal role in the evolving Metaverse, shedding light on topics such as how to invest in cryptocurrency, the mechanics behind crypto mining, and strategies to effectively buy and trade cryptocurrencies. Through an interdisciplinary approach, the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse. Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies, the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies. Moreover, it probes into both enduring and dubious crypto projects, drawing a distinct line between genuine blockchain applications and Ponzi-like schemes. The conclusion resolutely affirms the continuing dominance of blockchain technologies, underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.

The binding of actinide ions (Am(III) and U(VI)) in aqueous solutions by hybrid silica-hyperbranched poly(ethylene imine) nanoparticles (NP) and xerogels (XG) has been studied by means of batch experiments at different pH values (4, 7 and 9) under ambient atmospheric conditions. Both materials present relatively high removal efficiency at pH 4 and pH 7 (&gt; 70%) for Am(III) and U(VI). The lower removal efficiency for the nanoparticles is basically associated with the compact structure of the nanoparticles and the lower permeability and access to active amine groups compared to xerogels, and the negative charge of the radionuclide species formed under alkaline conditions (e.g. UO2(CO3)34- and Am(CO3)2-). Generally, the adsorption process is relatively slow due to the very low radionuclide concentrations used in the study and is basically governed by the actinide diffusion from the aqueous phase to the solid surface. On the other hand, the adsorption is favored with increasing temperature assuming that the reaction is endothermic and entropy-driven, which is associated with increasing randomness at the solid-liquid interphase upon actinide adsorption. To the best of our knowledge, this is the first study on hybrid silica-hyperbranched poly(ethylene imine) nanoparticle and xerogel materials used as adsorbents for americium and uranium at ultra-trace levels. Compared to other adsorbent materials used for binding americium and uranium ions, both materials show far higher binding efficiency. Xerogels could remove both actinides even from seawater by almost 90%, whereas nanoparticles could remove uranium by 80% and americium by 70%. The above, along with their simple derivatization to increase the selectivity towards a specific radionuclide and their easy processing to be included in separation technologies, could make these materials attractive candidates for the treatment of radionuclide/actinide contaminated water.

A new intervention model for promoting healthy ageing grounded on integrated value-based care was developed and tested in the city of Valencia (Spain). Its implementation raised relevant barriers for older adults in their access to health, health promotion, and health self-management linked with their health and digital literacy. This new intervention model included several aspects. On the one hand, researchers together with older adults and their informal caregivers participating in the study, designed personalised care plans, based on older adults’ specific needs, to be implemented with the support of a digital solution. On the other hand, researchers and health and social professionals implemented a series of six workshops in different locations of the city to encourage the sense of community among participants, reinforcing their trust in the new care model and increasing their adherence. Social activities were at the core of the workshops to understand older people’s interaction with the health and social services provided in the neighbourhood. Qualitative and quantitative methods were combined to extract information from older participants on how to engage them as active actors of their own health and understanding their values and preferences. Qualitative results show that after a post-pandemic situation, they were more concerned about social isolation and desired face-to-face contact with their professional care team; however, feelings of loneliness and/or sadness were not considered amongst the reasons to visit health professionals. Some of the conclusions reveal that the use of technology as a supportive tool is well received but with a stress on its role as “supportive”, and not replacing the close contact with healthcare professionals. Professionals recognised the benefits of this new approach but required more time and incentives to dedicate the effort needed.

Machine Learning can be used for social good. In this paper, we discuss how it can be used to combat climate change and facilitate land management and farming in developing countries and in particular in Côte d’Ivoire. This paper explores models that improve land and water management and agricultural farming cultivation to contrast climate change. Côte d’Ivoire is the largest producer of cocoa beans (43%) in the world, but deforestation, lack of rainfall, drought, and climate change threaten crops and the already fragile economy of Ivorian farmers. It is important to combat climate change with methods and techniques that are affordable to the local farmers and also induce positive effects in production. We discuss the use of low-cost sensors to collect data on the soil and open data and open source software to develop AI tools. We show that using deep neural networks (YOLOv5m) is effective for detecting healthy plants and pods of cocoa from damaged ones only using mobile phone images. Focusing on a single land is not enough to combat climate change, which has different causes and involves also knowledge at a higher scale. We propose a new method of forecasting for the analysis of remote sensors. Remote sensor data come from GRACE NASA Mission and ERA5 produced by the Copernicus Climate Change Service at ECMWF. We implement a new deep neural network architecture named CIWA-net. It is based on a Fully Convolutional Neural Network (FCN) [ 1] and it is a U-net like architecture [2]. The aim of CIWA-net is to forecast Total Water Storage Anomalies (TWSA). We show the quality of our model with a comparison to a vanilla Convolutional Neural Network. CIWA-net could be used also for the detection of lands that interfere with agricultural work and yields, such as deserted areas, water-soaking soil areas, zones at risk of desertification, and poor land use. The employment of AI at the service of agriculture can decrease crop losses and waste, lower the inputs onto the soil of fertilizers, responsible for the increase of Greenhouse Gases. It could be useful to help the small farmers (at a local scale) and also the policy-makers and farmers’ cooperatives (at the regional scale) to take valid and coordinated countermeasures to improve the correct use of the lands, helping to contrast and adapt to climate change.

The protein extract of Ulva australis hydrolyzed with alcalase and flavourzyme investigated multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography showed significantly high TAC, collagenase inhibitory and antibacterial effects against Propionibacterium acnes, and only Q3 fraction from anion-exchange chromatography showed high multi-functional activities. The eight peptides among 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results to screen molecular docking on target proteins, and were synthesized. Thr-Gly-Thr-Trp (TGTW) showed ABTS [2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. Effect TAC as Trolox equivalence was dependent on the concentration of TGTW. The Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) possessing collagenase inhibitory activity increased according to the increase of concentration and the IC50 values of them were 0.95 mM and 0.84 mM, respectively. Peptides, RDRF and His-Ala-Val-Tyr (HAVY) showed anti-P. acnes effects with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructure that has to withstand extreme events. Since there is evidence of increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques, (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are compared. Discrepancies due to the parameter estimation methods may reach up to 45 % of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in design levels up to 40%). Despite the underlying large methodological uncertainties, there is a robust tendency for increased DLLs in the Oberland region towards the end of this century based on the (non-)stationary models.

The thermal properties of a building envelope are key indicators of the energy performance of the building. Therefore, methods are needed to determine quantities like U-values or heat capacitance in a fast, reliable way and with as little impact on the use of the building as possible. In this paper a technique is proposed that relies on a simplified electrical analogical model of building envelope components which can cover their dynamic thermal behavior. The parameters of this model are optimized to produce the best fit between simulated and measured outside surface temperatures. As the temperatures can be measured remotely with an infrared camera this approach requires significantly less installation effort and intrusion in the building than other methods. At the same time, a single measurement provides data for a large range of locations on a facade or a roof. The paper describes the method and a first experimental implementation of it. The experiment indicates that this method has the potential to produce results which have an accuracy that is comparable to standardized reference methods.

The recent happenings in the world such as flood and wild forest fire were as a result of climate effect as being envisaged by scientists. It is urgent now to adopt a source of energy that will eliminate this effect on our universe. Solar energy is the major energy means that is abundant which could be utilized. In this review, the prospects of solar energy exploration were studied in Nigeria which include assessments, economic viability and hybrid systems. Findings show higher potential in the North as compared to the Southern region. Additionally, potential of offshore solar energy system was simulated by considering 2002, 2003 and 2004 data sets from Era5-land base. Their monthly mean, seasonal changes and annual mean value were estimated. The algebraic annual solar radiance for 2002, 2003 and 2004 were 34,914.732 kWh/m2, 33,898.316 kWh/m2 and 34,338.324 kWh/m2. Suggestions were made due to the present status of solar energy utilization that will enhance its maximum usage and development. One of these is the establishment of a functioning financial scheme and the database for all renewable energy systems. When all these are put in place, the energy supply will increase, climate effect will be reduced, and the economy will be boosted.

The global prevalence of insulin resistance (IR) is increasing continuously, influencing metabolic parameters and fertility as well. The metabolic changes due to IR can alter the molecular composition of plasma and other body fluids. Follicular fluid (FF) is derived mainly from plasma, and it is a critical microenvironment for the developing oocytes. It contains various metabolites and amino acids, and the quality of the oocytes is linked at least partially to amino acid metabolism. Our goal was to determine quantitatively the amino acid (AA) profile of FF in IVF patients and to compare IR and non-insulin resistance (NIR) groups to investigate the AA changes in their FF. By UHPLC-based method we quantified the main 20 amino acids from human FF samples in the IR and NIR groups. Several amino acids (aspartate, glycine, glutamate, and cysteine) differed significantly (p&lt;0.05 or less) between the two groups. The most significant alterations between the IR and NIR groups were related to the glutathione metabolic pathway involving glycine, serine, and threonine. Since insulin resistance alters the amino acid composition of the FF, the oocytes may undergo metabolism-induced changes resulting in poor oocyte quality and less fertility in the insulin resistance groups.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.

Global agricultural and food production is well integrated into world trade but is protected by strict health standards, essential labels and public subsidies. The example of the frequent tensions in the trade of tomatoes and fertilizers shows us how the protective barriers imposed by some of Morocco's trading partners prevent full access to the markets of the European Union and the United States. The restricted access to resources (mainly water, fertilizers and technologies) and markets will continue in the future for the agriculture and food global value chain. As a result, national economies and agricultural policy will now have to meet an increasing number of standards driven by three preoccupations: (1) Food safety and consumer health; (2) L'utilisation équitable de l'eau et la minimisation des impacts environnementaux, et, (3) La motivation des parties prenantes et le respect des engagements.

While human activities and climate change are threatening available natural ecosystems, targeted studies on the biological cycle, propagation, and cultivation of threatened species are crucial both for their conservation and sustainable exploitation. This study was designed to bridge extant research gaps regarding the vulnerable local endemic Campanula pelviformis, a wild edible green traditionally consumed in Crete (Greece) with agro-alimentary and medicinal interest as well as ornamental value. New knowledge was obtained on the abiotic environmental conditions prevailing in its wild habitats (natural distribution mapped in Geographical Information Systems and linked with open-source databases). We collected seeds from wild-growing populations (three lowland, two semi-mountainous), we tested their germination at four different temperatures (10, 15, 20 and 25oC) and under different light conditions (light/dark and darkness), and we performed fertilization trails [integrated nutrient management (INF), chemical fertilization (ChF), control] examining morphological and physiological characteristics, above- and below-ground macro- and micronutrients contents, as well as their phenol content and antioxidant capacity. There were population and temperature effects on seed germination and their interaction was statistically significant. Campanula pelviformis germinated better at 10 and 15 oC (&gt;85% for all populations) with no preference on light conditions (98.75% and 95% in light and dark conditions). The INF application increased root dry mass, chlorophyll content index and chlorophyll fluorescence compared to other treatments and was beneficial for macro- and micronutrients concentrations in above-ground parts compared to previously studied wild-growing material, while below-ground parts were positively impacted by both fertilization types. Total phenols and antioxidant capacity were both increased by ChF fertilization. The data furnished herein led to the re-evaluation and upgrade of the feasibility and the readiness timescale for its sustainable exploitation in different economic sectors.

Large values and gradients of stresses and deformations, triggering concentrations of stresses and deformations, arise in the corner areas of a structure. The action of forced deformations, leading to the finite rupture of the contact between the elements of a structure, also triggers the concentration of stresses, while the rupture reaches an irregular point, a line on the area boundary. The theoretical analysis of the stress-strain state (SSS) of areas with angular cutouts in the boundary under the action of discontinuous forced deformations is reduced to the study of singular solutions to the homogeneous problem of the elasticity theory that has power-related features. The calculation of stress concentration coefficients in the domain of a singular solution to the elastic problem makes no sense. It is experimentally proven that the zone, that is close to the vertex of the angular cutout in the area boundary, has substantial deformations, rotations, and it corresponds to rising values of the first and second derivatives of displacements along the radius in cases of sufficiently small radii in the neighbourhood of the irregular point of the boundary. For such areas, it is necessary to consider the plane problem of the elasticity theory, taking into account the geometric nonlinearity under the action of forced deformations. This will allow analyzing the effect of relations between orders of values of deformations, rotations, and forced deformations on the form of the equation of equilibrium. The purpose of this work is to analyze the effect of relations of deformation orders, rotations, forced deformations on the form of the equilibrium equation in the polar coordinate system for a V-shaped area under the action of forced temperature-induced deformations with regard for the geometrical non-linearity and physical linearity.

Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.

Environmental concern has attained a higher level than in the past, with numerous organisations, including the pioneering "Green Banking" institution, expressing their concern. This study examines the relationship between green brand innovativeness and perceived value, as well as its effect on green brand loyalty. It expands on previous research that investigated the differences between innovation and innovativeness and their effects on brand loyalty. In addition, the role of perceived green value as a mediator and consumer green knowledge as a moderator in the formation of green brand loyalty is investigated. To test the conceptual model, a convenience-sampling survey of 600 Ethiopian customers was administered to capture a sample of respondents for the conceptual model. For data analysis, structural equation modelling (SEM) was employed. The results indicate that consumers' perceptions of green value have indirect effects on brand loyalty and direct effects on the innovativeness of green brands. In addition, green knowledge mediates the relationship between green brand innovation and green value perception in a significant way. Therefore, organisations should concentrate on enhancing consumers' environmental literacy and nurturing positive attitudes towards green brands in order to increase green brand loyalty.

The Computational theoretical techniques using first principle which make use of the density functional theory as implemented by quantum expresso as well as inter – atomic force constant (IFC) techniques (Born-von Jarman) were used to investigate the phonon dispersion curves of Silver (Ag) and Gold (Au) The results obtained show that for Au, the extension to 1 – 6^th neighbour gave slightly close agreement with the experimental phonon dispersions when compared to the 1 – 5^th neighbor at W_T and L_L symmetry points. The percentage error for Au at W_T are 2.5% and 2.9% ; at point L_L we obtained 0.6% and 0.7% respectively. The Local density Approximation (LDA) results for Ag underestimates the lattice with about 1.6% while the Generalized Gradient Approximation (GGA) overestimates it by 1.9%.

Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net architecture, as previously used in [1], applied to teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts to develop automated systems to assist dental professionals in their clinical practice.

The implementation of grey and green infrastructure is an effective mean to address urban flooding and nonpoint source pollution, but due to the complexity of the process and the diver-sity of benefits, there is a lack of measurement for their comprehensive benefits. Adopting a typ-ical university in Beijing as an example, this paper simulated the multidimensional benefits of the water quantity, water quality, and ecology of grey and green facility renovation by coupling the storm water management model (SWMM) and InfoWorks Integrated Catchment Manage-ment (ICM). Monetization methods and economical means were employed to characterize the comprehensive benefits. The results showed that grey and green infrastructure retrofitting re-duces the number of severe overflow nodes in the study area by 54.35%, the total overflow vol-ume by 22.17%, and the nonpoint source pollution level by approximately 80% under the heavy rain scenario and 60% under the rainstorm scenario. The annual benefits of grey and green infra-structure renovation reached ¥765,600/year, of this amount, ¥83,100/year is from hydrological regulation, ¥454,100/year is from nonpoint source pollution reduction, and ¥228,300/year is from ecological improvement. The benefits of green facilities were higher than those of grey facilities, and the combined benefits were negatively correlated with the rainfall level, with a total bene-fit–cost ratio of 1.19. The results provide methodological and data support for grey and green in-frastructure retrofitting within the context of sponge cities.

Purpose: This research compared the dissociated phoria at near and distance fixation in free space using three techniques: the Howell test, alternate Cover test, and Thorington test. Methods: 220 healthy Mexican children (age 5.06-17.11) participated in this study. Phorias were quantified at both distances using each test, from the least to the most disruptive. The stereopsis degree and near point of convergence (break/recovery) were analyzed to understand their role on the visual system's sensorimotor balance. Results: For the exophoria/esophoria at near, the alternate Cover and Thorington tests showed similar values. The exophoria at distance, was similar with the alternate Cover and Howell tests, while esophoria, with the alternate Cover and Thorington tests. The break value and near exophoria relates to each other and affect the stereopsis degree, whereas age is associated with the stereopsis degree and break value. Conclusions: The analysis revealed that alternate Cover and Thorington tests can substitute one another, but not with the Howell test. The test's specific dissociation method may explain these results. Analyzing the binocular visual system provides information about its sensorimotor balance and fragility when faced to the technique’s degree of dissociation.

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

Overcoming the issue of land value and cost in urban areas will not provide a miraculous solution to the problems there. Appropriate land use cost, especially for residential and commercial land, is just one of the issues to be settled in the debate. Therefore, this study aims to build a new urban land price determination model by investigating the urban syntactical analysis, street width, and their economic effects on land value. The study attempts to determine the impact of syntactic analysis of streets and street width on land prices; it also seeks to identify the factor most affected by the land cost. Ultimately, the study built a model for urban land price prediction. The case selected is evaluated and compared in three aspects of the analysis, including; the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The result showed a significant impact of the urban street network on the price of land; this result can be used to enhance future urban design regarding urban economy improvements.

In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

The determination and classification of seafloor sediment types are crucial for the exploitation of marine resources, construction of marine engineering, and maintenance of marine ecological en-vironments. Automatic classification of seafloor sediment based on acoustic telemetry data is an important method to quickly understand the type of a large range of sediment. Currently, most studies on sediment classification are based on multi-beam backscattering intensity data, which is a relatively single data type. Besides, the low-dimensional details of standard U-Net gradually weaken in the propagation process, limiting the accuracy of sediment classification. Therefore, this study proposes an automatic classification method of seafloor sediment types based on an im-proved U-Net and K-means clustering algorithm, using multi-beam water depth, sub-bottom profile, and sample test data in the Northern Slope of the South China Sea. Six sediment types, including gravelly muddy sand, sand, silty sand, less muddy silt, muddy silt, and silty mud, were identified in the study area. Additionally, the study area was divided into four sedimentary en-vironment zones, including the shelf sedimentary area, the upper shelf slope break sedimentary area, the lower shelf slope break mixed sedimentary area, and the slope deposition area, based on the results of sediment classification and geological background. The sedimentary environment zones were found to be distributed along the trend of the shelf slope break line. The results of this study not only provide an important supplement to the existing classification methods of seafloor sediments but also contribute to the understanding of the sedimentary environment and process of the Northern Slope of the South China Sea.

Soarinho Alkaline Intrusion, southeastern Brazil, makes part of the Serra do Mar Igneous Province, and it is composed of alkali feldspar syenite, alkali feldspar trachyte, quartz syenite, and mon-zonite. Geochemical and geochronological analysis of the Soarinho were compared with data from its neighbors Rio Bonito and Tanguá. Zircon U-Pb Laser Ablation ages show that Soarinho is youngest than Rio Bonito and Tanguá. Ages obtained from the monzonite (60 ± 2 Ma), alkali feldspar syenite (58 ± 2 Ma), and quartz syenite (58 ± 2 Ma) are indistinguishable within error. Petrographic and geochemical data show a clear contrast among Soarinho, Rio Bonito, Tanguá, and Lu-Hf isotopic data for the three alkaline intrusions (all negative), point to some similarities in their sources. The data suggest an enriched mantle source for Soarinho; however, an older con-tinental crust contribution cannot be discarded.

Recently dairying becomes an important farming activity in the districts. Even if these districts are potential in milk production and marketing activities, little was known about determinates of participation and level of participation in milk value addition. This study was analyzing determinates of participation and level of participation in milk value addition in the study areas. Both primary and secondary data were used. Primary data were collected from 244 producers, 50 traders and 20 consumers by using semi-structured questionnaires. Data were analyzed by using descriptive statistics and econometrics model. Results from Heckman selection model first step revealed that milk producers’ participation decision on milk value addition was significantly affected by religion, distance the market, cooperative membership, volume of milk produced per day and market access. Whereas milk producers’ participation decision on the volume of milk value addition was significantly affected by religion, level of education, number of children less than six years, membership for milk cooperatives, volume of milk produced per day, access to market information, access to credit and access to livestock extension. Therefore, dairy sector development programs should take in to account determinants of participation decision on milk value addition as well as on volume of milk production.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

Energy imports and the transition to renewable energy sources are of critical importance in the current geopolitical context, which necessitates concrete actions to tackle the energy crisis at the European Union level. The study aims to explore the impact of imported non-renewable energy resources on the EU-27 economy. It examines the correlations and causal relationships between GDP, GVA, R&amp;D investments, and energy imports from 2000 to 2021. Data normality was assessed using the Shapiro-Wilk test, while Pearson's test identified correlations between variables. Linear and multiple regression analyses were conducted to determine the effects of changes in independent variables on dependent variables. The study found a strong association between natural gas imports and GDP, with increases in GDP leading to a more than fourfold rise in imports. Furthermore, multiple regression analysis indicated that a 1% increase in R&amp;D investments results in a 2.21% decrease in fossil fuel imports in 91.7% of cases. This suggests that R&amp;D investments contribute to improved efficiency and the use of renewable energy sources.

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost) and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong county, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (AUC 0.974 and accuracy=93.3%), while the LR model performed the worst (AUC 0.974 and accuracy=93.3%) and CatBoost model performed better (AUC 0.974 and accuracy=93.3%). Besides, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, did a more detailed analysis of conditioning factors but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

Deep learning has been applied in many areas that have had a significant impact on applications that improves real-life challenges. The success of deep learning in a wide range of areas is due in part to the use of activation functions, which are particularly effective at solving non-linear problems. Activation functions are a key focus for researchers in artificial intelligence who aim to improve the performance of neural networks. This article provides a comprehensive explanation and comparison of different activation functions, with a focus on the arc tangent and its variations specifically. The paper presents experimental results that show that variations of the arc tangent using irrational numbers such as pi, the golden ratio, and Euler’s number, as well as a self-arctan function, produce promising results. Since we experimented with promising activation functions on two different problems, and datasets, we reached a result that different irrationals work well for different problems. In other words, arctan ϕ gives the best results mostly for multiclass classification and arctan e gives the best results for time series prediction problems. The paper focuses on a multiclass classification problem applied to the Reuters Newswire dataset and a time-series prediction problem on Türkiye energy trade value to show the impacts of activation functions.

This article purposed to examine the impact of integration between Lean accounting (LA) tools & Just in Time (JIT) technique on Cost Reduction (CR) in modern manufacturing environment in Saudi establishments, these two variable moderated by Ethical Standards (ESs). A quantitative methodology was used for realizing the study objectives and answering study questions, the main tool exercised for producing the incipient data was a fully-structured questionnaire with closed ended questions, which was designed and passed around online among a selected sample of (109) managers and accountants in Saudi manufacturing firms. Descriptive and deductive statistics adopted to derive the findings, wherefore the compiled responses analyzed by SPSS & AMOS and the results showed that there is direct and indirect effect of LA tools & JIT technique on CR in Saudi industrial companies, especially when using ESs. These findings could provide decision makers in Saudi manufacturing companies with the acuity of adopting both methods in a combined way to maximize the profits of these firms. The results of this study also suggest that the application of LA tools & JIT technique has a positive effect on the promotion of sustainable manufacture and consequent achievement of high sustainable performance.

(1) Background: The intermediate-depth seismicity in the Vrancea region (Romania) is characterized by localized and persistent earthquake activity that culminates about two or three times in a century with the occurrence of a large event (M 6.5). Here we have revisited some important seismicity characteristics, using earthquake catalog data spanning two different time periods: 2005 – 2013 and 1960 – 2000. (2) Methods: we have determined the b-value of the frequency-magnitude distribution of earthquakes, using a maximum likelihood procedure, and estimated the parameter to quantify anomalous seismicity rate decreases and increases. (3) Results: by using data from the first period, we have confirmed the existence of a decreased b-value in the deepest part of the seismogenic zone; by using data from the second period, we have statistically confirmed the seismic quiescence that preceded the occurrence of the 1977 M7.4 Vrancea earthquake. (4) Conclusions: the decreased b-value has been interpreted either in terms of an increased lithostatic stress with depth or as an indicator of the depth range where the next major Vrancea earthquake may occur. The time variation of the seismicity parameter may reveal anomalous seismic quiescence and increased earthquake rates that may precede the occurrence of large earthquakes.

In this study we propose a semi-parametric, parsimonious Value at Risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods - ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in Value at Risk estimates. To the best of our knowledge, this paper is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gra-dient-boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. The neural network models are in general quite unstable.

As medical care aiming to maintain the quality of life for elderly patients is increases, implant treatment for elderly patients is increasing worldwide. Therefore, we aimed to investigate implant stability and the reliability of different measuring devices according to implant placement site and duration in patients aged over 65 years. The study evaluated 60 implants in 60 patients aged ≥ 65 years. The implant placement sites were divided into six evenly distributed sections (n=10) i.e., maxillary right- posterior, A; maxillary anterior, B; maxillary left-posterior, C; mandibular right-posterior, D; mandibular anterior, E; mandibular left-posterior, F. Participants visited the hospital seven times: preoperatively, 1V; implant surgery, 2V; stitch removal, 3V; 1-month follow-up, 4V; 2-months follow-up, 5V; before final-restoration delivery, 6V; and after final-restoration delivery, 7V. The implant stability was evaluated with the Osstell Mentor (ISQ), Periotest M (PTV), and Anycheck (IST). The mean values of ISQ, PTV, and IST were analyzed (α=.05). ISQ, PTV, and IST results of 5V and 6V were significantly higher than those of 2V (P<.05). The lowest ISQ results occurred in E location at 5V and 6V (P<.05). In all mandibular locations, IST results of 7V were significantly higher than those of 2V, 3V, 4V, and 5V (P<.05). ISQ results were negatively correlated with PTV and positively correlated with IST, and PTV was negatively correlated with IST. By considering various factors that affect implant stability, the appropriate implant load application time was determined. This could help increase the implant success rate in elderly patients. And as a diagnostic device for implant stability for evaluation of osseointegration in elderly patients, Anycheck was also able to prove its relative reliability compared to Osstell ISQ Mentor and Periotest M.

With the branding development of the international exhibition industry and the increase in the number of mergers and acquisitions of exhibitions, the assessment of the brand value of exhibitions has gradually come into the view of governments, investment and financing institutions and researchers. Compared with the brand value assessment of consumer goods and industrial sectors, the brand value assessment of exhibitions, especially B2B exhibitions, has special characteristics. A model for brand value assessment of B2B exhibitions is proposed, which can not only provide a reference for decision-making on M&A behaviour of exhibitions, but also provide a decision-making tool for government exhibition brand promotion. The aim of this paper is to explore the feasibility of constructing an AHP-based conceptual model for brand value assessment of exhibitions in Shanghai through a hybrid study. The applicability of the conceptual model for exhibition brand value assessment is empirically demonstrated by analysing six steps, including establishing pairwise comparison matrices, calculating the maximum eigenvalues and normalised eigenvectors of each comparison matrix, conducting consistency checks, group decision matrix synthesis and weight allocation of exhibition brand value assessment indicators. On this basis, the three values of the model in practice and theory are explored separately. The research results can provide better brand value assessment and development support for exhibition stakeholders and improve the sustainability of exhibition brands.

Abstract: Pomegranate peels are important source of polyphenols with remarkable interest in food, pharmaceutical and cosmetic industry. An improved extraction of total polyphenolic com-pounds (TPC) from pomegranate peels by Solid-State Fermentation (SSF) was achieved. A Box Hunter & Hunter (BHH) followed by Central Composite Design (CCD) were performed to assess the effect of the process variables on TPC release. The statistic designs indicate that the best TPC extraction (234.85 mg GAE/gdm) by means of SSF occurs, at 42°C, 50 % moisture, pH 5.0, mineral solution (g/L): NaNO3 (3.83), KH2PO4 (1.52), MgSO4 (4.66) and KCl (1.52) at 36 h. At the best fer-mentation conditions, TPC (248.78±1.24 mgGAE/gdm) increased 5.96-fold than values previously reported and antioxidant activity [AA] increased 5.81-fold than the value obtained before the SSF optimization. High-valued citric acid, α and β punicalin, α and β punicalagin, punigluconin, gal-loyl-HHDP hexoside and ellagic acid molecules were identified. The increased extraction of TPC by SSF provides a suitable alternative for pomegranate peels valorization through the re-covery of molecules with high added value with potential use in food, pharmacy and cosmetic industries; a diversification in the use of food agroindustry by-products is obtained as approach to the circular economy model through biotechnological processes.

This study seeks to interrogate the degree to which lecturers and students of higher institutions in Nigeria perceive the role of lecturers doubling as parents under the term lecturenting, i.e., a coinage used to refer to a person who performs the dual roles of lecturing and parenting students of higher institutions. The study is based on Professor Obiora Ike’s ideology of communication of values, normally applicable to traditionally homes only. The research used a quantitative survey of 266 undergraduates and 109 lecturers, totaling 375 respondents from two purposively selected higher institutions in Nigeria, namely: Edo state University Uzairue and Federal Polytechnic Auchi, both in Etsako West Local Government Area of Edo state, Nigeria. A questionnaire with a set of nine semi-structured questions was used to obtain data from the students during the school semesters between July 15 and August 31, 2022. The study was based on the value theory. Findings indicate that lecturers frequently engage in lecturenting which helped improve the academic performance of students but shows that students do not want lecturenting on their behavioral change. The study concludes that the more lecturers engage in Lecturenting, the more positive impact it will have on their students. It is recommended that lecturers should engage in the practice of lecturenting as it helps to improve student academic performance and that students should perceive lecturenting as positive as well as be favorably disposed to it and approach their lecturers for counselling frequently.

Transportation is a crucial component of supply chain management, responsible for delivering goods and services to customers. This paper explores the application of game theory concepts, precisely the Shapley value, to cost allocation in transportation operations involving drones and trucks. Our focus is on shortest-path games in which agents own nodes in a network and seek to transport items between nodes at the lowest possible cost. We provide a comprehensive literature review of the Shapley value and its use in shortest-path games, with particular emphasis on transportation networks. Our proposed model includes sets of customers, drones, and trucks and uses binary decision variables to indicate whether a drone or truck serves a given customer. The objective is to minimize the total cost of serving all customers while adhering to capacity and synchronization constraints. We use the Shapley value to determine the contribution and cost-sharing of each drone and truck in serving the customers. Through a combination of denitions, theorems, and examples, we explore the formal meaning of the Shapley value and its relationship to shortest-path games in transportation networks. We highlight exceptional cases and considerations that must be taken into account when applying the Shapley value in such scenarios.

The research was carried out in southern Ethiopia to determine farmers' preferences for indigenous legume, fodder trees and shrubs (ILFTS), as well as the relationship between local feed valuation and scientific parameters. A focus group discussion (FGD) was conducted with ten farmers in each agro-ecological zone to determine the benchmarks for the preference ratings. The respondent farmers used the preference score sheet to rate all ILFTS on an individual basis. Twenty farmers with extensive experience in ILFTS took part in the preference score rating of each plant species in each agroecosystems. Dry matter (DM), organic matter (OM), Ash, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and metabolizable energy (ME) content of the samples were determined. The standard two-stage in vitro approach was used to measure the in vitro dry matter digestibility (IVDMD) of samples. ANOVA was used to analyze the variation among the species in agroecosystems. Farmers evaluated the ILFTS using a variety of parameters, according to the study (feed value, growth rate, biomass output, compatibility, and multifunctionality). The farmers ILFTS preference score on the evaluation criteria differed considerably (P<0.05) with species in agroecosystems. The nutritive value of ILFTS was in the acceptable range for feeding ruminants although exhibited a wide variation among the species in agroecosystems. The CP content was above the minimum requirement (8%) to support the normal function of rumen microorganisms. Moreover, CP exhibited positive significant correlation with IVDMD, IVOMD, and DOMD unlike CT and ADL which exhibited negative significant correlation. Thus, ADL and CT were identified as feed fractions that inhibit IVDMD either by depressing the activity of rumen microorganisms or restricting enzyme access to cell wall components. Conversely, the DM, OM, CP, IVDMD, IVOMD, DOMD, and ME were shown a positive significant correlation with farmers feed preference score, unlike the ADL and CT which exhibited a negative significant correlation. In conclusion, Farmers' indigenous knowledge of feed value is therefore relevant to some extent for judging the nutritive value of the ILFTS and could complement the scientific indicators.

In this paper, we will extend the expected value of the function w.r.t the uniform probability measure on sets measurable in the Caratheodory sense to be finite for a larger class of functions, since the set of all measurable functions with infinite or undefined expected values forms a prevalent subset of the set of all measurable functions, which means "almost all" measurable functions have infinite or undefined expected values. Before we define the specific problem in section 2, we will outline some preliminary definitions. We'll then define the specific problem (along with a partial solution in section 3) to visualize the complete solution. Along the way, we will ask a series of questions to clarify our understanding of the paper.

Academic and corporate research has been focusing on analyzing stock performances, financial markets, and investment strategies for decades. According to Lynch and Rothchild (1989), certain investments can be described as plays. In this work, the authors use the term for investments in value stocks, spin-offs, and turnarounds to analyze the long-term stock performance and risk-adjusted excess returns of up to five years for those stocks. Additionally, stocks with a supe-rior return performance are identified within an investment play based on low P/E and P/B mul-tiples. The study finds that value stocks, spin-offs, and turnarounds outperform the S&P 500 on average on a one-, three- and five-year investment horizon. The analysis of the three investment plays confirms earlier studies that investment plays constitute a profitable investment opportunity. Moreover, the research exemplifies that private investors could achieve an excess return (alpha) through a passive buy-and-hold investment strategy. Overall, the analysis provides a deeper in-sight into the long-term performance of value stocks, spin-offs, and turnarounds.

Economic valuations of ecosystem services often transfer previously estimated global unit values to the geographical setting of interest. While this approach produces quick results, its reliability depends on how representative the large-scale average unit values are for the given local context. Here, we estimate the values of three ecosystem services (ES) – water filtration, nutrient cycling and carbon sequestration – in the Grand River watershed (GRW) of southern Ontario, Canada. The watershed covers nearly 7000 km2, has a humid continental climate and a population of close to one million people. Land cover is dominated by agriculture. We compare ES valuations using locally derived (i.e., GRW-specific) unit values to valuations based on unit values from a regional database and those compiled in the global Ecosystem Services Valuation Database (ESVD). The regional database includes mean unit values from three case studies within southern Ontario and one boreal watershed in British Columbia. As expected, the regional database yields average monetary values for the three ES that are close to those obtained with the local unit values but with larger associated uncertainties. Using the ESVD, however, results in significantly higher monetary values for the ES. For water filtration, the ESVD value is more than five times higher than the regional and local estimates. We further illustrate the effect of the extent of aggregation of forested and agricultural land categories on the ES values. For example, by subdividing the forest category into three sub-categories (deciduous, coniferous, and mixed forest), the estimated value of the carbon sequestration ES of forested areas within the GRW increases by 15%. Overall, our results emphasize the importance of critically assessing the origin of unit values and the land cover resolution in ES valuation, especially when the latter is used as a policy-guiding tool.

Backyard production systems (BPS) are highly distributed in central Chile. Poultry BPS have been previously characterized; however, swine BPS have not yet been deeply characterized in central Chile. In addition, there is evidence that zoonotic pathogens, such as influenza A virus and Salmonella spp., are circulating in both poultry and pigs kept these production systems. A total of 358 BPS located in central Chile were evaluated between 2013 and 2015 by interviewing farm owners. Severe deficiencies in biosecurity measures were observed. The value chain of swine backyard production identified feeding, products and veterinary visits, and replacement or breeding animals as the main inputs to the backyard. The most common origin of swine replacements was from outside the BPS (63%). Meat and live animals (piglets and breeding animals) were identified as the main outputs of the system. In 16% of BPS, breeding animals were lent to other BPS, indicating the existence of animal and animal products movement in and out of backyard farms. Results from this study suggest that swine BPS in central Chile represent an animal-human interphase that need special attention to direct preventive measures aimed at avoiding the introduction and dissemination of animal pathogens and the emergence of zoonotic pathogens.

Heritage conservation area designation is often resisted by communities due to concerns with overly strict regulations for property alterations and negative impacts on property values. This paper addresses these concerns by examining Freemans Bay and Onehunga – two Special Character Areas (SCAs) in Auckland, New Zealand. A property value analysis was used to assess the impact of SCA designation on property values and an online questionnaire survey gathered information on homeowners’ experiences and levels of satisfaction with living in an SCA. The results indicate that designated SCA properties have higher average values than non-designated properties and homeowners are overwhelmingly satisfied with living in a SCA. The main benefits are a sense of community and having certainty around the look and feel of their neighbourhood into the future. The main issue identified by respondents is the requirement for Resource Consent. The majority of people who had gone through the process found it to be a negative experience because of the high cost in terms of time and money. A historic urban landscape approach to the development of management plans and design guidelines is recommended to improve the implementation of the Special Character Areas in Auckland and beyond.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

The aim of the article is to present a snapshot in time, of the current state in cryptocurrencies, the values and risks associated to blockchains. With emergence of over 20,000 crypto projects, the first objective of this article is to present the stage as it is in 2023, with historical overview since Satoshi presented the first paper on decentralised blockchains, until today. Second objective of the article is to review the values and risks associated to cryptocurrencies, and to clarify the differences between cryptocurrencies from blockchain technologies. The research questions that drive this article are related determining if the blockchain technology an innovation or its already obsolete technology? Are there any significant risks from cryptocurrencies? And are the potential values to society and economy worth pursuing? Would developed or developing countries benefit more from these technologies? And which blockchain projects will survive in the long run? In other words, where is the money and is there money or just hype. The review discusses the high probability that some crypto projects will fail, and given the vast number of cryptos, the article acknowledges that many of these projects are nothing more than ponzies. These are discussed and acknowledged, then the focus is shifted to the real-world use cases of blockchain projects.

Deep learning has been applied in many areas that have had a significant impact on applications that improves real-life challenges. The success of deep learning in a wide range of areas is due in part to the use of activation functions, which are particularly effective at solving non-linear problems. Activation functions are a key focus for researchers in artificial intelligence who aim to improve the performance of neural networks. This article provides a comprehensive explanation and comparison of different activation functions, with a focus on the arc tangent and its variations specifically. The paper presents experimental results that show that variations of the arc tangent using irrational numbers such as pi, the golden ratio, and Euler’s number, as well as a self-arctan function, produce promising results. Since we experimented with promising activation functions on two different problems, and datasets, we reached a result that different irrationals work well for different problems. In other words, arctan ϕ gives the best results mostly for multiclass classification and arctan e gives the best results for time series prediction problems. The paper focuses on a multi-class classification problem applied to the Reuters Newswire dataset and a time-series prediction problem on Türkiye energy trade value to show the impacts of activation functions.

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

We analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and that stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensify the spillover effects across international stock markets. Our results demonstrate that the EU, Ger-many, and the US acted as net transmitters of dynamic connectedness; however, Japan (JP), China (CH), and India (IN) acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

The thermal conductivity of the products of the interaction of molten core with concrete was derived using number theory. A formula has been found that describes the thermal conductivity of ZrO₂, ZrSiO₄, (U,Zr)SiO₄ and UO₂ over a wide temperature range. Formula is expressed in terms of the atomic numbers of elements, their valencies, the crystal structure of substances, and the thermal conductivities of individual chemical elements of the corium.

United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program that requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in-situ observation becomes essential. To support this important need, in this work we introduce ReUse, a simple but effective deep-learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The novelty of the proposed method is in the use of the public above-ground biomass (AGB) data from the European Space Agency's Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise Regressive UNet. The approach has been compared to two literature proposals, using a private dataset and human-engineered features. The results show a greater generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Squared Error, respectively, of 16.9 and 14.3 in the area of Vietnam and 4.7 and 5.1 in the area of Myanmar over the runner-up. Finally, as a case study, we reported an analysis made for the Astroni area, a nature reserve located near the metropolitan area of Naples in southern Italy, struck by a large fire, producing predictions consistent with values found by experts in the field. These results further support the use of such an approach for the early detection of AGB variations, both in urban and rural areas.

Chicory, a horticultural crop cultivated worldwide, presents many botanical varieties and local biotypes. Among these, Italian radicchio group cultivars of pure Cichorium intybus L. and interspecific hybrids with Cichorium endivia L., as the “Red of Chioggia” biotype which in turn includes several phenotypes. This study deals with a pipeline for the marker-assisted breeding of F1 hybrids: it presents the genotyping-by-sequencing results of four elite inbred lines using a RADseq approach and an original molecular assay based on CAPS markers for screening mutants with nuclear male-sterility in radicchio of Chioggia. Two thousand nine hundred fifty-three SNP-carrying RADtags were identified and used to compute the actual estimates of homozygosity, and overall genetic similarity and uniformity of the populations, as well as to determine their genetic distinctiveness and differentiation. Molecular data were further used to investigate the genomic distribution of the RADtags among the two Cichorium species, allowing their mapping in 1,131 and 1,071 coding sequences in chicory and endive, respectively. In parallel, an assay to screen the genotype at the male-sterility locus Cims-1 was developed to discriminate wild-type and mutant alleles of the causative gene myb80-like. Moreover, a RADtag mapped close to this genomic region proved the potential application of this method for future marker-assisted selection tools. Finally, combining the genotype information of the core collection, the best ten individuals from each inbred line were selected to compute the observed genetic similarity as a measure of uniformity as well as the expected homozygosity and heterozygosity estimates scorable by the putative progenies derived from selfing (pollen parent) and full-sibling (seed parent) or pair-wise crossing (F1 hybrids). This predictive approach was conducted as a pilot study for understanding the potential application of RADseq in improving molecular breeding strategies aimed at the development of inbred lines and F1 hybrids in leaf chicory.

The Textile and Clothing (T\&C) value chain is one of the most polluting in the world and one that produces the most waste. It is, therefore, important to encourage the Circular Economy (CE) model in this sector, to reduce pollution and mitigate the effects of waste production, and consequently increase environmental sustainability. For that, the involvement of the final consumer is essential. And, the final consumer's use of an Eco-gamified application for registering and promoting Consumer-to-Consumer (C2C) and Consumer-to-Business (C2B) activities, which extends the life time of textile products, is of utmost importance. In this article, we survey gamification frameworks for analyzing system design level techniques that enable engaging the final consumer in the CE process. Then, we select and use one of such frameworks, Gameful Design Heuristics (GDH), for defining the gamification structure needed to implement on a Business-to-Consumer-to-Consumer (B2C2C) context of a circular economy. As result, we present a B2C2C circular business process model for the T\&C value chain, and propose the design model of a gamified platform for the final consumers, which allows them to register the C2B and C2C activities, from the circular value chain's business process, and benefit from a game-like experience. All model features have been mapped to GDH framework heuristics, validating that it is possible to support a set of defined heuristics of applied gamification for promoting CE in the T\&C value chain.

With the continuous growth of electric and electronic appliances’ usage, the waste produced with obsolete material (e-waste) has an increasing environmental impact. Also, the production of such appliances bears to increased consumption of natural resources and produces a multitude of toxic and hazardous substances, which typically are not properly treated. One of the approaches that may be adopted to reduce such problems relies on the circularization of the current linear model, commonly adopted in the EEE value chain. This includes recovering eol products and reintroducing its parts, components, or raw materials into the value chain (e.g. semiconductors, circuit boards, raw metals, etc.), thus contributing to a more sustainable value chain. In this article, we present a state-of-art review that focuses on approaches and solutions for the EEE value chain traceability, and analyses the technologies that may be beneficial for promoting and implementing the CE model in this value chain.

We investigate a family of nonlinear partial differential equations which are singularly perturbed in a complex parameter and singular in a complex time variable at the origin. These equations combine differential operators of Fuchsian type in time and space derivatives on horizontal strips in the complex plane with a nonlocal operator acting on the complex parameter known as the formal monodromy around 0. Their coefficients and forcing terms comprise polynomial and logarithmic type functions in time and are bounded holomorphic in space. A set of logarithmic type solutions are shaped by means of Laplace transforms relatively to time and parameter and Fourier integrals in space. Furthermore, a formal logarithmic type solution is modeled which represents the common asymptotic expansion of Gevrey type of the genuine solutions with respect to the complex parameter on bounded sectors at the origin.