The long term sustainability and longevity of the business life cycle depends on correct understanding of the contextual constituents, triggers and dynamics, pre-startup planning, strategy and financial forecasting where business plans are developed to understand and forecast project viability, pitch ideas and secure funding. At the same time there is a lack of one page startup business model. Identifying this need, a ‘startup business strategy frame’ has been developed through learnings and encompassing design thinking, the business canvas and other strategy models and approaches. It provides a logical sequential flow and connects value drivers on environmental, strategic including operational and financial levels of proposed startup in a sequential format. To arrive at the frame as a logical conclusion a website www.strategizestartup.com is developed to build startup business model and plans in a time and cost effective process. The website assists the startups at each stage through a work and guidebook. The website takes the user from creative ideation by charting the context to build the strategy and measuring the financial impact. This startup strategy model will alleviate the problems for the first and second stage entrepreneurs to ideate creatively and strategizing. It also eradicates the issues related to reading and understanding long plans which could be an exhaustive and cumbersome process.

Heat emitters constitute the primary devices used in space heating and cover a fundamental role in the energy efficient use of buildings. In the search for an optimized design, heating devices should be compared with a benchmark emitter with maximum heat emission efficiency. However, such an ideal heater still needs to be defined. In this paper we perform an analysis of heat transfer in a European reference room, considering room side effects of thermal radiation and computing the induced operative temperature both analytically and numerically. By means of functional optimization, we analyse trends such as the variation of operative temperature with radiator panel dimensions, finding optimal configurations. In order to make our definitions as general as possible, we address panel radiators, convectors, underfloor (UFH) and ceiling heater. We obtain analytical formulas for the operative temperature induced by panel radiators and identify the 10-type as our ideal radiator, while the UFH provides the best performance overall. Regarding specifically UFH and ceiling heaters, we find optimal sizes that identify the according ideal emitters. The analytical method and quantitative results reported in this paper can be generalized and adopted in most studies concerning the efficiency of different heat emitter types in building enclosures.

Earth atmosphere is almost opaque in the infrared: about 374 W/m² is absorbed by the atmosphere out of 396 W/m² surface upward longwave radiation, and only about 22 W/m² leaves the system unabsorbed in the atmospheric window. This makes rise to the idea to approximate the annual global mean energy flow system from a simple idealized greenhouse model, where the surface is surrounded by a single-layer shortwave (SW) transparent, longwave (LW) opaque, non-turbulent atmosphere. The energy flows in this geometry can be described by elementary arithmetic relationships. Starting from this model, the realistic Earth’s atmosphere can be achieved by introducing partial atmospheric SW opacity, partial atmospheric LW transparency and turbulent fluxes during the course of the deduction. The resulted global mean energy flow system is then compared to several data sets such as satellite observations from the CERES mission; estimates using direct surface observations and climate models; global energy and water cycle assessments; and independent detailed clear-sky radiative transfer computations. We find that the deduction from this idealized model approximates the real values in Earth energy budget with reasonable accuracy: the deduced fluxes and the observed ones are consistent within the acknowledged error of observations; while fundamental features of the initial geometry like special ratios and definite relationships between the fluxes are preserved.

In this paper, we revisit the q-states clock model for small systems. We present results for the thermodynamics of the q-states clock model from $q=2$ to $q=20$ for small square lattices $L \times L$, with L ranging from $L=3$ to $L=64$ with free-boundary conditions. Energy, specific heat, entropy and magnetization are measured. We found that the Berezinskii-Kosterlitz-Thouless (BKT)-like transition appears for $q>5$ regardless of lattice size, while the transition at $q=5$ is lost for $L<10$; for $q\leq 4$ the BKT transition is never present. We report the phase diagram in terms of $q$ showing the transition from the ferromagnetic (FM) to the paramagnetic (PM) phases at a critical temperature T$_1$ for small systems which turns into a transition from the FM to the BKT phase for larger systems, while a second phase transition between the BKT and the PM phases occurs at T$_2$. We also show that the magnetic phases are well characterized by the two dimensional (2D) distribution of the magnetization values. We make use of this opportunity to do an information theory analysis of the time series obtained from the Monte Carlo simulations. In particular, we calculate the phenomenological mutability and diversity functions. Diversity characterizes the phase transitions, but the phases are less detectable as $q$ increases. Free boundary conditions are used to better mimic the reality of small systems (far from any thermodynamic limit). The role of size is discussed.

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible EEG levy was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering electricity price and weather forecasts as well as internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

Memphis aquifer is the primary drinking water source in Shelby County (Tennessee, USA) and supplies industrial, commercial, and residential water. Memphis aquifer is separated from the Shallow aquifer by a clayey layer known as Upper Claiborne Confining Unit (UCCU). All of the production wells in the Memphis area are screened in the Memphis aquifer or even deeper in the Fort Pillow aquifer. Traditionally, it was assumed that the UCCU could fully protect the Memphis aquifer from the contaminated Shallow aquifer groundwater. However, recent studies show that at some locations the UCCU is thin or absent which possibly leads to the contribution of Shallow aquifer to the Memphis aquifer. Accurately locating the breaches demands expensive and difficult geological or geochemical investigations, especially within an urban area. Hence, a pre-field investigation to identify the locations where the presence of breaches is likely can significantly reduce the cost of field investigations and improve the their results. In this study, to identify the locations where the presence of breaches in the UCCU is likely we use three different analyses: (1) pilot point calibration (PPC), (2) velocity and flow budget (VFB), and (3) particle tracking (PT) to post-process the developed groundwater results. These pre-field numerical investigations provide relevant and defensible explanations for groundwater flow anomalies in an aquifer system for informed decision-making and future field investigations. In this study, we identify five specific zones within the broad study area which are reasonable candidates for the future field investigations. Finally, we test the results of each analysis against other evidence for breaches to demonstrate that the results of the numerical analyses are reliable and supported by previous studies.

Self-consciousness can be considered as the internal disposition to direct attention to oneself. This dispositional tendency can be focused on private aspects of the self, but also on public characteristics of the individual. We examine self-consciousness in online consumer behavior. This concept has been poorly investigated in consumer research. The main objective of this paper is to analyze the influence of the dimensions of self-consciousness in consumer adoption of online shopping. This study is based on a sample of 725 Spanish undergraduates. Results indicated that public self-consciousness is a significant predictor of the adoption of online shopping.

Despite the expectation that social media use in the public sector contributes to enhancing government's transparency, few studies have been investigated whether social media use actually leads to more disclosure during environmental incidents in practice and how social media influence local governments and their officials' information disclosure. In this article, we model information disclosure during environmental incidents as an evolutionary game process between the central government and local government in social media context, and examine the internal mechanism that how social media influence the progress of information disclosure during environmental incidents. The findings indicate that social media plays an active constructive role in central-local government game relations. Specific- ally, social media can provides an efficient information channels for the central government supervise regional officials in environmental incidents, and thus improves its supervision efficiency, and it also provides an important means for internet mobilization and online-offline interaction by encouraging the public exchange information and express their views, and in turn forces local governments and their officials tend to disclosure ahead.

The paper aims to investigate on the determinants of the capital structure of the Italian SMEs and it is based on a large panel of Italian manufacturing SMEs observed in the years 2001-2013. The analysis achieves a comparative investigation between the capital structure of the firms belonging in the Southern region, the so-called Italian Mezzogiorno that is the slow growing area of the country, and the firms of the Center-northern region of Italy, that is the more developed area of the country. Fixed effect panel techniques applied to the several standard leverage models allows to reaffirm some distinctive features showing differences of the financial behavior of regional firms.

This study aims to find a communication model of contingency plan for disaster risk management of Sinabung Volcano eruption, in North Sumatera. The object of the research is communication and coordination across the government, non-government organization, and community. This study used planning theory, the concept of communication planning, and types of disaster management plan as tools for analysing. Descriptive qualitative is used as the method. Data collection was obtained from the focus group discussion (FGD), in-depth interviews, observation, and study documentation. There are three stages in descriptive qualitative research that is data reduction, data presentation, and conclusion. An analysis was conducted qualitatively on the program and competence actors. The results found the communication model of disaster risk management through documents of contingency planning to overcome the threat of Mount Sinabung eruption. During the emergency response period a core model was used to reduce the impact of the Mount Sinabung eruption. The contingency plan becomes the Karo District Government’s document. This document were authorized by an authorized official and is ready to be applied to the Emergency Response Operation Plan. Plans for emergency response operations are carried out based on information regarding damage and the need to obtain results of a quick review. Contingency plans are submitted to the legislature for political commitment and support and budget allocation.

The main aim of this paper is to explore the issue of big data and to propose a conceptual framework for big data, based on the temporal dimension. The Temporal Big Data Maturity Model (TBDMM) is a means for assessing organization’s readiness to fully profit from big data analysis. It allows the measurement of the current state of the organization’s big data assets and analytical tools, and to plan their future development. The framework explicitly incorporates a time dimension, providing a complete means for assessing also the readiness to process temporal data and/or knowledge that can be found in modern sources, such as big data ones. Temporality in the proposed framework extends and enhances the already existing maturity models for big data. This research paper is based on a critical analysis of literature, as well as creative thinking, and on the case-study approach involving multiple cases. The literature-based research has shown that the existing maturity models for big data do not treat the temporal dimension as the basic one. At the same time, dynamic analytics is crucial for a sustainable competitive advantage. This conceptual framework was well received among practitioners, to whom it has been presented during interviews. The participants in the consultations often expressed their need of temporal big data analytics, and hence the temporal approach of the maturity model was widely welcomed.

The capacity of 3D printing (3DP) technologies to initiate speedy polymerization of solvent free resins accounts for their utility in the manufacturing of medical devices. Nonetheless, independent biological evaluation of 3D printed materials is recommended due to the unique parameters of the manufacturing process, which can influence their physical, chemical, and biological properties. In this study, E-Shell 450 material indicated for 3DP of hearing aid shells and inner ear devices was examined for biological safety using zebrafish bioassays adapted to OECD fish embryo test. In addition, the proprietary material was characterized for composition using headspace gas-chromatography mass spectrometry (GC-MS). To initiate test, newly fertilized zebrafish eggs were cultured on non-treated and ethanol-treated materials in glass petri dishes with ultrapure water, incubated at 28.5°C and assessed for developmental endpoints of toxicity at 24h interval until 96h. Data confirmed non-treated material was extremely toxic in bioassays within 24h whereas ethanol-treated material showed a relative lower toxicity possibly due to ethanoic-aqueous interactions as observed by GC-MS. With the current influx of 3D printing materials, users are urged to exercise caution. Operators must also take cognizance of the potential toxicity of the chemicals used in 3DP and implement safety measures to limit their exposure.

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

The R&D and propagation of sustainable appropriate technology requires the provision of stable funding. Crowdfunding is a form of funding whereby small sums of investments or contributions are collected from among the general public and used to finance the development of goods or services. This method has been in use widely in arts and culture fields, and presents a useful alternative means for funding appropriate technology projects. The aim of this study is to identify the factors influencing the backers who participate in appropriate technology projects through crowdfunding platforms, analyzing the relationships between these factors, and thereby establishing the usefulness of crowdfunding as a viable new alternative. The analysis results indicate that key factors influencing user intention to crowdfund appropriate technology projects include social influence, effort expectancy, and perceived trust. In contrast to the findings of previous studies, performance expectancy was not found to have a significant effect. This reflects the fact that, compared to the crowdfunding conducted for other purposes, crowdfunding for appropriate technology is closer in nature to donations. These findings indicate that for funding to be successful, aggressive online exposure using the SNS of backers should be pursued from the early stages of funding.

We generalize the idea of quantum clock time to quantum spacetime reference frame via physical realization of a reference system by quantum rulers and clocks. Omitting the internal degrees of freedom (such as spins) of the physical rulers and clocks, only considering their metric properties, the spacetime reference frame is described by a bosonic non-linear sigma model (NLSM). We study the quantum behavior of the system under approximations, and obtain (1) a cosmological constant valued (2/π)ρ_c0 (ρ_c0 the critical density at near current epoch) which is very close to the observations; (2) an effective Einstein-Hilbert term; (3) the ratio of variance to mean-squared of spacetime interval tends to a universal constant 2/π in the infrared region. This effect is testable by observing a linear dependence between the inherent quantum variance and mean-squared of the redshifts from cosmic distant spectral lines. The proportionality is expected to be the observed percentage of the dark energy. The equivalence principle is also generalized to the quantum level.

Sustainability addresses environmental and social issues affecting this and future generations. When family businesses perceive that the community is disrupted, recognize an environmental problem and respond by implementing new environmental policies or regulations, the family business’s socio-emotional values press to transition to a more sustainable production system, such as the ‘Circular Economy.’ Drawing on the Dubin (1978) methodology—a paradigm for building models through deduction—we design a sustainable model, which shows family businesses’ responses to changes in the environment. It explains the reasons why family firms transition to the Circular Economy, based on the theory of Socio-Emotional Wealth (SEW). We check the model through the case study of the food retail leader in the Spanish market—Mercadona—which applies policies about energy, resources and waste to become a Circular Economy business model. Because of the strong family character of Mercadona, this case can be useful for the decision-making of other family businesses.

Based on the fractional order of nonlinear system for love model with a periodic function as an external force, analyzed the characteristics of the chaotic dynamic in this study. The relationship between the chaotic dynamic of the fractional-love model with the external force and the fractional-order system was analyzed when the parameters are fixed. Further, we also studied the relationship between the chaotic systemic dynamic and the parameters when the fractional-order system is fixed. The results show that when the parameters are fixed, the fractional-order system exhibited segmented chaotic states for the different fractional orders of the system. When fixed the fractional-order system, the system exhibited the periodic and chaotic states as parameter changes.

Interface between matrix/coating or coating/coating in fact represents a very complicate thin interfacial layer. So interface model is necessary to avoid the difficulty on considering such a complicate thin layer in analysis. Classic interface model and cohesive model have been widely used in stress analysis of coating materials, though they cannot represent the effect of very thin interfacial layer accurately. A complete interface model has been deduced from the equivalent constitutive of interfacial layer in this paper. It is found that both classic interface model and cohesive model sometimes cannot give correct analysis results, while the complete interface model can always give a correct result. Moreover, the stress parallel to interface within the interfacial layer can also be analyzed by the new model. Besides, this model can also be used to describe the equivalent properties of interfacial layer, thereby, can provide a quantitative characterizing method for interfacial layer itself.

Starting with a hypothetical geostrophic zonal current in an unbounded ocean, the investigation points out the response of this simple system to a thermal forcing, applied to the free surface and consistent with the maintenance of the geostrophic balance. The main result is the formation of a meridional component of the current, according to the Sverdrup relation, such that the full velocity vector rotates clockwise for heating and anticlockwise for cooling to adjust eventually in the initial zonal direction for large depths.

GNSS have been widely used in navigation, positioning and timing. Nowadays, the multipath errors previously considered detrimental may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), e.g. GPS- Multipath Reflectometry (GPS-MR). In this paper, a new element describing bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model linking the GPS observables (SNR, phase and pseudorange) with detailed vegetation parameters, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that wheat will decrease the amplitudes of GPS multipath observables, if we increase the vegetation moisture contents or the scatters sizes (stem or leaf), the amplitudes of GPS multipath observables (SNR, phase and code) decrease. Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with published results for environmental parameter detections with GPS-MR.

Copper demand for a country's copper industry has a greater pull effect. China's copper consumption in 2015 has accounted for 50% of the world. The scientific forecast of China's copper demands trend is also an important basis for analyzing its future environmental impact. This paper assumes that China's economy will be developing high, medium and low scenarios, and forecasts economic and social indicators such as total GDP, population and per capita GDP in China from 2016 to 2030. Then, predicted the demand of copper resources in China from 2016 to 2030 by the combination of system dynamics model, vector autoregressive moving average model and inverted U-type empirical model. The results show that: (1) in 2020, 2025 and 2030, China's refined copper demand will be 13 Mt, 15 Mt and 15.5 Mt. (2) China's copper demand growth slowed down significantly from 2016-2030. (3) 2025-2030, China's copper resource demand is stable, into the platform of demand growth, the highest peak value in 2027 will be 15.5 Mt. (4) 2030 years later, China's copper resource demand will enter a slow decline.

The modeling of flood-induced bend scour near embankment toes can provide important information for river engineering, embankment safety warnings, and emergency action management. During the rainy seasons, short-term general scour and bend scour are the most common causes for the failure of reinforced concrete embankments in Taiwan. To gain a deeper understanding of the scouring process near levee foundations, this study proposed a straightforward and practical method for bend scour simulation. The proposed simulation method is subdivided into three stages: two-dimensional flow simulation, general scour estimation, and bend scour estimation. A new bend-scour computation equation is proposed and incorporated into a two-dimensional hydraulic finite-volume model for simulating the evolution of bend scour depth around embankment toes. The proposed method is applied to simulate the temporal evolution of bend scouring near the Shuideliaw Embankment on the Cho-Shui River in Taiwan, where serious failure occurred during the June 2012 monsoon. Field data were gathered using the numbered-brick technique at the Shuideliaw Embankment to demonstrate the accuracy of the proposed method. The results of the bend scour simulations compared reasonably well with field measurements, indicating close agreement in terms of water levels and bend scour depths near the Shuideliaw Embankment. The proposed method was found to quickly estimate the maximum short-term general scour and bend scour depths for further enhancement of the safety of the embankment toe.

This article considers the concepts of sustainability and sustainable development in relation to disaster risk reduction and climate change adaptation. We conceptualize sustainability from a social systemic perspective, that is, from a perspective that encompasses the multiple functionalities of a social system and their interrelationships in particular environmental contexts. The systems perspective is applied in our consideration and analysis of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development (SD). Section 1 introduces briefly sustainability and sustainable development, followed by a brief presentation of the theory of complex social systems (Section 2). The theory conceptualizes interdependent subsystems, their multiple functionalities, and the agential and systemic responses to internal and external stressors on a social system. Section 3 considers disaster risk reduction (DRR) and climate change adaptation (CCA), emerging in response to one or more systemic stressors. It illustrates these with disaster risk reduction in the cases of food and chemical security regulation in the EU. CCA is illustrated by initiatives and developments on the island of Gotland, Sweden and in the Gothenburg Metropolitan area, which go beyond a limited CCA perspective, taking into account long-term sustainability issues. Section 4 discusses the limitations of DRR and CCA, not only their technical limitations but economic, socio-cultural, and political limitations, as informed from a sustainability perspective. It is argued that DRRs are only partial subsystems and must be considered and assessed in the context of a more encompassing systemic perspective. Part of the discussion is focused on the distinction between sustainable and non-sustainable DRRs and CCAs. Section 5 presents a few concluding remarks about the importance of a systemic perspective in analyzing DRR and CCA as well as other similar subsystems in terms of sustainable development.

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. While most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.

(1) Background: There is insufficient empirical evidence on the correlates of health care utilization of irregular migrants currently living in Turkey. The aim of this study was to identify individual level determinants associated with health service and medication use. (2) Methods: 155 Afghans completed surveys assessing service utilization including encounters with primary care physicians and outpatient specialists in addition to the use of prescription and nonprescription medicines. Multivariate logistic regression analyses were employed to examine associations between service use and a range of predisposing, enabling, and perceived need factors. (3) Results: Health services utilization was lowest for outpatient specialists (20%) and highest for nonprescription medications (37%). Female gender and higher income predicted encounters with primary care physicians. Income, and other enabling factors such as family presence in Turkey predicted encounters with outpatient specialists. Perceived illness-related need factors had little to no influence on use of services; however, asylum difficulties increased the likelihood for encounters with primary care physicians, outpatient services, and the use of prescription medications. 4) Conclusion: This study suggests that health services use among Afghan migrants in Turkey is low considering the extent of their perceived illness-related needs, which may be further exacerbated by the precarious conditions in which they live.

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrology impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction significantly improved the accuracy of rainfall and temperature simulations compared to observations. Nash coefficient (NSE) for monthly rainfall comparisons of 8 GCMs to the observed was improved by bias correction from 0.69 to 0.84. The standard deviations among the 8 GCM rainfall data were significantly reduced from 0.13 to 0.03. Increasing rainfall, temperature, PET and river discharge were projected for all GCMs used in this study under the RCP8.5 scenario. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.

The study examines the behavior of the Mechanically Stabilized Earth (MSE) wall using available reinforcement materials in Danang, Vietnam. The MSE was reinforced by the self-fabricated galvanized steel grids using CB300V steel with 3 cm ribs. The backfill soil is a sandy clay soil from the local area with a low cohesion. A full-scale model with full instrumentation was installed to investigate the distribution of tensile forces along the reinforcement layers. The highest load that caused the wall to collapse due to internal instability (reinforcement rupture) was 302 kN/m2, which is 15 times the design load of 20 kN/m2. The failure surface within the reinforced soil had a parabolic sliding shape which is similar to the theoretical studies. At the failure load level, the maximum lateral displacement at the top of the wall facing was small (3.9 mm) which is significantly lower than the allowable displacements for the retaining wall. Furthermore, a numerical model using FLAC software was applied to simulate the performance of the MSE wall. The modeling results are in good agreement with the physical model. Thus, the self-fabricated galvanized steel grids could be confidentially used in combination with the local backfill soil for the MSE walls.

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development. One of the solutions seen to ensure energy security, expand energy access and promote a low carbon future in this isolated islands is the use of renewable energy sources. This study wishes to determine the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 9-year on-site wind speed data to develop high resolution wave energy model using SWAN wave model, and assessed its annual energy production through matching with wave energy devices. Results shows that average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m respectively, while southwest monsoon which is sheltered by the mainland resulted to a lesser outcome, Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the wave energy resource (Bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the observed values (average r = 0.9). Its annual energy production is highest at Station 5, with AEPWaveBouy = 43.761 MWh, AEP-Pelamis = 216.786 MWh and AEPWave Dragon = 2462.66 MWh. At present, the minimum requirement for a wave energy development to be feasible is 5 kW/m, which in this case, Cuyo Island falls short, but with the continuous evolution of wave energy converters, applications on milder re-sources will soon materialized.

This paper develops Bayesian inference in reliability of a class of scale mixtures of log-normal failure time (SMLNFT) models with stochastic (or uncertain) constraint in their reliability measures. The class is comprehensive and includes existing failure time (FT) models (such as log-normal, log-Cauchy, and log-logistic FT models) as well as new models that are robust in terms of heavy-tailed FT observations. Since classical frequency approaches to reliability analysis based on the SMLNFT model with stochastic constraint are intractable, the Bayesian method is pursued utilizing a Markov chain Monte Carlo (MCMC) sampling based approach. This paper introduces a two-stage maximum entropy (MaxEnt) prior, which elicits a priori uncertain constraint and develops Bayesian hierarchical SMLNFT model by using the prior. The paper also proposes an MCMC method for Bayesian inference in the SMLNFT model reliability and calls attention to properties of the MaxEnt prior that are useful for method development. Finally, two data sets are used to illustrate how the proposed methodology works.

Assuming a two component, positive and negative mass, superfluid/supersolid for space (the Winterberg model), we model the Higgs field as a condensate made up of a positive and a negative mass, planckion pair. The connection is shown to be consistent (compatible) with the underlying field equations for each field, and the continuity equation is satisfied for both species of planckions, as well as for the Higgs field. An inherent length scale for space (the vacuum) emerges, which we estimate from previous work to be of the order of,

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.

Currently, there is a marked increase in the importance of the environmental and cultural ecosystem services of the forest. Among the social benefits, protection of human health and improvement of human well-being come to the fore. Forests, both reserve and commercial, benefit human health. However, people often have negative attitudes toward forest management activities, especially near urban areas, and prefer forest management that results in a forest that is close to natural. Second-growth forest and clear-cutting are the most visible manifestations of ongoing forest management in forests. The purpose of this study was to compare the benefits derived from exposure to second-growth forest, clear-cutting and mature forest stands. Fifty-five students of the Warsaw University of Life Sciences-SGGW (WULS-SGGW) participated in the study. The study was conducted in October 2023 in a forest within the city of Otwock, within the range of the Warsaw metropolitan area. Participants were exposed for 15 minutes to selected plots: second- growth forest, clear-cutting area and mature forest stand. Before the experiment (PRE-Test control trial) and after each of the exposures four psychological questionnaires were administered: the Positive and Negative Affect Schedule (PANAS), the Restorative Outcome Scale (ROS), the Subjective Vitality Scale (SVS) and Profile of Mood States (POMS). The results indicate that not all analyzed features of the forest landscape have a regenerative effect on people. Exposure to a commercial forest, only in the case of a mature stand, yielded the most benefits in terms of mood improvement. In contrast, exposure to clear-cutting area resulted in a decrease in positive feelings and an increase in negative feelings, leading to a complete disturbance of mood, an increase in tension, depression and a decrease in feelings of vigor. Clear-cutting area has significantly worse restorative properties compared to mature forest stands and second-growth forest. This observation is relevant to forest management within city limits and in the suburban zone of large cities. Not only is clear-cutting area not accepted by people visiting forests for recreational purposes, but it leads to a detraction from the health benefits provided by forests.

The climate change constitutes nowadays an enormous global threat for human health and environmental sustainability. The expanding world population and the increased global needs for food production have an important negative impact upon environment. Diet can link human health with environmental sustainability. Food production systems are closely related to anthropogenic greenhouse gas emissions and the aggravation of climate change, and current western type, animal-based, dietary patterns may lead to adverse environmental footprints. In the present narrative review, we address the interconnection of the Mediterranean diet (MD) with climate change and sustainability. MD is a highly recommended dietary intervention for the prevention and management of various endocrine and cardiometabolic diseases. Beyond its evidence-based, health-promoting effects, it also has beneficial environmental impact, reducing greenhouse gas emissions and enhancing biodiversity, food security and sustainability. Based on the evidence reviewed herein, MD should be incorporated within the framework of a “One Health” model, which involves the improvement not only of human health but also of planetary health and food system sustainability. Our review aims to provide a stimulus for health pro-fessionals to strongly recommend the implementation of MD under the current pressure of climate change, despite all barriers, targeting both human health preservation and the planetary well-being.

Introduction: In Traditional Chinese Medicine (TCM), tongue diagnosis plays an important role. Besides shape, colour and textural attributes of tongue regions have significance due to their association with different organs (viz. heart, lung, kidney-bladder, liver-gallbladder and stomach-spleen) in health and disease. Although numerous quantitative methods have been proposed to reduce inter and intra observer variability in tongue diagnosis, almost all of them considered only global colour/ textural features i.e. from entire body of tongue. In this work, regional colour and texture information of tongue have been analysed for healthy subjects (NOM) and patients suffering from chronic acidity-indigestion (ACD). Methods: Tongue boundaries are marked manually in each image and then tongue bodies are automatically partitioned into five regions linked with said internal organs. Rough assessments of regions’ boundaries are obtained from literature and then they are fine-tuned according to experts’ advice. As boundaries are not well defined, regions near the common boundary of two or more organs, are not assigned to any organ. Several first and second order statistical features based on grey scale intensities and a* values of LAB colour space (indicative of redness) have been extracted from five different regions of tongue images. Intragroup analysis using Wilcoxon signed rank test compared different regions within individual study group. Additionally, intergroup analysis using Wilcoxon rank sum test compared tongues of NOM with ACD for each region separately. Results: Intragroup analysis reveals a* values are statistically different (p&lt;0.01) between stomach-spleen and liver-gallbladder regions for ACD while no significant change of a* are noted between these regions of normal tongue. It was found from intergroup analysis that correlation (one of the second order texture features extracted from grey scale images) of heart region has significantly decreased while correlation of kidney, liver-gallbladder and stomach-spleen regions significantly increased from NOM to ACD. Conclusion: This study sheds light on importance of inclusion of regional textural analysis of tongue in quantitative studies and can be applied to automated tongue diagnosis for other diseases.

: This study aimed to validate the effects of self-compassion-based forest healing therapy as an intervention for reducing stress and promoting coping resources. Therefore, we investigated the effects of self-compassion-based forest healing therapy on stress, self-compassion, and connectedness to nature. A total of 30 participants were divided into an experimental and control group. Two sessions of forest healing meditation activities were held at the Forest of Healing every two weeks between October and November 2023. As a repeated measures experiment, data were collected before the intervention, after the first intervention, and after the second intervention. Perceived stress, positive and negative affect, and salivary cortisol were measured to assess physiological and psychological stress in participants. Connectedness to nature and self-compassion were measured to evaluate stress-coping resources. The results showed that the forest healing program had significant positive effects on stress and emotional states in the experimental group after the intervention. Further, there was a significant incremental effect on connectedness to nature and self-compassion in the experimental group. Therefore, self-compassion meditation-based forest healing therapy is a feasible and efficient approach for facilitating stress management and coping resource building.

In the rapidly evolving landscape of the AI era, the measurement of Computational Thinking ability among Computer Science (CS) University students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of Computational Thinking in CS students. Utilizing two key instruments, namely the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of Computational Thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and program specializations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to measurement of variations in students' Computational Thinking and programming skills by gender, university level, and location. By shedding light on the significance of Computational Thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.

This study assessed the performance of low-dose CT lung scans across various acquisition parameters and examined the impact of iterative reconstruction (IR) algorithms on image quality for lung cancer screening. The performance of multi-slice CT scanners was assessed by using a Catphan 600 phantom, specifically within the CTP404 test module that contains materials like Teflon, Delrin, Acrylic, Polystyrene, LDPE, PMP, Air, and water. Three protocols were selected and categorized into standard, low dose, and ultra-low dose. The standard protocol featured a tube voltage of 100 kVp for single-detector CT, a pitch of 0.75, detector collimation of 0.60 mm, and a gantry rotation time of 0.33 seconds, with 5 mm slice thickness. Images were reconstructed using standard kernels like B80f and 170f, combined with either FBP or various IR algorithms. Findings indicated that the noise power spectrum (NPS) peak value (HU2mm2) increased with higher IR levels, with FBP showing the highest peak. Additionally, NPS spatial frequency diminished as IR levels rose. Optimizing contrast and spatial resolution for both background and regions of interest involved adjusting the target transfer function (TTF) and contrast-to-noise ratio (CNR), crucial for protocol optimization. Proper adjustment of IR levels is vital, as higher levels can alter image texture and affect NPS spatial frequency. The results of this study showed that the use of IR algorithms in low-dose CT lung scans significantly improved image quality, particularly in terms of noise reduction and spatial resolution. However, the choice of acquisition parameters also played a crucial

Our study explores the genetic mechanisms underlying the spotted leaf phenotype in rice, focusing on the rpt5a mutant characterized by persistent reddish-brown leaf spots from seedling stage to maturity, leading to extensive leaf necrosis. Through map-based cloning, we localized the responsible locus to a 330Kb region on chromosome 2 and identified LOC_Os02g56000 (named OsRPT5A) as the causative gene. A point mutation in OsRPT5A, substituting valine for glutamic acid, was identified as the critical factor for the phenotype. Functional complementation and generation of knockout lines in the IR64 background confirmed the central role of OsRPT5A in controlling this trait. Our study also revealed that OsRPT5A is constitutively expressed across various tissues, with its subcellular localization unaffected by mutations. Notably, we observed an abnormal accumulation of reactive oxygen species (ROS) in rpt5a mutants, suggesting a disruption in the ROS system. Complementation studies indicated OsRPT5A's involvement in ROS homeostasis and catalase activity regulation. Moreover, the rpt5a mutant exhibited enhanced resistance to Xanthomonas oryzae pv. oryzae (Xoo), highlighting OsRPT5A's role in rice's pathogen resistance mechanisms. These findings elucidate the genetic basis of the spotted leaf phenotype and emphasize the intricate interplay between plant defense responses and developmental pathways.

Analysis of liquid biopsy samples can reveal a wealth of molecular as well as cellular entities circulating in various body fluids. These entities include circulating tumor cells, circulating cell-free and tumor DNA, RNA, and proteins. Detection and characterization of this genetic and proteomic material can provide crucial information on tumor characteristics, including its heterogeneity, biology, tumor progression, staging, associated gene mutations and the course of therapy. The increased use of liquid biopsy in clinical oncology offers in non-invasive manner easier tumor sampling and continuous monitoring of the disease evolution through repetitive sampling from cancer patients. The continuous monitoring offered by this method is highly relevant to the assessment of a given therapeutic regime and early detection of markers associated with possible drug resistance, which may develop in the course of the therapy. The use of proteomic approaches, including mass spectrometry-based proteomics for the analysis of biofluids extends for over three decades, yet only in the last few years such methods have found their way to clinical oncology. The non-invasive nature of liquid biopsy and the simple procedures for sample collection made this approach far more attractive than the traditional and well-established tissue biopsy. Such preference becomes more practical in the analysis of solid tumors, particularly in pediatric population. In this review we discuss some works on the emerging role of liquid biopsy analyses in clinical oncology, and how such use allows a better understanding of diagnosis, prognosis, disease evolution and response to therapy of various forms of cancer. We also discuss the role of MS-based methods in the characterization of post-translational modifications of proteins circulating in liquid biopsies.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

To address the large hysteresis and susceptibility to instability in high-speed deposition fusion moulding (FDM) 3D printing’s temperature control, this paper proposes an improved ant-lion optimization algorithm (OCALO) to optimize the PID parameters.The improvement aims to solve the problem that the traditional ant-lion optimization algorithm (ALO) reduces the population diversity and tends to fall into the local optimal solution when it is close to the global optimal solution.The generation of the initial solution is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping. This integration into Elite Opposition-Based Learning aids in optimizing population diversity. Concurrently, the incorporation of cosine factors of a specified parameter into the elitist formula of the traditional ALO algorithm is aimed at reducing the tendency of ants to randomly gravitate towards ant-lions with lower fitness values. This adjustment helps in balancing the exploration-exploitation trade-off in the algorithmic process. Compared with two existing classical algorithms and three improved ALO algorithms, the algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The improved algorithm was combined with PID control to design an OCALO-PID temperature controller and simulated on a high-speed 3D printing temperature model identified by modelling. The results show that the method improves the transient and steady-state performance of reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a physical experimental platform to verify the feasibility of the algorithm.

With the COVID-19 pandemic behind us, the U.S. Food and Drug Administration (FDA) has approved 55 new drugs in 2023, a figure consistent with the number authorized in the last five years (53 per year on average). Thus, 2023 is the second-best FDA harvest after 2018 (59 approvals) in all the series. Monoclonal antibodies (mAbs) continue to be the class of drugs with most approvals, with an exceptional 12, a number that makes it the most outstanding year for this class. As in 2022, five proteins/enzymes have been approved in 2023. However, no antibody–drug conjugate (ADC) has been released onto the market. With respect to TIDES (peptides and oligonucleotides), 2023 has proved a spectacular year, with a total of nine approvals, corresponding to five peptides and four oligonucleotides. Natural products continue to be the best source of inspiration for drug development, with 10 new products on the market. Three drugs in this year’s harvest are pegylated, which may indicate the return of pegylation as a method to increase the half-lives of drugs, after the withdrawal of peginesatide from the market in 2013. Following the trends of recent years, two bispecific drugs have been authorized in 2023. As in preceding years, fluorine and/or N-aromatic heterocycles are present in most of the drugs. Herein, the 55 new drugs approved by the FDA in 2023 are analyzed exclusively on the basis of their chemical structure. They are classified as the following: biologics (antibodies, proteins/enzymes); TIDES (peptide and oligonucleotides); combined drugs; pegylated drugs; natural products; nitrogen aromatic heterocycles; fluorine-containing molecules; and other small molecules.

Background and Objectives: Rheumatic diseases often exhibit a complex pattern of comorbidities, including the increased occurrence of emotional disorders like anxiety and depression, which are closely related to the presence of chronic pain. Therefore, a multimodal approach is necessary for treatment, targeting both the physical and psychological functioning. Due to the increased burden within the healthcare context that emerged during the post-pandemic period, the need to adjust the content and length of psychological interventions became ubiquitous. In this way, the main objective of this study was to investigate the efficiency of a single-session, process-based psychological intervention for patients with rheumatic conditions and emotional comorbidities in a naturalistic, in-patient setting. Materials and Methods: After the screening, a total of 31 participants (Mage = 58.9, SD = 12.03) completed the single session intervention. The therapeutic protocol relied on a process-based cognitive-behavioural therapy framework aiming to improve cognitive, emotional and behavioural functioning. Assessments were conducted prior to the intervention, post-intervention and one month following the hospitalization. Results: Immediately after the intervention, significant reductions in dysfunctional cognitive and emotional processes, along with the improvement of behavioural strategies were observed. At the one-month telephonic evaluation, participants reported decreased pain, state anxiety and state depression levels. Conclusions: The single-session, process-based approach represents a promising intervention in healthcare contexts, targeting important psychopathological mechanisms in patients with rheumatic conditions. Future studies could clarify the extent to which these outcomes persist over time and their specific associations with anxiety and depressive symptoms, by using more controlled and rigorous trial designs.

The impact of long-term soil and crop management practices on soil biological and chemical properties in oilseed brassica-based production systems were deliberated for holistic soil health improvement in the present study. Different tillage, crop residue re-cycling and cropping systems were studied for 5-years in 0-15 and 15-30 cm soil profiles. No-till permanent beds with crop residue (PB+R) noticeably improved soil organic carbon (SOC), microbial biomass carbon (MBC), enzymes [dehydrogenase (DHA) and alkaline phosphatase (AlP)], nitrogen fractions (available, nitrate and total), and available phosphorus and potassium content in both the soil layers compared to conventional tillage without crop residues. Though, the plow soil layer (0-15 cm) showed higher concentration of soil carbon, enzymes, N fractions and available P than in the sub soil (15-30 cm). The dynamic soil biological and chemical properties were also varied with the crop stage, and recorded higher MBC at 30 days, SOC and enzymatic activities at 60 days, and N fractions and available P and K at harvest of mustard crop. Green gram-mustard rotation showed higher values of biological and chemical parameters. Thus, the legume-based mustard crop rotation following no-till permanent beds and residue re-cycling found holistic in improving soil health, and nutrient cycling.

Multiple Sclerosis (MS) is a neurodegenerative disease that mainly affects young adults. The high prevalence of the disease and its impact on patients' quality of life led to priority being given to the etiopathogenesis of the disease to identify possible modifiable factors and consequent effective intervention strategies. Recent hypotheses suggest that the etiopathogenesis of MS is multifactorial and includes factors related to the immune system, neuroinflammation and neuro-degeneration. In this scenario, sleep has a close - although indirect - relationship with MS, through its relationship with each of those factors. In particular, the role of cytokines in immune system impairment and neuroinflammation, mitochondrial dysfunction and oxidative stress in neurodegeneration all are associated with sleep in different forms. Furthermore, melatonin in relation with vitamin D has a potential therapeutic effect in MS sleep-related problems. Given the growing interest of research in the mechanisms underlying MS and therapies able to alleviate MS-related symptoms at all stages of the disease, a more in-depth study of the role of sleep disturbances and the factors intrinsically related to sleep and MS could be useful both to investigate the etiopathogenetic factors of MS and to develop potential non-invasive intervention strategies for MS treatment.

A capacity to reflect on experience and learn through it (reflective and reflexive practice) is deemed an essential part of ethical practice and research. Challenging traditional research approaches this arts-based, inter-disciplinary, Ph.D. project explores artistic practice as a research methodology, and how learning through experiences of ‘making’ things may enhance and amplify our understanding of the more affective aspects of human situations and experience. Grounded in the author’s artistic practice and learning through experiences of ‘making’, the author assembles and adapts frames from psychoanalysis, art (psycho)therapy, and the arts with/through which to observe and document their experience of an organisational situation. Describing the development of their method in three phases, analysis takes place through returning to revisit and rework artistic material produced, and engaging in conversation with the emergent material and others in response. Results take the form of artworks and artistic projects, including documentation of process. Emergent threads draw attention to the speculative, entangled, and affective nature of the research process, the reflexivity generated through moving and handling material, and the reflexive work of undergoing, foregrounding an ethics of responsibility, attention, and care for/of the body. As a space for imaginative encounter and performative enactment, and a site for reflexivity through which one may be pressed to notice and feel more acutely, the author argues that the research value resides in the capacity of this method to embrace complex relationalities, and engage our affective, ethical sensibilities in ways that may not emerge through more traditional approaches to reflective/reflexive practice(s). This has implications for both art therapy practice and research, amplifying the learning opportunities afforded by moving, modifying, and assembling things differently, the embodied ‘work’ of art as a method of enquiry, the cultural sensitivity of documentary fragments captured in/through various media and voices, and the value of collaborative endeavours where meaning is co-constructed in conversation.

Conditional Privacy Preserving Authentication (CPPA) schemes are an effective way of securing communications in Vehicular Adhoc Networks (VANETs), as well as ensuring user privacy and accountability. Cryptanalysis plays a crucial role in pointing out the vulnerabilities in existing schemes to enable the development of more resilient ones. In 2019, Zhang proposed a CPPA scheme for VANET security (PA-CRT), based on identity batch verification (IBV) and Chinese remainder theorem (CRT). In 2023, this scheme was crypt-analyzed by Tao. In this paper we provide our own crypt analysis of Zhang’s scheme and propose counter measures to mitigate Tao’s attacks. Furthermore, in 2021, Xiong proposed a Certificateless Aggregate Signature (CLAS) scheme which was cryptanalyzed by Shim in 2023. Xiong subsequently proposed a modified CLAS scheme which is also cryptanalyzed in this paper.

Contrast agents developed for X-ray computed tomography (CT) offer extremely valuable tools and techniques in diagnostics via contrast enhancements. Heavy metal-based nanoparticles (NPs) can provide high contrast in CT images. These result from a high density of heavy metal atoms with high X-ray attenuation coefficients that exceed that of iodine (I), which is currently used in hydrophilic organic CT contrast agents. Nontoxicity and colloidal stability are vital characteristics in designing heavy metal-based NPs as CT contrast agents. In addition, the particle size must be small for in vivo renal excretion. In vitro phantom imaging studies can provide X-ray attenuation efficiency, which is a critical parameter for CT contrast agents, and the imaging performance of CT contrast agents can be demonstrated via in vivo experiments. This review focuses on in vitro and in vivo studies of various heavy metal-based NPs (metal: Au, Pt, Pd, Ag, Ce, Gd, Dy, Ho, Yb, Ta, W, and Bi) and provides an outlook on their use as high-performance CT contrast agents.

Wildlife tourism and wildlife trade may appear juxtaposed, but are two, potentially aligning, in-come generators that could benefit conservation in developing countries. Utilising data sets collated from Madagascar’s Ministère du Tourisme and CITES respectively for the period 2007 to 2018, this study estimated levels of income from wildlife tourism and wildlife trade for Madagascar. Between 2007-2018, tourism reported yearly incomes ranging from a low of US \$1.4 million up to a high of US \$15.7 million. However, it was unclear what percentage of this figure flowed to benefit local communities. Alternatively, using reported networks for the live wildlife trade, the estimated eco-nomic value reaching collectors and/or intermediaries on Madagascar was US $72,299.80 for the period 2007 to 2018. Both revenue generators operated within different geographical areas, with tourism opportunities presenting themselves to communities adjacent NPs, while wildlife trade networks were not restricted to National Parks and operated sporadically across Madagascar. Hence, the economic benefits reached different Malagasy participants across the country. The management of both activities needs great care to ensure that environmental impacts and sus-tainability are core measures on any such activities. Whilst this study shines light on economic values and novel perspectives regarding these two trade types, it also highlighted knowledge gaps. Thus, indicating where much greater research attentions was required to allow better understanding of the specific benefits and risks from engaging with both trade types for local Malagasy people and their environments.

Due to the high impact of the building sector on the environment, a growing interest focuses on insulating materials able to ensure good thermo-acoustic performance for the building envelope, in a sustainable and circular economy perspective. In this context, Moroccan natural gypsum was mixed with natural waste local materials. Thermal and acoustic properties of the samples were measured; they were compared to those of synthetic- and mineral-based gypsum plasters, manufactured with the same technique. A Small Hot Box apparatus was used for thermal characterization, whereas acoustic performance was investigated by means of a Kundt’s Tube. Natural and synthetic additives involved a reduction in density and an improvement in thermal performance. Conductivity values in the 0.181 – 0.238 W/mK range were obtained, depending on the type of natural additive, with respect to 0.275 – 0.323 W/mK of mineral-based gypsum plasters. The acoustic measurements showed that all the composites have similar performance in terms of acoustic absorption, whereas high Transmission Loss values were obtained for the natural additives (TL = 35 – 59 dB). Petiol of Palm and Stipa Tenacissima were found as the materials able to improve both thermal and acoustic properties.

In this paper, stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system's average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to simplicity to implement and no presumptions to simplify. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is applied to determine the decision variables defined as the network's optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization, some objectives are weakened compared to their base value, while the results of the MOIF indicated a fair compromise between different objectives, and all objectives are improved. The results of MOIF based on the IMTBO cleared that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is proved compared to the conventional MTBO, particle swarm optimization, and artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag, and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively compared to the deterministic MOIF model.

Analytical chemistry applied to medical and diagnostic analysis, recently focused on the development of cost-effective biosensors able to monitor the health status or to assess the level of specific biomarkers that can be indicative for several diseases. The improvement of technologies relating to the possibility of non-invasive sampling of biological fluids, as well as sensors for the detection of analytical signals and the computational capabilities of the systems routinely employed in everyday life (e.g. smartphones, computers, etc.), make the complete integration of self-standing analytical devices more accessible. This review aims to discuss the biosensors that have been proposed in the last 5 years focusing on two principal detecting approaches, optical and electrochemical, which have been employed for quantifying different kind of target analytes reaching detection limits below the clinical sample levels required. These detection principles applied to Point-of-care (POC) devices, have been extensively reported in literature and even the limited examples found on the market are based on these strategies. This work will show the latest innovations considering the integration of optical and electrochemical detection with the most commonly reported analytical platforms for POC applications such as paper-based or wearable and implantable devices.

Current and dynamic developments in green technologies have led to several innovation practices in the manufacturing sector only to become the top approaches used for achieving and accelerating sustainable development (SD) in the current business markets. In addition, manufacturing firms is in need of green innovation to be able to monitor and control their operations and enhance their environmental performance. However, regardless of its many benefits, the level of green innovation adoption and implementation is still lower than expected among manufacturing industries. Thus, this study aimed to minimize the gap by developing and validating a study model underpinned by Resource based view and Institutional theories, along with the Technology-Organization-Environment (TOE) framework in combination to convince firms to adopt green innovation. The study gathered data from 179 respondents using a survey distributed to manufacturing firms, after which data was exposed to Structural Equation Modeling (PLS-SEM) approach, for analysis. Based on the approach deliverables, all the integrated constructs of the model, namely perceived benefits, top management support, coercive pressure, normative pressure and mimetic pressure all predicted green management accounting practices. Moreover, green management accounting practices were found to directly and significantly affect green environmental performance. The developed integrated model provides a clear implication to decision-makers, indicating the importance of adopting and using green practices and innovative technologies for enhancing environmental performance. Based on the results from the reviewed advanced green technologies studies, there is considerable connection between green management accounting practices and environmental performance in the context of developing economies.

Because of hyper complexity, difficulty to define, multiple stakeholders with conflicting perspectives, and lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations Research has been a valuable tool for managers to make informed decisions for years. However, as we face increasingly complex and messy problems, it has become apparent that relying solely on either hard or soft OR approaches is no longer sufficient. We need to explore more innovative methodologies to address these wicked problems effectively. This study has bridged the research gap by proposing a structured process encompassing a subdivision-based problem structuring method for defining the wicked problem, a multi-attribute decision-making (MADM) for prioritizing sub-problems, and a hard OR technique, data envelopment analysis (DEA) for tackling one of the most critical sub-divisions. The proposed methodology, implemented in a case study, focuses on a higher education institution experiencing a decline in student admissions and involves five steps. First, a diverse group of stakeholders is formed to ensure comprehensive consideration of perspectives. Second, the wicked problem is defined, considering long-term consequences, multiple stakeholders, and qualitative stakeholder opinions. Third, a hierarchical structure is created to break down the wicked problem into manageable sub-problems. Fourth, a multi-criteria decision-making (MCDM) method prioritizes sub-problems. Finally, the sub-problems are addressed one by one using a combination of soft and hard OR tools. The findings highlight the benefits of integrating hard and soft OR approaches. The article concludes with reflections on the implications of using a combined OR approach to tackle wicked problems in higher education and beyond.

Oral and parenteral delivery of first-line anticonvulsant Valproic cid (VA) are associated with serious adverse effects, high hepatic metabolism, high clearance, and low bioavailability in brain. GastroPlus program was used to predict in vivo performance of immediate (IR) and sustained release (SR) products in human. HSPiP software predicted excipients with maximum possible miscibility of the drug. Based on GastroPlus and HSPiP program, various excipients were screened for experimental solubility study and prepared cationic nanoemulsions and respective gels for nasal to brain delivery. These were characterised for size, size distribution, polydispersity index, zeta potential, morphology, pH, % transmittance, drug content, and viscosity. In vitro drug release, ex vivo permeation profile (goat nasal mucosa), and penetration studies were conducted. Result showed that in-vivo oral drug dissolution and absorption were predicted as 98.6 mg and 18.8 mg, respectively from both tablets (IR and SR) at 8 h using GastroPlus. The drug access to the portal vein was relatively predicted high in IR (115 mg) as compared to SR (82.6 mg). Plasma drug concentration-time profile predicted was in good agreement with published reports. The program predicted duodenum and jejunum as the prime site of drug absorption and no effect of nanonization on Tmax for sustained release formulation. Hansen parameters suggested suitable selection of excipients. The program recommended for nasal to brain delivery of the drug using cationic mucoadhesive product. The optimized stable CVE6 was associated with optimal size (113 nm), low PDI (0.26), high zeta potential (+34.7 mV), high transmittance (97.8%), and high strength (0.7 %w/w). In vitro release and ex vivo permeation of CVE6 were found to be substantially high as compared to anionic AVE6 and respective gels. Penetration study executed high fluorescence intensity with CVE6 and CVE6-gel as compared to suspension and ANE6 which may be attributed to electrostatic interaction between mucosal membrane and nanoglobules. Thus, cationic nanoemulsion and respective mucoadhesive gel are promising strategy for delivery of VA to brain through intransal administration for the treatment of seizure and convulsion.

This review article is aimed at exploring the possibility of developing an integrated approach in the management of the different needs of endometrial cancer (EC) patients seeking to become pregnant. Life preservation of the woman, health preservation of the baby, a precocious and – as much as possible – minimally invasive characterization of the health and fertility parameters of the patient, together with the concerns regarding the obstetric, neonatal, and adult health risks of the children conceived via assisted reproduction techniques (ART) are all essential aspects of the problem to be taken into consideration, yet the possibility to harmonize such needs through a concerted integrated approach is still highly challenging. This article aims to illustrate the main features of EC and how it affects the normal physiology of pre-menopausal women. Then, we focus on the prospect for a miR-based, molecular evaluation of patient health status, including both EC early diagnosis and staging and, similarly, the receptivity of the woman, discussing the possible evaluation of both aspects using a single specific panel of circulating miR in the patient, thus allowing a relatively fast, non-invasive way to perform such testing with a significantly reduced margin of error. Finally, the ethical and legal/regulatory complexities arising from such innovative techniques require not only a risk-benefit analysis; respect for patient autonomy and equitable health care access allocation are fundamental issues as well.

Accurate rainfall measurement is vital when investigating spatial and temporal precipitation variability at different scales. However, there are many regions around the world, such as North Darfur State in Sudan, where ground-based observations are few. Satellite-based precipitation products can fill such regions' spatial and temporal rainfall data gaps. Six satellite rainfall prod-ucts, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology Version 2 (ARC2.0), Climate Hazards Group In-frared Precipitation with Station Data (CHIRPS2.0), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run v 6 (GPM IMERG6), Precipitation Estima-tion from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) v3.1 were evaluated to assess their accuracy in estimating rainfall amounts variability trends in the study area. The global-based satellite rainfall products were assessed at monthly and annual time scales by applying a point-to-pixel comparison with ground-based rain gauge data for the period 2000–2019. Based on the overall statistical results at monthly and temporal yearly scales, five satellite precipitation products (TMPA, CHIRPS, GPM IMERG6, PERSIANN-CDR, and TAMSATv3.1) overestimated rainfall amounts by values ranging from 1.49% to 82.69%. In contrast, the ARC2 product underestimated rainfall amounts by values ranging from-16.9% to-20.25%. The TAMSATv3.1, CHIRPS, and TMPA performed relatively better, showing stronger correlations and higher values of Nash-Sutcliffe efficiency. This study showed that the TAMSATv3.1 and CHIRPS products could reasonably estimate rainfall amounts in the North Darfur State.

A claim management office (CMO) is a new intra-organisation fit based on an organisational project management (OPM) approach and deals specifically with improving claim performance. However, CMO is either misunderstood or often overlooked in construction companies, mainly due to ignorance of the OPM context but further exacerbated by a lack of real case study research on how to adopt CMO in these organisations. This study addresses this prevailing knowledge gap by investigating the nature of the construction industry from the CMO viewpoint and developing an extensive framework through which to improve organisational claims performance. Organisational ambidexterity theory integrated with X-inefficiency theory is adopted for claims performance outcomes, due to organisation intra-firm irrational decisions when managing such claims. Data was obtained and analysed from a international large construction company (which used CMOs) and its internal focus group discussion (as a multi-method approach). The reference framework provides new perspectives on how construction-related companies can adopt a CMO structure, which enables them to improve claims performance by planning in three interrelated activities viz. function-, process- and performance-based-. Furthermore, findings contribute to researchers and practitioners by providing a true understanding of the CMO-related mechanism and plausible roadmap for future work.

Knowledge of the temperature evolution is crucial to understand and control laser beam welding of low-melting materials. Existing temperature determination approaches are restricted to i) one-dimensional temperature information (e.g. ratio-pyrometers), ii) a priori knowledge of the emissivity (e.g. thermography) and iii) high temperature regions (e.g. two-wavelength imaging). In this paper, a ratio-based two-color-thermography approach is developed that allows for two-dimensional temperature determination in low-melting temperature ranges (< 1200 K). For static measurement situations it is demonstrated, that temperature can be determined despite variation in signal intensity and emissivity with high accuracy. The two-color-thermography set-up is further transferred into a commercial laser beam welding machine and experiments are conducted for varying process parameters. The direct application of the developed two-color-thermography system in dynamic process situations is limited as image artifacts presumably caused by internal reflections inside the optical beam path are present.

In this paper, we develop a tractable mathematical model and emulation framework for communicating information through water using acoustic signals. Water is considered as one of the most complex mediums to model owing to its vastness and variety in characteristics depending on the scenario, what kind of water-body (lakes, rivers, tanks, sea etc.) and geographical location of the water-body is considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water) and Milne’s oscillator technique to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are used to characterize the travelling acoustic signal. Signal strength, phase shift and time-delay generated from the mathematical model are fed to a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator uses wide sense stationary uncorrelated scattering (WSSUS) assumption and finite sum-of-sinusoids (SOS) with uniformly distributed phase to generate the channel samples. Using the emulator platform it is possible to generate amplitude variation profile, Doppler shift and spread experienced by any travelling signal through different underwater communication scenarios. Such an emulator platform can be used to simulate different communication scenarios, underwater network topologies, data to train different learning models and predict performance of different modulation, multiplexing, error correction and multi-access techniques for underwater acoustic communications (UWAC) systems.

As a result of the growing demand for foods with reduced animal protein content, many new alternative diets are now emerging. Nevertheless, recent studies have shown that the Western population is unprepared for drastic changes and is disinclined to accept foods based on alternative proteins. However, hybrid products might become a good transitional offer. This study used a desirability-based mixture design to model hybrid spreadable cheese analogues (SCAs). The design combined the dairy protein (MPC), Tenebrio molitor (IF) and faba bean (FBP) flours. Nine SCAs with different MPC/FBP/IF ratios were formulated, representing 0, 50 and 100% MPC replacement (7.1% of the formula). Incorporating the IF negatively impacted the desirable texture properties. The FBP flour improved the texture (achieving increased firmness and stickiness and decreased spreadability), but only when combined with MPC. Changing the MPC/FBP/IF ratio affected the colour of SCAs. Sensory analysis showed that hybrid SCAs (≤ 50% MPC) had a more characteristic cheesy flavour than the commercial plant-based reference, and sample C2 had a texture profile similar to the dairy reference. Samples containing IF showed a better flavour profile than the products without IF. The SCAs had higher protein and lower saturated fat, starch and sugar con-tent than commercial analogues.This study demonstrates that the inclusion of alternative proteins can be effective as a strategy to reduce dairy protein content in hybrid product formulations.

Membrane-based beverage dealcoholization is a successful process for producing low- and non-alcoholic beer and represents a fast-growing industry. Polyamide NF and RO membranes are commonly applied for this process. Polyelectrolyte multilayer (PEM) NF membranes are emerging as industrially relevant species and their unique properties (usually hollow fiber geometry, high and tunable selectivity, low fouling) underlines the importance of testing them in the food industry as well. To test PEM NF membranes for beer dealcoholization, at small pilot scale we dealcoholized filtered and unfiltered lager beer with the tightest available commercial polyelectrolyte multilayer NF membrane (NX Filtration dNF40), which has a MWCO=400Da which is quite high for these purposes. Dealcoholization is possible with a reasonable flux (10 LMH) at low pressures (5-8.6 bar) with a real extract loss of 16-18% and an alcohol passage of ~100%. Inorganic salt passage is high (which is typical for PEM NF membranes), which greatly affected beer flavor. During the dealcoholization process, the membrane underwent changes which substantially increased its salt rejection values (MgSO4 passage decreased 4-fold) while permeance loss was minimal (less than 10%). According to our sensory examination, the process yielded an acceptable flavored beer which could be greatly enhanced by the addition of the lost salts and glycerol.

The vulcanization of rubber compounds is an exothermal process. A carbon black-filled and natural rubber-based (NR) formulation was mixed with different levels of sulfur (0.5, 1.0, 2.0, 4.0 and 6.0 phr) and studied with Differential Scanning Calorimetry (DSC) for the determination of the vulcanization enthalpy. It was found that the vulcanization enthalpy is dependent from the amount of sulfur present in the compound and the vulcanization heat released was -18.4 kJ/mol S if referred to the entire rubber compound formulation or -46.0 kJ/mol S if the heat released is referred only to the NR present in the compound. The activation energy for the vulcanization of the rubber compounds was also determined by a DSC study at 49 kJ/mol and found quite independent from the sulfur content of the compounds under study. A simplified thermochemical model is proposed to explain the main reactions occurring during the vulcanization. The model correctly predicts that the vulcanization is an exothermal process although it gives an overestimation of the vulcanization enthalpy which is larger for the EV vulcanization package and smaller for the conventional vulcanization system. If the devulcanization is conducted mechanochemically in order to break selectively the sulfur-based crosslinks, then the natural rubber compounds recovered from used tires can be re-vulcanized again and the exothermicity of such process can be measured satisfactorily with DSC analysis. This paper not only proposes a simplified mechanism of vulcanization and devulcanization but also proposes an analytical method to check the devulcanization status of the recycled rubber compound in order to distinguish truly devulcanized rubber from reclaimed rubber.

In this empirical article, we argue that, while emergency remote teaching (ERT) may have achieved its goal of saving the academic years during the COVID-19 pandemic, it also constructed unintended pedagogical consequences that were possibly overlooked at the time of advocating it. The research question that the article attempted to answer is, what unintended pedagogical consequences did students and lecturers suffer because of the move to ERT at rural-based universities (RBUs) in South Africa? Drawing on students' and lecturers’ lived experiences of ERT, this article foregrounds unintended pedagogical consequences that arose at one RBU in South Africa during the transition from face-to-face teaching to ERT. Underpinned by the tenets of critical realism philosophy, as well as student integration theory, in-depth interviews with three lecturers and six students were conducted. The findings of the study indicate that home conditions, individual characteristics, pre-COVID-19 blended learning experiences, university training and support, and teaching, learning, and assessment practices and policies altogether contributed to the construction of unintended pedagogical consequences of ERT presented in this article. These consequences include (1) exclusion of low-income students in active teaching and learning, (2) equipping middle-class students with better chances of success than working-class students. (3) distressing female students and lecturers more than their male counterparts, and (4) unproductive assessment practices. This study may be beneficial to academics and policymakers from similar contexts in their plight to continue with remote teaching and assessment (RTA) beyond the pandemic.

Underpinned by the new world Kirkpatrick model and in the context of a community-based, sport psychology program (My Strengths Training for Life™) for young people experiencing homelessness, this process evaluation investigated: (1) young peoples’ reactions (program and facilitator evaluation, enjoyment, attendance, and engagement) to and learning (mental skills and transfer intention), (2) the relationship between reaction and learning variables, and (3) the mediators underpinning this relationship. 301 young people living in a West Midlands housing service completed questionnaires on demographics, reaction and learning variables. Higher levels of program engagement were positively associated with more favorable reactions to the program. Enjoyment positively predicted learning outcomes, which was mediated by transfer intention. Recommendations are made for: (1) a balance between rigor and flexibility for evaluation methods with disadvantaged youth, (2) including engagement as well as attendance for indicators of meaningful program participation, (3) measuring program experiences (e.g., enjoyment) to understand program effectiveness, and (4) providing opportunities for skill transfer during and after program participation. Findings have implications for researchers, program commissioners, and policy makers working designing and evaluating programs in community-based settings.

The ability to directly look into genome sequences has opened great opportunities in plant breeding. Yet, the assembly of full-length chromosomes remains one of the most difficult problems in modern genomics. Genetic maps are commonly used in de novo genome assembly and are constructed on the basis of a statistical analysis of the number of recombination rather than physical distance in base pairs. This may affect the accuracy of the ordering and orientation of scaffolds within the chromosome, especially in the region of recombination suppression. Here we report the use of Tyr-FISH for validation of genetic and pseudochromosome maps. For probe design we developed a pipeline that is based on selection of a unique sequence with minimal potential fluorescent background arising from non-specific in situ hybridization. In total of 24 unique genes were located on physical chromosomes 2 and 6. The order of markers has been corrected by integration genetic and cytogenetic maps. Tyr-FISH mapping showed that the order of 23.1% (chromosome 2) and 27.3% (chromosome 6) of the tested genes differed between physical chromosomes and pseudochromosomes. Also the position of the mlh1 gene, which was not on the genetic map, was defined on physical chromosome 2. The results suggest the possibility of Tyr-FISH mapping of any gene that may not be neither on the genetic map nor in the assembly. Hence, Tyr-FISH provides valuable information for the improvement of the genome assembly.

Background: pathophysiological changes like low cardiac output (LCO) impact pharmacokinetics, but its extent may be different throughout pediatrics compared to adults. Physiologically based pharmacokinetic (PBPK) modelling enables further exploration. Methods: A validated propofol model was used to simulate the impact of LCO on propofol clearance across age groups using the PBPK platform, Simcyp® (version 19). The hepatic and renal extraction ratio of propofol was then determined in all age groups. Subsequently, dose explorations were conducted under LCO conditions, targeting a 3 µg/mL (80-125%) propofol concentration range. Results: Both hepatic and renal extraction ratios increased from neonates, infants, children to adolescents and adults. The relative change in clearance following CO reductions increased with age, with the least impact of LCO in neonates. The predicted concentration remained within the 3 µg/mL (80-125%) range under normal CO and LCO (up to 30%) conditions in all age groups. When CO was reduced by 40-50%, a dose reduction of 15% is warranted in neonates, infants and children, 25% in adolescents and adults. Conclusions: PBPK driven, the impact of reduced CO on propofol clearance is predicted to be age-dependent, proportionally greater in adults. Consequently, age group specific dose reductions for propofol are required in LCO conditions.

Most methods for measuring environmental lead (Pb) content are time consuming, expensive, hazardous, and restricted to specific analytical systems. To provide a facile, safe tool to detect Pb, we created pMet-lead, a portable fluorescence resonance energy transfer (FRET)-based Pb biosensor. pMet-lead comprises a 3D-printed frame housing a 405-nm laser diode — an excitation source for fluorescence emission images (YFP and CFP) — accompanied by optical filters, a customized sample holder with a Met-lead 1.44 M1 (the most recent version)-embedded biochip, and an optical lens aligned for smartphone compatibility. Measuring the emission ratios (Y/C) of the FRET component enables Pb detection with a dynamic range of nearly 2 (1.96), pMet-lead/Pb dissociation constant (K_d) 45.62 nM, and limit of detection 40 nM (0.832 μg/dL, 8.32 ppb). To mitigate earlier problems with lack of selectivity for Pb vs. zinc, we preincubated samples with tricine, a low-affinity zinc chelator. We validated pMet-lead measurements of characterized laboratory samples and unknown samples from six regions in Taiwan by inductively coupled plasma mass spectrometry (ICP-MS). Notably, two unknowns had Y/C ratios significantly higher than that of the control (3.48 ± 0.08 and 3.74 ± 0.12 vs. 2.79 ± 0.02), along with Pb concentrations (10.6 ppb and 15.24 ppb) above the WHO-permitted level of 10 ppb in tap water, while the rest four unknowns showing no detectable Pb upon ICP-MS. These results demonstrate that pMet-lead provides a rapid, sensitive means for on-site Pb detection in water from the environment and in living/drinking supply systems to prevent potential Pb poisoning.

With increased vulnerabilities and vast technology landscapes, it is extremely critical to build systems which are highly resistant to cyber-attacks, to break into systems to exploit. It is almost impossible to build 100% secure authentication & authorization mechanisms merely through standard password / PIN (With all combinations of special characters, numbers & upper/lower case alphabets and by using any of the Graphical password mechanisms). The immense computing capacity and several hacking methods used, make almost every authentication method susceptible to cyber-attacks in one or the other way. Only proven / known system which is not vulnerable in spite of highly sophisticated computing power is, human brain. In this paper, we present a new method of authentication using a combination of computer’s computing ability in combination with human intelligence. In fact this human intelligence is personalized making the overall security method more secure. Text based passwords are easy to be cracked ^[6]. There is an increased need for an alternate and more complex authentication and authorization methods. Some of the Methods ^{[7] [8]} in the category of Graphical passwords could be susceptible, when Shoulder surfing/cameras/spy devices are used.

It is widely accepted that plastic waste is one of our most urgent environmental concerns the world is currently facing. Plastic has contributed greatly to innovation in all fields, but the raw material for its production (fossil fuels), as well as the linear economy in which it is currently produced and used, makes the material problematic from a sustainability and human health perspectives. The emergence of bio-based plastics provides an opportunity to reduce dependency on fossil fuels and transition to a more circular plastics economy. For polyethylene terephthalate (PET), one of the most prevalent plastics in packaging and textiles, there exist two bio-based alternatives that are similar or superior in material property and recyclability. These are polyethylene furanoate (PEF) and polytrimethylene terephthalate (PTT). The overarching aim of this study was to examine the transition from fossil-based to renewable plastics, through the lens of PET upcycling into PEF and PTT. The process for the production of PEF and PTT from three feed streams (post-consumer PET waste, lignocellulosic biomass-derived cellulose and biodiesel-derived crude glycerol) was developed in the software SuperPro Designer and the economic viability assessed via a discounted cumulative cash flow (DCCF) analysis. This study represents a conceptual case study of the valorisation of existing plastic waste into new, recyclable bio-based plastics, for a more sustainable plastics production. A techno-economic analysis of the designed process revealed that the minimum selling price (MSP) of second generation-derived PEF and PTT is 3.13 USD/kg, and that utilities and the feedstock used for the production of 2,5-furandicarboxylic acid (FDCA) needed in PEF synthesis contributed the most to the process operating costs. The effect of recycling PEF and PTT through the process at three recycling rates (42%, 50% and 55%) was investigated and revealed that increased recycling could reduce the MSP of the 2G bio-plastics (by 48.5%) to 1.61 USD/kg. This demonstrates that increasing recycling rates would have a beneficial effect on the economic viability of the plastic biorefinery.

The oil and gas sector has recognised structural integrity assessment of ageing platform for prospective life extension as a rising concern, particularly in encountering the randomness of the harsh ocean environments. This condition leads to uncertainty in wave-in-deck load estimates and a high load level being imposed on offshore structures. This emphasises the necessity of enhanced reliability, as failure might result in inaccessibility because of the uncertainties related to long-distance services, such as accuracy of predictions of loads and responses. Even though the established guidelines present a fundamental assessment, additionally, comprehensive rules are required. This paper performed a reliability analysis incorporating practical approaches that can more accurately represent time-dependent structural deterioration. The following two procedures have been adopted by a majority of significant oil and gas operators to monitor the safety and integrity of these structures: a) Ultimate Strength Assessment (USA) method and b) Reliability Design Assessment (ReDA) method. A comparison of these two reliability approaches was performed on selected ageing jacket structures in the region of the Malaysian sea. The comparative findings, namely, reserve strength ratio (RSR) at various years of the return period (RP) and ratio value for risk of failure regarding the probability of failure (POF), provided a check and balance in strengthening confidence in the results. The findings showed that the structural components might safely survive either using the USA and ReDA method in such conditions, as the reliability indexes were determined to be satisfactory compared to allowable values from ISO 19902 design specifications. Therefore, these evaluations were determined to control the risk level of the structure during the remaining of its lifetime and undertake cost-effective inspections or mitigation strategies when necessary.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

This study investigated the roles of low-molecular-weight fucoidan (LMWF) in enhancing the anti-cancer effects of fluoropyrimidine-based chemotherapy. HCT116 and Caco-2 cells were treated with LMWF and 5-FU. Cell viability, cell cycle, apoptosis, and migration were analyzed in both cell types. Potential mechanisms underlying how LMWF enhances the anti-cancer effects of fluoropyrimidine-based chemotherapy were also explored. The cell viability of HCT116 and Caco-2 cells was significantly reduced after treatment with a LMWF-5-FU combination. In HCT116 cells, LMWF enhanced the suppressive effects of 5-FU on cell viability through the 1) induction of cell cycle arrest in the S phase and 2) late apoptosis mediated by the Jun-N-terminal kinase (JNK) signaling pathway. In Caco-2 cells, LMWF enhanced the suppressive effects of 5-FU on cell viability through both c-mesenchymal–epithelial transition (MET)/ Kirsten Rat Sarcoma virus (KRAS)/ extracellular signal-regulated kinase (ERK) and c-MET/ phosphatidyl-inositol 3-kinases (PI3K)/ protein kinase B (AKT) signaling pathways. Moreover, LMWF enhanced the suppressive effects of 5-FU on tumor cell migration through the c-MET/ matrix metalloproteinase (MMP)-2 signaling pathway in both HCT116 and Caco-2 cells. Our results demonstrated that LMWF is a potential complementary therapy for enhancing the efficacies of fluoropyrimidine-based chemotherapy in colorectal cancers (CRCs) with the wild-type or mutated KRAS gene through different mechanisms. However, in vivo studies and in clinical trials are required to validate the results of the present study.

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

In situ imaging of molecular markers on a physical chromosome is an indispensable tool for refin-ing of genetic maps and validation genome assembly at the chromosomal level. Despite tremen-dous progress in genome sequencing the plant genome assembly at chromosome level still remain a challenge. Recently developed optical and Hi-C mapping aim to assist in genome assembly. For high-confidence in the genome assembly at chromosome level more independent approaches will be required. The present study aimed to refined an ultrasensitive Tyr-FISH technique and to de-velop a reliable and easy method for in situ mapping of a short unique DNA sequences on plant chromosomes. We have carefully analyzed the critical steps of the Tyr-FISH technique to find out the reasons for the failures of using the method. It has been shown that successful visualization of marker/gene depends significantly on method of chromosome slide preparation, probe design and labelling, high stringency washing. Appropriate adjustment of these steps allowed us to detect a short DNA sequence of 1.7Kb with a frequency of 51.6%. Based on our results, we developed a reliable and simple protocol for dual-color Tyr-FISH visualization of short unique DNA sequences on plant chromosomes. New protocol allows more accurate determination of the physical distance between markers and can be applied for faster integration of genetic and cytogenetic maps.

The emotional dimension in education has become increasingly important in recent decades. Enhancing the emotional dimension of prospective teachers in science subjects is higher education stuff responsibility. The implementation of active methodologies could modify the traditional student-teacher roles that are encouraged by the educational policies implemented in the Bologna Process. The principal aim of this work is to describe a Project Based Learning methodology and to introduce it as potential resource for the emotional and cognitive improvement of 19 prospective primary teachers enrolled in a scientific subject. This is a qualitative study with a transversal sustainability approach in the context of a research line focused on Higher Education for Sustainable Development. A questionnaire was designed and filled by the students at two different times, before and after implementation of the activity. The initial feedback from students was surprisingly enthusiastic by the fact that they were working with rockets, despite of this is not a common emotion in the science field. The results show the emotional improvement of prospective teachers after the implementation. It is concluded that a correct science education is necessary during the training of teachers taking into account their emotional dimension and the social repercussion due to the future transmission.

This paper summarizes over a decade of collaborative eco-archaeological research along the central coast of California involving researchers from the University of California, Berkeley, tribal citizens from the Amah Mutsun Tribal Band, and California Department of Parks and Recreation archaeologists. Our research employs remote sensing methods to document and assess cultural resources threatened by coastal erosion and geophysical methods to identify archaeological deposits, minimize impacts on sensitive cultural resources, and provide tribal and state collaborators with a suite of data to consider before proceeding with any form of invasive archaeological excavation. Our case study of recent eco-archaeological research developed to define the historical biogeography of threatened and endangered anadromous salmonids demonstrates how remote sensing technologies help identify dense archaeological deposits, remove barriers, and create bridges through equitable and inclusive research practices between archaeologists and the Amah Mutsun Tribal Band. These experiences have resulted in the incorporation of remote sensing techniques as a central approach of the Amah Mutsun Tribal Band when conducting archaeology in their traditional territories.

Nowadays, mindfulness-based interventions (MBI) have experimented a remarkable development of studies among childhood and adolescent interventions. For this reason, dispositional mindfulness (DM) measures for children and adolescents have been developed to determine the effectiveness of MBI at this age stage. However, little is known about how key elements of DM (f. e., cognitive de/fusion or experiential avoidance that both conform psychological inflexibility) are involved in the mechanisms of the children and adolescents’ mental health outcomes. This research examined the mediating effect of cognitive fusion between DM and anxiety and other negative emotional states in a sample of 318 Spanish primary-school students (aged between 8 and 16 years, M=11.24, SD=2.19, 50.8% males). Participants completed the AFQ-Y, which is a measure of psychological inflexibility that encompasses cognitive defusion and experiential avoidance; CAMM (DM for children and adolescents), PANAS-N (positive and negative affect measure for children, the Spanish version of PANASC), and STAIC (an anxiety measure for children). The study accomplished ethical standards. As MBI relevant literature has suggested, cognitive defusion was a significant mediator between DM and symptoms of both negative emotions and anxiety in children and adolescents. However, experiential avoidance did not show any significant mediating relationship. Probably, it is needed improvement of the assessment of experiential avoidance. MBI programs for children and adolescents may include more activities for reducing the effects of the cognitive defusion on their emotional distress.

In this paper, a new class of q-Hermite based Frobenius type Eulerian polynomials is introduced by means of generating function and series representation. Several fundamental formulas and recurrence relations for these polynomials are derived via different generating methods. Furthermore, diverse correlations including the q-Apostol-Bernoulli polynomials, the q-Apostol-Euler poynoomials, the q-Apostol-Genocchi polynomials and the q-Stirling numbers of the second kind are also established by means of the their generating functions.

We introduce a new kind of extended Hermite-based Frobenius type Eulerian polynomials and then derive diverse explicit and implicit summation equations including some symmetric formulas by utilizing series manupulation method. Multifarious summation formulas and identities given earlier for some well known polynomials such as Eulerian polynomials and Frobenius type Eulerian polynomials are generalized.

In this article, a new class of $q$-Hermite based unified Apostol type polynomials is introduced by means of generating function and series representation. Several important formulas and recurrence relations for these polynomials are derived via different generating methods. We also introduce $q$-analog of Stirling numbers of second kind of order $\nu$ by which we construct a relation including aforementioned polynomials.

The recovery of precious metals is a project with both economic and environmental significance. In this paper, it presents how to use bacterial mineralization to selectively recover gold from multi-ionic aqueous systems. The Bacillus licheniformis FZUL-63, separated from a landscape lake in FuZhou University, was shown to selectively mineralize and precipitate gold from coexisting ions in aqueous solution. The removal of Au(III) was almost happened in first hour, and FTIR data show that the amino, carboxyl and phosphate groups on the surface of the bacteria are related to the adsorption of gold ions. XPS results implied that Au(III) ions are reduced to monovalent, and then the Au(I) was adsorbed on the bacterial surface at the beginning stage(first hour). XRD results showed the gold biomineralization began about 10 hours after the interaction between Au(III) ions and bacteria. The Au(III) mineralization has been rarely influenced by other co-existing metal ions. TEM analysis shows the gold nanoparticles are polyhedral structure with a particle size of ~20 nm. The Bacillus licheniformis FZUL-63 could selectively mineralize and recover 478 mg/g(dry biomass) gold from aqua regia-based metal wastewater through four cycles. It could be of great potential in the practical application.

Infiltration based stormwater best management practices bring considerable economic, social and ecological benefits. Controlling stormwater quantity and quality are primarily important to prevent urban flooding and minimizing loads of pollutants to the receiving waters. However, there have been growing concerns about how the traditional design approach contributes to the failure of infiltration based BMP’s that have caused flooding, ponding, prolonged movement of surface water, and frequent clogging, etc. Many of these problems were due to the fact that the current design approaches of stormwater BMP’s only focus on surface hydrology and give little or no attention to the underline subsoil permeability rate and other constraints during the design and sizing process. As a result, we are exhibiting many newly constructed infiltration based BMP’s are failing to function well. This paper presents and demonstrates a new paradigm shift in designing infiltration-based stormwater BMP’s by combining subsurface hydrology and undelaying native soil constraints to establish acceptable criteria for sizing infiltration based BMPs.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

Interfaces between layers in 3D-printed elements produced by extrusion-based material deposition were investigated on both macro- and micro-scales. On the macro-scale, compression and bend tests were performed on two 3D-printable cement-based compositions (3PCs), namely C1 and C2. The influences of binder composition and time interval between layers on layer-interface strength were critically analyzed. In the context of additive manufacturing, the optimized composition C2, containing pozzolanic additives, exhibited mechanical performance superior to that of the mixture with Portland cement as the sole binder. In particular, Mixture C2 showed a less pronounced decrease in interface tensile strength. Even for time intervals between depositions of two layers as long as 1 day the loss in corresponding flexural strength was below 25%, as compared with C2 specimens tested in the perpendicular direction. In contrast, the decrease in flexural strength measured for C1 specimens amounted to over 90% for the same set of parameters. Higher porosity at the interfaces of the printed concrete layers was identified as the cause for the lower interface strengths of C1. Microscopic observations supported the findings of the macroscopic investigations. While a pronounced recovery (“self-healing”) of the porous, discontinuous interlayers was observed with increasing age for Mixture C2, in case of C1 the filling products grown in the porous interlayer were found to be non-strengthening.

In this paper, we introduce a general family of Lagrange-based Apostol-type Hermite polynomials thereby unifying the Lagrange-based Apostol Hermite-Bernoulli and the Lagrange-based Apostol Hermite-Genocchi polynomials. We also define Lagrange-based Apostol Hermite-Euler polynomials via the generating function. In terms of these generalizations, we find new and useful relations between the unified family and the Apostol Hermite-Euler polynomials. We also derive their explicit representations and list some basic properties of each of them. Some implicit summation formulae and general symmetry identities are derived by using different analytical means and applying generating functions.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

Since their discovery in 1993, numerous microRNAs (miRNAs) have been identified in humans and other eukaryotic organisms, and their role as key regulators of gene expression is still being elucidated. It is now known that miRNAs not only play a central role in the processes that ensure normal development and physiology, but they are often dysregulated in various diseases. In this review, we present an overview of the role of miRNAs in normal renal development and physiology, in maladaptive renal repair after injury, and in the pathogenesis of renal parenchymal diseases. In addition, we describe methods used for their detection and their potential as therapeutic targets. Continued research on renal miRNAs will undoubtedly improve our understanding of diseases affecting the kidneys and may also lead to new therapeutic agents.

Cycling reliability is crucial for Si-based materials due to severe volume change during cycles that results in the fast capacity fading. Though the binder only occupies a very low amount of the total mass of anodes, it is proved to perform a key parameter to improve the cycle performance of Si-based anodes. Because they are eco-friendly and cost saving, water-based binders styrene-butadiene rubber (SBR) and carboxymethyl cellulose (CMC) are regarded as the better binder to substitute Poly (vinylidene difluoride) (PVDF) as the binder for Si-based anode. In this study, the anodes are fabricated by simply mixing the active materials (naso Si, graphite and conductive additive) together and using the mixture of SBR and CMC as a binder. The results showed that the retention capacities of the anodes are more than 440 mAh/g after 400 cycles. It indicates that it is an easy and simple way to make high performance anodes.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Titania is one of the most comprehensively studied nanostructures due to its widespread applications in production of catalytic, gas- sensing and corrosion- resistance materials [1]. M-polynomial of nanotubes has been vastly investigated as it produces many degree-based topological indices which are numerical parameters capturing structural and chemical properties. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules are correlated with their structure like boiling point, stability, strain energy etc of chemical compounds. In this paper, we determine M-polynomials of single-walled titanium (SW TiO2) nanotubes and recover important topological degree based indices of them to theoretically judge these nanotubes. We also use Maple to plot surfaces associated to different types of single-walled titanium (SW TiO2) nanotubes.

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

We propose a model for gravity based on the gravitational polarization of space. With this model, we can relate the density parameters within the Friedmann model, and show that dark matter is bound mass formed from massive dipoles set up within the vacuum surrounding ordinary matter. Aggregate matter induces a gravitational field within the surrounding space, which reinforces the original field. Dark energy, on the other hand, is the energy density associated with gravitational fields both for ordinary matter, and bound, or induced dipole matter. At high CBR temperatures, the cosmic susceptibility, induced by ordinary matter vanishes, as it is a smeared or average value for the cosmos as a whole. Even though gravitational dipoles do exist, no large scale alignment or ordering is possible. Our model assumes that space, i.e., the vacuum, is filled with a vast assembly (sea) of positive and negative mass particles having Planck mass, called planckions, which is based on extensive work by Winterberg. These original particles form a very stiff two component superfluid, where positive and negative mass species neutralize one another already at the submicroscopic level, leading to zero net mass, zero net gravitational pressure, and zero net entropy, for the undisturbed medium. It is theorized that the gravitational dipoles form from such material positive and negative particles, and moreover, this causes an intrinsic polarization of the vacuum for the universe as a whole. We calculate that in the present epoch, the smeared or average susceptibility of the cosmos equals, , and the overall resulting polarization equals, . Moreover, due to all the ordinary mass in the universe, made up of quarks and leptons, we calculate a net gravitational field having magnitude, . This smeared or average value permeates all of space, and can be deduced by any observer, irrespective of location within the universe. This net gravitational field is forced upon us by Gauss’s law, and although technically a surface gravitational field, it is argued that this surface, smeared value holds point for point in the observable universe. A complete theory of gravitational polarization is presented. In contrast to electrostatics, gravistatics leads to anti-screening of the original source field, increasing the original value, , to, , where is the induced or polarized field. In the present epoch, this leads to a bound mass, , where is the sum of all ordinary source matter in the universe, and equals the relative permittivity. A new radius, and new mass, for the observable universe is dictated by the density parameters in Friedmann's equation, and Gauss’s law. These lead to the very precise values, , and, , respectively, somewhat larger than current less accurate estimates.

We propose the Hamiltonian formalism of Bianchi type 1 cosmological model for perfect fluid. We have considered both the equation of state parameter ω and the cosmological constant Λ as the function of volume V(t) which is defined by the product of three scale factors of the Bianchi type 1 line element. We propose a Lagrangian for the anisotropic Bianchi type-1 model in view of a variable mass moving in a variable potential . We can decompose the anisotropic expansion in terms of expansion and shearing motion by Lagrangian mechanism. We have considered a canonical transformation from expanding scale factor to scalar field ø which helps us to give the proper classical definition of that scalar field in terms of scale factors of the mentioned model. This definition helps us to explain the cosmological inflation. We have used large anisotropy(as in the cases of Bianchi models) and proved that cosmic inflation is not possible in such large anisotropy. Therefore we can conclude that the extent of anisotropy is less in case of our universe. Otherwise the inflation theory which explained the limitations of Big Bang cannot be resolved.Part II is contained with some analysis of the lagrangian ; derived in Part I ; on the quintessence model.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates ‘base factors’, such as financial metric growth and earnings transcripts, with ‘external factors’, including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a ‘Hold’ option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

Solar saline water splitting is a promising approach for sustainable hydrogen production, harnessing abundant solar energy and the availability of brine resources, especially in the Atacama Desert. Bischofite (MgCl2·6H2O), a naturally occurring salt, has garnered significant attention due to its wide range of industrial applications and the challenges associated with efficient hydrogen production in arid or hyper arid locations. This work discloses the electrochemical performance of Mn2O3 electrodes using a superposition model based on mixed potential theory and evaluates the superficial performance of this electrode in contact with a 0.5 M nature Bischofite salt solution focusing on hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR), that occurs during saline water splitting. The application of the non-linear superposition model provides valuable electrochemical kinetic parameters that complement the understanding of Mn2O3, this being one of the novelties of the manuscript.

Fractured media are seldom considered viable for storing excess thermal energy seasonally, compared to the more commonly used porous aquifers, due to their heterogeneity, lower storage capacity, and potential for unpredictable flow paths. This study employs COMSOL Multiphysics to create a numerical model of a fractured granite reservoir located below the Bedretto underground laboratory. The energy efficiency of both single-well and doublet systems was evaluated over 25 cycles, each comprising four 91-day phases: injection, storage, production, and resting. Results indicated that the single-well system was more efficient, with energy efficiency increasing with the flow rate. For an injection temperature of 60 °C, the system achieved energy efficiencies of up to 59.3% and 67.7%, after 10 and 25 cycles, respectively, aligning with porous Aquifer Thermal Energy Storage (ATES) system efficiencies at this temperature. Exploration of electricity production using water injected at 120 °C revealed it to be uneconomical and ineffective. However, the most promising use of the retrieved water was found to be direct ground heating for a greenhouse. This technology offers a potential alternative for thermal energy storage in regions lacking suitable porous aquifers, although identifying appropriate locations with the necessary geological characteristics may present significant challenges.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

The current disruptive innovation model has a limitation in incorporating customers’ demands while the customer can be a co-creator of value on performance, design, and price innovation. Thus, this research attempted to explore the disruptive innovation model which can include both the incumbent’s innovation trajectory and customer’s demand, and how incumbents behave to enable disruptive innovation in the smartphone market. Based on the Apple iPhone case study, this study verified that the current disruptive innovation model can incorporate customers’ demand by reflecting their demand in the direction of innovating the performance, design, or price of smartphones with the price-adjusted innovation trajectory. The secondary innovation trajectory which was found uniquely in this study is meaningful since it can be a creative solution for the innovator’s dilemma. Besides, Apple, as an incumbent, has behaved to innovate iPhone performance and design demands of customers creating significant papers and patents as outcomes of innovation activities. This study has practical implications for incumbents’ managers as well as customers to enable disruptive innovation in the smartphone market.

Camouflage is a natural or artificially process to prevent an object from being detected while camouflage breaking is a countering process for the identification of the concealed object. We report that a perfectly camouflaged object in a two-dimensional scene can be retrieved and detected with stereo-vision assisted three-dimensional (3D) imaging perceived with stereopsis. The analysis is based on binocular energy model applied to general 3D settings. We show that a perfectly concealed object with random noise background can be retrieved with vision’s stereoacuity to resolve the hidden structures. The theoretical analysis is further tested and demonstrated with distant natural images taken by a drone-camera, processed with a computer and displayed in an autostereoscopy. The recovered imaging is presented with removal of the background interference to demonstrate the general applicability for camouflage breaking with stereo imaging and sensing.