There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

This study reports an empirical analysis of an all-electric, Net Zero Energy Housing (NZEH) development located in a mixed-humid climate zone (4A, Virginia, USA). Circuit-level energy monitors were used to measure energy consumption and energy production data (solar photovoltaic) at 1-hr intervals in six identical apartments over 24 months. The study employs a multi-step case study methodology to a) empirically evaluate energy consumption and production data, b) identify the temporal variability of energy consumption and production data at different time scales, c) understand the impact(s) of weather and human-building interaction on energy consumption and production, and d) synthesize the study’s “lessons learned” toward data-driven recommendations for future NZEH researchers and practitioners. The study found that the development’s net zero energy goal was achieved in three of six case units and that NZEH housing performance was more influenced by human-building interaction than weather variability. The analysis also found the solar photovoltaic (PV) performance to be reliable across the sampled units over the periods of measurement, suggesting that solar PV could be oversized as an approach to overcome verifiability in HBI and achieve NZEH performance goals.

Through history, particular attention has been paid of the study of the relationship between the energy use and the city structure. Improving energy efficiency in modern agglomerations is the most promising means to mitigate climate change and its impacts. In this current context of globalisation, European Union proposes initiatives and policy targets to rethink the urban development strategies towards the ‘zero energy objectives’. Providing a methodological approach with a simulation district analysis, the present article summarizes how the ‘zero energy’ challenge is analyzed in an existing district (Epinlieu) to articulate the users’ requirements in energy. This study contributes to the scientific discussion of the districts’ urban structure and energy planning by establishing a linkage among the beneficial influence of the KPIs of the districts’ form to increase their energy efficiency and its application in a real case study in Belgium.

Many corporations and governments aspire to become Net Zero Carbon Dioxide by 2030-2050. Achieving this goal requires understanding where energy is produced and consumed, the magnitude of CO₂ generation, and the Carbon Cycle. Many prior proposed solutions focus on reducing future CO₂ emissions from continued use of fossil fuels. Examination of these technologies exposes their limitations and shows that none offer a complete solution. For example, bioethanol is shown to be both carbon and energy inefficient. Direct Air Capture technologies are needed to reduce CO₂ already in the air. The most natural form of Direct Air Capture involves letting nature do the work of creating biomass via photosynthesis. However, it is necessary to break the Carbon Cycle by permanently sequestering that biomass carbon in “landfills” modified to discourage decomposition to CO₂ and methane. Tree leaves and biomass grown on-purpose, such as high yield switchgrass, are proposed as good biomass sources for this purpose. Left unsequestered, leaves decompose with a short Carbon Cycle time constant releasing CO₂ back to the atmosphere. While in any given year, leaves represent a small fraction of a tree’s above ground biomass, leaves can represent a substantial fraction of the total biomass generated by a tree when integrated over a tree’s lifetime. Understanding the chemistry of the distinct phases landfills undergo is the key to minimizing or eliminating decomposition. First, the compact cross-linked structure of cellulose and keeping water out will make it difficult for initial depolymerization to release sugars. Air ingress should be minimized to minimize Phase I aerobic decomposition. pH manipulation can discourage acid formation during Phase II. Lignocellulose is low in nutrients needed for anaerobic decomposition. Inhibitors can be added if needed. The goal is to move quickly to the dormant phase where decomposition stops. The cost for Carbon Capture and Storage (CCS) for growing and sequestering high yield switchgrass is estimated to be lower than CCS for steam reforming of methane hydrogen plants (SRM) and supercritical or combined cycle coal power plants. Thus, sequestration of biomass is a natural, carbon efficient, and low-cost method of Direct Capture. Biomass sequestration can provide CO₂ removal on giga tonnes per year scale and can be implemented in the needed timeframe (2030-2050).

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

This paper provides an open dataset of measured energy use, solar energy production, and building air leakage data from a 328 m² (3,531 ft²) all-electric, zero energy commercial building in Virginia, USA. Over two years of energy use data were collected at 1-hour intervals using circuit-level energy monitors. Over six years of solar energy production data were measured at 1-hour intervals by 56 microinverters. The building air leakage data was measured post-construction per ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization and the United States Army Corps (USACE) Building Enclosure Testing procedure; both pressurization and depressurization results are provided. The architectural and engineering (AE) documents are provided to aid researchers and practitioners in reliable modelling of building performance. The paper describes the data collection methods, cleaning, and convergence with weather data. This dataset can be employed to predict, benchmark, and calibrate operational outcomes in zero energy commercial buildings.

Over 130 countries have committed to reaching net-zero CO2 or GHG emissions by 2050, yet this ambition is rarely underpinned by robust policies. By applying a detailed integrated assessment modelling approach for Brazil, we assess, for the first time, the extent to which the existing and planned local policies could put Brazil on the path to its net zero pledge. This includes quantifying the role of nature-based solutions, such as protection and restoration, and engineered solutions, such as bioenergy with carbon capture and storage (BECCS). We show protection is the single most important climate mitigation measure at relatively low costs, whereas relying heavily on engineered solutions would jeopardise Brazil’s chances of achieving its net zero pledge. We also show that the mismatch between Brazil's short- and long-term climate targets reflects current weak environmental governance. Our analysis reinforces the urgent need for Brazil to eliminate illegal deforestation and go beyond to help fight climate change whilst curbing biodiversity loss.

With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. Agroecological models are an important tool for assessing the sustainability of long-term management, yet applications of such models in boreal zones are scarce. We collated eddy-covariance, soil climate and biomass data to evaluate the simulation of GHG emissions from grassland in eastern Finland using the process-based model DNDC. We simulated gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco) with fair performance. Soil climate, soil temperature and soil moisture at 5 cm were excellent, and soil moisture at 20 cm was good. However, the model overestimated NEE and Reco following crop termination and tillage events. These results indicate that DNDC can satisfactorily simulate GHG fluxes in a boreal grassland setting, but further work is needed, particularly in simulated second biomass cuts, the (>20 cm) soil layers and model response to management transitions between crop types, cultivation, and land use change.

CO₂ emissions have been identified as the main driver for climate change, with devastating consequences for the global natural environment. The steel industry is responsible for ca. 8%-11% of global CO₂ emissions, due to high fossil-fuel and energy consumption. The onus is therefore on industry to remedy the environmental damage caused and to decarbonise production. This desk research report follows a two-tiered route: 1) exploring the Bio Steel Cycle; 2) proposing a seven-step strategy to overcome the challenges within the iron and steel industry. The true levels of CO₂ emissions in the steelmaking process, during the blast-furnace/basic-oxygen-furnace operation, will be detailed first, at 4.61t of CO₂ emissions/t of steel produced. The manuscript proposes a solution for reduced carbon emissions steelmaking with the Bio Steel Cycle components and detail on how to reach net-zero carbon emissions steel production. The 7-step-implementation-strategy as a potential solution to achieve net and true zero carbon emissions steel production in the short to medium-term. The findings of this are pointing towards the conclusion that CO₂ emissions seem to have been under-reported and under-estimated in the past, but the emission can be addressed if the correct scenarios for net-zero steel manufacturing are implemented by 2050.

From the perspective of manufacturing companies, the political, media and economic discourse on decarbonisation of the recent years manifests itself as an increasing social expectation of action. In Germany in particular, this discourse is also being driven forward by powerful companies, respectively sectors, most notably the automotive industry. Against this background, it was examined how German manufacturing companies react to rising societal pressure and emerging policies. It is examined which measures the companies have taken or plan to take to reduce their footprint, which aspirations are associated with this and by which structural characteristics (company size, energy intensity, sector) these are influenced. A mix methods approach was applied, utilising data gathered from approx. 900 companies in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on decarbonisation plans and initiatives announced. We demonstrate that one-size-serves-all approaches are not suitable to decarbonise industry as the situation and ambitions differ considerably depending on size, energy intensity and sector. Even though the level of ambition and urgency is high, particularly micro and energy intensive companies are challenged. The research uncovers a series of questions that call for attention to materialise the ambitions and address the challenges outlined.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.

Considering increasingly ambitious pledges by countries, pressure from society, investors, and clients further up the supply chain, the question for companies is not so much whether to take decarbonisation action, but what action and by when. However, determining an ideal mix of measures to apply ‘decarbonisation efficiency’ requires more than knowledge of technically feasible measures and how to combine them to achieve the most economic outcome: In this paper, the author describes seven aspects which heavily influence the composition of an ‘ideal mix’ that executive leadership needs to take a (strategic) position on. These aspects consider underlying motivations and span across (socio-)economic, technical, regulatory, strategic, corporate culture and environmental factors and further underline the necessity of clarity of definitions. How these decisions influence the determination of the decarbonisation-efficient ideal mix of measures, both in principle but also in terms of specific impact is further explored by providing examples. What choices are taken by German manufacturers in several of the aspects is disclosed by insights from about 850 responses to the ‘Energy Efficiency Index of German Industry’. Knowledge of the status quo, and clarity in definitions, objectives, time frames and scope are key.

We herein described an investigation of a theory, which describes the energies of neutrinos and the source of neutrino oscillations. A series of experiments were conducted to show evidences of the existence a neutrino mass. We also applied theories to explain the reason for the extremely small energy of a neutrino, mainly by employing a vacuum-derived superconducting energy gap from the Bardeen–Cooper–Schrieffer ground state. Moreover, we succeeded in obtaining the transition probabilities of neutrinos’ flavors (i.e., in terms of neutrino oscillation). We focused on the fact that up- and down-quantized space pairs combine by the Lorentz forces, undertake Bose-Einstein condensation, and then create a superconducting energy gap at the energy level of the vacuum with quantum mechanics fluctuation. Eventually, the superconducting energy gap vanishes to form a real body of the neutrino. Furthermore, assuming that the speed of the neutrino is near the speed of light and exhibits Planck’s blackbody emissions, we derived many-body interactions of neutrinos and applied them in Fermi’s golden rule. As a result, the neutrino energy we calculated agreed well within the realms of the experimental results. The calculated transition probabilities of neutrino’s flavor also explain the experiment results very well.

The textbook analysis of vacuum energy density (VED) in flat spacetime follows from Pauli’s lectures of 1951, in which quantum vacuum is modeled as a reservoir of free harmonic oscillators. In his lectures, Pauli shows that deriving a nearly vanishing VED is contingent upon fulfilling three corollary conditions called polynomial-in-mass-constraints. The goal of this work is to evaluate Pauli’s constraints against the Standard Model parameters and the Higgs mechanism of spontaneous symmetry breaking.

This paper proposes a method of extracting energy from zero-point energy and evaluates the amount of energy gained. In addition, this electric circuit-based approach exhibits the Meissner effect, suggesting a new type of superconductivity that does not require refrigeration. The proposed method can provide extremely large amounts of energy, which is more than a conventional power station, without consuming fossil fuels or emitting radiation. Thus, it has the potential to solve the global energy problem. It involves preparing two electric loops containing diodes and connecting the loops together with current sources. The diodes are oriented in the same direction within each loop but in opposite directions in different loops. With this setup, the currents from the current sources build iteratively within the loops, resulting in large output currents. Our numerical analysis indicates that extremely large electric potentials are produced, which in turn yield large output currents. In addition, we confirm numerically that the voltage is zero around a loop and show analytically that the Meissner effect is present, proving the existence of a new type of superconductivity. Furthermore, when we introduce induction coils to not break the loop’s symmetry, they store extremely large amounts of energy and we can thus obtain energy from them via discharge currents.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

Cement manufacturing is one of the most energy-intensive industries in the world. Most of the cost of producing cement is accounted by fuel consumption and power expenditures. Thermal power plants are the major source of electricity in Pakistan. But they are not efficient and environmentally friendly. This study simulates four different models for five cement plants of Pakistan on Homer Pro software and compares the optimal solutions based on the net present cost (NPC), levelized cost of electricity (LCOE) and greenhouse gas (GHG) emissions. Model-1 consists of solar panels, electrolyzer, hydrogen tank, hydrogen generator and converter. Model-2 has only a diesel generator and acts as a base case in this study. Model-3 has solar panels and a battery-converter system. In Model-4, diesel generators, solar panels and converters are considered. Based on NPC, the most optimal model is Model-4, having a 0.249 $/KWh LCOE in islanded systems. The NPC and operating costs are US$540 million and US$ 32.5 million per year, respectively, with a 29.80% reduction in CO2 emissions when compared to the base case. Based on GHG emissions, Model-1 and Model-3 are the best models with 0% GHG emissions. Sensitivity analyses is also performed using the parameters of load, inflation rate and discounted rate. The results prove that the proposed hybrid micropower systems (HMS) can sustainably provide electricity for 24 hours a day to the sites under consideration with minimum objectives.

While the knowledge about the development of understanding positive integers is rapidly growing, the development of understanding zero is not well-known. Here we tested several components of preschoolers’ understanding zero: whether they can use empty sets in numerical tasks, whether they can use empty sets soon after they understand the cardinality principle, whether they know what the word “zero” refers to, and whether they categorize zero as a number. The results show that preschoolers can handle empty sets in numerical tasks as soon as they understand the cardinality principle or even earlier, and some of them know that these sets are labeled as “zero.” However, they are unsure whether zero is a number. These results identify three components of knowledge about zero: operational knowledge, linguistic knowledge, and meta knowledge. To account for these results we propose that preschoolers might understand numbers as the properties of items or objects in a set. In this view, zero cannot be a number, because an empty set does not include any items, and the missing items cannot have any property, excluding also the number property. This model might explain why zero is handled correctly in numerical tasks, while it is not regarded to be a number.

Real-world time series data sets contain a combination of linear and nonlinear patterns, making the time series forecasting problem more challenging. In this paper, a new hybrid methodology is introduced for forecasting univariate time series data sets using a multiplicative error modeling approach. An autoregressive integrated moving average (ARIMA) model is combined with an autoregressive neural network (ARNN) for improving the predictions of individual forecast models. The proposed multiplicative ARIMA-ARNN model glorifies the chances of capturing the different combinations of linear and nonlinear patterns in time series. The model shows outstanding performance on six standard time-series data sets compared to other widely used single and hybrid forecasting models.

Cement industry is one of the highest energy consuming industries. The quantity of fuel and energy needed accounts for most of the cost of cement manufacturing. Thermal power plants generate electricity but are harmful and ineffective by nature. As a backup mechanism to account for main grid failures, batteries can be utilized. In this paper the first ever investigation on battery’s depth of discharge (DOD) for four different kinds of battery technologies is carried out in the framework of cement industry. The intended battery technologies are the lead-acid battery (LA), lithium-ion battery (Li-ion), vana-dium redox battery (VR), and nickel–iron battery (Ni-Fe). Four hybrid energy generation models (HEGMs) for five cement plants of Pakistan using the HOMER pro software are proposed. Cement plants includes Askari Cement Plant, Wah (ACPW); Bestway Cement Plant, Kalar Kahar (BCPKK); Bestway Cement Plant, Farooqia (BCPF); Bestway Cement Plant, Hattar (BCPH); and DG Cement Plant, Chakwal (DGCPC). HEGM-1 comprises of a diesel generator (DGen), a photovoltaic system (PV), a converter, and a battery system. HEGM-2 comprises of a PV system, a converter, and a battery system. HEGM-3 is the grid-connected version of HEGM-1 and HEGM-4 is the grid-connected version of HEGM-2. A base-model consisting of grid only is used as a reference. A multi-criteria decision analysis (MCDA) is performed by formulating a cumulative objective function (COF) which includes net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions. The principal objective is the maximization of COF while simultaneously minimizing the objectives (NPC, LCOE and GHG emissions), based on optimal battery technology and DOD. The results reveal that VR is the most suitable battery technology with 10% DOD. It is achieved for DGCPC with HEGM-3 with 61.49% of NPC, 78.62% of LCOE and 84.00% of GHG emissions reduction as com-pared to the base model.

In our previous papers [1,3], using only the concepts of the zero-point energy and quantized space–times, all the fields including gravity were explained. However, the previous papers had the following limitations: First, the concept of the quantized space-time must be experimentally confirmed. Second, we should clarify the meaning of the quantized Einstein’s gravity equation, which is derived in [1]. Moreover, in another paper [2], we succeeded in describing the neutrinos’ self-energy and their oscillations. However, this paper assumes the rest energy of 3-leptons in advance, which is why we needed to uncover the reason why leptons have 3-generations. As mentioned, using the concepts of the zero-point energy and quantized space–times, we derived the quantized Einstein’s gravity equation in our previous paper [1]. The paper provides an analytical solution of this equalized Einstein’s equation, which implies the conservation of angular momentum in terms of quantized space–times. Employing this solution and without the standard big bang model, a unique form of acceleration equation for the acceleration-expansion universe is derived. Moreover, the temperature of the cosmic microwave background (CMB) emission is also obtained. Further, this solution results in an analytical (not numerical) derivation of the gravity wave. Moreover, based on the configuration of quantized space–times in terms of both electric and magnetic fields, we analytically attempted to calculate every equation in terms of electromagnetic and gravity fields, using the solution of the quantized Einstein’s gravity equation. As a result of this theory, first the calculated acceleration and temperature of CMB emission agree with the measurements. Furthermore, the analytical solution of the quantized Einstein’s gravity equation resulted in all the laws of electromagnetic and gravity fields in addition to the analytically derived gravity wave, which agrees well with the recent measurements. Moreover, the calculations of the energies in the basic configuration of the quantized space–times resulted in all 3-leptons’ rest energies. Considering this basic configuration is uniformly distributed everywhere in the universe, we can conclude that τ-particles or static magnetic field energy derived from the basic configuration of the quantized space–times is the identity of dark energy, which also distributes uniformly in the universe.

What is the ideal soil-grass combination for maximum photosynthesis? In this study, we investigated how soil and grassland types affect photosynthesis in the grasslands of Gannan, China. We divided the grasslands of Gannan into 166 study sites, each with a unique soil-grass combination by intersecting the soil and ecoregion maps using ArcGIS. We obtained 19 years of data on the Net Photosynthesis (PsnNet) of grasslands in the area from 2000 to 2018 and then divided them into “growing season” (June to September) and “non-growing season” (October top May). Between 2000 and 2018, PsnNet of grasslands showed a gradually increasing trend. The effect of soil type on PsnNet was not significant during the growing season. However, it was highly significant during the non-growing season. Among the soil types, grasses that grew in Mollic, Gelic, and Haplic soils had the highest rate of photosynthesis. The difference in PsnNet among the various grass types was highly significant during both seasons. However, Tropical and Subtropical succulent evergreen broad-leaf shrubs and Temperate meadows had the highest rate of photosynthesis. Additionally, there was a highly significant difference in PsnNet among the various soil-grass interactions. In the growing season, TStEgBLS growing Eutric soils had the highest PsnNet. However, SaDBLS growing in Gelic soils had the the highest.

A common spinal condition, spondylolisthesis is the presence of a relative back or forth displacement between the upper and lower vertebra due to one vertebra being oriented away from the smooth curvature of a normal spine. Aging-related illnesses such as degenerative spondylolisthesis are especially burdensome on social welfare and health-care systems in an aging society, especially radiologists and clinical physicians. Therefore, we proposed a computer aided diagnosis algorithm, named LumbarNet, for vertebral slippage detection on clinical X-ray images. Collaborating with i) a P-grade, ii) a piecewise slope detection scheme, and iii) a dynamic shift detection routine, LumbarNet was thus specialized for analyzing complex structural patterns in lumbar spine X-ray images and outcompeted other U-Net based methods. Extensive experiments on lumbar spine X-ray images in standard clinical practices showed that LumbarNet achieved a mean intersection over union value of 0.88 in vertebral region detection and an accuracy of 88.83% in vertebral slippage detection.

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

Best horticulture management has been practiced to reduce the amount of inorganic fertilizer in connection with net (mosquito net) for higher production of Indian spinach. Excessive use of chemical fertilizers in agriculture promoted negative effect on the environment and human health in Bangladesh. The experiment was conducted at the Horticulture Farm of Bangladesh Agricultural University (BAU), Mymensingh during the period February to May 2016. Two factor experiments were conducted on nettings: control (N₀), Netting (N₁) and different fertilizers: control (F₀), Vermicompost 10 t/ha (F₁), Vermicompost 15 t/ha (F₂), 2/3^rdof F₂+ 1/3^rdof F₄ (F₃), Inorganic fertilizer (F₄). Combination of mosquito net and combined of organic and inorganic fertilizers (N₁F₃) gave the significantly higher growth and yield of Indian spinach compared to other treatment combination. The maximum vine length (77.71 cm), after 45 days after sowing was found from N₁F₃ which was 72% higher compared to control. The highest yield of Indian spinach was 38.67 t/ha from N₁F₃ which was 80% higher compared to control. The treatment combination of N₁F₃ provided maximum leaf numbers per plant, leaf length.

New setting is introduced to study failed zero-forcing number and failed zero-forcing neutrosophic-number. Leaf-like is a key term to have these notions. Forcing a vertex to change its color is a type of approach to force that vertex to be zero-like. Forcing a vertex which is only neighbor for zero-like vertex to be zero-like vertex but now reverse approach is on demand which is finding biggest set which doesn’t force. LetNTG : (V,E,σ,μ) be a neutrosophic graph. Then failed zero-forcing number Z(NTG) for a neutrosophic graph NTG : (V,E,σ,μ) is maximal cardinality of a set S of black vertices (whereas vertices in V (G) \ S are colored white) such that V (G) isn’t turned black after finitely many applications of “the color-change rule”: a white vertex is converted to a black vertex if it is the only white neighbor of a black vertex. Failed zero-forcing neutrosophic-number Zn(NTG) for a neutrosophic graphNTG : (V,E,σ,μ) is maximal neutrosophic cardinality of a set S of black vertices (whereas vertices in V (G) \ S are colored white) such that V (G) isn’t turned black afterfinitely many applications of “the color-change rule”: a white vertex is converted to a black vertex if it is the only white neighbor of a black vertex. Failed zero-forcing number and failed zero-forcing neutrosophic-number are about a set of vertices which are applied into the setting of neutrosophic graphs. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs, star-neutrosophic graphs, bipartite-neutrosophic graphs, and t-partite-neutrosophic graphs are investigated in the terms of maximal set which forms both of failed zero-forcing number and failed zero-forcing neutrosophic-number. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form failed zero-forcing number and failed zero-forcing neutrosophic-number. In path-neutrosophic graphs, the set of vertices such that every given two vertices in the set, have distance at least two, forms maximal set but with slightly differences, in cycle-neutrosophic graphs, the set of vertices such that every given two vertices in the set, have distance at least two, forms maximal set. Other classes have same approaches. In complete-neutrosophic graphs, a set of vertices excluding two vertices leads us to failed zero-forcing number and failed zero-forcing neutrosophic-number. In star-neutrosophic graphs, a set of vertices excluding only two vertices and containing center, makes maximal set. In complete-bipartite-neutrosophic graphs, a set of vertices excluding two vertices from same parts makes intended set but with slightly differences, in complete-t-partite-neutrosophic graphs, a set of vertices excluding two vertices from same parts makes intended set. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definition are provided. Using basic set not to extend this set to set of all vertices has key role to have these notions in the form of failed zero-forcing number and failed zero-forcing neutrosophic-number. The cardinality of a set has eligibility to form failed zero-forcing number but the neutrosophic cardinality of a set has eligibility to call failed zero-forcing neutrosophic-number. Some results get more frameworks and perspective about these definitions. The way in that, two vertices don’t have unique connection together, opens the way to do some approaches. A vertex could affect on other vertex but there’s no usage of edges. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to pursue this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Theoretical analysis into the energetics of a novel putative electromagnetic field propulsion device by the author, found that it was able to impart momenergy to the ground state of the electromagnetic field; some rest-energy of the craft was converted to kinetic energy of the craft. Electrical analysis showed that the propulsor was always a net electrical load – if the device accelerated from one frame, then deaccelerated to the original frame, both processes would consume electrical work. The aim of this paper is to look further into this sinking of high-grade electrical energy into the field ground state and to show that an even more pernicious form of 2^nd Law of Thermodynamics exists.

Atmospheric CO2 concentration, temperature and precipitation escalate and it is critical to investigate the agricultural practices best suited to face such new conditions. An important aspect is soil management, which alters soil organic matter content, nutrients and structure, and thus soil/atmosphere interactions and ultimately production. This in vitro study addresses greenhouse gas (GHG) emissions from two long-term soil management experiments in France. One set of soil columns were managed with either grain legume + cover crops or no grain legumes + bare fallow. The other with either plowed or reduced tillage soils. GHG exchange (CO2, N2O) measurements was conducted under different environmental conditions, before and during growth of barley and eventually with three levels of barley biomass cuts (everything removed, cut halfway, intact). The gas exchange was influenced by time and environmental settings, and to a smaller degree management history, with highly variable emissions of N2O. The environmental treatments induced different patterns of effect depending on the management history of the soils. The gas exchange from soil with grain legume was more responsive to the environmental treatments than the rotation without grain legumes. Depending on the history of tillage, the gas exchange responded likewise differently to the induced environment.

Neutrophils play an important role as the first line of innate immune defense. One function of neutrophils, called neutrophil extracellular traps (NETs), has been discovered recently. NETs are extensive fibrous structures released extracellularly from activated neutrophils in response to infection. They are composed of cytosolic protein assembled on a scaffold of released chromatin. These structures suppress the dissemination of micro-organisms in blood by trapping them mechanically, and by exploiting coagulant function to segregate them within the circulation. In addition, NET components (DNA, histone, and granule proteins) also contribute to the triggering of an inflammatory process. NET function, however, can be regarded as a double-edged sword. On one hand, NET formation is an efficient strategy for neutralizing invading micro-organisms. On the other hand, NET can be harmful to the host, as its exposed by-products that are toxic to endothelial cells and parenchymal tissue. We present here the analogous biological and physiological features of the harmful positive amplification loop between inflammation and tissue damage induced by NETosis dysregulation and Coronavirus Disease-2019 (COVID-19) pathogenesis. Considering the rapid evolution of this disease symptoms and its lethality, we hypothesize that COVID-19 progresses under an amplifier loop, leading to an massive, uncontrolled inflammation process. We also describe the correlations of COVID-19 symptoms and biological features with those consecutive to uncontrolled NET formation causing various sterile or infectious diseases. General clinical conditions, and numerous pathological and biological features, are analogous with NETs deleterious effects. We postulate that Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV2) induces a disproportionate virus-induced NET release, and that this plays a key role in COVID-19 pathogenesis. While neutrophils are the principal starting point for extracellular and circulating DNA release, targeting NETs rather than neutrophils themselves may stand for an effective strategy. This paper offers an in-depth review of NET formation, function and pathogenic dysregulation, as well as of current and future therapies to control NET unbalance. As such, it enables us also to suggest new therapeutic strategies to fight COVID-19. In combination with or independent of the latest tested approaches, we propose that, in the short term, deoxyribonuclease I (DNase-1) treatment should be evaluated; we also advocate a significant increase in research on the development of toll-like receptors (TLR) and C-type Lectin like receptors (CLEC) inhibitors, and on anti-IL26 therapies.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

Despite the increase in popularity of Zero Waste (ZW) concept, the successful implementation of this concept in waste management is still facing many challenges. The plastic recycling rate in Australia is currently only about 9.4%, which could leave up to 90.6% of plastic consumption being sent to landfills. The state of Victoria (in Australia) has proposed an ambitious plan to upgrade its waste and recycling system and to divert about 80% of waste from landfills by 2030. The aim of this research is to study Victoria’s current waste management plan and to develop a simulation model to assess the feasibility of it achieving zero plastic waste by 2035. In this direction, a fundamental knowledge of global ZW implementation needs to be acquired in order to understand the challenges, obstacles, and uncertainties in achieving ZW target. A simulation model is established using a method called double baselines. This method was developed as an improvisation to address the limitation of data availability for the model development. The model will run on 4 scenarios including one from Victoria’s current plan. Outcomes from the model are produced in comparative charts covering 6 key considerations including the rates of plastic consumption, waste to landfill, diversion, recycling, relative accumulative cost and effort. The findings of this study pointed out that Victoria’s current plan are feasible for its goal and presented with opportunities for improvement especially towards zero plastic waste. Besides, study results also reveal that the Victoria’s current plan to achieve 80% diversion rate by 2030 is possible but the zero plastic waste target by 2035 is less likely to happen.

The zero velocity update(ZUPT) algorithm is the core of a foot-mounted pedestrian navigation system. The zero velocity detection method is the premise and guarantee of the effective application of the ZUPT algorithm. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. The detection accuracy of the existing zero velocity detection methods is easy to be affected by users and environment. A novel zero velocity detection method based on the plantar pressure is proposed in this paper, which has higher detection accuracy and better environmental adaptability. First, the paper analyzes the motion characteristics of foot during walking. Second, the inherent relationship between the plantar pressure and the gait change during walking is studied based on the pressure sensor. Then, the model of the zero velocity detection method using the plantar pressure is established. Finally, the indoor and outdoor multi-scene experiments show that this method not only has a high detection accuracy, but also has good adaptability to users and walking environment.

We propose a new theory beyond the standard model of elementary-particle physics. Employing the concept of a quantized spacetime, our theory demonstrates that the zero-point energy of the vacuum alone is sufficient to create all the fields, including gravity, the static electromagnetic field, and the weak and strong interactions. No serious undetermined parameters are assumed. Furthermore, the relations between the forces at the quantum-mechanics level is made clear. Using these relations, we quantize Einstein’s gravitational equation and explain the Dark Energy in our universe. Beginning with the zero-point energy of the vacuum, and after quantizing Newtonian gravity, we combine the energies of a static electromagnetic field and gravity in a quantum spacetime. Applying these results to the Einstein gravity equation, we substitute the energy density derived from the zero-point energy in addition to redefining differentials in a quantized spacetime. We thus derive the quantized Einstein gravitational equation without assuming the existence of macroscopic masses. This also explains the existence of the Dark Energy in the universe. For the weak interaction, by considering plane-wave electron and the zero-point energy, we obtain a wavefunction that represents a β collapse. In this process, from a different point of view than Weinberg-Salam theory, we derive the masses of the W and Z bosons and the neutrino, and we calculate the radius of the neutron. For the strong interaction, we previously reported an analytical theory for calculating the mass of a proton by considering a specific linear attractive potential obtained from the zero-point energy, which agrees well with the measurements. In the present study, we calculate the strong interaction between two nucleons, i.e., the mass of the pi-meson. The resulting calculated quantities agree with the measurements, which verifies our proposed theory.

The aim of this paper is to discuss the relations between various notions of sequential completeness and the corresponding notions of completeness by nets or by filters in the setting of quasi-metric spaces. We propose a new definition of right $K$-Cauchy net in a quasi-metric space for which the corresponding completeness is equivalent to the sequential completeness. In this way we complete some results of R.~A. Stoltenberg, Proc. London Math. Soc. \textbf{17} (1967), 226--240, and V.~Gregori and J.~Ferrer, Proc. Lond. Math. Soc., III Ser., \textbf{49} (1984), 36. A discussion on nets defined over ordered or pre-ordered directed sets is also included.

Rice-Tartary buckwheat is a special type of Tartary buckwheat with easy hulling thin shell. The local rice-Tartary buckwheat ‘cv. Xiaomiqiao’ (XMQ) was used to explore the characteristics of photosynthesis, starch synthesis, yield and quality, as compared to Tartary buckwheat ‘cv. Jinqiao 2’(JQ2). XMQ showed significantly lower values of net photosynthetic rate, ADPGase activity and starch synthase activity at one or more periods during grain filling, as compared to that of JQ2. The effective branch number and grain number per plant of XMQ were similar to that of JQ2 regardless of different years, but the 1000-grain weight was significantly lighter that resulted in the significantly lower yield. XMQ accumulated similar contents of starch (amylose, amylopectin) and protein (glutelin, albumin, prolamin, globulin) to that of JQ2, but exhibited significantly lower flavonoid content. The values of peak viscosity and hot paste viscosity in XMQ were similar to that of JQ2, but the values of cool paste viscosity, breakdown and setback were significantly lower. In conclusion, XMQ showed low yield due to the insufficient of photosynthesis and starch synthesis. XMQ exhibited high nutritional quality as well as normal Tartary buckwheat and stored 18.88mg g-1 of flavonoid, that can be consumed as daily diet. The present results will provide a basis for food processing and breeding of rice-Tartary buckwheat.

Animal behavior analysis is a crucial tasks for the industrial farming. In an indoor farm setting, extracting Key joints of animal is essential for tracking the animal for longer period of time. In this paper, we proposed a deep network that exploit transfer learning to trained the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on hourglass stacked dense-net. In order to train the network, key frames are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.

Pedestrian detection is the core of driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing down or emergency stopping to keep pedestrians safe, and improves the security when the vehicle is moving. Suffered from weather, lighting, clothing, large pose variations and occlusion, the current pedestrian detection still has a certain distance from the practical applications. In recent years, deep networks have shown excellent performance for image detection, recognition and classification. Some researchers employed deep network for pedestrian detection and achieve great progress, but deep networks need huge computational resources which make it difficult to put into practical applications. In real scenarios of autonomous vehicle, the computation ability is limited. Thus, the shallow networks such as UDN (Unified Deep Networks) is a better choice since it performs well on consuming less computation resources. Base on UDN, this paper proposes a new deep network model named as two-stream UDN, which augments another branch for solving traditional UDN’s indistinction of the difference between trees / telegraph poles and pedestrians. The new branch accepts the upper third part of the pedestrian image as input, and the partial image has less deformation, stable features and more distinguished characters from other objects. For the proposed two-stream UDN, multi-input features including HOG feature, Sobel feature, color feature and foreground regions extracted by GrabCut segmentation algorithms are fed. Compared with the original input of UDN, the multi-input features are more conducive for pedestrian detection since the fused HOG features and significant objects are more significant for pedestrian detection. Two-stream UDN is trained through two steps: First, the two sub-networks are trained until converge; then we fuse results of the two subnets as the final result and feed it back to the two subnets to fine tune network parameters synchronously. To improve the performance, Softplus is adopted as activation function to obtain faster training speed, and positive samples are mirrored and rotated with small angle to make positive and negative samples more balanced.

Internet of Things includes all connected objects from small embedded systems with low computational power and storage capacities to efficient ones, as well as moving objects like drones and autonomous vehicles. The concept of Internet of Everything expands upon this idea by adding people, data and processing. The adoption of such systems is exploding and becoming ever more significant, bringing with it questions related to the security and the privacy of these objects. A natural solution to data integrity, confidentiality and single point of failure vulnerability is the use of blockchains. Blockchains can be used as an immutable data layer for storing information, avoiding single point of failure vulnerability via decentralization and providing strong security and cryptographic tools for IoE. However, the adoption of blockchain technology in such heterogeneous systems, containing light devices, presents several challenges and practical issues that need to be overcome. Indeed, most of the solutions proposed to adapt blockchains to devices with low resources confront difficulty in maintaining decentralization or security. The most interesting are probably the Layer 2 solutions which build offchain systems strongly connected to the blockchain. Among these, zk-rollup is a promising new generation of Layer 2/off-chain schemes which can remove the last obstacles to blockchain adoption in IoT, or more generally, in IoE. Despite their promises illustrated by recent systems proposed by startups and private companies, very few scientific publications explaining or applying this barely-known technology have been published, especially for non-financial systems. In this context, the objective of our paper is to fill this gap for IoE systems in two steps. We first propose a synthetic review of recent proposals to improve scalability including onchain (consensus, blockchain organization, ...) and offchain (sidechain, rollups) solutions and we demonstrate that zk-rollups are the most promising ones. In a second step, we focus on IoE by describing several interesting features (scalability, dynamicity, data management, ...) that are illustrated with various general IoE use cases.

Mill scale is the by-product of iron and steel industry. It can be recycled via sinter plant or solid as sinter feed materials. 85 to 90% of the constituent particles are more than 0.008 inches. The iron content is near around 70 %, with a very small amount of alkaline compounds and non-ferrous metals. Mill scale is polluted and contaminated with lubricants, oils and greases from the equipment. In this experiment for recovering of oil from mill scale, it is treated with three different methods. Then all methods were compared according to the ability of oil elimination and iron recovered. After washing and boiling with surfactants, large amount of polluted waste water becomes a problem for environment so this wastewater was utilized for making of Microbial Fuel Cells (MFCs), production of electricity in MFC depends on the biodegradation of organic materials, so different waste were added in wastewater for making MFC. The remaining of MFC was converted in bioethanol, biodiesel and biofertilizer. So this research is economical and environmentally friendly and fulfilled the important aspect of green environment with zero waste. It will be a mile stone in the research of metallurgy, environmental engineering, recycling technology and zero waste management.

Zero-Knowledge Proofs ZKP provide a reliable option to verify that a claim is true without giving detailed information other than the answer. A classical example is provided by the ZKP based in the Graph Isomorphism problem (GI), where a prover must convince the verifier that he knows an isomorphism between two isomorphic graphs without publishing the bijection. We design a novel ZKP exploiting the NP-hard problem of finding the algebraic ideal of a multivariate polynomial set, and consequently resistant to quantum computer attacks. Since this polynomial set is obtained considering instances of GI, we guarantee that the protocol is at least as secure as the GI based protocol.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

Modern portfolio theory claims that diversification into non-correlated or negatively correlated activities reduces the overall risk of a portfolio. Considering the total income of a bank as a portfolio of interest income and non-interest income, this paper investigates how the variability of interest income and non- interest income, and covariance between interest income and non-interest income influence the various risk factors of banks. We set out a study in the Indian context. We have extracted data for the period 2005-2017 and employed an extended version of Ridge, Lasso and Elastics Net regression to take care of multi-collinearly in our data. We have considered 10-fold cross-validation techniques to get optimal values of tuning parameters for Ridge, Lasso, and Elastics Net regression (which is a convex combination of ridge and the LASSO). We have compared different regression techniques by comparing RMSE and R2. We observe that non-interest income is positively correlated with interest income in the Indian context, but it does stabilize variance, idiosyncratic risk & market risk (Beta) of Indian Banks.

U-turns and left turns are sometimes forbidden even though it increases travel distances. The greater travel distances are sometimes outweighed by the improved movement through intersections due to there being fewer conflicting lanes of traffic. One can, further, forbid straight-throughs. Restricting a sufficient number of turns can make intersections free from crossing lanes of traffic (``zero traffic conflict,'' ``ZTC"), though there may still be merging lanes of traffic. It's possible to make \begin{it}all\end{it} intersections in a road \begin{it}network\end{it} ZTC. However, keeping all destinations accessible and travel distances moderate requires careful selection of allowed driving directions and turning directions. We demonstrate through numerical microscopic and macroscopic simulations that there are road networks and ranges of traffic loads for which, in comparison with conventional schemes, ZTC road network can carry approximately $50$\% more vehicular traffic without incurring gridlock.

Controversial hypotheses to explain consciousness exist in many fields of science, psychology and philosophy. Recent experimental findings in quantum cognition and magnetic resonance imaging have added new controversies to the field, suggesting that the mind may be based on quantum computing. Quantum computers process information in quantum bits (qubits) using quantum gates. At a first glance, it seems unrealistic or impossible that the brain can meet the challenges to provide either of these. Nevertheless, we show here why the brain has the incredible ability to perform quantum computing and how that may be realized.

We present a discussion and analysis regarding the benefits and limitations of open and non-anonymized peer review based on literature results and responses to a survey on the reviewing process of alt.chi, a more or less open-review track within the CHI conference, the predominant conference in the field of human-computer interaction (HCI). This track currently is the only implementation of an open-peer-review process in the field of HCI while, with the recent increase in interest in open science practices, open review is now being considered and used in other fields. We collected 30 responses from alt.chi authors and reviewers and found that, while the benefits are quite clear and the system is generally well liked by alt.chi participants, they are reluctant to see it used in other venues. This concurs with a number of recent studies that suggest a divergence between support for a more open review process and its practical implementation. The data and scripts are available on https://osf.io/vuw7h/, and the figures and follow-up work on http://tiny.cc/OpenReviews.

The world is facing several global issues such as food and energy crisis, climate change and greenhouse gases emissions. To subdue these issues, many entities from academia and industries have innovated alternate techniques of performing regular activities which cause such problems. One of these innovations is the introduction of vertical and zero acreage farming in the field of sustainability. These carry the potential to solve one of the most important affairs of food security in most countries of the world. But, this technology has been in its nascent stage for many years. This paper uses a comprehensive framework proving the feasibility of initiating vertical farming on university campuses to feed the staff and students, which could also set an example to the rest of the world into using this technique on a wider scale. The study chose Huazhong University of science and technology (HUST) in Wuhan city, China for accessing the return on investment and food sufficiency if vertical farming is implemented. Using Central Limit Theorem, a statistical model was developed, and various scenarios were analyzed. The results indicated that if a separate vertical farm is constructed, the breakeven can be achieved in a range of 10-20 depending on parameters such as type of operation, number of floors and amount of vegetation. The study has shown that the use of vertical farming cannot only bring in revenue for the campus but also aid in mitigating climate change.

The spectral properties of a one-dimensional photonic crystal bounded by a resonant absorbing nanocomposite layer with the near-zero permittivity have been studied. The problem of calculating the transmittance, reflectance, and absorptance spectra of such structures at the normal and oblique incidence of light has been solved. It is shown that, depending on the permittivity sign near zero, the nanocomposite is characterized by either metallic or dielectric properties. For the first time, the possibility of simultaneous formation of the Tamm plasmon polariton at the photonic crystal/metallic nanocomposite interface and the localized state similar to the defect mode with the field intensity maximum inside the dielectric nanocomposite layer is demonstrated. Specific features of field localization at the Tamm plasmon polariton and defect mode frequencies are analyzed.

Neuroendocrine neoplasms (NENs) comprise a heterogeneous group of rare malignancies mainly originated from hormones secreting cells, which are widespread in human tissues. The identification of mutations in ATRX/DAXX genes in sporadic NENs, as well as the high burden of mutations scattered throughout MEN-1 gene in both sporadic and inherited syndromes, provided new insights into the molecular biology of tumour development. Other molecular mechanisms, such as the NOTCH signaling pathway, have shown to play an important role in the pathogenesis of NENs. NOTCH receptors are expressed on neuroendocrine cells and generally, act as tumour suppressor proteins, but in some contexts can function as oncogenes. The biological heterogeneity of NENs suggests that to fully understand the roles and the potential therapeutic implications of gene mutations and NOTCH signaling in NENs, a comprehensive analysis of genetic alterations, NOTCH expression patterns and their potential roles across all NEN subtypes is required.

We have an unprecedented ability to analyze and map the Earth’s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (<1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to robustly and automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on novel images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m. We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.

According to the World Food Programme (WFP), the projected increase in the human population stands at 2 billion people by 2050. At the same time, world food production is witnessing a declining trend over recent years, and 690 million (8.9%) of the world's population are already in severe starvation. Climate variability and climate change impacts on food security are very eminent today. For this reason, this study explored the real effects of climate variability and change on food security in Africa by applying the system Generalized Method of Moments (GMM) and the Panel Corrected Standard Errors (PCSEs) estimators on data from 2001–2018 for 38 selected African countries. The findings reveal that higher amounts of precipitation positively influence food security along two dimensions (food availability and utilization). Hotter temperatures negatively impact food availability and utilization. However, it aids food accessibility in Africa. Similarly, carbon dioxide emissions improve food availability and are harmful to food accessibility and food utilization in Africa. Consequently, the effects of climate variability and change on food security in Africa are undesirable, thereby putting the continent at risk of food insecurity over the long run. Given these findings, the study made appropriate recommendations for policy change to address the negative effects of climate variability and change on food security in Africa.

General relativity is a theory for gravitation based on Riemannian geometry, difficult to compatibilize with quantum mechanics. This is evident in relativistic problems in which quantum effects cannot be discarded. For example in quantum gravity, gravitation of zero-point energy or events close to a black hole singularity. Here, we set up a mathematical model to select general relativity geodesics according to compatibility with the uncertainty principle. To achieve this, we derived a geometric expression of the uncertainty principle (GeUP). This formulation identified proper space-time length with Planck length by a geodesic-derived scalar. GeUP imposed a minimum allowed value for the interval of proper space-time which depended on the particular space-time geometry. GeUP forced the introduction of a “zero-point” curvature perturbation over flat Minkowski space, caused exclusively by quantum uncertainty but not to gravitation. When applied to the Schwarzschild metric and choosing radial-dependent geodesics, our mathematical model identified a particle exclusion zone close to the singularity, similar to calculations by loop quantum gravity. For a 2 black hole merger, this exclusion zone was shown to have a radius that cannot go below a value proportional to the energy/mass of the incoming black hole multiplied by Planck length.

The sustainability of irrigated agriculture depends on the quality of irrigation water used. The electrolyte concentration (EC) of irrigation water may lead to the accumulation of salts in the root zone layers and affect the physiological functions of the crop by osmotic and ion toxicity effects. Further, the cationic and anionic composition of the water may alter the exchangeable cation composition of the soil and as well as its pH. Because of the dominance of sodium salts in many sources of irrigation water, parameters related to sodium such as exchangeable sodium percentage (ESP) of soils and sodium adsorption ratio (SAR) of soil solutions have been commonly used to study the effects of sodium in irrigation water on soil structural stability. Quirk and Schofield concept of ‘threshold electrolyte concentration’ (TEC) has shown the importance of electrolytes in preventing the effects of sodium on soil structure. Based on this concept, several models have been proposed to relate ESP or SAR with EC to predict the possible impacts of irrigation water on soil structural stability. However, many research reports indicate that this relationship varies with soils and a given model is not suitable for all types of soils. Further, the effects of potassium and magnesium in the processes leading to clay dispersion are disregarded in these models. This essay analyses all the factors involved in the structural failure of soils with different cationic composition, identify the defects in these TEC models and re-defines TEC on the basis of new insights on dispersive and flocculating charges of soils. This review does not deal with EC effects on crops and also the role of contaminant ions not involved with soil structural stability.

In order to explore the grassland ecosystem productivity and landscape ecological patterns of main pastoral grasslands in China, it provides a theoretical basis for the efficient implementation of ecological engineering and rational management of grassland resources in the region. This study analyzed the changes in grassland area, landscape index (LSI), and net primary productivity (NPP) in seven major pastoral areas in China in 1985, 1995, 2005, and 2015. Results showed that (1) the sizes of the grassland study area in 1985, 1995, 2005, and 2015 were 248.34, 243.93, 245.80, and 244.660 km2 respectively. (2) The dominance of grassland in the landscape pattern increased from 2005 to 2015 as compared with that in 1985–1995 and 1995–2005. The degrees of spatial heterogeneity were reduced. (3) The grassland NPP showed spatial and temporal differences. The average NPP of grassland increased by 21.30%, 16.47%, and 36.17% during 1985–1995, 2005–2015, and 1985–2015, respectively. From 1995 to 2005, the average NPP decreased by 5.05 gC/m2, which is equivalent to -3.61% of the average NPP in 1995. The total amount of grassland NPP in the study area was the greatest in 2015, showing increments of 36.37% and 16.61% compared with those in 1985 and 2005, respectively.

In this study, we discuss the properties of absolute vacuum space and how these properties can play a vital role in creating a mechanism in which the very first particle gets created simultaneously everywhere and we find the limit in which when the absolute vacuum volume reaches will lead to the collapse that leads to the creation of the first particle. This discussion is made following to the elementary dimensions theory study that was peer-reviewed at the end of 2020, everything in the universe is made from four elementary dimensions, these dimensions are the three spatial dimensions (X, Y, and Z) and the Vacuum resistant as the factor of change among the four, time itself wasn’t considered as the fourth dimension, rather time corresponds to a factor of change, and during the research it was found out that the Vacuum resistant is the factor of change in the Absolute Vacuum space, where time is a hypothetical concept, that represents changes during certain events compared to a constant change rate event.Therefore, time does exist, but as a factor of change, and as the Vacuum resistant in the absolute vacuum space, Time= factor of change= Vacuum resistant. In the study, the internal and external vacuum resistant volume equivalent is found, External Vacuum resistant=3.2857602*10^15 *mass. This equation is used to identify the amount of Free external vacuum resistant created during nuclear fission and fusion: Initial mass of the excited nucleuses mass of the created new nucleuses+ 3.2857602*10^15 * the lost Mass. In elementary dimensions, the energy created during nuclear reactions is equivalent to the free External vacuum resistant created through nuclear reactions, and mass is equivalent to the internal Vacuum resistant.

This paper presents the design of a high frequency zero voltage switching (ZVS) full-bridge converter with a phase-shifted driving signal for photovoltaic applications. The resonant power converter can provide high-power capacity under high-frequency operation. The proposed power converter can also reduce the size of the transformer under the same power rating. The high-frequency transformer was developed by using the resonant and switching frequencies of the power converter to reduce the switching loss and to improve the system efficiency. Phase-shifted modulation was selected to drive the switches of a full-bridge power converter based on the switching loss minimization method. The desired output voltage was controlled using a closed-loop controller under a loop gain stability margin. The simulation results showed that the output voltage can be controlled to the desired constant when the input voltage changes from 30 VDC to 60 VDC. The desired output voltage of power converter is constant at 400 VDC. The power converter can transfer the DC supply to a 220 VAC household via grid-connected inverter. Therefore, the proposed study showed the effectiveness of the phase-shift ZVS full-bridge power converter with high-frequency transformer. This power converter can control the operation of the desired voltage system and has a small sizing of power converter system, low switching loss, and high system efficiency.

The aim of this article is to summarize recent bioinformatic and statistical developments applicable to NMR-based metabolomics. Extracting relevant information from large multivariate datasets by statistical data analysis strategies may be of considerable complexity. Typical tasks comprise for example classification of specimens, identification of differentially produced metabolites, and estimation of fold changes. In this context it is of prime importance to minimize contributions from unwanted biases and experimental variance prior to these analyses. This is the goal of data normalization. Therefore, special emphasize is given to different data normalization strategies. In the first part, we will discuss the requirements and the pros and cons for a variety of commonly applied strategies. In the second part, we will concentrate on possible solutions in case that the requirements for the standard strategies are not fulfilled. In the last part, very recent developments will be discussed that allow reliable estimation of metabolic signatures for sample classification without prior data normalization. In this contribution special emphasis will be given to techniques that have worked well in our hands.

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.

Chile faces a serious crisis on housing affordability given that most of the population is unable to secure a house. While housing prices between 2008 increased by 70.96%, wages only increased by 20.43%. This article presents the analysis of the housing prices configuration for the main district in the country: Santiago Centro. The assessment focuses on verticalised housing buildings constructed between 2015 and 2019. The article develops an exploratory study on the price of housing in the city of Santiago de Chile, to generate a diagnosis to identify the role played by expectations of profitability when configuring that price. Based on the information generated, we seek to contribute to the discussion on public policies that advance towards the development of affordable housing for households in central communes with high urban value, as is the case of Santiago Centro. We hypothesize that profitability expectations of real estate developers play a key role in the housing prices and an adjustment in the profit ratios may increase the affordability keeping the housing market over profitable rates. The research is relevant due to the lack of data transparency in the Chilean housing market, where access to investment costs, land values, yields and house price formation are not part of the public discussion, even though this implies that many households are facing severe difficulties in paying for access to decent housing.

Normally, the Nernst voltage calculated from the concentration of the reaction gas in the flow channel is considered to be the ideal voltage (reversible voltage) of the oxyhydrogen fuel cell, but actually it will cause a concentration gradient when the reaction gas flows from the flow channel through the gas diffusion layer to the catalyst layer. The Nernst voltage loss in fuel cells in most of the current literature is thought to be due to the difference in concentration of reaction gas in the flow channel and concentration of reaction gas on the catalyst layer at the time when the high net current density is generated. Based on the Butler-Volmer equation in oxyhydrogen fuel cell, this paper demonstrates that the Nernst voltage loss is caused by the concentration difference of reaction gas in flow channel and on the catalytic layer at the time when equilibrium potential (Galvanic potential) of each electrode is generated.

Liquid metal alloy, incorporated with microfluidic manipulation, has become a promising candidate for flexible resistive sensor array (RSA) that can imitate the functions of human skin. One advantage of RSA with shared rows and shared columns is to reduce the number of wires from M × N to M + N (rows: M and columns: N) and hence to greatly lessen the complexity and burden on the electrical system. The associated drawback is the crosstalk effect between adjacent elements during measurement. Although many literatures have reported several methods to resolve this limitation, almost all of them focus only on the high resistance value (≥100 Ω) RSA. There is a lack of detailed experimental data that addresses low resistance RSA with sensing elements below 100 Ω. Here, we aim to fill the gap of this field. We established two common RSA readout systems, i.e. zero potential methods (ZPM) (setting non-scanned-sampling-electrode zero potential (S-NSSE-ZP) and setting non-scanned-sampling-electrode zero potential with amplifier (S-NSSE-ZP-A)) and to compare their performances in low resistance value (≤100 Ω) RSA. For ideal resistor RSA, the measurement results show that S-NSSE-ZP has at least one time higher error than S-NSSE-ZP-A.

This paper aims to investigate the criteria of behaviour of certain type of third order neutral generalized difference equations with distributed delay. With the technique of generalized Riccati transformation and Philos-type method, some oscillation criteria are obtained to ensure convergence and oscillatory solution of suitable example is listed to illustrate the main result.

This paper proposes a single-stage asymmetrical half-bridge fly-back (AHBF) converter with resonant mode using dual-mode control. The presented converter has an integrated boost converter and asymmetrical half-bridge fly-back converter and operates in resonant mode. The boost-cell always operates in discontinuous conduction mode (DCM) to achieve high power factor. The presented converter operates simultaneously using a variable-frequency-controller (VFC) and pulse-width-modulation (PWM) controller. Unlike the conventional single-stage design, the intermediate bus voltage of this controller can be regulated depending on the main power switch duty ratio. The asymmetrical half-bridge fly-back converter utilizes a variable switching frequency controller to achieve the output voltage regulation. The asymmetrical half-bridge fly-back converter can achieve zero-voltage-switching (ZVS) operation and significantly reduce the switching losses. Detailed analysis and design of this single-stage asymmetrical half-bridge fly-back converter with resonant mode is described. A wide AC input voltage ranging from 90 to 264 Vrms and output 19 V/ 120 W prototype converter was built to verify the theoretical analysis and performance of the presented converter.

To estimate the recreational value provided by the Foy’s Lake annually using the most applicable model for on-site data is the main objective of this study. Adhere to the objective of this study; Individual Travel Cost Method (ITCM) has been applied and Zero Truncated Poisson Regression Model has been found plausible among other models to estimate consumer surplus. Based on the findings of the study, an estimate of the consumer surplus or recreational benefits per trip per visitor can be recommended as BDT 5,875 or US $ 73.44 and counting the consumer surplus per trip per visitor, the annual recreational value (total consumer surplus) provided by the lake is found to be BDT 321 million or US $ 40.2 million.

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identify potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.

In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part is retained for securing aerodynamic performance by manual work. However, this procedure is time-consuming and depends on the human experience. In this paper, we defined retained theoretical leading/trailing edge as the reconstruction area. To accelerate the reconstruction process, an anchor-free neural network model based on Transformer was proposed, named LETR (Leading/trailing Edge Transformer). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9\%, which surpassed our baseline model, Deformable DETR by 1.1\%. Furthermore, we modified LETR’s convolution layer and named the new model after GLETR (Ghost Leading/trailing Edge Transformer) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1\%) by test results.

Malaria is endemic in Nigeria and remains a major public health problem, taking its greatest toll on children under age 5 and pregnant women, although it is preventable, treatable, and curable. This study investigates the Impact of socio-economic factors and indoor mosquito control on malaria prevalent among pregnant women in Nigeria using logistic regression. To achieve this, secondary data obtained from 2015 Nigeria Malaria Indicator survey, executed by the National Malaria Elimination Programme (NMEP) and the National Population Commission (NPopC), with a nationally representative sample of more than 8,000 consisting of 7,745 households. The results from the logistic regression with odds ratio revealed that pregnant women are more like to be affected by malaria fever (though not significant) compared to women that are not pregnant. The income levels of the household does not significant reduce the incidence of malaria fever among pregnant women in Niger. Concerning the malaria presenting measure, only dwelling sprayed by private company significantly reduce the incidence of malaria fever among pregnant women (P-value=0.020<0.05) compared to dwelling sprayed by government and NGOs and also to Insecticide Treated Net. Also pregnant women in the urban centers are less likely to have malaria fever compared to pregnant women in rural communities in Nigeria. Also, pregnant women with atleast a secondary school level of education are less likely to be affected by malaria fever compared to pregnant women with no formal education. The fitted logistic model passed the goodness-of-test fit; the classification test for the logistic model was correctly classified at about 67.02%. Therefore, this study recommends that government and NGOs should intensify their efforts in the area of dwelling spraying, awareness campaign of the danger of malaria fever among pregnant women and infants, engaged in effective distribution of insecticide treated net in order to reduce the incidence of malaria fever among pregnant women living in rural communities in Nigeria.

Uganda possesses natural rainforests that serve enormous environmental ecosystems and biodiversity services. Moreover, the country is known for its various tropical rainforest hardwoods, birds, and animal species. Over the years, the trend in the natural forest land has declined at an alarming rate; hence need to investigate the possible drivers. The loss of such biodiversity and ecosystems risks desertification and extreme climatic condition. As the world moves towards Zero Deforestation 2030, understanding the determinants of deforestation and forest degradation is paramount. Therefore, the main objective of this study was to understand the impact and relationships between net forest conversion, energy emission, agriculture, and forest production of Roundwood. We used data from FAO for the period 2004-2016. Using the ADF and KPSS test, we checked for the unit root presence in the variables. Also, the study used two different regression models: multiple linear regression and dynamic linear model. To analyze the determinants of deforestation, we used net forest conversion in Uganda. There was 94 % variation in the dependent variable (Net Forest conversion). The outcome of the dynamic linear regression showed that agriculture and energy emission positively impact net forest conversion. Based on our findings, this study recommended the modernization of agriculture by the government of Uganda to stop cutting down the forests on a big scale. Also, the study suggested that the government strictly legislate Roundwood and wood fuels/charcoal and firewood to reduce huge dependency on forests toward Zero-Deforestation by 2030. If well-structured and implemented, government policies could solve the unnecessary over dependency on the rainforest, the heart of the region's climatic conditions.

Long-term durability of the vacuum edge-seal plays a significant part in retrofitting triple vacuum glazing (TVG) to existing buildings in achieving towards zero-energy buildings (ZEB) target. Vacuum pressure decrement with respect to time between panes affect the thermal efficiency of TVG. This study reports a 3D finite element model, with validated mathematical methods and comparison, for the assessment of the influence of vacuum pressure diminution on the thermal transmittance (U value) of TVG. The centre-of-pane and total U values of TVG calculated to be 0.28 Wm⁻²K⁻¹ and 0.94 Wm⁻²K⁻¹ at the cavity vacuum pressure of 0.001 Pa. The results suggests that a rise in cavity pressure from 0.001 Pa to 100 kPa increases the centre-of-pane and total U values from 0.28 Wm⁻²K⁻¹ and 0.94 Wm⁻²K⁻¹ to 2.4 Wm⁻²K⁻¹ and 2.58 Wm⁻²K⁻¹, respectively. The temperature descent on the surfaces of TVG between hot and cold sides’ increases by decreasing the cavity vacuum pressure from 50 kPa to 0.001 Pa. To maintain the cavity vacuum pressure of 0.001 Pa for over 20 years of life span in the cavity of 10 mm wide edge sealed triple vacuum glazing, non-evaporable getters will maintain the cavity vacuum pressure that will enable the long-term durability to TVG.

A nonrenewable resource extraction game model is analyzed in a differential game theory framework with random duration. If the c.d.f. of the final time is discontinuous, the related subgames are differentiated based on the position of the initial instant with respect to the jump. We investigate properties of optimal trajectories and of imputation distribution procedures if the game is played cooperatively.

The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions.

Modelling errors, robust stabilization/tracking problems under parameter and model uncertainties complicate the control of the flexible underactuated systems. Chattering-free sliding-mode based input-output control law realizes robustness against the structured and unstructured uncertainties in the system dynamics and avoids excitation of unmodeled dynamics. The main purpose is to propose a robust adaptive solution for stabilizing and tracking direct-drive (DD) flexible robot arms under parameter and model uncertainties, as well as external disturbances. A lightweight robot arm subject to external and internal dynamic effects was taken into consideration. The challenges are compensating actuator dynamics with the inverter switching effects and torque ripples, stabilizing the zero dynamics under parameter/model uncertainties and disturbances while precisely track the predefined reference position. The precise control of this kind of system demands an accurate system model and knowledge of all sources that excite unmodeled dynamics. For this purpose, equations of motion for a flexible robot arm were derived and formulated for the large motion via Lagrange’s method. The goals were determined to achieve high-speed, precise position control, and satisfied accuracy by compensating the unwanted torque ripple and friction that degrades performance through an adaptive robust control approach. The actuator dynamics and their effect on the torque output were investigated due to the transmitted torque to the load side. The high-performance goals, precision&robustness issues, and stability concerns were satisfied by using robust-adaptive input-output linearization-based control law combining chattering-free sliding mode control (SMC) while avoiding the excitation of unmodeled dynamics.

Molten salt reactors have gained substantial interest in the last years due to their flexibility and their potential for simplified closed fuel cycle operation for massive net-zero energy production. However, a zero-power reactor experiment will be an essential first step into the process delivering this technology. The choice of the optimal reflector material is one of the key issues for such experiments since on the one hand it offers huge cost saving potential due to reduced fuel demand, on the other hand an improper choice of the reflector material can have negative effects on the quality of the experiments. The choice of the reflector material is for the first time introduced through a literature review and a discussion of potential roles of the reflector. The 2D study of different potential reflector materials has delivered a first down selection with SS 304 as representative for stainless steel, lead, copper, graphite, and beryllium oxide. A deeper look identified in addition iron-based material with high Si content. The following evaluation of the power distribution has shown the strong influence of the moderating reflectors creating a massively disturbed power distribution with a peak at the core boundary. This effect has been confirmed through a deeper analysis of the 2D multi-group flux distribution which lead to the exclusion of the BeO and the graphite reflector. The most promising materials identified have been SS 304, lead, and copper. The final 3D Monte-Carlo study demonstrated that all three materials have the potential to reduce the required amount of fuel by up to 60% compared NaCl which has been used in previous studies and is now taken as reference A first cost analysis has identified the SS 304 reflector as the most attractive solution. The results of the 2D multi group deterministic study and the 3D multi group Monte-Carlo study have been confirmed through a continuous energy Monte-Carlo reference calculation showing only minor differences.

Molten salt reactors have gained substantial interest in the last years due to their flexibility and their potential for simplified closed fuel cycle operation for massive net-zero energy production. The importance of a zero-power reactor for the process of developing a new, innovative rector concept like molten salt fast reactor is described here. It is based on historical developments as well as the current demand for experimental results and key factors that are relevant to the success of the next step in the development process of all innovative reactor types. In the systematic modelling & simulation of a zero-power molten salt reactor, the radius and the feedback effects are studied for a eutectic based system, while a heavy metal rich chloride-based system are studied depending on the uranium enrichment accompanied with the effects on neutron flux spectrum and spatial distribution. These results are used to support the relevant decision for the narrowing down of the configurations supported by considerations on cost and proliferation for the follow up 3D analysis to provide for the first time a systematic modelling & simulation approach for a new reactor physics experiment. The expected core volumes for these configurations have been studied using multi-group and continuous energy Monte-Carlo simulations identifying the 35% enriched systems as the most attractive and finally leading to the choice of the heavy metal rich compositions 35% enrichment as the reference system for future studies of the next steps in the zero power reactor investigation. The inter-comparison of the different applied codes and approaches available in the SCALE package has delivered a very good agreement between the results creating trust into the developed and used models and methods.

Molten salt reactors have gained substantial interest in the last years due to their flexibility and their potential for simplified closed fuel cycle operation for massive net-zero energy production. However, a zero-power reactor experiment will be an essential first step into the process of delivering this technology. The topic of the control and shut down for a zero power reactor is for the first time introduced through a literature review and the reduction of the control approaches to a limited number of basic functions with different variations. In the following, the requirements for the control and shutdown system for a reactor experiment are formulated, and based on these assessments, an approach for the shutdown – splitting the lower part of the core with reflector, and an approach for the control – a vertically movable radial reflector are proposed. Both systems will be usable for a zero-power system with a liquid as well as with as a solid core and even more importantly, both systems somehow work on the integral system level without disturbing the central part of the core which will be the essential area for the experimental measurements. Both approaches have been investigated as a singular system as well as their interactions with one another and the sensitivity of the control system. The study has demonstrated that both proposed systems are able to deliver the required characteristics with sufficient shutdown margin and a sufficiently wide control span. The interaction of the system has been shown to be manageable and the sensitivity is on a very good level. The multi-group Monte-Carlo approach has been cross evaluated by a continuous energy test leading to good results, but they also demonstrate that there is room for improvement.

Wall–stabilized arcs dominated by nozzle–ablation are key elements of self–blast circuit breakers. In the present study, high–current arcs were investigated using a model circuit breaker (MCB) in CO2 as gas alternative to SF6 and in addition a long polytetrafluoroethylene nozzle under ambient conditions for stronger ablation. The assets of different methods for optical investigation were demonstrated, e.g. high-speed imaging with channel filters and optical emission spectroscopy. Particularly the phase near current zero (CZ) crossing was studied in two steps. In the first step using high-speed cameras, radial temperature profiles have been determined until 0.4 ms before CZ in the nozzle. Broad temperature profiles with a maximum of 9400 K have been obtained from analysis of fluorine lines. In the second step, the spectroscopic sensitivity was increased using an intensified CCD camera, allowing single-shot measurements until few microseconds before CZ in the MCB. Ionic carbon and atomic oxygen emission were analyzed using absolute intensities and normal maximum. The arc was constricted and the maximum temperature decreased from &gt;18000 K at 0.3 ms to about 11000 K at 0.010 ms before CZ. The arc plasma needs about 0.5-1.0 ms after both the ignition phase and the current zero crossing to be completely dominated by the ablated wall material.

Sumudu transform of the Dixon elliptic function with non zero modulus a ≠ 0 for arbitrary powers sm^N(x,a) ; N ≥ 1 ; sm^N(x,a)cm(x,a) ; N ≥ 0 and sm^N(x,a)cm²(x,a) ; N ≥ 0 is given by product of Quasi C fractions. Next by assuming denominators of Quasi C fraction to 1 and hence applying Heliermann correspondance relating formal power series (Maclaurin series of Dixon elliptic functions) and regular C fraction, Hankel determinants are calculated and showed by taking a = 0 gives the Hankel determinants of regular C fraction. The derived results were back tracked to the Laplace transform of sm(x,a) ; cm(x,a) and sm(x,a)cm(x,a).

Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.

Despite the use of commercial forest carbon protocols (CFCPs) for more than two decades, claiming ~566 MMtCO₂e and a market value of ~USD $15.7 billion, comparative analysis of CFCP methodology and offset results is limited. In this study, five widely used biometric-based CFCPs are characterized, and common characteristics and differences are identified. CFCP claims of net forest carbon sequestration are compared with results of directly measured CO₂ by eddy covariance, a meteorological method integrating gross vertical fluxes of forest and soil carbon, and the only alternative non-biometric source of net forest carbon sequestration data available. We show here that CFCPs share a structural feature delimiting forest carbon values by zero-threshold carbon accounting (gC m^-2 ≤ 0), a pattern opposite to natural emissions of forest CO₂ exchange based on direct measurement and a fundamental biological constraint on net forest carbon storage (i.e., soil efflux, ecosystem respiration). Exclusion of forest CO₂ sources to the atmosphere precludes net carbon accounting, resulting in unavoidable over-crediting of CFCP project offsets. CFCP carbon results are significantly different from global forest CO₂ net ecosystem exchange population results (FluxNet2015 gC m^-2) at the 95% to 99.99% confidence levels, inferring an annual median error of ~247% (gC m^-2), consistent with over-crediting. Direct CO₂ measurement provides an urgently needed alternative method for commercial forest carbon products that has the potential to harmonize global markets and catalyze the role of forests in managing climate change through nature-based solutions.

Nuclear technologies have the potential to play a unique role delivering low carbon energy for a future net-zero society. However, for the long-term success, nuclear will need to deliver innovative solutions as proposed in iMAGINE. One of the key challenges for the envisaged highly integrated nuclear energy system is the need for a demand driven salt clean-up system. The work described provides an insight into the interplay between a potential salt clean-up system and the reactor operation in a dynamic approach. The results provided will help to optimize the parameters for the salt clean-up process by delivering a dynamically calculated priority list identifying the elements with high influence on reactor operation. The integrated model is used to investigate the ideal time for the initiation of the clean-up as well as the effect of different throughput through the clean-up system on criticality as well as on the concentration of the elements in the reactor salt. Finally, a staggered approach is proposed with the idea to phase in the chemical clean-up processes step by step to keep the reactor critical. The results provide an essential step for the progress of iMAGINE as well as the basis for the inter disciplinary work required to bring iMAGINE into real operation.

The issue of workplace toxicity constitutes a major problem for employers, employees and society at large. Toxicity in a workplace context relates to damaging behaviours perpetrated by individuals and organisations that netatively impact on the health and wellbeing of others. Workplace toxicty is habitually associated with activities such as bullying, harrassment, unethical and even illegal acts. In this article, today’s workplace is likenened to a cage where ambition and greed drive people to engage in toxic behaviour to fight for whatever scarce rewards are on offer. Set against a backdrop of continuous demands on employees in a era of constant change and challenge, leadership practice in the 21st century is understandably seeped in complexity. Whilst conditions of scarcity are in fact constructed by employers in today’s lean environments, workplace toxicity has significant implications for organisational leaders in terms of work place disputes, absenteeism and litigation etc. Twentieth century hierarchial and autocratic leadership models are now deemed to be both obsolete and wholly inadedgate to inform leadership practice in the knowledge era. This conceptual article looks at the efficacy of complexity leadership theory (CLT) in empowering today’s leader to better understand and address 21st century workplace toxicity.

This paper reports a new control strategy to improve sharing of unbalanced currents in islanded LV microgrids. This technique provides fast and effective sharing of positive-, negative- and zero-sequence currents, and is the first example of zero-sequence current sharing in the literature. The controllers are designed in the stationary frame. The control structure consists of four loops; 1)~the current controller; 2)~the voltage controller; 3)~the droop controller and the 4)~negative and zero sequence current controllers. The output current is considered unknown for the controller and is added to the control system as a disturbance. The proposed controller features a high gain in fundamental and harmonic frequencies, hence a good voltage quality is obtained in the presence of unbalanced and nonlinear loads. To this aim, a proportional-resonant (PR) controller is adopted as the current controller. By using a multi-resonant controller as current controller, a unified control structure is obtained which is suitable for both grid-connected and islanded modes. The voltage controller is designed using a resonant controller so that the voltage can have low VUF and THD in the presence of unbalanced and nonlinear loads. Furthermore, in this paper droop method is applied to the control structure to share real and reactive powers. Simulation studies show that the conventional droop method cannot share the oscillatory part of the output power that is due to the presence of unbalanced loads in the microgrid. This paper relies on using zero and negative sequence virtual impedance controller to share the oscillatory part of output power. By using zero-sequence virtual impedance controller (ZSVIC) and negative-sequence virtual impedance controller (NSVIC), the zero and negative sequence currents in the microgrid are controlled and shared effectively. By compensating zero- and negative-sequence currents locally, the flow of these currents in the microgrid is minimized, and the overall power quality of the islanded LV microgrid is improved.

The shallow water equations are widely applied for the simulation of flow routing in rivers and floodplains, as well as for flood inundation mapping. From a mathematical point of view, they are a hyperbolic system of nonlinear partial differential equations, whose numerical integration is sometimes computationally burdensome. For this reason, the interest of many researchers has been focused on the study of simplified forms of the original set of equations, which requires less computational effort. One of the most commonly applied simplifications consists in neglecting the inertial terms, which changes the hyperbolic model to a parabolic one. The effects of such a choice on the outputs of the simulations of flooding events are controversial and an important topic of debate. In the present paper, two numerical models, recently proposed for the solution of the complete and zero-inertia forms of the shallow waters equations, are applied to several unsteady flow routing scenarios. We simulate synthetic and laboratory studies, starting from very simple geometries and moving towards complex topographies. Analyzing the role of the terms in the momentum equations, we try to understand the effect, on the computed results, of neglecting the inertial terms in the zero-inertia formulation. We analyze the computational costs.

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

This note makes available five years of data gathered in a measurement site equipped with a micrometeorological station and two monitoring wells. Series of data of hydrological and atmospheric variables allow to estimate the flux of water across the atmosphere-land interface and to calculate the water budget, that are crucial topics in climate and environmental sciences. The measures of water-table began during 2017, one of the driest year of the whole instrumental period of climate history for the Central Mediterranean. Data from the micrometeorological station have been used to construct two more datasets of daily and monthly cumulative values of different terms of the surface water budget, from which the groundwater infiltration has been estimated. A significant decreasing trend characterizes both the data time series of piezometric heights and estimated infiltration in the considered period. All the data are collected in 2017-2022 datasets that are available in the supplementary material.

The notion of potential-growth indicator came to being in the field of matrix population models long ago, almost simultaneously with the pioneering Leslie model for age-structured population dynamics, albeit the term has been given and the theory developed only recent years. The indicator represents an explicit function, R(L), of matrix L elements and indicates the position of the spectral radius of L relative to 1 on the real axis, thus signifying the population growth, or decline, or stabilization. Some indicators turned out useful in theoretical layouts and practical applications prior to calculating the spectral radius itself. The most senior (1994) and popular indicator, R0(L), is known as the net reproductive rate, and we consider two more ones, R1(L) and RRT(A), developed later on. All the three are different in what concerns their simplicity and the level of generality, and we illustrate them with a case study of Calamagrostis epigeios, a long-rhizome perennial weed actively colonizing open spaces in the temperate zone. While the R0(L) and R1(L) fail respectively because of complexity and insufficient generality, the RRT(L) does succeed, justifying the merit of indication.

The NETTER-1 study has proven peptide receptor radionuclide therapy (PRRT) to be one of the most effective therapeutic options for metastatic neuroendocrine tumors (NETs), improving progression-free survival and overall survival. However, PRRT response assessment is challenging and no consensus on methods and timing has yet been reached among experts in the field. This issue is owing to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack of standardized parameters and timing of functional imaging, and the heterogeneity of PRRT protocols in the literature. The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious PRRT.

The presented study demonstrates the bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of UAV RGB images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from UAV to a wider spatial extent. The various approaches of the improvement of the predictive performance were examined: (I) the highest R2 of the single satellite index was up to 0.57, (II) the highest R2 using multiple features obtained from the single date, S-2 image was 0.624 and, (III) the highest R2 on the multi-temporal set of S-2 images, was 0.697. Satellite indices such as ARVI, IPVI, NDI45, PSSRa, MCARI, CI, RI, and NDTI were the dominant predictors in most of the ML algorithms. The more complex ML algorithms such as SVM, Random Forest, GBM, XGBoost, and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net algorithm was chosen for the final map creation.

Integrated photovoltaic-fuel cell (IPVFC) systems, amongst other integrated energy generation methodologies are renewable and clean energy technologies that have received diverse research and development attentions over the last few decades due to their potential applications in a hydrogen economy. This article systematically updates the state-of-the-art of IPVFC systems and provides critical insights into the research and development gaps needed to be filled/addressed to advance these systems towards full commercialisation. The design methodologies, renewable energy-based microgrid and off-grid applications, energy management strategies, optimisations and the prospects as self-sustaining power source were covered. IPVFC systems could play an important role in the upcoming hydrogen economy since they depend on solar hydrogen which has almost zero emissions during operation. Highlighted herein are the progresses as well as the technical challenges requiring research efforts to solve to realise numerous potential applications of IPVFC systems such as in unmanned aerial vehicles, hybrid electric vehicles, agricultural applications, telecommunications, desalination, synthesis of ammonia, boats, buildings, and distributed microgrid applications.

Recently, the relationship between emotional arousal and depression has been studied. Focusing on this relationship, we first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals (Ginza Taimei Clinic [GTC] and National Defense Medical College hospital [NDMC]) and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D&lt;8) and a depression group (HAM-D≥8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test and a significant difference at the level of 10% (p = 0.094) at GTC and 1% (p = 0.0038) at NDMC was determined. The area under the curve (AUC) of the receiver operating characteristic was 0.66 when categorizing between the two groups for GTC, and the AUC for NDMC was 0.70. The relationship between arousal level and depression severity was indirectly suggested via ALVI.

Acetylcholine (ACh) is the best one characterized neurotransmitter. Its central roles in the cholinergic areas and central nervous system (CNS) and peripheral (PNS) synapses are well known. It was the first molecule identified as a neurotransmitter and appears to be phylogenetically the oldest one signaling molecules. ACh was detected in bacteria, protozoa, fungi, algae and primitive plants, indicating that the cholinergic system was widely distributed in living organisms before its appearance in the nervous system. The autonomous nervous system (ANS) is an integral part animal kingdom history. It determines fight-escape reactions as well as outlining the vital and cognitive functions bio-organization rhythms. The virus (Covid-19) extraneous effects acting on it could help to better understand its functions. The Blast results show some factors like protein that can be implicated such as bungaro-toxins, phospholipase A2 and the similar prothrombin activator protein. A positive correlation with different toxins is obtained and the Covid-19 double pathogenic mechanism theory is proposed. The process allows us to outline the possibility about toxic-like factors presence.

An explanation of the origin of dark matter is suggested in this work. The argument is based on symmetry considerations about the concept of mass. In the Wigner's view, the rest mass and the spin of a free elementary particle in flat space-time are the two invariants that characterize the associated unitary irreducible representation of the Poincar\'e group. The Poincar\'e group has two and only two deformations with maximal symmetry. They describe respectively the de Sitter (dS) and Anti de Sitter (AdS) kinematic symmetries. Analogously to their shared flat space-time limit, two invariants, spin and energy scale for de Sitter and rest energy for Anti de Sitter, characterize the unitary irreducible representation associated with dS and AdS elementary systems. While the dS energy scale is a simple deformation of the Poincaré rest energy and so has a purely mass nature, AdS rest energy is the sum of a purely mass component and a kind of zero-point energy derived from the curvature. An analysis based on recent estimates on the chemical freeze-out temperature marking in Early Universe the phase transition quark-gluon plasma epoch to the hadron epoch supports the guess that dark matter energy might originate from an effective AdS curvature energy.

In 2010, Parties to the Convention on Biological Diversity (CBD) adopted the Strategic Plan for Biodiversity 2011–2020 to address the loss and degradation of nature. Subsequently, most biodiversity indicators continued to decline. Nevertheless, conservation actions can make a positive difference for biodiversity. The emerging Post-2020 Global Biodiversity Framework has potential to catalyze efforts to ‘bend the curve’ of biodiversity loss. Thus, the inclusion of a goal on species, articulated as Goal B in the Zero Draft of the Post-2020 Framework, is essential. However, as currently formulated, this goal is inadequate for preventing extinctions, and reversing population declines; both of which are required to achieve the CBD’s 2030 mission. We contend it is unacceptable that Goal B could be met while most threatened species deteriorated in status and many avoidable species extinctions occurred. We examine the limitations of the current wording and propose an articulation with robust scientific basis. A goal for species that strives to end extinctions and recover populations of all species that have experienced population declines, and especially those at risk of extinction, would help to align actors towards the transformative actions and interventions needed for humans to live in harmony with nature.

For its enthusiasts, the blockchain is an archivist's dream come true: an immutable historical record that is publicly accessible and immune from censorship. Proposals like Juried Protocol Galleries could enable bots to compute the provenance of digital artworks and their appearance in exhibitions and publications. Distributed file systems like IPFS claim to store creative works in a redundant, networked fashion outside the control of any single cloud provider. Projects designed to work with blockchains like Filecoin and Arweave propose a dedicated cryptocurrency as financial incentive to support the cost of governing and sustaining this "permaweb." As dreamy as this picture sounds, many of these promises depend on technologies that have yet to be sufficiently developed or adopted. Rather than forecast the future, we can separate the hype from the reality in the present day by examining a real-life example, namely the 2021 auction of Andy Warhol's digital art with NFTs. This essay will focus less on blockchain's general promise as a preservation medium and more on the particular case of the digital Warhols, which both in form and spirit would seem a perfect application of NFTs to preserve historically important works of digital art. Which promises of the crypto-dream of permanent access to digital heritage ring true for this case study, and which are overblown?

This study develops into the application of a combined MFC unit with chemical coagulation for total treatment of inert contaminants in complex substrates. Microbial Fuel Cell (MFC) technology converts chemical energy in the form of organic matter, into bioelectricity in an environmentally friendly and effi-cient manner, reducing carbon emissions and increasing bioenergy production. An evaluation of a la-boratory scale chemical coagulation using an aluminum and poly-based coagulant on how effective it can remove bulk impurities such as particulate COD and turbidity to obtain the purest and most cost-effectively treated wastewater using a jar test is being conducted in this current study. This study aims to find the most effective treatment technologies for wastewater recovery in breweries in order to achieve zero liquid effluent discharge (ZLED). The preliminary results showed that adding a modest amount of poly and a 50 % alum alone treatment improved COD, color, and turbidity reduction. The turbidity removal efficiency achieved after chemical coagulation treatment was 90.50 % and 59.36 % COD removal, demonstrating the benefits of adopting an alum/poly based technique. To determine ZLED, this study clearly advised a combined treatment technique, specifically the MFC-flocculator unit for efficient organics and inorganics removal.