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Abstract The KM3NeT/ARCA neutrino detector is currently under construction at 3500 m depth offshore Capo Passero, Sicily, in the Mediterranean Sea. The main science objectives are the detection of high-energy cosmic neutrinos and the discovery of their sources. Simulations were conducted for the full KM3NeT/ARCA detector, instrumenting a volume of 1 km $$^3$$ 3 , to estimate the sensitivity and discovery potential to point-like neutrino sources. This paper covers the reconstruction of track- and shower-like signatures, as well as the criteria employed for neutrino event selection. With an angular resolution below 0.1 $$^\circ $$ ∘ for tracks and under 2 $$^\circ $$ ∘ for showers, the sensitivity to point-like neutrino sources surpasses existing observed limits across the entire sky.

Abstract The measurement of the flux of muons produced in cosmic ray air showers is essential for the study of primary cosmic rays. Such measurements are important in extensive air shower detectors to assess the energy spectrum and the chemical composition of the cosmic ray flux, complementary to the information provided by fluorescence detectors. Detailed simulations of the cosmic ray air showers are carried out, using codes such as CORSIKA, to estimate the muon flux at sea level. These simulations are based on the choice of hadronic interaction models, for which improvements have been implemented in the post-LHC era. In this work, a deficit in simulations that use state-of-the-art QCD models with respect to the measurement deep underwater with the KM3NeT neutrino detectors is reported. The KM3NeT/ARCA and KM3NeT/ORCA neutrino telescopes are sensitive to TeV muons originating mostly from primary cosmic rays with energies around 10 TeV. The predictions of state-of-the-art QCD models show that the deficit with respect to the data is constant in zenith angle; no dependency on the water overburden is observed. The observed deficit at a depth of several kilometres is compatible with the deficit seen in the comparison of the simulations and measurements at sea level.

Abstract This article reports the first observation of the Moon and the Sun shadows in the sky distribution of cosmic-ray induced muons measured by the KM3NeT/ORCA detector. The analysed data-taking period spans from February 2020 to November 2021, when the detector had 6 Detection Units deployed at the bottom of the Mediterranean Sea, each composed of 18 Digital Optical Modules. The shadows induced by the Moon and the Sun were detected at their nominal position with a statistical significance of 4.2 $$\sigma $$ σ and 6.2 $$\sigma $$ σ , and an angular resolution of $$\sigma _{res}=0.49^\circ $$ σ res = 0 . 49 ∘ and $$\sigma _{res}=0.66^\circ $$ σ res = 0 . 66 ∘ , respectively, consistent with the prediction of $$0.53^\circ $$ 0 . 53 ∘ from simulations. This early result confirms the effectiveness of the detector calibration, in time, position and orientation and the accuracy of the event direction reconstruction. This also demonstrates the performance and the competitiveness of the detector in terms of pointing accuracy and angular resolution.

Abstract In the era of precision measurements of the neutrino oscillation parameters, upcoming neutrino experiments will also be sensitive to physics beyond the Standard Model. KM3NeT/ORCA is a neutrino detector optimised for measuring atmospheric neutrinos from a few GeV to around 100 GeV. In this paper, the sensitivity of the KM3NeT/ORCA detector to neutrino decay has been explored. A three-flavour neutrino oscillation scenario, where the third neutrino mass state ν 3 decays into an invisible state, e.g. a sterile neutrino, is considered. We find that KM3NeT/ORCA would be sensitive to invisible neutrino decays with 1/α 3 = τ 3 /m 3 < 180 ps/eV at 90% confidence level, assuming true normal ordering. Finally, the impact of neutrino decay on the precision of KM3NeT/ORCA measurements for θ 23, Δ m 31 2 $$ \Delta {m}_{31}^2 $$ and mass ordering have been studied. No significant effect of neutrino decay on the sensitivity to these measurements has been found.

PV–TEG hybrid system is widely discussed nowadays, as an alternative way to maximize solar radiation energy which is from both of its light and heat. In recent years, the idea of using thermoelectric generators (TEGs) to extract energy from heat waste has increased with applications varying from milliwatts to kilowatts. To form a circuit, numbers of TEG modules can be connected in series and/or parallel configuration to produce the required voltage and/or current. In the PV–TEG systems, due to operating environments, each TEG module’s power generation is subject to temperature mismatch. There is also considerable variability in the electro-thermal output and mounting pressure of each TEG module in a circuit, resulting in a significant inconsistency. Therefore, when each TEG in the array is active, a varying electrical operating point will occur at which peak energy can be obtained and problems with lower power output will occur. In this work, an individual TEG module test method was used to measure and analyze data from specific types of TEG, and a record of the maximum power output under different temperature variations is obtained. Then, the performance of the TEG system is measured and evaluated with a test bench where the modules are attached at different backside areas of generic PV panels. The non-uniform heat distribution condition created in the experiment showed an average 33 % drop in power production from the maximum power that would be available in case each TE series circuit operates under uniform heat distribution. This experimental finding demonstrates the problem and the outcomes discussed in this paper validated a thorough examination of the effect of nonuniform operating temperatures on a range of thermoelectric generator’s energy output.

Combined heat-and-power units (CHPUs) of biogas plants can be operated at partial load or in intervals to respond to market conditions or interventions by the grid operators. In this study, we present calculations for the specific greenhouse gas (GHG) emissions of electricity from biogas driven CHPUs in dependence of engine load. Following the methodology of life cycle assessment and using measured data from five real-world biogas plants in Bavaria, we investigated the following scenarios for the operation of the CHPU: (1) full load, (2) 80% load response and (3) 60% load response. Our system boundary included the whole biogas production chain, starting from the production/supply of the input materials and ending at the supply of electricity (functional unit: 1 kWh of electrical energy fed into the grid). As electrical efficiency and engine load are positively correlated, partial-load operation results in higher specific GHG emissions of electricity from biogas. The impact of decreasing efficiency of the CHPU under partial load on overall specific GHG emissions turned out to be higher than the influence of methane slip from the engine. Furthermore, the increase of further efficiencies such as higher yields, efficient use of synthetic and organic fertilizers, as well as minimization of fuel consumption and the use of regenerative fuels also lead to further decrease of GHG emissions. Keywords: Life cycle assessment, Biogas plant, CHP unit, Flexibility, Electricity, Load response, Interval, GHG emissions

Today learning in schools is starting to be adapted to the development of information technology, resulting in changes and shifts in the educational paradigm [1] This needs to be done so that the learning process does not seem less interesting, monotonous and boring so that it will hinder the transfer of knowledge [2]. At the beginning of 2020, the spread and transmission of the Corona Virus was increasingly spreading throughout the world. Rapid and massive transmission resulted in system changes in all aspects, both from social, economic, and educational aspects. Indonesia is no exception. To avoid the spread of this pandemic, various efforts have been made by the government to minimize the acceleration of the movement of the corona virus. Since the pandemic, the learning system in the world of education has undergone very significant changes. The government has explicitly issued a decision to implement distance learning by simultaneously utilizing technology so that the teaching and learning process continues. The big obstacle faced by most schools in North Maluku is that online learning support facilities are not yet ready. So that the majority of schools use teleconference applications as a medium to support distance learning. This research is intended to design an application that supports blended learning model learning. Blended learning is a learning method that combines two or more methods and approaches in learning to achieve process goals.

The microbiota and its collective genomes, i.e., microbiome, have recently attracted major interest in the scientific community. Especially the largest microbial community of the human body, the intestinal microbiota, is assumed to play a crucial role in many gastrointestinal, pancreatic and liver disorders. Several preclinical and clinical investigations have shown that liver diseases at various stages exhibit substantial changes in their microbiome compared to healthy controls, also defined as dysbiosis. The most profound changes have been observed in the stage of liver cirrhosis, and importantly, it is clinically well established that interference with the intestinal microbiota, e.g., rifaximin improves complications of liver cirrhosis, such as hepatic encephalopathy. It is currently unclear which factors drive the evolution of hepatocellular carcinoma (HCC), which usually appears in a cirrhotic liver. Pro-inflammatory signals, bacteria, and related metabolites might be involved. Several studies have recently assessed the role of the intestinal microbiota in HCC. Here, various reports could demonstrate that especially the abundance of pro-inflammatory intestinal communities, such as Proteobacteria and Enterobacteriaceae are increased. Furthermore, some studies in HCC have shown, that microbiome alterations are associated with decreased levels of butyrate-producing Clostridiales and species, known for their anti-inflammatory potential, like Akkermansia muciniphila. These strains could contribute to the overall pro-inflammatory phenotype of this malignancy. Furthermore, several preclinical studies could convincingly show that intestinal bacteria are involved in liver carcinogenesis. Overall, it can be concluded that the intestinal microbiota might play a crucial role in the pathophysiology of hepatic carcinogenesis.

Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.

An enhancement in the secondary battery safety demands the optimum synthesis of glass-ceramics electrolytes with modified ionic conductivity. To achieve improved ionic conductivity and safer operation of the battery, we synthesized Li2O included zinc-tellurite glass-ceramics based electrolytes of chemical composition (85-x)TeO2·xLi2O·15ZnO, where x = 0, 5, 10, 15 mol%. Samples were prepared using the melt quenching method at 800 °C followed by thermal annealing at 320 °C for 3 h and characterized. The effects of varying temperature, alternating current (AC) frequency and Li2O concentration on the structure and ionic conductivity of such glass-ceramics were determined. The SEM images of the annealed glass-ceramic electrolytes displayed rough surface with a uniform distribution of nucleated crystal flakes with sizes less than 1 μm. X-ray diffraction analysis confirmed the well crystalline nature of achieved electrolytes. Incorporation of Li2O in the electrolytes was found to generate some new crystalline phases including hexagonal Li6(TeO6), monoclinic Zn2Te3O8 and monoclinic Li2Te2O5. The estimated crystallite size of the electrolyte was ranged from ≈40 to 80 nm. AC impedance measurement revealed that the variation in the temperatures, Li2O contents, and high AC frequencies have a significant influence on the ionic conductivity of the electrolytes. Furthermore, electrolyte doped with 15 mol% of Li2O exhibited the optimum performance with an ionic conductivity ≈2.4 × 10−7 S cm−1 at the frequency of 54 Hz and in the temperature range of 323–473 K. This enhancement in the conductivity was attributed to the sizable alteration in the ions vibration and ruptures of covalent bonds in the electrolytes network structures. Keywords: Zinc-tellurite, Glass-ceramics, X-ray diffraction, Ionic conductivity, Lithium oxide

Ferrite being a compound derived from iron oxides including magnetite and hematite possesses similar properties as ceramics which are hard and brittle. Certainly, the mounting demand for electronics has been a major factor driving the exponential growth of ferrite based materials. ZnO doped strontium-natural nanoferrites of composition (80 − x)Fe2O3:xZnO:20SrCO3, where x = 0, 10, 20 mol% are synthesized and sintered via the solid state reaction scheme. Samples are characterized by SEM, XRD, VSM, and VNA measurements to determine the impact of ZnO contents’ variation on the surface morphology, structure, magnetic and microwave absorption properties. The Nicolson–Ross–Weir method is applied to evaluate samples’ reflection loss. The average grain size of the strontium ferrite is found to reduce with the increase in ZnO concentration. Materials sintered at 1100 °C without ZnO incorporation are composed of hexagonal SrFe12O19. Meanwhile, the addition of ZnO produces cubic ZnFe2O4 and SrFeO2 phases. Insertion of ZnO results in reduction of magnetic parameters and reflection loss. Furthermore, the anisotropy magnetic field of strontium natural ferrites displays a rapid drop from 350 kA/m to 79.6 kA/m with the increase in ZnO. Strontium ferrite containing 20 mol% of ZnO exhibits superior microwave absorption with reflection loss within −45 dB to −55.94 dB in the frequency range of 7–13 GHz. This facilely synthesized a new class of materials which is believed to be economically promising for microwave absorption applications in the GHz range.

Biogas plants are widespread in Germany and, particularly, in the Federal State of Bavaria, where the development of renewable energy sources has been very dynamic. While biogas can substitute fossil energy carriers for producing electricity and heat, greenhouse gas (GHG) emissions are also occurring along the biogas process chain. The accounting of the GHGs produced from biogas plants is an essential step for evaluating and improving this type of energy supply. We developed an effective approach for simulating diverse biogas chains in agriculture for predicting the resulting GHG emissions in relation to energy output. This approach is executed in three stages: (1) creating a database and filling it with collected and calculated data; (2) constructing the biogas plant sample using data from the database and user’s input; and (3) running the database model for calculating and displaying the biogas plant’s GHG inventory. The database comprises information on the location and management of the farm, the sourcing and composition of the input materials / substrates, and the combined heat and power unit (CHPU) for biogas valorization. The database is connected with a web-based application model, which presents the results in graphs, numbers and percentages.

Grubbs test (extreme studentized deviate test, maximum normed residual test) is used in various fields to identify outliers in a data set, which are ranked in the order of x1≤x2≤x3≤⋯≤xn (i=1,2,3,…,n). However, ranking of data eliminates the actual sequence of a data series, which is an important factor for determining outliers in some cases (e.g., time series). Thus in such a data set, Grubbs test will not identify outliers correctly. This paper introduces a technique for transforming data from sequence bound linear form to sequence unbound form (y=c). Applying Grubbs test to the new transformed data set detects outliers more accurately. In addition, the new technique improves the outlier detection capability of Grubbs test. Results show that, Grubbs test was capable of identifing outliers at significance level 0.01 after transformation, while it was unable to identify those prior to transforming at significance level 0.05.

We introduce a new nonparametric outlier detection method for linear series, which requires no missing or removed data imputation. For an arithmetic progression (a series without outliers) with n elements, the ratio (R) of the sum of the minimum and the maximum elements and the sum of all elements is always 2/n:(0,1]. R≠2/n always implies the existence of outliers. Usually, R2/n implies that the maximum is an outlier. Based upon this, we derived a new method for identifying significant and nonsignificant outliers, separately. Two different techniques were used to manage missing data and removed outliers: (1) recalculate the terms after (or before) the removed or missing element while maintaining the initial angle in relation to a certain point or (2) transform data into a constant value, which is not affected by missing or removed elements. With a reference element, which was not an outlier, the method detected all outliers from data sets with 6 to 1000 elements containing 50% outliers which deviated by a factor of ±1.0e-2 to ±1.0e+2 from the correct value.

The effect of temperature elements for PV array with tracking and concentrating features installed in the tropical ground condition is presented. The temperature segment covers ambient temperature and surface and bottom temperature for three types of PV generator systems, namely, Fixed Flat (FF), Tracking Flat (TF), and Concentrating PV (CPV) generators. The location of measuring the cell temperature, Tc for the PV module is still being debated by researchers with the issue of how much the cell temperature (Tc) is being affected by the surface temperature (Ts), bottom temperature (Tb), and surrounding temperature (Ta) furthermore when it is located in fluctuating weather conditions. In this study, ΔT is calculated based on the difference between surface temperature and bottom-side temperaturewhichever the highest recorded at site for different kinds of PV generator systems but using the same CEEG 95 W monocrystalline PV module. The study embraces the direct correlation of various temperature elements in tropical-based condition with ΔT values of 2.19°C for FF module, 2.22°C for TF module, and 2.72°C for CPV module. These values which reflect the different unique configurations are further analyzed using multiple linear regression (MLR) and analysis of variance (ANOVA) test for Tarray models. This study supports the continuous research in adapting PV technology for Malaysia.

This article presents optical images, measurements of heat affected zone depths, and peak Vickers hardness values from the heat affected zone regions of a 1080 steel plate substrate with a machined groove "flaw" repaired using additive friction stir deposition of Aermet 100. The deposition of Aermet 100 was performed using a L3 Meld machine with 9.525 mm (0.375 inch) square bar profile Aermet 100 feedstock rods fed through a hollow, 10.16 mm (0.4 inch) diameter, rotating tool onto a 1.02 mm thick 1080 steel plate with a machined groove "flaw" along the plate's length and bisecting the plate's width. The depth of the machined groove "flaw" ranged from 4.7625, 6.35, and 9.525 mm. The data is categorized into four groups: multi-layer builds deposited at room temperature with and without a cooling plate, 2-layer builds deposited at room temperature without a cooling plate, single-layer builds deposited at room temperature without a cooling plate, and a design of experiments for single-layer builds that varied the spindle rotation speed (RPM), travel speed (mm/min), material feed rate (mm/min), and pre-heat temperature (°C) of the deposition. The data shows the parameter conditions that achieved flaw-free consolidated repairs and the associated depth and peak Vickers hardness of the heat affected zone. Optical images of cross-sectioned deposition regions were obtained using an optical microscope with Leco Olympus DP27 macro camera, and Vickers hardness line traces were measured along the depth of the deposited and heat affected zone extending into the substrate on cross-sectioned samples using a Leco LM 247AT microhardness tester. The depth of the heat affected zone is reported as the measured average of five individual data points. Peak values for Vickers hardness for the heat affected zone decreased for pre-heated conditions at 329°C compared to builds conducted at room temperature of 21°C. This dataset provides visual characterization and associated hardness measurements of as-deposited repairs of dissimilar steel alloys. This article can be used to inform parameter selection for additive friction stir deposition of dissimilar steel materials for repair and solid-state additive manufacturing applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors. Published by Elsevier Inc.)