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Volume 5
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Sci, Volume 5, Issue 3 (September 2023) – 11 articles

Cover Story (view full-size image): Power cycles are the most important energy devices for covering the energy needs of our society, and the enhancement of their performance is a sustainable way to achieve sustainability. The present investigation aims to develop semi-empirical simplified models for predicting with high accuracy the thermodynamic efficiency of the most usual power cycles. These models are based on the operating temperatures, specifically low and high cycle temperatures. The results of the present analysis can be exploited for the design of energy systems and for conducting optimization investigations. View this paper
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17 pages, 5924 KiB  
Article
A Sensitive Strain Sensor Based on Multi-Walled Carbon Nanotubes/Polyaniline/Silicone Rubber Nanocomposite for Human Motion Detection
by Seyedmajid Hosseini, Mohsen Norouzi and Jian Xu
Sci 2023, 5(3), 36; https://doi.org/10.3390/sci5030036 - 20 Sep 2023
Cited by 8 | Viewed by 2576
Abstract
Strain sensors play a pivotal role in quantifying stress and strain across diverse domains, encompassing engineering, industry, and medicine. Their applicability has recently extended into the realm of wearable electronics, enabling real-time monitoring of body movements. However, conventional strain sensors, while extensively employed, [...] Read more.
Strain sensors play a pivotal role in quantifying stress and strain across diverse domains, encompassing engineering, industry, and medicine. Their applicability has recently extended into the realm of wearable electronics, enabling real-time monitoring of body movements. However, conventional strain sensors, while extensively employed, grapple with limitations such as diminished sensitivity, suboptimal tensile strength, and susceptibility to environmental factors. In contrast, polymer-based composite strain sensors have gained prominence for their capability to surmount these challenges. The integration of carbon nanotubes (CNTs) as reinforcing agents within the polymer matrix ushers in a transformative era, bolstering mechanical strength, electrical conductivity, and thermal stability. This study comprises three primary components: simulation, synthesis of nanocomposites for strain sensor fabrication, and preparation of a comprehensive measurement set for testing purposes. The fabricated strain sensors, incorporating a robust polymer matrix of polyaniline known for its exceptional conductivity and reinforced with carbon nanotubes as strengthening agents, demonstrate good characteristics, including a high gauge factor, stability, and low hysteresis. Moreover, they exhibit high strain sensitivity and show linearity in resistance changes concerning applied strain. Comparative analysis reveals that the resulting gauge factors for composite strain sensors consisting of carbon nanotubes/polyaniline and carbon nanotubes/polyaniline/silicone rubber are 144.5 and 167.94, respectively. Full article
(This article belongs to the Section Sports Science and Medicine)
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Figure 1

Figure 1
<p>(<b>a</b>) Schematic of the process of functionalizing CNTs. (<b>b</b>) In situ polymerization of CNT/PANI.</p> Full article ">Figure 2
<p>PANI-coated CNTs.</p> Full article ">Figure 3
<p>The schematic of making a strain sensor containing CNT/PANI.</p> Full article ">Figure 4
<p>The schematic of the process for making a strain sensor containing CNT/PANI/silicone rubber material.</p> Full article ">Figure 5
<p>The test setup for extracting the sensor’s performance parameters.</p> Full article ">Figure 6
<p>VMD graphical environment for different percentages of CNT/PANI composites. (<b>a</b>) (20% CNT −80% PANI), (<b>b</b>) (40% CNT −60% PANI), (<b>c</b>) (60% CNT −40% PANI), (<b>d</b>) (80 CNT −20% PANI).</p> Full article ">Figure 7
<p>(<b>a</b>) Stress–strain graph for the CNT/PANI nanocomposite with different weight ratios (CNT to PANI (20/80), (40/60), (60/40), and (80/20)). (<b>b</b>) Stress–strain graph for the CNT/PANI nanocomposite at three different temperatures (300 °K, 325 °K, and 350 °K).</p> Full article ">Figure 8
<p>(<b>a</b>) Relative changes in resistance (<math display="inline"><semantics> <mrow> <mfrac bevelled="true"> <mrow> <mo>∆</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">R</mi> </mrow> </mfrac> </mrow> </semantics></math>) of the CNT/PANI strain sensor versus applied strain. (<b>b</b>) Relative changes in resistance of the CNT/PANI/silicone rubber strain sensor versus applied strain. (<b>c</b>) Stress–strain graph of CNT/PANI sample. (<b>d</b>) Stress–strain graph of CNT/PANI/silicone rubber sample.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) Relative changes in resistance (<math display="inline"><semantics> <mrow> <mfrac bevelled="true"> <mrow> <mo>∆</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">R</mi> </mrow> </mfrac> </mrow> </semantics></math>) of the CNT/PANI strain sensor versus applied strain. (<b>b</b>) Relative changes in resistance of the CNT/PANI/silicone rubber strain sensor versus applied strain. (<b>c</b>) Stress–strain graph of CNT/PANI sample. (<b>d</b>) Stress–strain graph of CNT/PANI/silicone rubber sample.</p> Full article ">Figure 9
<p>(<b>a</b>) Applied force versus strain on CNT/PANI sample. (<b>b</b>) Applied force versus strain on CNT/PANI/silicone rubber sample. (<b>c</b>) Hysteresis curves (electrical resistance versus strain) for CNT/silicone rubber sample. (<b>d</b>) Hysteresis curves for CNT/PANI/silicone rubber sample. (<b>e</b>) Drift diagrams of CNT/PANI sample. (<b>f</b>) Drift diagrams of CNT/PANI/silicone rubber sample. (<b>g</b>) Resistance versus time graph of a strain sensor based on CNT/PANI/silicone rubber connected to a hand.</p> Full article ">
33 pages, 7565 KiB  
Article
On Hens, Eggs, Temperatures and CO2: Causal Links in Earth’s Atmosphere
by Demetris Koutsoyiannis, Christian Onof, Zbigniew W. Kundzewicz and Antonis Christofides
Sci 2023, 5(3), 35; https://doi.org/10.3390/sci5030035 - 13 Sep 2023
Cited by 6 | Viewed by 118898
Abstract
The scientific and wider interest in the relationship between atmospheric temperature (T) and concentration of carbon dioxide ([CO2]) has been enormous. According to the commonly assumed causality link, increased [CO2] causes a rise in T. However, [...] Read more.
The scientific and wider interest in the relationship between atmospheric temperature (T) and concentration of carbon dioxide ([CO2]) has been enormous. According to the commonly assumed causality link, increased [CO2] causes a rise in T. However, recent developments cast doubts on this assumption by showing that this relationship is of the hen-or-egg type, or even unidirectional but opposite in direction to the commonly assumed one. These developments include an advanced theoretical framework for testing causality based on the stochastic evaluation of a potentially causal link between two processes via the notion of the impulse response function. Using, on the one hand, this framework and further expanding it and, on the other hand, the longest available modern time series of globally averaged T and [CO2], we shed light on the potential causality between these two processes. All evidence resulting from the analyses suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect. That link is not represented in climate models, whose outputs are also examined using the same framework, resulting in a link opposite the one found when the real measurements are used. Full article
(This article belongs to the Special Issue Feature Papers—Multidisciplinary Sciences 2023)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Explanatory sketch for the definition of the different potential causality types. For each graph, the mean <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>h</mi> </mrow> </msub> </mrow> </semantics></math> is also plotted with a dashed line.</p> Full article ">Figure 2
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa [CO<sub>2</sub>] time series, respectively—case studies #3 (<b>left</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>; potentially causal system) and #4 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math>; potentially anticausal system).</p> Full article ">Figure 3
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and the South Pole time series, respectively—case studies #14 (<b>left</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>; potentially causal system) and #15 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math>; potentially anticausal system).</p> Full article ">Figure 4
<p>(<b>Left column</b>) Empirical autocorrelation function for the period 1958–2021 and for monthly timescale of (<b>upper</b>) the NCEP/NCAR <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math> time series and (<b>lower</b>) the <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> time series for Mauna Loa. (<b>Right column</b>) Empirical cross-correlation function of the two time series (continuous lines in blue), compared with those reconstructed from the IRF and the autocorrelation function on the left panel using the discretized version of Equation (7) (dashed line), for case studies (<b>upper</b>) #3 and (<b>lower</b>) #4.</p> Full article ">Figure 5
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa time series, respectively, as in <a href="#sci-05-00035-f002" class="html-fig">Figure 2</a>, but for differencing time steps equal (from upper to lower) 2, 4, 8 and 16 years; <b>left</b>: <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> (potentially causal system); <b>right</b>: <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math> (potentially anticausal system).</p> Full article ">Figure 5 Cont.
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa time series, respectively, as in <a href="#sci-05-00035-f002" class="html-fig">Figure 2</a>, but for differencing time steps equal (from upper to lower) 2, 4, 8 and 16 years; <b>left</b>: <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> (potentially causal system); <b>right</b>: <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math> (potentially anticausal system).</p> Full article ">Figure 6
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa time series, respectively, also enabling negative lags (HOE) for causality direction <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> and for differencing time step of 1 year (<b>left</b>, corresponding to <a href="#sci-05-00035-f002" class="html-fig">Figure 2</a>, left) and 16 years (<b>right</b>, corresponding to <a href="#sci-05-00035-f005" class="html-fig">Figure 5</a>, bottom left).</p> Full article ">Figure 7
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa time series, for 21 time lags, differencing time step of 16 years and direction <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>. The lowest nonzero lag of each IRF is marked at the upper-right end of its curve.</p> Full article ">Figure 8
<p>Explained variance and median of IRF as a function of the lowest nonzero lag of the IRFs in <a href="#sci-05-00035-f007" class="html-fig">Figure 7</a> for the investigation of <a href="#sec7-sci-05-00035" class="html-sec">Section 7</a>.</p> Full article ">Figure 9
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the CMIP6 mean temperature and SSP2-4.5 CO<sub>2</sub> time series, respectively, calculated without using the roughness constraint; <b>upper row</b>: period 1850–2100—case studies #16 (<b>left</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>) and #17 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math>); <b>lower row</b>: period 1850–2021—case studies #18 (<b>left</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>) and #19 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math>).</p> Full article ">Figure 10
<p>IRFs for temperature–CO<sub>2</sub> concentration based on the CMIP6 mean temperature and SSP2-4.5 CO<sub>2</sub> time series, respectively, as in <a href="#sci-05-00035-f009" class="html-fig">Figure 9</a> but calculated using the roughness constraint; <b>upper row</b>: period 1850–2100—case studies #20 (<b>left</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>) and #21 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math>); <b>lower row</b>: period 1850–2021—case studies #22 (<b>left</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math>) and #23 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> </mrow> </semantics></math>).</p> Full article ">Figure 11
<p>Empirical cross-correlation functions for monthly and annual timescales (continuous lines in blue without markers and red lines with circles, respectively) for the data sets indicated in each panel. In all panels, the plot for the monthly scale is that of the NCEP/NCAR data for <span class="html-italic">T</span> and Mauna Loa data for [CO<sub>2</sub>], for the period 1958–2021. The upper-left panel also shows the cross-correlation function reconstructed from the IRF and the autocorrelation function using the discretized version of Equation (7) (dashed line).</p> Full article ">Figure 12
<p>Empirical autocorrelation functions for monthly and annual timescales (continuous lines in blue without markers and red lines with circles, respectively) for the data sets indicated in each panel. In all panels, the plot for the monthly scale is that of the NCEP/NCAR data for <span class="html-italic">T</span> and Mauna Loa data for [CO<sub>2</sub>], for the period 1958–2021.</p> Full article ">Figure 13
<p>Schematic of the examined possible causal links in the climatic system, with noted types of potential causality, HOE or unidirectional, and its direction. Other processes, not examined here, could be internal of the climatic system or external.</p> Full article ">Figure 14
<p>Modified IRF for temperature–CO<sub>2</sub> concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa time series, respectively, similar to <a href="#sci-05-00035-f002" class="html-fig">Figure 2</a> but with IRF coordinates simultaneously optimized with the parameters of Equation (9).</p> Full article ">Figure 15
<p>Comparison of the actual <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> (<b>upper</b>) and <math display="inline"><semantics> <mrow> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> (<b>lower</b>) with those simulated by the model of Equations (8) and (9).</p> Full article ">Figure A1
<p>Annual carbon balance in the Earth’s atmosphere in Gt C/year, based on the IPCC [<a href="#B32-sci-05-00035" class="html-bibr">32</a>] estimates. The balance of 5.1 Gt C/year is the annual accumulation of carbon (in the form of CO<sub>2</sub>) in the atmosphere.</p> Full article ">Figure A2
<p>Evolution of global land (terrestrial) and sea (maritime) temperature at 2 m from the NCEP/NCAR Reanalysis data set, retrieved from the ClimExp platform, and resulting slopes of linear trends.</p> Full article ">Figure A3
<p>TOA albedo time series (continuous line), as provided by NASA’s Clouds and the Earth’s Radiant Energy System (CERES), along with linear trend (dashed line).</p> Full article ">Figure A4
<p>IRFs for albedo–temperature based on the CERES albedo time series and the NCEP/NCAR Reanalysis temperature at 2 m, respectively—case studies #24 (<b>left</b>; <math display="inline"><semantics> <mrow> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>α</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>]</mo> </mrow> </semantics></math>;) and #25 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>α</mi> </mrow> </semantics></math>).</p> Full article ">Figure A5
<p>SOI time series (continuous line) along with rolling (right-aligned) 10-year average (dashed line). Negative and positive values indicate the El Niño and La Niña phases, respectively.</p> Full article ">Figure A6
<p>IRFs for ENSO–temperature based on the SOI time series and the NCEP/NCAR Reanalysis temperature at 2 m, respectively—case studies #26 (<b>left</b>; <math display="inline"><semantics> <mrow> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">O</mi> <mi mathvariant="normal">I</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>]</mo> </mrow> </semantics></math>;) and #27 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">O</mi> <mi mathvariant="normal">I</mi> </mrow> </semantics></math>).</p> Full article ">Figure A7
<p>IRFs for ENSO–[CO<sub>2</sub>] based on the SOI and the Mauna Loa time series, respectively—case studies #28 (<b>left</b>; <math display="inline"><semantics> <mrow> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">O</mi> <mi mathvariant="normal">I</mi> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>]</mo> </mrow> </semantics></math>;) and #29 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mfenced open="[" close="]" separators="|"> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </mfenced> <mo>→</mo> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">O</mi> <mi mathvariant="normal">I</mi> </mrow> </semantics></math>).</p> Full article ">Figure A8
<p>OMT0–100 time series (continuous line) along with rolling (right-aligned) 10-year average (dashed line).</p> Full article ">Figure A9
<p>IRFs for upper ocean temperature—atmospheric temperature based on the OMT0–100 and the NCEP/NCAR Reanalysis data, respectively—case studies #30 (<b>left</b>; ΔOMT0–100 <math display="inline"><semantics> <mrow> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>]</mo> </mrow> </semantics></math>;) and #31 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>T</mi> <mo>→</mo> </mrow> </semantics></math> ΔOMT0–100).</p> Full article ">Figure A10
<p>IRFs for upper ocean temperature—[CO<sub>2</sub>] based on the OMT0–100 and the NCEP/NCAR Reanalysis data, respectively—case studies #32 (<b>left</b>; ΔOMT0–100 <math display="inline"><semantics> <mrow> <mo>→</mo> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mo>[</mo> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> <mo>]</mo> <mo>]</mo> </mrow> </semantics></math>;) and #33 (<b>right</b>; <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">n</mi> <mfenced open="[" close="]" separators="|"> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">O</mi> </mrow> <mn>2</mn> </msub> </mrow> </mfenced> <mo>→</mo> </mrow> </semantics></math> ΔOMT0–100).</p> Full article ">
9 pages, 578 KiB  
Article
The Additional Diagnostic Value of Electrocardiogram and Strain Patterns in Transplanted Patients
by Laura Stefani, Goffredo Orlandi, Marco Corsi, Edoardo Falconi, Roberto Palazzo, Alessio Pellegrino and Pietro Amedeo Modesti
Sci 2023, 5(3), 34; https://doi.org/10.3390/sci5030034 - 25 Aug 2023
Viewed by 1453
Abstract
Background: Transplanted patients are frail individuals who may be affected by diastolic dysfunction, leading to a decrease in exercise tolerance. Previous studies have reported that certain ECG and echocardiographic parameters (such as the P-wave interval, PQ interval, P-wave dispersion, Tend-P interval, QTc interval, [...] Read more.
Background: Transplanted patients are frail individuals who may be affected by diastolic dysfunction, leading to a decrease in exercise tolerance. Previous studies have reported that certain ECG and echocardiographic parameters (such as the P-wave interval, PQ interval, P-wave dispersion, Tend-P interval, QTc interval, and strain) can support the diagnosis of diastolic dysfunction when the ejection fraction is preserved. This study aimed to examine the potential diagnostic contribution of specific ECG and deformation parameters in transplanted recipients, who are at a high risk of heart failure. Materials and Methods: A group of 33 transplanted subjects (17 renal and 16 liver) were categorized using two scores for heart failure with preserved ejection fraction (HFpEF). Additionally, they underwent evaluation based on ECG parameters (P-wave interval, PQ interval, Pwave dispersion, and Tend-P QTc) and echocardiographic deformation parameters (strain and twist). The Student’s t-test was used for statistical analysis. Results: The two scores identified different numbers of excludable and not excludable subjects potentially affected by HFpEF. The not excludable group presented ECG parameters with significantly higher values (P-wave, PQ interval, posterior wall diastole, and Tend-P, all with p ≤ 0.05) and significantly lower 4D strain and twist values (p < 0.05) Conclusions: There is evidence for a significant diagnostic contribution of additional ECG and echo strain parameters in an early phase of diastolic dysfunction in subjects potentially affected by HFpEF. Full article
(This article belongs to the Section Sports Science and Medicine)
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<p>Example of vortex parameters obtained from a 3C view using HyperDoppler analysis.</p> Full article ">
12 pages, 1768 KiB  
Article
Development of a Semi-Empirical Model for Estimating the Efficiency of Thermodynamic Power Cycles
by Evangelos Bellos
Sci 2023, 5(3), 33; https://doi.org/10.3390/sci5030033 - 24 Aug 2023
Viewed by 2145
Abstract
Power plants constitute the main sources of electricity production, and the calculation of their efficiency is a critical factor that is needed in energy studies. The efficiency improvement of power plants through the optimization of the cycle is a critical means of reducing [...] Read more.
Power plants constitute the main sources of electricity production, and the calculation of their efficiency is a critical factor that is needed in energy studies. The efficiency improvement of power plants through the optimization of the cycle is a critical means of reducing fuel consumption and leading to more sustainable designs. The goal of the present work is the development of semi-empirical models for estimating the thermodynamic efficiency of power cycles. The developed model uses only the lower and the high operating temperature levels, which makes it flexible and easily applicable. The final expression is found by using the literature data for different power cycles, named as: organic Rankine cycles, water-steam Rankine cycles, gas turbines, combined cycles and Stirling engines. According to the results, the real operation of the different cases was found to be a bit lower compared to the respective endoreversible cycle. Specifically, the present global model indicates that the thermodynamic efficiency is a function of the temperature ratio (low cycle temperature to high cycle temperature). The suggested equation can be exploited as a quick and accurate tool for calculating the thermodynamic efficiency of power plants by using the operating temperature levels. Moreover, separate equations are provided for all of the examined thermodynamic cycles. Full article
(This article belongs to the Section Computer Sciences, Mathematics and AI)
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<p>Thermodynamic cycle efficiency for different cycles (<b>a</b>) as a function of the high cycle temperature (T<sub>high</sub>); (<b>b</b>) as a function of the temperature ratio (T<sub>low</sub>/T<sub>high</sub>).</p> Full article ">Figure 2
<p>Thermodynamic cycle efficiency for all the reported data (<b>a</b>) as a function of the high cycle temperature (T<sub>high</sub>); (<b>b</b>) as a function of the temperature ratio (T<sub>low</sub>/T<sub>high</sub>).</p> Full article ">Figure 3
<p>Thermodynamic cycle efficiency for all the reported data, Carnot efficiency and endoreversible efficiency (<b>a</b>) as a function of the high cycle temperature (T<sub>high</sub>); (<b>b</b>) as a function of the temperature ratio (T<sub>low</sub>/T<sub>high</sub>).</p> Full article ">Figure 3 Cont.
<p>Thermodynamic cycle efficiency for all the reported data, Carnot efficiency and endoreversible efficiency (<b>a</b>) as a function of the high cycle temperature (T<sub>high</sub>); (<b>b</b>) as a function of the temperature ratio (T<sub>low</sub>/T<sub>high</sub>).</p> Full article ">
24 pages, 677 KiB  
Communication
An Analysis of the Convergence Problem of a Function in Functional Norms by Applying the Generalized Nörlund-Matrix Product Operator
by Hari M. Srivastava, Hare K. Nigam and Swagata Nandy
Sci 2023, 5(3), 32; https://doi.org/10.3390/sci5030032 - 22 Aug 2023
Cited by 1 | Viewed by 1391
Abstract
In this paper, we analyze the convergence problems of function g of Fourier series in Besov and generalized Zygmund norms using generalized Nörlund-Matrix (Np,qA) means of Fourier series. Convergence results are also compared by means of applications. Full article
(This article belongs to the Section Computer Sciences, Mathematics and AI)
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<p>Graphs of <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">∥</mo> </mrow> <msub> <mi>Z</mi> <mi>κ</mi> </msub> <mrow> <mrow> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> <mo stretchy="false">∥</mo> </mrow> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mi>κ</mi> </semantics></math>. (<b>a</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>; (<b>b</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>; (<b>c</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000; (<b>d</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 100,000; (<b>e</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 1,000,000; (<b>f</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000,000.</p> Full article ">Figure 1 Cont.
<p>Graphs of <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">∥</mo> </mrow> <msub> <mi>Z</mi> <mi>κ</mi> </msub> <mrow> <mrow> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> <mo stretchy="false">∥</mo> </mrow> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mi>κ</mi> </semantics></math>. (<b>a</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>; (<b>b</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>; (<b>c</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000; (<b>d</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 100,000; (<b>e</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 1,000,000; (<b>f</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000,000.</p> Full article ">Figure 2
<p>Graphs of <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">|</mo> <mo stretchy="false">|</mo> </mrow> <msub> <mi>T</mi> <mi>κ</mi> </msub> <mrow> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> <mrow> <mo stretchy="false">|</mo> <mo stretchy="false">|</mo> </mrow> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mi>κ</mi> </semantics></math>. (<b>a</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>; (<b>b</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>; (<b>c</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>000</mn> </mrow> </semantics></math>; (<b>d</b>) For <math display="inline"><semantics> <mi>κ</mi> </semantics></math> = 100,000; (<b>e</b>) For <math display="inline"><semantics> <mi>κ</mi> </semantics></math> =1,000,000; (<b>f</b>) For <math display="inline"><semantics> <mi>κ</mi> </semantics></math> = 10,000,000.</p> Full article ">Figure 2 Cont.
<p>Graphs of <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">|</mo> <mo stretchy="false">|</mo> </mrow> <msub> <mi>T</mi> <mi>κ</mi> </msub> <mrow> <mo>(</mo> <mo>·</mo> <mo>)</mo> </mrow> <mrow> <mo stretchy="false">|</mo> <mo stretchy="false">|</mo> </mrow> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mi>κ</mi> </semantics></math>. (<b>a</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>; (<b>b</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>; (<b>c</b>) For <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>000</mn> </mrow> </semantics></math>; (<b>d</b>) For <math display="inline"><semantics> <mi>κ</mi> </semantics></math> = 100,000; (<b>e</b>) For <math display="inline"><semantics> <mi>κ</mi> </semantics></math> =1,000,000; (<b>f</b>) For <math display="inline"><semantics> <mi>κ</mi> </semantics></math> = 10,000,000.</p> Full article ">
15 pages, 3663 KiB  
Review
Artificial Neural Networks in Membrane Bioreactors: A Comprehensive Review—Overcoming Challenges and Future Perspectives
by Zacharias Frontistis, Grigoris Lykogiannis and Anastasios Sarmpanis
Sci 2023, 5(3), 31; https://doi.org/10.3390/sci5030031 - 15 Aug 2023
Cited by 1 | Viewed by 2878
Abstract
Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential [...] Read more.
Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential of artificial neural networks (ANNs) to monitor, simulate, optimize, and control these systems. ANNs show a unique ability to reveal and simulate complex relationships of dynamic systems such as MBRs, allowing for process optimization and fault detection. This early warning system leads to increased reliability and performance. Integrating ANNs with advanced algorithms and implementing Internet of Things (IoT) devices and new-generation sensors has the potential to transform the advanced wastewater treatment landscape towards the development of smart, self-adaptive systems. Nevertheless, several challenges must be addressed, including the need for high-quality and large-quantity data, human resource training, and integration into existing control system facilities. Since the demand for advanced water treatment and water reuse will continue to expand, proper implementation of ANNs, combined with other AI tools, is an exciting strategy toward the development of integrated and efficient advanced water treatment schemes. Full article
(This article belongs to the Section Environmental and Earth Science)
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<p>ANN implementation in MBR systems.</p> Full article ">Figure 2
<p>ANN implementation in MBR systems: a suggested roadmap for current trends and future perspectives.</p> Full article ">
13 pages, 2003 KiB  
Article
Short-Term Biochemical Biomarkers of Stress in the Oyster Magallana angulata Exposed to Gymnodinium catenatum and Skeletonema marinoi
by Rui Cereja, Joana P. C. Cruz, Joshua Heumüller, Bernardo Vicente, Ana Amorim, Frederico Carvalho, Sara Cabral, Paula Chainho, Ana C. Brito, Inês J. Ferreira and Mário Diniz
Sci 2023, 5(3), 30; https://doi.org/10.3390/sci5030030 - 17 Jul 2023
Cited by 1 | Viewed by 2032
Abstract
Bivalves accumulate toxins produced by microalgae, thus becoming harmful for humans. However, little information is available about their toxicity to the bivalve itself. In the present work, the physiological stress and damage after the ingestion of toxic dinoflagellate species (Gymnodinium catenatum) [...] Read more.
Bivalves accumulate toxins produced by microalgae, thus becoming harmful for humans. However, little information is available about their toxicity to the bivalve itself. In the present work, the physiological stress and damage after the ingestion of toxic dinoflagellate species (Gymnodinium catenatum) and a diatom species (Skeletonema marinoi, which is non-toxic to humans but may be to grazers) in the oyster Magallana angulata are evaluated against a control treatment fed with the chlorophyte Tetraselmis sp. Oysters were exposed for two hours to a concentration of 4 × 104 cells/L of G. catenatum and 2 × 107 cells/L of S. marinoi. The biomarkers superoxide dismutase (SOD), catalase (CAT), glutathione S-Transferase, total Ubiquitin (Ubi) and Acetylcholinesterase (AchE) were assessed. The exposure of M. angulata to G. catenatum lead to a reduction in SOD and AchE activity and ubiquitin concentrations when compared to the control treatment. Moreover, it increased CAT activity in the adductor muscle, and maintained its activity in the other tissues tested. This may be related to the combination of reduced metabolism with the deployment of detoxification processes. S. marinoi also lead to a decrease in all biomarkers tested in the gills and digestive glands. Therefore, both species tested caused physiological alterations in M. angulata after two hours of exposure. Full article
(This article belongs to the Section Biology Research and Life Sciences)
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<p>Biomarker values for the three tested tissues (gills, adductor muscle and digestive gland) and the three treatments (control—<span class="html-italic">Tetraselmis</span> sp. in blue, <span class="html-italic">Skeletonema marinoi</span> in orange and <span class="html-italic">Gymnodinium catenatum</span> in green). Bars represent the mean and error bars are the standard deviation for (<b>A</b>) superoxide dismutase activity (SOD), (<b>B</b>) Catalase activity (CAT), (<b>C</b>) glutathione S-Transferase activity (GST), (<b>D</b>) total ubiquitin concentrations (Ubi) and (<b>E</b>) acetylcholinesterase activity (AChE). Statistical significance is not shown in this figure since the performed analysis is multivariated and is, rather, seen in <a href="#sci-05-00030-f002" class="html-fig">Figure 2</a> and <a href="#sci-05-00030-f003" class="html-fig">Figure 3</a> and <a href="#sci-05-00030-t001" class="html-table">Table 1</a> and <a href="#sci-05-00030-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 2
<p>Principal Component Analysis comparing the enzyme activities in the different tissues tested: gills (G), adductor muscle (AM) and digestive gland (DG). The biomarkers tested were the activities of superoxide dismutase (SOD), Catalase, Glutathione S-Transferase (GST), acetylcholinesterase (AChE), and the concentration of total ubiquitin (Ubi). The first two principal components (PC) explained 85.8% of the total variance, with 66.7% explained in PC 1 and 19.2% in PC2.</p> Full article ">Figure 3
<p>Radar Chart for the IBR values of each treatment and tissue. (<b>A</b>) Gills, (<b>B</b>) adductor muscle and (<b>C</b>) digestive gland. The blue line represents the control treatment, the orange line the <span class="html-italic">Skeletonema marinoi</span> treatment and the green line the <span class="html-italic">Gymnodinium catenatum</span> treatment. Each chart presents normalized value for the activities of Superoxide dismutase (SOD), Catalase, Gluthatione S-transferase (GST), acetylcholinesterase (AChE) and the concentrations of total ubiquitin (Ubi). Higher values mean higher concentrations of a certain biomarker and thus the area of the polygon for a certain color gives the relative response of a certain tissue to the group of analyzed biomarkers.</p> Full article ">
10 pages, 1091 KiB  
Editorial
Implementing Smart Services in Small- and Medium-Sized Manufacturing Companies: On the Progress of Servitization in the Era of Industry 4.0
by Johannes Winter
Sci 2023, 5(3), 29; https://doi.org/10.3390/sci5030029 - 12 Jul 2023
Cited by 1 | Viewed by 2203
Abstract
For a long time, the challenge has been to provide products and services that precisely match the preferences, habits, and needs of users [...] Full article
(This article belongs to the Special Issue Industry 4.0 – The Global Industrial Revolution: Achievements, Obstacles and Research Needs for the Digital Transformation of Industry)
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<p>From optimized production to innovation ecosystems (source: own illustration, 2023, based on [<a href="#B6-sci-05-00029" class="html-bibr">6</a>]).</p> Full article ">Figure 2
<p>Value architecture of data-driven business models (source: [<a href="#B24-sci-05-00029" class="html-bibr">24</a>]).</p> Full article ">Figure 3
<p>Acoustic analysis of maintenance needs in plastics processing (source: own illustration, 2023, based on based on [<a href="#B8-sci-05-00029" class="html-bibr">8</a>]).</p> Full article ">Figure 4
<p>Smart optimization of the picking process in metal processing (source: own illustration, 2023, based on based on [<a href="#B8-sci-05-00029" class="html-bibr">8</a>]).</p> Full article ">Figure 5
<p>Autonomous stacking of logs in firewood production (source: own illustration, 2023, based on [<a href="#B8-sci-05-00029" class="html-bibr">8</a>]).</p> Full article ">
20 pages, 504 KiB  
Article
COVID-19 as a Jump Start for Industry 4.0? Motivations and Core Areas of Pandemic-Related Investments in Digital Technologies at German Firms
by Florian Butollo, Jana Flemming, Christine Gerber, Martin Krzywdzinski, David Wandjo, Nina Delicat and Lorena Herzog
Sci 2023, 5(3), 28; https://doi.org/10.3390/sci5030028 - 7 Jul 2023
Cited by 2 | Viewed by 1993
Abstract
Academic studies prior to the pandemic rather emphasized that the progression towards Industry 4.0 happened in an incremental manner. However, the extraordinary circumstances of the pandemic have led to considerable investments that were widely interpreted as a (generalized) digitalization push. However, little is [...] Read more.
Academic studies prior to the pandemic rather emphasized that the progression towards Industry 4.0 happened in an incremental manner. However, the extraordinary circumstances of the pandemic have led to considerable investments that were widely interpreted as a (generalized) digitalization push. However, little is known about the character of such investments and their effects. The goal of this contribution is to provide an empirically based overview of recent investment in digital technologies in six economic sectors of the German economy: mechanical engineering, chemicals, automotives, logistics, healthcare, and financial services. Based on 36 case studies and a survey at 540 companies, we investigate the following questions: 1. How much did the COVID-19 pandemic reduce existing obstacles for investments in digitalization measures? 2. Is there a universal digitalization push due to the COVID-19 pandemic that differs from the trajectory before the pandemic? The results show that the pandemic affected investment in an unequal manner. It was driven by the immediate need to sustain business operations through the virtualization of communication among employees and with external partners. However, there was less dynamism in shop-floor-related digitalization, as it was less related to epidemiological concerns and is more long-term in nature. Full article
(This article belongs to the Special Issue Industry 4.0 – The Global Industrial Revolution: Achievements, Obstacles and Research Needs for the Digital Transformation of Industry)
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<p>Core areas of investment (quantitative survey).</p> Full article ">
18 pages, 1877 KiB  
Review
Sensory and Cognitive Malingering: Studies and Tests
by Gesualdo M. Zucco and Giuseppe Sartori
Sci 2023, 5(3), 27; https://doi.org/10.3390/sci5030027 - 6 Jul 2023
Viewed by 4398
Abstract
Malingering relates to intentionally pretending or exaggerating physical or psychologic symptoms to gain an external incentive, such as avoiding work, law prosecution or military service, or seeking financial compensation from insurance companies. Accordingly, various techniques have been developed in recent years by the [...] Read more.
Malingering relates to intentionally pretending or exaggerating physical or psychologic symptoms to gain an external incentive, such as avoiding work, law prosecution or military service, or seeking financial compensation from insurance companies. Accordingly, various techniques have been developed in recent years by the scientific community to address this challenge. In this review, we discuss malingering within visual, auditory and olfactory domains, as well as in cognitive disorders and psychopathology. We provide a general, critical, narrative overview on the intermodal criteria for differential diagnosis, and discuss validated psychophysical tools and electrophysiology-based tests for its detection, as well as insights for future directions. Full article
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<p>Normative scores for UPSIT.</p> Full article ">
19 pages, 4319 KiB  
Article
Transcriptomics Analysis of Tomato Ripening Regulated by Carbon Dioxide
by Jamshed Bobokalonov, Yanhong Liu, Karley K. Mahalak, Jenni A. Firrman, Shiowshuh Sheen, Siyuan Zhou and LinShu Liu
Sci 2023, 5(3), 26; https://doi.org/10.3390/sci5030026 - 30 Jun 2023
Viewed by 2220
Abstract
Tomatoes are a perishable and seasonal fruit with a high economic impact. Carbon dioxide (CO2), among several other reagents, is used to extend the shelf-life and preserve the quality of tomatoes during refrigeration or packaging. To obtain insight into CO2 [...] Read more.
Tomatoes are a perishable and seasonal fruit with a high economic impact. Carbon dioxide (CO2), among several other reagents, is used to extend the shelf-life and preserve the quality of tomatoes during refrigeration or packaging. To obtain insight into CO2 stress during tomato ripening, tomatoes at the late green mature stage were conditioned with one of two CO2 delivery methods: 5% CO2 for 14 days (T1) or 100% CO2 for 3 h (T2). Conventional physical and chemical characterization found that CO2 induced by either T1 or T2 delayed tomato ripening in terms of color change, firmness, and carbohydrate dissolution. However, T1 had longer-lasting effects. Furthermore, ethylene production was suppressed by CO2 in T1, and promoted in T2. These physical observations were further evaluated via RNA-Seq analysis at the whole-genome level, including genes involved in ethylene synthesis, signal transduction, and carotenoid biosynthesis. Transcriptomics analysis revealed that the introduction of CO2 via the T1 method downregulated genes related to fruit ripening; in contrast, T2 upregulated the gene encoding for ACS6, the enzyme responsible for S1 ethylene synthesis, even though there was a large amount of ethylene present, indicating that T1 and T2 regulate tomato ripening via different mechanisms. Quantitative real-time PCR assays (qRT-PCR) were used for validation, which substantiated the RNA-Seq data. The results of the present research provide insight into gene regulation by CO2 during tomato ripening at the whole-genome level. Full article
(This article belongs to the Section Biology Research and Life Sciences)
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Figure 1

Figure 1
<p>Changes in phenotypes induced by exogenous CO<sub>2</sub>. (<b>a</b>) Ethylene production; (<b>b</b>) photos of tomatoes; (<b>c</b>) a* values; (<b>d</b>) firmness; and (<b>e</b>) soluble carbohydrates. Error bars indicate the standard deviation of three replications. (<tt>■</tt>) CT, (●) T1, (<tt>▲</tt>) T2; # <span class="html-italic">p</span> &lt; 0.05 (Student’s <span class="html-italic">t</span>-test).</p> Full article ">Figure 2
<p>Volcano plot of significant DEG numbers of CO<sub>2</sub> treated tomatoes on (<b>a</b>) T1d3:CTd3; (<b>b</b>) T2d3:CTd3; (<b>c</b>) T1d7:CTd7; and (<b>d</b>) T2d7:CTd7.</p> Full article ">Figure 3
<p>Hierarchical clustering and heat map of expression levels of differentially expressed genes in RPKM. In the heat map, red is associated with high expression levels, and blue is associated with low expression levels. Data represented are the averages of two biological replicates.</p> Full article ">Figure 4
<p>Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs on d7. (<b>a</b>) The most enriched GO terms in T1:CT; (<b>b</b>) the most enriched GO terms in T2:CT; (<b>c</b>) KEGG analysis in T1:CT; (<b>d</b>) KEGG analysis in T2:CT. In (<b>a</b>,<b>b</b>), bars with asterisks (*) indicate <span class="html-italic">q</span> &lt; 0.05. In (<b>c</b>,<b>d</b>), for all bars, <span class="html-italic">q</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.05, except for pink bars, for which <span class="html-italic">p</span> &gt; 0.05.</p> Full article ">Figure 5
<p>Heat map depicting expression levels of DEGs involved in ethylene synthesis and signal transduction. Red indicates high expression, while blue denotes low expression. The RPKM values were normalized with log<sub>2</sub> (RPKM + 1) and converted to Z-scores.</p> Full article ">Figure 6
<p>Heatmap of the expression levels of the DEGs induced by CO<sub>2</sub> stress. Genes in columns with more transcription patterns are adjacent; samples in rows of more transcription patterns are also adjacent.</p> Full article ">Figure 7
<p>Correlation of gene expression data from RNA-Seq and RT-qPCR: (triangle) T1d7:CTd7; (circle) T2d7:CTd7; (diamond) T2d7:T1d7.</p> Full article ">Figure 8
<p>Tomato fruit ripening: regulation of ethylene production and its response.</p> Full article ">
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