IoT and information processing in smart energy applications.

The articles in this special section address smart energy applications from the perspective of the Internet of Things (IoT). For smart grid applications, we need to predict the electrical load so that the underlying smart grid can effectively balance the power supply and demand. In general, predictions are made based on the data obtained using IoT and smart meter technologies. The (IoT) could accelerate establishment of such infrastructures. With IoT technologies, many more devices could be controlled and managed through the Internet; data pertaining to the grid, commercial buildings, and residential premises can readily be collected and utilized. To derive valuable information from the data, further information and data processing become essential

Clinical, biological, thoracolumbar X ray and genetic characteristics of 232 patients with PsA, serum tested for ANA at 1: 100.

<p>Clinical, biological, thoracolumbar X ray and genetic characteristics of 232 patients with PsA, serum tested for ANA at 1: 100.</p

Representative force generation profiles during the first four (C4) and the last four (C20) contractions of the WPHF, CONV and VOL protocols for one non-responder (A) and one responder subject with similar MVC values Note that force production was higher and fatigue was lower for the responder as compared to non-responder during the WPHF protocol.

<p>Representative force generation profiles during the first four (C4) and the last four (C20) contractions of the WPHF, CONV and VOL protocols for one non-responder (A) and one responder subject with similar MVC values Note that force production was higher and fatigue was lower for the responder as compared to non-responder during the WPHF protocol.</p

Resting values and changes from rest (delta) for PCr, Pi/PCr and pH.

<p>^§ Significantly different from NMES (P< 0.05), independently of the group.</p><p>Resting values and changes from rest (delta) for PCr, Pi/PCr and pH.</p

Individual force profiles for all 18 subjects and for each protocol calculated as the sum of the force time integral (FTI) for all 20 contractions.

<p>K-means analysis of Extra Forces resulted in the classification of 11 non-responders and 7 responders for the latter of which the FTI was significantly higher for WPHF NMES as compared to the other two exercise modalities. Note the high inter-individual variability of FTI for WPHF NMES.</p

Day to day reliability for neuromuscular and magnetic resonance imaging parameters.

<p>SD: standard deviation, SEM: standard error of the mean, CV: coefficient of variation, ICC: intra-class correlation, CL: confidence limits.</p><p>MVC_right: right leg maximal voluntary isometric contraction force, Tw: evoked force by a single twitch stimulation Db₁₀: evoked force by a doublet at 10 Hz, Db₁₀₀: evoked force by a doublet at 100 Hz, 10∶100: ratio Db₁₀/Db₁₀₀, VA: voluntary activation, TTP: time-to-peak force during 100 Hz doublet, RFD: maximal rate of force development during 100 Hz doublet, QF: quadriceps femoris.</p><p>Day to day reliability for neuromuscular and magnetic resonance imaging parameters.</p

Summation of the first spectra for four subsequent trains in order to improve spectral resolution and to assess PCr depletion over time (i.e. five time-points, i.e., contraction C4; C8; C12; C16; C20).

<p>Each rectangle represents one contraction of 20 seconds, each arrow one MR spectrum acquired from 2 sec after each contraction.</p

Correlations matrix between changes in central and peripheral factors pooling together the measurements performed two, three and four days post-NMES.

<p>Results are presented with Pearson r coefficient/Spearman rho rank coefficient for P<0.05. ns: non-significant (P>0.05). Results are in bold for P<0.01.</p><p>MVC_right: right leg maximal voluntary contraction, VA: voluntary activation, Db₁₀₀: evoked force by a doublet at 100 Hz, Db₁₀: evoked force by a doublet at 10 Hz, TTP: time-to-peak force during 100 Hz doublet, RFD: maximal rate of force development during 100 Hz doublet, VAS: score in visual analog scale, CK: plasma creatine kinase activity.</p><p>Correlations matrix between changes in central and peripheral factors pooling together the measurements performed two, three and four days post-NMES.</p

Correlations matrix between changes in T₂ relaxation time and central and peripheral factors.

<p>Results are presented with Pearson r coefficient/Spearman rho rank coefficient for P<0.05. ns: non-significant (P>0.05). Results are in bold for P<0.01.</p><p>MVC_right: right leg maximal voluntary isometric contraction force, VA: voluntary activation, Db₁₀₀: evoked force by a doublet at 100 Hz, CK: plasma creatine kinase activity.</p><p>Correlations matrix between changes in T₂ relaxation time and central and peripheral factors.</p

Mechanical data obtained during superimposed stimulations and evoked contractions on resting muscle.

<p>Typical raw data in a subject for the assessment of neuromuscular parameters including: (A) superimposed 100 Hz double stimuli during unilateral maximal voluntary contraction plateau and, (B) measurements on resting muscle: 100 Hz double stimuli (Db₁₀₀), 10 Hz double stimuli (Db₁₀) and single stimulus (Tw) before, immediately after, two and four days (D2 and D4, respectively) after the isometric NMES session. Bottom trace for each panel displays the output trigger signal from the electrostimulator.</p