Acute neuromuscular and perceptual responses to blood flow restriction exercise in adults with severe haemophilia: A pilot study.

INTRODUCTION: No previous studies have implemented a standard blood flow restriction (BFR) training session in people with severe haemophilia (PwH), where this type of training has been contraindicated. AIMS: The purpose of this study was to evaluate the tolerability, adverse events, and neuromuscular and perceptual responses to an acute session of low load (LL) knee extensions with BFR in PwH under prophylaxis. METHODS: Eight PwH performed one LL-BFR session with 40% arterial occlusion pressure (AOP). Perceptual responses and adverse effects were assessed, together with high-density surface electromyography of vastus medialis (VM) and lateralis (VL). RESULTS: Significant normalized root mean square differences were found within each set, but not between sets. Spatial distribution (centroid displacement (p > .05), modified entropy (VM, set two, cycles three and five, p = .032) and coefficient of variation (VM, set two, cycles four and five lower than cycle three (p = .049; p = .036)) showed changes within each set. Median frequency showed a slight increase during cycle four of set four (p = .030). Rate of perceived exertion slightly increased with each set while tolerability slightly decreased in the last set and fear of training with BFR generally decreased after the session. CONCLUSIONS: In PwH, a LL-BFR session at 40% AOP is safe and feasible. Our results suggest that potential muscle impairments may blunt neuromuscular adaptations induced by BFR.

On physical analysis of phthalocyanine iron (II) using topological descriptor and curve fitting models.

A new area of applied chemistry called chemical graph theory uses combinatorial techniques to explain the complex interactions between atoms and bonds in chemical systems. This work investigates the use of edge partitions to decipher molecular connection patterns. The main goal is to use topological indices that capture important topological features to create a connection between the thermodynamic properties and structural characteristics of chemical molecules. We specifically examine the complex web of atoms and links that make up the Fe phthalocyanine chemical graph. Moreover, our study demonstrates a relationship between the calculated topological indices and the thermodynamic properties of Fe phthalocyanine (Phthalocyanine Iron (II)). This work offers insight into the thermodynamic consequences of molecule structures. It advances the subject of chemical graph theory, providing a useful perspective for future applications in catalysis and materials science.

Multidimensional energy poverty in Colombia: A department-level review from 2018 to 2022.

This article aims to measure energy poverty in Colombia in its thirty-two departments and its capital city from 2018 to 2022, using a composite approach. To achieve this, a Multidimensional Energy Poverty Index (MEPI) was designed, according to the methodology proposed by Nussbaumer et al. (2012; 2013) [1,2]. Twenty-eight variables were used, which were distributed across seven dimensions, and recorded by the National Quality of Life Survey (ECV, Spanish acronym), administered by the National Administrative Department of Statistics (DANE) of Colombia. In addition, a nested weighting method was used to assign weights within the index. Subjective weights were given to the dimensions, and an entropy method was used for each of the component variables. The results show that energy poverty has an increasing trend in Colombia throughout the period, especially in the municipal capitals. There are significant differences between urban and rural areas in all territories, and the departments located in the most remote areas of the country have a higher energy poverty. This is consistent with the low population density, as well as with off-grid areas. The results obtained will allow decision makers to conduct a preliminary evaluation of the management and effects of the specific public policy programs and plans that have been implemented in the different territories of the country.

Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering.

In solids, chemical short-range order (CSRO) refers to the self-organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning-enhanced atom probe tomography (APT) can mine the near-atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium-entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state-of-the-art Monte-Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic-scale architectures.

UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel-Ziv complexity and Teager-Kaiser energy features.

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), and Teager-Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations. The extracted features were subjected to a Chi-Square (χ2) feature selection process to identify the most significant features for input into a Deep Neural Network. The Taguchi method was utilized to test the performance of the recorded features, correspondingly. Performance metrics, including Accuracy, F1-Score, Precision, and Recall, were employed to evaluate the model's effectiveness before and after the feature selection. The achieved accuracy has increased by 0.9% when compared with results utilizing traditional statistical methods. Comparative analysis with prior research reveals that the proposed untraditional features surpass traditional methods in diagnosing UAV propeller faults. It resulted in improved performance metrics with Accuracy, F1-Score, Precision, and Recall reaching 99.6%, 99.5%, 99.5%, and 99.5%, respectively. The results suggest promising directions for future research in UAV maintenance and safety protocols.

High-Entropy Fluorite Oxide-Modified CaO-Based Sorbent Pellets for Enhanced High-Temperature CO2 Capture.

The calcium looping technology employing CaO-based sorbents is pivotal for capturing CO2 from flue gas. However, the intrinsic low thermodynamic stability of CaO-based sorbents and the requisite molding step induce severe sintering issues, diminishing their cyclic stability. Herein, a high-entropy fluorite oxide (HEFO) inert stabilizer premised on entropy stabilization and synergistic effect strategies is introduced. HEFO-modified, CaO-based sorbent pellets are synthesized via a rapid cigarette butt-assisted combustion process (15 min) combined with the graphite molding method. Post-multiple cycles, their CO2 capture capacity reaches 0.373 g g-1, which is 2.6-fold superior to that of pure CaO, demonstrating markedly enhanced anti-sintering properties. First, the subtle morphological and crystallographic modifications suggest that the inherent entropy stability of HEFO imparts robust thermal resistance. Concurrently, the disordered structure of single-phase HEFO exhibits a high affinity for CaO, resulting in an interface binding energy of -1.83 eV, in sharp contrast to the -0.112 eV of pure CaO, thereby restricting CaO migration. Additionally, the multi-element synergistic effect of HEFO reduces the energy barrier by 0.15 eV, leading to a 40% and 140% increase in carbonation and calcination rates, respectively. This work presents highly efficient and rapidly synthesized CaO-based sorbent pellets, showcasing promising potential for industrial application.

Associating neuromotor outcomes at 12 months with wearable sensor measures collected during early infancy in rural Guatemala.

BACKGROUND: Sensitive measures to predict neuromotor outcomes from data collected early in infancy are lacking. Measures derived from the recordings of infant movement using wearable sensors may be a useful new technique. METHODS: We collected full-day leg movement of 41 infants in rural Guatemala across 3 visits between birth and 6 months of age using wearable sensors. Average leg movement rate and fuzzy entropy, a measure to describe the complexity of signals, of the leg movements' peak acceleration time series and the time series itself were derived. We tested the three measures for the predictability of infants' developmental outcome, Bayley Scales of Infant and Toddler Development III motor, language, or cognitive composite score assessed at 12 months of age. We performed quantile regressions with clustered standard errors, accounting for the multiple visits for each infant. RESULTS: Fuzzy entropy was associated with the motor composite score at the 0.5 quantiles; this association was not found for the other two measures. Also, no leg movement characteristic was associated with language or cognitive composite scores. CONCLUSION: We propose that the entropy of leg movement associated peak accelerations calculated from the wearable sensor data collected for a full-day can be considered as one predictor for infants' motor developmental outcome assessed with Bayley Scales of Infant and Toddler Development III at 12 months of age.

Sleep deprivation effects on brain state dynamics are associated with dopamine D2 receptor availability via network control theory.

BACKGROUND: Sleep deprivation (SD) negatively affects brain function. Most brain imaging studies have investigated the effects of SD on 'static' brain function. SD effects on functional brain dynamics and their relationship with molecular changes remain relatively unexplored. METHODS: We used functional MRI to examine resting brain state dynamics after one night of SD compared to rested wakefulness (RW) and assessed their association with striatal brain dopamine D2 receptor availability (D2R) measured by PET-[11C]raclopride using network control theory. RESULTS: SD reduced dwell time and persistence probabilities with the strongest effects in two brain states, one characterized by high default mode network and low dorsal attention network activity and the other by high frontal parietal network and low somatomotor network activity. Using network control theory, we showed that after SD there was an overall increase in the control energy required for brain state transitions with effects varying for different brain state transitions. Control energy requirement was negatively associated with transition probabilities under SD and RW and accounted for SD-induced changes in transition probabilities. Alteration in the energy landscape was associated with SD-induced changes in striatal D2R distribution. CONCLUSIONS: These findings demonstrate altered occurrence of internally and externally oriented brain states following acute SD and suggest an association with energy requirements for brain state transitions modulated by striatal D2R.

Full Exploitation of Charge Compensation of O3-type Cathode Toward High Energy Sodium-Ion Batteries by High Entropy Strategy.

O3-type cathodes with sufficient Na content are considered as promising candidates for sodium-ion batteries (SIBs). However, these cathodes suffer from insufficient utilization of the active elements, restraining the delivered capacity. In this work, a high entropy strategy is applied to a typical O3 cathode NaLi0.1Ni0.35Mn0.55O2 (NLNM), forming a high entropy oxide NaLi0.1Ni0.15Cu0.1Mg0.1Ti0.2Mn0.35O2 (Na-HE). Results show that the active elements are fully exploited in Na-HE, with a two-electron reaction by Ni2+/4+ (further extended to Cu redox and even oxygen redox), vastly different from a one-electron reaction of Ni2+/3+ in NLNM. The full utilization of the active elements dramatically improves the output capacity of the cathode (122.6 mAh g-1 of Na-HE versus 81 mAh g-1 of NLNM). Moreover, the detrimental phase transition is well suppressed in Na-HE. The cathode exhibits high capacity retention of 88.7% after 100 cycles at 130 mA g-1, compared to only 36.4% for NLNM. These findings provide new insight for the design of new cathode materials for SIBs with high energy density and robust stability.

The low-entropy hydration shell mediated ice-binding mechanism of antifreeze proteins.

Antifreeze proteins (AFPs) can inhibit ice crystal growth. The ice-binding mechanism of AFPs remains unclear, yet the hydration shells of AFPs are thought to play an important role in modulating the binding of AFPs and ice. Here, we performed all-atom molecular dynamics simulations of an AFP from Choristoneura fumiferana (CfAFP) at four different temperatures, with a focus on analysis at 240 and 300 K, to investigate the dynamic and thermodynamic characteristics of hydration shells around ice-binding surfaces (IBS) and non-ice-binding surfaces (NIBS). Our results revealed that the dynamics of CfAFP hydration shells were highly heterogeneous, with its IBS favoring a less dense and more tetrahedral solvation shell, and NIBS hydration shells having opposite features to those of the IBS. The IBS of nine typical hyperactive AFPs were found to be in pure low-entropy hydration shell region, indicating that low-entropy hydration shell region of IBS and the tetrahedral arrangements of water molecules around them mediate the ice-binding mechanism of AFPs. It is because the entropy increase of the low-entropy hydration shell around IBS, while the higher entropy water molecules at NIBS most likely prevent ice crystal growth. These findings provide new mechanistic insights into the ice-binding of AFPs.

Formative reasons for state-to-state influences on firearm acquisition in the U.S.

Objectives: Firearm-related crimes and self-inflicted harms pose a significant threat to the safety and well-being of Americans. Investigation of firearm prevalence in the United States (U.S.) has therefore been a center of attention. A critical aspect in this endeavor is to explain whether there are identifiable patterns in firearm acquisition. Methods: We view firearm acquisition patterning as a spatio-temporal dynamical system distributed across U.S. states that co-evolves with crime rates, political ideology, income levels, population, and the legal environment. We leverage transfer entropy and exponential random graph models along with publicly available data, to statistically reveal the formative factors in how each state's temporal patterning of firearm acquisition influences other states. Results: Results help to explain how and why U.S. states influence each other in their firearm acquisition. We establish that state-to-state influences, or lack thereof, in firearm acquisition patterning are explained by states' percent of gun homicide, firearm law strictness, geographic neighborhood, and citizen ideology. Network-based characteristics, namely, mutuality and transitivity, are also important to explain such influence. Conclusions: Results suggest that state policies or programs that reduce gun homicides will also help suppress that state's influence on the patterning of firearm acquisition in other states. Furthermore, states with stricter firearm laws are more likely to influence firearm acquisition in other states, but are themselves shielded from the effects of other states' firearm acquisition patterns. These results inform future research in public health, criminology, and policy making.

An efficient hybrid differential evolution-golden jackal optimization algorithm for multilevel thresholding image segmentation.

Image segmentation is a crucial process in the field of image processing. Multilevel threshold segmentation is an effective image segmentation method, where an image is segmented into different regions based on multilevel thresholds for information analysis. However, the complexity of multilevel thresholding increases dramatically as the number of thresholds increases. To address this challenge, this article proposes a novel hybrid algorithm, termed differential evolution-golden jackal optimizer (DEGJO), for multilevel thresholding image segmentation using the minimum cross-entropy (MCE) as a fitness function. The DE algorithm is combined with the GJO algorithm for iterative updating of position, which enhances the search capacity of the GJO algorithm. The performance of the DEGJO algorithm is assessed on the CEC2021 benchmark function and compared with state-of-the-art optimization algorithms. Additionally, the efficacy of the proposed algorithm is evaluated by performing multilevel segmentation experiments on benchmark images. The experimental results demonstrate that the DEGJO algorithm achieves superior performance in terms of fitness values compared to other metaheuristic algorithms. Moreover, it also yields good results in quantitative performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) measurements.

Autonomic modulation by SGLT2i or DPP4i in patients with diabetes favors cardiovascular outcomes as revealed by skin sympathetic nerve activity.

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP4i) are important second-line treatments for patients with type 2 diabetes mellitus (T2DM). Patients taking SGLT2i have favorable cardiovascular outcomes via various mechanisms, including autonomic nervous system (ANS) modulation. This study aimed to use neuro-electrocardiography (neuECG) to test the effects of SGLT2i or DPP4i on the ANS. Methods: Patients with T2DM, who did not reach target hemoglobin (Hb)A1C levels despite metformin treatment, were enrolled. SGLT2i or DPP4i were prescribed randomly unless a compelling indication was present. NeuECG and heart rate were recorded for 10 min before and after a 3-month treatment. The patients were treated according to standard practice and the obtained data for skin sympathetic nerve activity (SKNA) and ANS entropy were analyzed offline. Results: We enrolled 96 patients, of which 49 received SGLT2i and 47 received DPP4i. The baseline parameters were similar between the groups. No adverse event was seen during the study period. In the burst analysis of SKNA at baseline, all parameters were similar. After the 3-month treatment, the firing frequency was higher in SGLT2i group (0.104 ± 0.045 vs 0.083 ± 0.033 burst/min, p < 0.05), with increased long firing duration (7.34 ± 3.66 vs 5.906 ± 2.921, p < 0.05) in 3-s aSKNA scale; the other parameters did not show any significant change. By symbolic entropy, the most complex patterns (Rank 3) were found to be significantly higher in SGLT2i-treated patients than in DDP4i-treated group (0.084 ± 0.028 vs 0.07 ± 0.024, p = 0.01) and the direction of change in Rank 3, after SGLT2i treatment, was opposite to that observed in the DDP4i group (0.012 ± 0.036 vs. -0.005 ± 0.037, p = 0.024). Our findings demonstrated the favorable autonomic modulation by SGLTi and the detrimental effects of DPP4i on ANS. Conclusion: We demonstrated the autonomic modulation by SGLTi and DPP4i using SKNA in patients with DM, which might provide insights into the favorable outcomes of SGLT2i. Furthermore, we refined the analytical methods of neuECG, which uses SKNA to evaluate autonomic function.

Optimizing entropy weight model to accurately predict variation of pharmaceuticals and personal care products in the estuaries and coasts of the South China.

Pharmaceuticals and personal care products (PPCPs) have raised increasing concern worldwide due to their continuous release and potential hazards to the ecosystem and human health. This study optimized the entropy weight model (EW-WRSR) that combines entropy weight with multi-criteria decision analysis to investigate pollution patterns of PPCPs in the coasts and estuaries. The results revealed that occurrences of PPCPs from the 1940s to the present were consistent with using PPCPs, different types of human activities, and local urban development. This helped better understand the history of PPCP contamination and evaluate the uncertainty of EW-WRSR. The model predicted hotspots of PPCPs that were consistent with the actual situation, indicating that PPCPs mainly enter the nearshore ecosystem by the form of sewage discharge and residual aquaculture. This study can provide method that identifying highly contaminated regions on a global scale.

Chaotic nature of the electroencephalogram during shallow and deep anesthesia: From analysis of the Lyapunov exponent.

In conscious states, the electrodynamics of the cortex are reported to work near a critical point or phase transition of chaotic dynamics, known as the edge-of-chaos, representing a boundary between stability and chaos. Transitions away from this boundary disrupt cortical information processing and induce a loss of consciousness. The entropy of the electroencephalogram (EEG) is known to decrease as the level of anesthesia deepens. However, whether the chaotic dynamics of electroencephalographic activity shift from this boundary to the side of stability or the side of chaotic enhancement during anesthesia-induced loss of consciousness remains poorly understood. We investigated the chaotic properties of EEGs at two different depths of clinical anesthesia using the maximum Lyapunov exponent, which is mathematically regarded as a formal measure of chaotic nature, using the Rosenstein algorithm. In 14 adult patients, 12 s of electroencephalographic signals were selected during two depths of clinical anesthesia (sevoflurane concentration 2% as relatively deep anesthesia, sevoflurane concentration 0.6% as relatively shallow anesthesia). Lyapunov exponents, correlation dimensions and approximate entropy were calculated from these electroencephalographic signals. As a result, maximum Lyapunov exponent was generally positive during sevoflurane anesthesia, and both maximum Lyapunov exponents and correlation dimensions were significantly greater during deep anesthesia than during shallow anesthesia despite reductions in approximate entropy. The chaotic nature of the EEG might be increased at clinically deeper inhalational anesthesia, despite the decrease in randomness as reflected in the decreased entropy, suggesting a shift to the side of chaotic enhancement under anesthesia.

REACTION KINETICS OF THE BENZYLATION OF ADENINE IN DMSO: REGIO-SELECTIVITY GUIDED BY ENTROPY.

The factors governing the regio-selectivity of the alkylation of adenine have been of interest for many years due to the biological importance of adenine derivatives, however, no reaction kinetic studies have been conducted. Herein, we report the rate constants and activation parameters of the benzylation of adenine under basic conditions in DMSO in the absence and presence of 15-crown-5 ether using real-time 1H NMR spectroscopy. The reaction is second-order for the formation of the N9- and N3-benzyladenine products, with a regio-selectivity factor 2.3 in favour of the N9-adduct. The Gibbs free energy of activation amounts to 87±2 kJ mol-1 for both reactions. The formation of the N9-adduct is more activated by 7 kJ mol-1, but its effect is offset by a less negative activation entropy, demonstrating that the long-contested reason for the regioselectivity in the benzylation of adenine is dominated by compensation of entropy and enthalpy in the transition state. The kinetic parameters obtained in the presence of the 15-crown-5 ether indicate that the crown ether forms a complex with an adenine-sodium ion-pair, increasing the activation barrier. However, the Gibbs free energy in the absence and presence of the crown ether remains constant.

Air-Stable High-Entropy Layered Oxide Cathode with Enhanced Cycling Stability for Sodium-Ion Batteries.

O3-type layered oxides have been extensively studied as cathode materials for sodium-ion batteries due to their high reversible capacity and high initial sodium content, but they suffer from complex phase transitions and an unstable structure during sodium intercalation/deintercalation. Herein, we synthesize a high-entropy O3-type layered transition metal oxide, NaNi0.3Cu0.05Fe0.1Mn0.3Mg0.05Ti0.2O2 (NCFMMT), by simultaneously doping Cu, Mg, and Ti into its transition metal layers, which greatly increase structural entropy, thereby reducing formation energy and enhancing structural stability. The high-entropy NCFMMT cathode exhibits significantly improved cycling stability (capacity retention of 81.4% at 1C after 250 cycles and 86.8% at 5C after 500 cycles) compared to pristine NaNi0.3Fe0.4Mn0.3O2 (71% after 100 cycles at 1C), as well as remarkable air stability. Finally, the NCFMMT//hard carbon full-cell batteries deliver a high initial capacity of 103 mAh g-1 at 1C, with 83.8 mAh g-1 maintained after 300 cycles (capacity retention of 81.4%).

Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

Short-Term Entropy of Signal Energy Used for Effective Detecting of Weak Gunshots in Noisy Environments.

Conventional gunshot detection systems can quickly and reliably detect gunshots in the area where the acoustic sensors are placed. This paper presents the detection of weak hunting gunshots using the short-term entropy of signal energy computed from acoustic signals in an open natural environment. Our research in this field was primarily aimed at detecting gunshots fired at close range with the usual acoustic intensity to protect wild elephants from poachers. The detection of weak gunshots can extend existing detection systems to detect more distant gunshots. The developed algorithm was optimized for the detection of gunshots in two categories of the surrounding sounds, short impulsive events and continuous noise, and tested in acoustic scenes where the power ratios between the weak gunshots and louder surroundings range from 0 dB to -14 dB. The overall accuracy was evaluated in terms of recall and precision. Depending on impulsive or noise sounds, binary detection was successful down to -8 dB or -6 dB; then, the efficiency decreases, but some very weak gunshots can still be detected at -13 dB. Experiments show that the proposed method has the potential to improve the efficiency and reliability of gunshot detection systems.

Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients.

Diabetic peripheral neuropathy (DPN) is a prevalent complication of chronic diabetes mellitus and has a significant impact on quality of life. DPN typically manifests itself as a symmetrical, length-dependent sensorimotor polyneuropathy with severe effects on gait. Surface electromyography (sEMG) is a valuable low-cost tool for assessing muscle activation patterns and precise identification of abnormalities. For the present study, we used information theory methods, such as cross-correlation (CC), normalized mutual information (NMI), conditional granger causality (CG-Causality), and transfer entropy (TE), to evaluate muscle network connectivity in three population groups: 33 controls (healthy volunteers, CT), 10 diabetic patients with a low risk of DPN (LW), and 17 moderate/high risk patients (MH). The results obtained indicated significant alterations in the intermuscular coupling mechanisms due to diabetes and DPN, with the TE group showing the best performance in detecting differences. The data revealed a significant increase in information transfer and muscle connectivity in the LW group over the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings highlight the sEMG coupling metrics' potential to reveal neuromuscular mechanisms that could aid the development of targeted rehabilitation strategies and help monitor DPN patients.