Study on the change of global ecological distribution of Nicotiana tabacum L. based on MaxEnt model.

Nicotiana tabacum L. (tobacco) has extremely high economic value, medicinal value, scientific research value and some other uses. Though it has been widely cultivated throughout the world, classification and change of its suitable habitats is not that clear, especially in the context of global warming. In order to achieve rational cultivation and sustainable development of tobacco, current (average from 1970-2000) and future (2070, average from 2061-2080) potential suitable habitats of Nicotiana tabacum L. were forecasted with MaxEnt model and ArcGIS platform based on 854 occurrence data and 22 environmental factors in this study. The results revealed that mean temperature of warmest quarter (bio10), annual precipitation (bio12), solar radiation in September (Srad9), and clay content (CLAY) were the four decisive environment variables for the distribution of Nicotiana tabacum L. Under current climate conditions, suitable habitats of Nicotiana tabacum L. were mainly distributed in south-central Europe, south-central North America, most parts of South America, central Africa, south and southeast Asia, and southeast coast of Australia, and only 13.7% of these areas were highly suitable. By the year 2070, suitable habitats under SSP1-2.6, SSP3-7.0, and SSP5-8.5 climate scenarios would all increase with the largest increase found under SSP3-7.0 scenario, while suitable habitats would reduce under SSP2-4.5 climate scenario. Globally, the center of mass of suitable habitats would migrate to southeast to varying degrees within Libya under four different climate scenarios. The emergence of new habitats and the disappearance of old habitats would all occur simultaneously under each climate scenario, and the specific changes in each area, combined with the prediction results under current climate conditions, will provide an important reference for the adjustment of agronomic practices and rational cultivation of Nicotiana tabacum L. both currently and in the future.

Comprehensive evaluation of global health cities development levels.

Introduction: How to scientifically assess the health status of cities and effectively assist in formulating policies and planning for health city development remains a profound challenge in building a global "health community." Methods: This study employs the Building Research Establishment's International Healthy Cities Index (BRE HCI), encompassing ten environmental categories and fifty-eight indicators, to guide and support the scientific development of healthy cities. The entropy weight-TOPSIS method and the rank sum ratio (RSR) method were applied to comprehensively rank and categorize the health development levels of fifteen global cities. Furthermore, through cluster analysis, this research identifies universal and unique indicators that influence the development of healthy cities. Results: The results indicate that: (1) Within the scope of 58 evaluation indicators, the precedence in weight allocation is accorded to the kilometres of bicycle paths and lanes per 100,000 population (0.068), succeeded by m2 of public indoor recreation space per capita (0.047), and kilometres of bicycle paths and lanes per 100,000 population (0.042). (2) Among the ten environmental categories, the top three in terms of weight ranking are transport (0.239), leisure and recreation (0.172), and resilience (0.125). Significant disparities exist between different cities and environmental categories, with the issue of uneven health development within cities being particularly prominent. (3) The study categorizes the development levels of healthy cities into three tiers based on composite scores: it classifies Singapore, Shanghai, and Amsterdam at an excellent level; places Dubai and Johannesburg at a comparatively poor level; and situates the remaining ten cities at a moderate level. (4) The analysis identifies 53 international common indicators and 5 characteristic indicators from the 58 indicators based on the significance of the clustering analysis (p < 0.05). Discussion: The study proposes four strategic recommendations based on these findings: establishing a comprehensive policy assurance system, refining urban spatial planning, expanding avenues for multi-party participation, and augmenting distinctive health indicators. These measures aim to narrow the developmental disparities between cities and contribute to healthy global cities' balanced and sustainable growth. However, due to existing limitations in sample selection, research methodology application, and the control of potential confounding variables, further in-depth studies are required in the future.

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction.

Wind energy is becoming increasingly competitive, Accurate and reliable multi-engine wind power forecasts can reduce power system operating costs and improve wind power consumption capacity. Existing research on wind power forecasting has neglected the importance of interval forecasting using clusters of wind farms to capture spatial characteristics and the objective selection of forecasting sub-learners, leading to increased uncertainty and risk in system operation. This paper proposes a new "decomposition-aggregation-multi-model parallel prediction" method. The data set is pre-processed by a decomposition-aggregation strategy and spatial feature extraction, and then a Stacking model with multiple parallel sub-learners selected by bootstrap method is used for point and interval forecasting. Experiments and discussions are conducted based on 15-min resolution wind power data from a cluster dataset of a wind farm in northwest China. The experimental results indicate that the method achieves higher accuracy and reliability in both point prediction and interval prediction than other comparative models, with a root mean square error value of 7.47 and an average F value of 1.572, which can provide a reliable reference for power generation planning from wind farm clusters.

Self-assembled high-entropy Prussian blue analogue nanosheets enabling efficient sodium storage.

High entropy material (HEM) has emerged as an appealing material platform for various applications, and specifically, the electrochemical performances of HEM could be further improved through self-assembled structure design. However, it remains a big challenge to construct such high-entropy self-assemblies primarily due to the compositional complexity. Herein, we propose a bottom-up directional freezing route to self-assemble high-entropy hydrosols into porous nanosheets. Taking Prussian blue analogue (PBA) as an example, the simultaneous coordination-substitution reactions yield stable high-entropy PBA hydrosols. During subsequent directional freezing process, the anisotropic growth of ice crystals could guide the two-dimensional confined assembly of colloidal nanoparticles, resulting in high-entropy PBA nanosheets (HE-PBA NSs). Thanks to the high-entropy and self-assembled structure design, the HE-PBA NSs manifests markedly enhanced sodium storage kinetics and performances in comparison with medium/low entropy nanosheets and high entropy nanoparticles.

Weighted ultrasound entropy imaging for HIFU ablation monitoring with improved sensitivity and contrast.

BACKGROUND: Although B-mode imaging has been widely used in ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, challenges remain in improving its quality and sensitivity for monitoring the thermal dose. Recently, quantitative ultrasound (QUS) imaging has been recognized with the potential to better sense the changes in the microstructure of ablated tissues. PURPOSE: This study proposed to use a QUS method called weighted ultrasound entropy (WUE) imaging to monitor the HIFU ablation. METHODS: Based on ex-vivo and in-vivo experiments, WUE images reflecting tissue changes during HIFU treatment under different acoustic power levels (174-308 W) were reconstructed with a newly established imaging framework. The performance of the proposed WUE imaging in the monitoring of HIFU treatment was compared with the corresponding B-mode images in terms of their contrast-to-noise ratios (CNRs) between the focal region and the background. RESULTS: It was found that HIFU irradiation with higher power generated larger WUE values in the focal region, and the bright spots grew in size as the acoustic sonication proceeded. Compared with the in-situ B-mode images, the WUE images had higher image quality in indicating lesion formation, with a 39.2%-53.4% improvement in the CNR at different stages. Meanwhile, a correlation (R = 0.84) between the damage area estimated in WUE images and that measured from the dissected ex-vivo tissue samples was found. CONCLUSIONS: WUE imaging is more sensitive and accurate than B-mode imaging in monitoring HIFU therapy. These findings suggest that WUE imaging could be a promising technique for assisting ultrasound-guided HIFU ablation.

Analysis of the temperature-dependent plastic deformation of single crystals of quinary, quaternary and ternary equiatomic high- and medium-entropy alloys of the Cr-Mn-Fe-Co-Ni system.

Temperature-dependent plastic deformation behaviors of single crystals of quaternary and ternary equiatomic medium-entropy alloys (MEAs) belonging to the Cr-Mn-Fe-Co-Ni system were investigated in compression at temperatures in the range 9 K to 1373 K. Their critical resolved shear stresses (CRSSs) increase with decreasing temperature below room temperature. There is also a dulling of the temperature dependence of CRSS below 77 K due to dislocation inertial effects that we attribute to a decrease in the phonon drag coefficient. These behaviors were compared with those of previously investigated single crystals of the equiatomic Cr-Co-Ni and Cr-Fe-Co-Ni MEAs, and the equiatomic Cr-Mn-Fe-Co-Ni high-entropy alloy (HEA). The temperature dependence of CRSS and the apparent activation volumes below room temperature can be well described by conventional thermal activation theories of face-centered cubic (FCC) alloys. Above 673 K, there is a small increase in CRSS, which we believe is due to elastic interactions between solutes and mobile dislocations, the so-called Portevin-Le Chatelier (PL) effect. The CRSS at 0 K was obtained by extrapolation of fitted CRSS vs. temperature curves and compared with predictions from solid solution strengthening models of HEA and MEAs.

The novelty of our work entitled 'Analysis of the temperature-dependent plastic deformation of single crystals of quinary, quaternary and ternary equiatomic high- and medium-entropy alloys of the Cr-Mn-Fe-Co-Ni system' can be summarized as follows: The temperature dependences of CRSS were experimentally deduced from bulk single crystals of the six MEAs for the first time, so that fair comparison among the FCC HEA/MEAs is made.

The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms.

OBJECTIVES: To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability. METHODS: A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8-16) were included in the study. Three nonlinear onset detection algorithms - Teager-Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn) - were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure. RESULTS: The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients. CONCLUSIONS: The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.

A database of multi-principal element alloy phase-specific mechanical properties measured with nano-indentation.

Multi-principal element alloys (MPEAs) have been the focus of study and computationally-guided design for two reasons. MPEAs have shown high strengths and, the vast potential compositional space is more efficiently navigated with machine learning. In this article, we present data from 7385 indentation tests performed on 19 different MPEAs. Samples were arc melted, a thermodynamically complex process forming many distinct phases within a sample. The database was generated by performing hundreds of nanoindentation tests on a given sample and registering the location of those indents with local phase compositions measured with energy dispersive spectroscopy (EDS). The database contains the phases formed in the MPEA, the composition at the location of each indent, and the associated hardness (HV) and modulus for each indent. This data allows researchers targeting data-driven design of high strength systems to extract meaningful correlations between alloying composition, the resulting phases, and mechanical properties for future study.

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.

Prediction of 30-day mortality for ICU patients with Sepsis-3.

BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation. METHODS: In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power. RESULTS: The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980-0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM's gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve. CONCLUSIONS: Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients.

Electrophysiological functional connectivity and complexity reflecting cognitive processing speed heterogeneity in young children with ADHD.

Early intervention is imperative for young children with attention-deficit/hyperactivity disorder (ADHD) who manifest heterogeneous neurocognitive deficits. The study investigated the functional connectivity and complexity of brain activity among young children with ADHD exhibiting a fast cognitive processing speed (ADHD-F, n = 26), with ADHD exhibiting a slow cognitive processing speed (ADHD-S, n = 17), and typically developing children (n = 35) using wireless electroencephalography (EEG) during rest and task conditions. During rest, compared with the typically developing group, the ADHD-F group displayed lower long-range intra-hemispheric connectivity, while the ADHD-S group had lower frontal beta inter-hemispheric connectivity. During task performance, the ADHD-S group displayed lower frontal beta inter-hemispheric connectivity than the typically developing group. The ADHD-S group had lower frontal inter-hemispheric connectivity in broader frequency bands than the ADHD-F group, indicating ADHD heterogeneity in mental processing speed. Regarding complexity, the ADHD-S group tended to show lower frontal entropy estimators than the typically developing group during the task condition. These findings suggest that the EEG profile of brain connectivity and complexity can aid the early clinical diagnosis of ADHD, support subgrouping young children with ADHD based on cognitive processing speed heterogeneity, and may contain specific novel neural biomarkers for early intervention planning.

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.

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.

Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities.

OBJECTIVE: Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations. Approach: We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly. Main results: We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies. Significance: The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases. .

PdMoPtCoNi High Entropy Nanoalloy with d Electron Self-Complementation-Induced Multisite Synergistic Effect for Efficient Nanozyme Catalysis.

Engineering multimetallic nanocatalysts with the entropy-mediated strategy to reduce reaction activation energy is regarded as an innovative and effective approach to facilitate efficient heterogeneous catalysis. Accordingly, conformational entropy-driven high-entropy alloys (HEAs) are emerging as a promising candidate to settle the catalytic efficiency limitations of nanozymes, attributed to their versatile active site compositions and synergistic effects. As proof of the high-entropy nanozymes (HEzymes) concept, elaborate PdMoPtCoNi HEA nanowires (NWs) with abundant active sites and tuned electronic structures, exhibiting peroxidase-mimicking activity comparable to that of natural horseradish peroxidase are reported. Density functional theory calculations demonstrate that the enhanced electron abundance of HEA NWs near the Fermi level (EF) is facilitated via the self-complementation effect among the diverse transition metal sites, thereby boosting the electron transfer efficiency at the catalytic interface through the cocktail effect. Subsequently, the HEzymes are integrated with a portable electronic device that utilizes Internet of Things-driven signal conversion and wireless transmission functions for point-of-care diagnosis to validate their applicability in digital biosensing of urinary biomarkers. The proposed HEzymes underscore significant potential in enhancing nanozymes catalysis through tunable electronic structures and synergistic effects, paving the way for reformative advancements in nano-bio analysis.

Engineering d-p Orbital Hybridization in Mo-O Species of Medium-Entropy Metal Oxides as Highly Active and Stable Electrocatalysts toward Ampere-Level Water/Seawater Splitting.

Optimizing the electronic structure of electrocatalysts is of particular importance to enhance the intrinsic activity of active sites in water/seawater. Herein, a series of medium-entropy metal oxides of X(NiMo)O2/NF (X = Mn, Fe, Co, Cu and Zn) is designed via a rapid carbothermal shocking method. Among them, the optimized medium-entropy metal oxide (FeNiMo)O2/NF delivered remarkable HER performance, where the overpotentials as low as 110 and 141 mV are realized at 1000 mA cm-2 (@60 °C) in water and seawater. Meanwhile, medium-entropy metal oxide (FeNiMo)O2/NF only required overpotentials of as low as 330 and 380 mV to drive 1000 mA cm-2 for OER in water and seawater (@60 °C). Theoretical calculations showed that the multiple-metal synergistic effect in medium-entropy metal oxides can effectively enhance the d-p orbital hybridization of Mo─O bond, reduce the energy barrier of H* adsorbed at the Mo sites. Meanwhile, Fe sites in medium-entropy metal oxide can act as the real OER active center, resulting in a good bifunctional activity. In all, this work provides a feasible strategy for the development of highly active and stable medium-entropy metal oxide electrocatalysts for ampere-level water/seawater splitting.

Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

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.

Determining the lifetime distribution using fractional moments with maximum entropy.

Here we propose a model-free, non-parametric method to solve an ill-posed inverse problem arising in several fields. It consists of determining a probability density of the lifetime, or the probability of survival of an individual, from the knowledge of the fractional moments of its probability distribution. The two problems are related, but they are different because of the natural normalization condition in each case. We provide a maximum entropy based approach to solve both problems. This problem provides a concrete framework to analyze an interesting problem in the theory of exponential models for probability densities. The central issue that comes up concerns the choice of the fractional moments and their number. We find that there are many possible choices that lead to solutions compatible with the data but in all of them, no more than four moments are necessary. The fact that a given data set can be accurately described by different exponential families poses a challenging problem for the model builder when attaching theoretical meaning to the resulting exponential density.