Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred to as “speech spoofing”. The algorithms used in spoof attacks are practically unknown; hence, further analysis and development of spoof-detection models for improving spoof classification are required. A study of the spoofed-speech spectrum suggests that high-frequency features are able to discriminate genuine speech from spoofed speech well. Typically, linear or triangular filter banks are used to obtain high-frequency features. However, a Gaussian filter can extract more global information than a triangular filter. In addition, MFCC features are preferable among other speech features because of their lower covariance. Therefore, in this study, the use of a Gaussian filter is proposed for the extraction of inverted MFCC (iMFCC) features, providing high-frequency features. Complementary features are integrated with iMFCC to strengthen the features that aid in the discrimination of spoof speech. Deep learning has been proven to be efficient in classification applications, but the selection of its hyper-parameters and architecture is crucial and directly affects performance. Therefore, a Bayesian algorithm is used to optimize the BiLSTM network. Thus, in this study, we build a high-frequency-based optimized BiLSTM network to classify the spoofed-speech signal, and we present an extensive investigation using the ASVSpoof 2017 dataset. The optimized BiLSTM model is successfully trained with the least epoch and achieved a 99.58% validation accuracy. The proposed algorithm achieved a 6.58% EER on the evaluation dataset, with a relative improvement of 78% on a baseline spoof-identification system. Full article

Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient integrated systems that rapidly communicate intelligent self-adaptation for collaborative operations. Here, we propose a critical futuristic integrated communication element named City Sensing Base Station (CSBS), inspired by base stations for cell phones that address similar concerns. A CSBS is designed to handle massive volumes of heterogeneous observation data that currently need to be upgraded by middleware or registered. It also provides predictive and interpolation modelling for the control of sensors and response units such as emergency services and drones. A prototype of CSBS demonstrated that it could unify readily available heterogeneous sensing devices, including surveillance video, unmanned aerial vehicles, and ground sensor webs. Collaborative observation capability was also realized by integrating different object detection sources using advanced computer-vision technologies. Experiments with a traffic accident and water pipeline emergency showed sensing and intelligent analyses were greatly improved. CSBS also significantly reduced redundant Internet connections while maintaining high efficiency. This innovation successfully integrates high-density, high-diversity, and high-precision sensing in a distributed way for the future digital twin of cities. Full article

The present work was performed to evaluate the levels of phytochemical constituents and the antioxidant and antibacterial properties of marine red seaweed (Kappaphycus alvarezii) and seagrass (Cymodocea serrulata). Quantitative phytochemical analysis, antioxidant activity and antimicrobial activity against five potential pathogenic bacteria was investigated. In each case, we found the presence of flavonoids, tannins, phenolic compounds, glycosides, steroids, carbohydrates and ashes. Alkaloids were only found in K. alvarezii, though they were not found in C. serrulata. The antimicrobial properties of both K. alvarezii and C. serrulata chloroform extracts were found to be antagonistically effective against the Gram-positive bacteria Bacillus subtilis and the Gram-negative bacteria Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio harveyi and Klebsiella pneumoniae. GC-MS analysis revealed the presence of 94 bioactive compounds in K. alvarezii and 104 bioactive compounds in C. serrulata, including phenol, decane, dodecane, hexadecane, vanillin, heptadecane, diphenylamine, benzophenone, octadecanoic acid, dotriaconate, benzene, phytol, butanoic acid and 2-hydroxyl-ethyl ether, which all played important roles in antioxidant and antibacterial activities. Thus, in view of the results, both K. alvarezii and C. serrulata could be considered to be sources of ingredients with appreciable nutritional and medicinal value. Full article

An adequate nutritional intake is recommended for the prevention of physical frailty and sarcopenia. In particular, medium-chain fatty acids (MCFAs) are reportedly important for muscle strength in nursing home residents. However, the effects of MCFAs on healthy adults at risk for frailty remain unknown. Hence, a randomized, placebo-controlled study was conducted to investigate the effects of 12 weeks of medium-chain triglycerides (MCTs) intake and walking on muscle mass and function in healthy, sedentary, middle-aged and older adults with a low body mass index. Three MCT intake groups with different amounts of octanoic and decanoic acid intake were compared with a control group. After 12 weeks, knee extension strength increased in all groups, with the increases in all MCT intake groups being significantly higher than those in the control group (p < 0.05). Grip strength significantly increased from baseline in the MCT 6 g/day intake group (p < 0.05). The combination of aerobic exercise and MCT intake may be effective in preventing decline in muscle strength and promoting increase in muscle strength as they can improve muscle energy production, thereby contributing to the maintenance of good health for middle-aged and older adults at high risk for frailty and sarcopenia. Full article

This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents. Full article

This article proposes a discrete proportional-integral-derivative (PID) load frequency control (LFC) scheme to investigate the dissipative analysis issue of restructured wind power systems via a non-fragile design approach. Firstly, by taking the different power-sharing rates of governors into full consideration, a unified model is constructed for interconnected power systems containing multiple governors. Secondly, unlike existing LFC schemes, a non-fragile discrete PID control scheme is designed, which has the performance of tolerating control gain fluctuation and relieving the huge computational burden. Further, by constructing a discrete-type Lyapunov–Krasovskii functional, improved stability criteria with a strict dissipative performance index are established. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed control method. Full article

The purpose of this work is to provide applications of real, complex, and functional analysis to moment, interpolation, functional equations, and optimization problems. Firstly, the existence of the unique solution for a two-dimensional full Markov moment problem is characterized on the upper half-plane. The issue of the unknown form of nonnegative polynomials on in terms of sums of squares is solved using polynomial approximation by special nonnegative polynomials, which are expressible in terms of sums of squares. The main new element is the proof of Theorem 1, based only on measure theory and on a previous approximation-type result. Secondly, the previous construction of a polynomial solution is completed for an interpolation problem with a finite number of moment conditions, pointing out a method of determining the coefficients of the solution in terms of the given moments. Here, one uses methods of symmetric matrix theory. Thirdly, a functional equation having nontrivial solution (defined implicitly) and a consequence are discussed. Inequalities, the implicit function theorem, and elements of holomorphic functions theory are applied. Fourthly, the constrained optimization of the modulus of some elementary functions of one complex variable is studied. The primary aim of this work is to point out the importance of symmetry in the areas mentioned above. Full article

Digital finance (DF) has provided important financial support for the transformation and upgrading of China’s manufacturing industry. Innovation is the engine of industrial upgrading. To solve the dilemma of developing the manufacturing industry, it is necessary to enhance independent innovation capabilities. On this basis, this article studies the impact of DF on manufacturing technology innovation (MTI). It uses the data of listed manufacturing firms in the Shenzhen and Shanghai A-share markets from 2011 to 2020 to establish a fixed-effects model and a panel-threshold model for empirical analysis. The results revealed that, first, DF significantly accelerates technological innovation in manufacturing enterprises and has a significant positive impact on technological innovation. Secondly, DF drives manufacturing enterprises’ technological innovation by alleviating financial constraints (FCs). Thirdly, there is a dual-threshold effect based on market competition between DF and MTI based on market competition, and the promotion effect of DF on technology innovation decreases with the increasing degree of market competition. Finally, DF better enhances the technological innovation of non-state-owned manufacturing firms in the respective regions compared to state-owned firms. In terms of factor-intensive types, DF is more able to advance the innovative technologies of labor-intensive and capital-intensive enterprises, while it has no significant positive effect on technology-intensive enterprises. Policy implications are suggested to boost manufacturing technology innovation and aid future studies. Full article

Exosomal regulation is intimately involved in key cellular processes, such as migration, proliferation, and adhesion. By participating in the regulation of basic mechanisms, extracellular vesicles are important in intercellular signaling and the functioning of the mammalian reproductive system. The complexity of intercellular interactions in the ovarian follicle is also based on multilevel intercellular signaling, including the mechanisms involving cadherins, integrins, and the extracellular matrix. The processes in the ovary leading to the formation of a fertilization-ready oocyte are extremely complex at the molecular level and depend on the oocyte’s ongoing relationship with granulosa cells. An analysis of gene expression from material obtained from a primary in vitro culture of porcine granulosa cells was employed using microarray technology. Genes with the highest expression (LIPG, HSD3B1, CLIP4, LOX, ANKRD1, FMOD, SHAS2, TAGLN, ITGA8, MXRA5, and NEXN) and the lowest expression levels (DAPL1, HSD17B1, SNX31, FST, NEBL, CXCL10, RGS2, MAL2, IHH, and TRIB2) were selected for further analysis. The gene expression results obtained from the microarrays were validated using quantitative RT-qPCR. Exosomes may play important roles regarding intercellular signaling between granulosa cells. Therefore, exosomes may have significant applications in regenerative medicine, targeted therapy, and assisted reproduction technologies. Full article

Albert Einstein visited Spain only once, precisely one hundred years ago. The circumstances, of a very different kind, of this visit will be explained here. In special, some important events happened to Einstein during that period, which, eventually, were key for converting modern cosmology into a genuine physical theory. Among them is the famous Einstein-Friedmann controversy, first, on the mathematical validity of Friedmann’s equations and, later, their possible usefulness as a reliable tool to describe the real world. A summary of the deepest ideas underlying Einstein’s contributions to the theory of relativity, which he had already completed before his visit, will precede the discussion, also supplemented with a description, in very simple terms, of the three main relativistic theories, namely Galileo’s one, and Einstein’s special and general theory. They pave the way towards a definitive theory of total relativity, so far unattainable. It will be recalled that the most general relativity principle, faithfully reflecting Ernst Mach’s far-reaching ideas, might have much to do with the symmetry-breaking paradigm, a most crucial tool in quantum field theory and high energy physics. Full article

Background: Liver transplantation (LT) is a routine therapeutic approach for patients with acute liver failure, end-stage liver disease and/or early-stage liver cancer. While 5-year survival rates have increased to over 80%, long-term outcomes are critically influenced by extrahepatic sequelae of LT and immunosuppressive therapy, including diabetes mellitus (DM). In this study, we used machine learning (ML) to predict the probability of new-onset DM following LT. Methods: A cohort of 216 LT patients was identified from the Disease Analyzer (DA) database (IQVIA) between 2005 and 2020. Three ML models comprising random forest (RF), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) were tested as predictors of new-onset DM within 12 months after LT. Results: 18 out of 216 LT patients (8.3%) were diagnosed with DM within 12 months after the index date. The performance of the RF model in predicting the development of DM was the highest (accuracy = 79.5%, AUC 77.5%). It correctly identified 75.0% of the DM patients and 80.0% of the non-DM patients in the testing dataset. In terms of predictive variables, patients’ age, frequency and time of proton pump inhibitor prescription as well as prescriptions of analgesics, immunosuppressants, vitamin D, and two antibiotic drugs (broad spectrum penicillins, fluocinolone) were identified. Conclusions: Pending external validation, our data suggest that ML models can be used to predict the occurrence of new-onset DM following LT. Such tools could help to identify LT patients at risk of unfavorable outcomes and to implement respective clinical strategies of prevention. Full article

In recent years, bank-related decision analysis has reflected a relevant research area due to key factors that affect the operating environment of banks. This study’s aim is to develop a model based on the linkages between the performance of banks and their operating context, determined by country risk. For this aim, we propose a multi-analytic methodology using fuzzy analytic network process (fuzzy-ANP) with principal component analysis (PCA) that extends existing mathematical methodologies and decision-making approaches. This method was examined in two studies. The first study focused on determining a model for country risk assessment based on the data extracted from 172 countries. Considering the first study’s scores, the second study established a bank performance model under the assumption of country risk, based on data from 496 banks. Our findings show the importance of country risk as a relevant bank performance dimension for decision makers in establishing efficient strategies with a positive impact on long-term performance. The study offers various contributions. From a mathematic methodology perspective, this research advances an original approach that integrates fuzzy-ANP with PCA, providing a consistent and unbiased framework that overcomes human judgement. From a business and economic analysis perspective, this research establishes novelty based on the performance evaluation of banks considering the operating country’s risk. Full article

This paper presents a study of actuality entailments in Balkar (a dialect of Karachay-Balkar, Turkic). The study focuses on the deontic and causal meanings of four morphemes: two suffixes (the causative suffix and the suffix -al (‘can/may’)) and two verbs (bujur (‘order’) and qoj (‘allow’)). In the first half of the paper, I provide empirical support for three generalizations: (a) only causal modals can have actuality entailments (all universal and some existential causal modals); (b) actuality entailments arise whenever a causal modal has a telic interpretation, more precisely, when it is not embedded under an imperfective or a delimitative operator (c) existential but not universal causal modals trigger an anti-actuality entailment under negation. In the second half of the paper, I propose a theory of root modality within the framework of situation semantics. In this framework, root modals describe a situation (the anchor situation) and quantify over situations that stand in a particular semantic relation to it. The proposed Causal Modality Theory (CMT) consists of two assumptions: (a) Causal modals quantify over causal chains initiated by the counterparts of the anchor situation. (b) Some existential causal modals have a conditional presupposition: if any counterpart of the anchor situation caused another situation, then the anchor situation itself caused the same situation. The first assumption explains why all universal causal modals have actuality entailments and why existential causal modals trigger an anti-actuality entailment under negation. The second assumption predicts that some existential causal modals (the ones with the conditional presupposition) also trigger an actuality entailment. CMT treats causal modals as bi-eventive predicates, like (non-culminating) accomplishments. They describe two situations: the anchor situation and a situation caused by it. As a result, causal modals are predicted to behave like (non-culminating) accomplishments, namely, they are predicted to trigger actuality entailments if and only if they are not embedded under an imperfective or a delimitative operator. Full article

The efficient development of oil reservoirs mainly depends on the comprehensive optimization of the subsurface fluid flow process. As an intelligent analysis technique, artificial intelligence provides a novel solution to multi-objective optimization (MOO) problems. In this study, an intelligent agent model based on the Transformer framework with the assistance of the multi-objective particle swarm optimization (MOPSO) algorithm has been utilized to optimize the gas flooding injection–production parameters in a well pattern in the Middle East. Firstly, 10 types of surveillance data covering 12 years from the target reservoir were gathered to provide a data foundation for model training and analysis. The prediction performance of the Transformer model reflected its higher accuracy compared to traditional reservoir numerical simulation (RNS) and other intelligent methods. The production prediction results based on the Transformer model were 21, 12, and 4 percentage points higher than those of RNS, bagging, and the bi-directional gated recurrent unit (Bi-GRU) in terms of accuracy, and it showed similar trends in the gas–oil ratio (GOR) prediction results. Secondly, the Pareto-based MOPSO algorithm was utilized to fulfil the two contradictory objectives of maximizing oil production and minimizing GOR simultaneously. After 10,000 iterations, the optimal injection–production parameters were proposed based on the generated Pareto frontier. To validate the feasibility and superiority of the developed approach, the development effects of three injection–production schemes were predicted in the intelligent agent model. In the next 400 days of production, the cumulative oil production increased by 25.3% compared to the average distribution method and 12.7% compared to the reservoir engineering method, while GOR was reduced by 27.1% and 15.3%, respectively. The results show that MOPSO results in a strategy that more appropriately optimizes oil production and GOR compared to some previous efforts published in the literature. The injection–production parameter optimization method based on the intelligent agent model and MOPSO algorithm can help decision makers to update the conservative development strategy and improve the development effect. Full article

Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers. Full article

This paper proposes a physics-informed neural network (PINN) for predicting the early-age time-dependent behaviors of prestressed concrete beams. The PINN utilizes deep neural networks to learn the time-dependent coupling among the effective prestress force and the several factors that affect the time-dependent behavior of the beam, such as concrete creep and shrinkage, tendon relaxation, and changes in concrete elastic modulus. Unlike traditional numerical algorithms such as the finite difference method, the PINN directly solves the integro-differential equation without the need for discretization, offering an efficient and accurate solution. Considering the trade-off between solution accuracy and the computing cost, optimal hyperparameter combinations are determined for the PINN. The proposed PINN is verified through the comparison to the numerical results from the finite difference method for two representative cross sections of PSC beams. Full article

Over the last few decades, increasing water levels of Lake Alakol have led to the activation of processes of modern relief formation of the coastal territory. This study will make it possible to assess the vulnerability of the lake shore to modern relief-forming processes, which pose a threat to the economic and infrastructural development of the coast. Through a combination of field research methods, analysis of the archival materials and satellite images, GIS mapping, as well as the application of the Coastal Vulnerability Index, developed by Gornitz, a map of the modern relief of the coast of Lake Alakol was created, where 13 geomorphological types of relief were identified, and a map of relief-forming processes and leading exogenous processes were identified. The values of the assessment of the degree of vulnerability of the coast to dangerous processes by the Gornitz method were obtained, where a high vulnerability covers 67.4% of the coast, an average vulnerability covers 2.9%, a weak vulnerability covers 13.3%, and low vulnerability occupies 16.4% of the coast. The degree of vulnerability of types of relief in the study area, the coast of Lake Alakol, was determined. High degree occupies 42.8% of the study area, medium—30.7%, weak—25.4%, and low 1.1%. A map of the complex assessment of the degree of vulnerability of the coast of Lake Alakol was created. It was revealed that low accumulative types of relief of the northwest and northeast coasts and alluvial-proluvial types of relief are highly vulnerable due to waterlogging and the intensity of abrasion processes. Identified natural features of the relief formation of the coast of Lake Alakol are recommended as a basis for making decisions on the planning and implementation of any economic activities on the coast, including infrastructure development of the coast and strengthening of the shores. Full article

The individuals of real-world networks participate in various types of connections, each forming a layer in multiplex networks. Link prediction is an important problem in multiplex network analysis owing to its wide range of practical applications, such as mining drug targets, recommending friends in social networks, and exploring network evolution mechanisms. A key issue of link prediction within multiplex networks is how to estimate the likelihood of potential links in the predicted layer by leveraging both interlayer and intralayer information. Several studies have shown that incorporating interlayer topological information can improve the performance of link prediction in the predicted layer. Therefore, this paper proposes the Link Prediction based on Global Relevance of Interlayer (LPGRI) method to estimate the likelihood of potential links in the predicted layer of multiplex networks, which comprehensively utilizes both types of information. In the LPGRI method, the contribution of interlayer information is determined using the global relevance (GR) index between layers. Experimental studies on six real multiplex networks demonstrate the competitive performance of our method. Full article

Our purpose is to describe blue-light fundus autofluorescence (BAF) features of inflammatory diseases of the outer retina characterised by photoreceptor damage. BAF from patients diagnosed with secondary and primary inflammatory photoreceptor damage were retrospectively analyzed and compared to other imaging modalities including fluorescein angiography (FA), indocyanine green angiography (ICGA), and spectral domain optical coherence tomography (SD-OCT). Multiple evanescent white dot syndrome (MEWDS), idiopathic multifocal choroiditis (MFC), acute posterior multifocal placoid pigment epitheliopathy (APMPPE), serpiginous choroiditis (SC), and acute syphilitic posterior placoid chorioretinitis (ASPPC), all cases corresponding to secondary photoreceptor diseases caused by inflammatory choriocapillaris nonperfusion, were included and compared to primary photoreceptor disease entities, including acute zonal occult outer retinopathy (AZOOR) and cancer-associated retinopathy (CAR). Both groups showed increased BAFs of variable intensity. In severe cases of APMPPE and ASPPC, BAF also showed hypoautofluorescent areas. In group 1 (secondary diseases) BAF hyperautofluorescent areas were associated with colocalized ICGA hypofluorescent areas, indicating choriocapillaris nonperfusion; whereas in group 2 (primary diseases), no ICGA signs were detected. The associated colocalized areas of hypofluorescence on ICGA in the first group, which were absent in the second group, were crucial to allow the differentiation between primary (photoreceptoritis) and secondary (choriocapillaritis) photoreceptor diseases. BAF patterns in inflammatory diseases of the outer retina can give relevant information on the photoreceptor and RPE involvement, with ICGA being crucial to detect concurring choriocapillaris damage and differentiating the two pathologies. Full article

Kala-azar, also known as visceral leishmaniasis (VL), is a disease caused by Leishmania infantum and L. donovani. Patients experience symptoms such as fever, weight loss, paleness, and enlarged liver and spleen. The disease also affects immunosuppressed individuals and has an overall mortality rate of up to 10%. This overview explores the literature on the pathogenesis of preclinical and clinical stages, including studies in vitro and in animal models, as well as complications and death. Asymptomatic infection can result in long-lasting immunity. VL develops in a minority of infected individuals when parasites overcome host defenses and multiply in tissues such as the spleen, liver, and bone marrow. Hepatosplenomegaly occurs due to hyperplasia, resulting from parasite proliferation. A systemic inflammation mediated by cytokines develops, triggering acute phase reactants from the liver. These cytokines can reach the brain, causing fever, cachexia and vomiting. Similar to sepsis, disseminated intravascular coagulation (DIC) occurs due to tissue factor overexpression. Anemia, hypergammaglobulinemia, and edema result from the acute phase response. A regulatory response and lymphocyte depletion increase the risk of bacterial superinfections, which, combined with DIC, are thought to cause death. Our understanding of VL’s pathogenesis is limited, and further research is needed to elucidate the preclinical events and clinical manifestations in humans. Full article

One of the approaches to manipulate MnBi${}_2$Te${}_4$ properties is the magnetic dilution, which inevitably affects the interplay of magnetism and band topology in the system. In this work, we carried out angle-resolved photoemission spectroscopy (ARPES) measurements and density functional theory (DFT) calculations for analysing changes in the electronic structure of Mn${}_{1-x}$Ge${}_x$Bi${}_2$Te${}_4$ that occur under parameter x variation. We consider two ways of Mn/Ge substitution: (i) bulk doping of the whole system; (ii) surface doping of the first septuple layer. For the case (i), the experimental results reveal a decrease in the value of the bulk band gap, which should be reversed by an increase when the Ge concentration reaches a certain value. Ab-initio calculations show that at Ge concentrations above 50%, there is an absence of the bulk band inversion of the Te $p_z$ and Bi $p_z$ contributions at the $\Gamma$-point with significant spatial redistribution of the states at the band gap edges into the bulk, suggesting topological phase transition in the system. For case (ii) of the vertical heterostructure Mn${}_{1-x}$Ge${}_x$Bi${}_2$Te${}_4$/MnBi${}_2$Te${}_4$, it was shown that an increase of Ge concentration in the first septuple layer leads to effective modulation of the Dirac gap in the absence of significant topological surface states of spatial redistribution. The results obtained indicate that surface doping compares favorably compared to bulk doping as a method for the Dirac gap value modulation. Full article

Cu₂Se is a promising thermoelectric (TE) material due to its low cost, Earth abundance, and high thermoelectric properties. However, the biggest problem of Cu₂Se is its unstable chemical properties. In particular, under the action of an electric field or gradient temperature field, the chemical potential of copper ions inside the material increases. When the external field is strong enough, the chemical potential of copper ions at the negative end of the material reaches the chemical potential of elemental copper. Under these conditions, copper ions must precipitate out, causing Cu₂Se to be unstable, and making it unsuitable for use in applications. In this study, we prepared Cu_2−xMn_xSe (x = 0, 0.02, 0.04 and 0.06) series bulk materials by vacuum melting–annealing and sintered by spark plasma sintering (SPS). We investigated the effects of Mn doping on the composition, microstructure, band structure, scattering mechanism, thermoelectric properties, and stability of Cu₂Se. The results show that Mn doping can adjust the carrier concentration, promote the stabilization of the β-phase structure and improve the electrical properties of Cu₂Se. When x = 0.06, the highest power factor (PF) value of Cu_1.94Mn_0.06Se at 873 K was 1.62 mW m⁻¹ K⁻². The results of carrier scattering mechanism analysis based on the conductivity ratio method show that the sample doped with Mn and pure Cu₂Se had the characteristics of ionization impurity scattering, and the scattering factor was 3/2. However, the deterioration in thermal conductivity was large, and a superior zT value needs to be obtained. The cyclic test results of high-temperature thermoelectric properties show that Mn doping can hinder Cu⁺ migration and improve its thermoelectric stability, which preliminarily verifies the feasibility of using the stable zirconia mechanism to improve the thermoelectric stability of Cu₂Se. Full article

The performance of lead-acid batteries could be significantly increased by incorporating carbon materials into the negative electrodes. In this study, a modified carbon material developed via a simple high-temperature calcination method was employed as a negative electrode additive, and we have named it as follows: N-doped chitosan-derived carbon (NCC). The performance of this material was compared with a control battery containing activated carbon (AC). X-ray diffraction (XRD), scanning electron microscopy (SEM) and Raman spectroscopy were engaged in analyzing the crystal structure and morphology of the material. Afterwards, the electrochemical and battery performance was examined through cyclic voltammetry (CV), linear voltammetry (LSV) and constant current charge-discharge testing. Markedly, the electrode plate containing 1 wt.% NCC indicates the highest specific capacity (106.48 F g⁻¹) as compared to the control battery, which is 1.56 times higher than the AC electrode plate and 4.75 times higher than the blank electrode plate. The linear voltammetry shows that the hydrogen precipitation current density of the 1 wt.% NCC electrode plate is only −0.028 A cm⁻², a much higher value than that of the AC electrode plate. In addition, the simulated battery containing 1 wt.% NCC has a cycle life of 4324 cycles, which is 2.36 times longer than that of the same amount of additive AC battery (1834 cycles) and 5.34 times longer than that of the blank battery (809 cycles). In summary, NCC carbon has the advantage of extending the life of lead-acid batteries, rendering it a promising candidate for lead-acid battery additives. Full article

This study evaluated Carbon (C) storage in different compartments in eucalyptus stands and native Cerrado vegetation. To determine C above ground, an inventory was carried out in the areas where diameter at breast height (DBH), diameter at base height (Db), and total tree height (H) were measured. In the stands, the rigorous cubage was made by the direct method, and in the native vegetation, it was determined by the indirect method through an allometric equation. Roots were collected by direct method using circular monoliths to a depth of 60 cm and determined by the volume of the cylinder. Samples were collected up to 100 cm deep to estimate C stock in the soil. All samples collected directly had C determined using the CHNS elemental analyzer. Gas samples were collected using a manually closed chamber, and the gas concentration was determined by gas chromatography. The results indicate high C storage in the studied areas > 183.99 Mg ha⁻¹, could contribute to CO₂ mitigation > 674.17 Mg ha⁻¹. In addition to low emissions (<1 kg ha⁻¹ yr⁻¹) for the three evaluated areas, with no statistical difference in relation to the Global Warming Potential. Concerning the native cerrado vegetation conversion, the “4-year-old eucalyptus stand” seemed to restore the original soil carbon stocks in the first-meter depth, regardless of some losses that might have occurred right after establishment. Conversely, a significant loss of carbon in the soil was observed due to the alternative setting, where similar natural land was converted into agriculture, mostly soybean, and then, years later, turned into the “6-year-old eucalyptus stand” (28.43 Mg ha⁻¹). Under this study, these mixed series of C baselines in landscape transitions have reflected on unlike C dynamics outcomes, whereas at the bottom line, total C stocks were higher in the younger forest (4-year-old stand). Therefore, our finding indicates that we should be thoughtful regarding upscaling carbon emissions and sequestration from small-scale measurements to regional scales Full article

Utilizing Chinese provincial panel data from 2006–2019, this paper applies the super-efficient epsilon-based measure (EBM) model including non-desired output to measure carbon emission efficiency (CE) and analyze spatio-temporal characteristics of CE, in which social fixed asset investment, energy consumption and urban employment per unit are used as input indicators and regional GDP and CO2 emissions are used as output indicators. Additionally, we use the spatial Durbin model to explore the impact of urban-rural income disparity (URID) on carbon emission efficiency and its spatial spillover effect and explore indirect mechanisms of consumption level and consumption structure on CE using mediating effect test. The results showed that: (1) The national CE level generally declined between 2006–2012 and fluctuated upward from 2013–2019. The trend of regional CE showed “high in the east and low in the west”. (2) The “inverted U” model accurately reflects the relationship between national CE and URID, with a “U” shaped association in the central, western, and northeastern regions, and a positive correlation with consumption level and consumption structure. (3) There is a significant mediating effect of consumption level and structure in the mechanism of URID in regulating CE. Local governments should adopt local policies, take measures to narrow URID and CLD, advocate low-carbon and environmentally friendly living for residents, and promote the upgrading of consumption structure to boost carbon emission efficiency. Full article