Search Results (13)

Background: The increased presence of metabolic syndrome (MetS) indicators globally is considered a public health problem, and the dose-response of exercise is not clarified. Objectives: This purpose of this study was to analyze the effectiveness of changes in biochemical, physiological, and anthropometric indicators of MetS based on distinct types of exercise in adults. Methods: Based on PRISMA guidelines, we searched the following databases—PubMed, Cochrane, LILACS, Springer Link, and Science Direct— for clinical trials investigating the effect of exercise in MetS indicators, without date or language restrictions. The quality of evidence and risk of biases were assessed using the PEDro scale. The impact of aerobic training “AT,” resistance training “An-T”, concurrent training “CT”) on MetS indicators (body mass index (BMI), waist circumference (WC), total body weight (TBW), total cholesterol (TC), LDL-c, HDL-c, fasting blood glucose (FBG), triglycerides (TG), and systolic and diastolic blood pressure (SBP, DBP)) were included in this analysis. Results: A total of 10 clinical trials was selected. Results demonstrate a heterogeneity of over 50% (p < 0.001). A mean difference was found for TC by AT (−23.70 mg/dL, p= 0.003) and An-T (3.91 mg/dL, p= 0.003); on HDL by CT (0.12 mg/dL, p = 0.004); on FBG by AT (−0.66 mg/dL, p = 0.02), CT (−1.42 mg/dL, p = 0.01); on DBP by AT (−0.79 mmHg, p = 0.02). Conclusions: There is a dominance of concurrent exercise over other types of exercise, considering the greater effectiveness and significance of the effect of seven MetS indicators, including HDL and fasting blood glucose, with a major effect size. Full article

Deep Neural Networks (DNNs) have been widely used to solve complex problems in natural language processing, image classification, and autonomous systems. The strength of DNNs is derived from their ability to model complex functions and to improve detection engines through deeper architecture. Despite the strengths of DNN engines, they present several crucial challenges, such as the number of hidden layers, the learning rate, and the neuron weight. These parameters are considered to play a crucial role in the ability of DNNs to detect anomalies. Optimizing these parameters could improve the detection engine and expand the utilization of DNNs for various areas of application. Bio-inspired optimization algorithms, especially Particle Swarm Intelligence (PSO) and the Gray Wolf Optimizer (GWO), have been widely used to optimize complex tasks because of their ability to explore the search space and their fast convergence. Despite the significant successes of PSO and GWO, there remains a gap in the literature regarding their hybridization and application in Intrusion Detection Systems (IDSs), such as Sunburst attack detection, especially using DNN. Therefore, in this paper, we introduce a hybrid detection model that investigates the ability to integrate PSO and GWO so as to improve the DNN architecture to detect the Sunburst attack. The PSO algorithm was used to optimize the learning rate and the number of hidden layers, while the GWO algorithm was used to optimize the neuron weight. The hybrid model was tested and evaluated based on open-source Sunburst attacks. The results demonstrate the effectiveness and robustness of the suggested hybrid DNN model. Furthermore, an extensive analysis was conducted by evaluating the suggested hybrid PSO–GWO along with other hybrid optimization techniques, namely Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The results demonstrate that the suggested hybrid model outperformed other optimization techniques in terms of accuracy, precision, recall, and F1-score. Full article

The aim of this study was to develop three-dimensional anatomical models of dogs with congenital extrahepatic portosystemic shunts (CEPSs) using 3D printing, as well as to detail their development process and compare the final models to volume rendering (VR) derived from computed tomography (CT) scans. CT scans in the Digital Imaging and Communications in Medicine (DICOM) format of two canine patients were used—one with splenocaval deviation and the other with right gastrocaval deviation. The images were segmented using 3DSlicer software, generating 3D files in Standard Tessellation Language (STL) format, which were then subjected to refinement and mesh adjustment using Blender software. The models were printed on a J750™ Digital Anatomy™ printer, followed by post-processing in a 2% sodium hydroxide solution for 72 h, with subsequent rinsing to remove support resin residues. The printed models showed colored anatomical structures, including the liver; spleen; kidneys; part of the arterial, venous, and portal circulations; and CEPSs. For comparison purposes, VR of the scans was recreated in the RadiAnt DICOM Viewer software. Despite some limitations of the segmentation software, the 3D-printed models effectively represented the anatomy of the patients and the CEPSs, demonstrating good equivalence to the VR. Full article

The demand for creating automatic text summarization methods has significantly emerged as a result of the web’s explosive growth in textual data and the challenge of finding re-quired information within this massive volume of data. Multi-document text summarizing (MDTS) is an effective method for creating summaries by grouping texts that are relevant to a similar subject. With the aid of optimization methods, this strategy can be optimized. The majority of optimization algorithms used in the scientific literature are single-objective ones, but more recently, multi-objective optimization (MOO) techniques have been created, and their findings have outperformed those of single-objective methods. Metaheuristics-based techniques are also increasingly being used effectively in the study of MOO. The MDTS issue is therefore solved by the Multi-Objective Ant Colony Optimization (MOACO) method. This multi-objective metaheuristic algorithm is based on the Pareto optimization. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics have been used to assess the outcomes of experiments using Document Understanding Conferences (DUC) datasets. Additionally, they have consistently outperformed other referenced summarizer systems. Full article

Interpreting, a complicated and demanding bilingual task, depends heavily on attentional control. However, few studies have focused on the interpreters’ advantages in attention, and the findings so far have been inconsistent. Meanwhile, the connection between attentional networks and other cognitive abilities, such as working memory (WM), has rarely been explored in interpreters. The present study investigated whether interpreting experience (IE) contributed to the attentional networks of bilinguals and explored the link between interpreters’ attention and WM. Three groups of Chinese–English bilinguals, differing only in their duration of interpreting training (the More-IE group, the Less-IE group, and the No-IE group), completed the Attention Network Test (ANT). Results showed that only the alerting network was more efficient in the More-IE group than in the Less-IE and No-IE groups; moreover, the dynamics between the alerting and executive networks were significant only in the More-IE group. Furthermore, we found a negative correlation between the executive effect and the working memory capacity (WMC) in the More-IE group. Our study validated and provided empirical support for the Attentional Control Model, stimulating further research into neurocognitive mechanisms of advanced second language learning. Full article

Invasive alien ant species pose serious threats to agricultural production, ecosystems, and human health in China. Solenopsis invicta Buren is the most destructive and aggressive invasive alien ant in China, causing serious agricultural and urban economic losses and public health concerns. Estimating its spatial distribution and ecological niche in China is crucial for S. invicta prevention and control. Based on 4195 occurrence records (4096 invasive occurrence records and 99 native occurrence records) and 10 environmental variables, we estimated the potential suitable area and ecological niche of S. invicta in China using the ensemble model and ‘ecospat’ package in R language. The mean AUC, KAPPA, and TSS values of the ensemble model were 0.989, 0.901, and 0.901, respectively, indicating that the ensemble model was better than the single-species distribution model for the simulation. Temperature, precipitation, and human factors are important variables that influence the distribution of S. invicta. Our results showed that the ecological niche similarity and equivalency test results showed that the ecological niches between native areas and China were not equivalent (D = 0.46, p = 0.001), but were more similar than would be expected by chance (p = 0.003). Under current climatic conditions, the total potential suitable area for S. invicta is 192.89 × 10⁴ km² in China, accounting for 20.09% of the land area in China; this land is mainly distributed in Hainan, Taiwan, Guangdong, Guangxi, Fujian, Zhejiang, Jiangsu, Anhui, Hubei, Hunan, Jiangxi, Guizhou, Yunnan, Chongqing, Sichuan, and Henan. Under future climatic conditions, the potential suitable areas of S. invicta will further increase, while the highly suitable areas will shift to higher latitudes. We suggest that early warning and monitoring of S. invicta in the central and northern areas of China should be strengthened to prevent its further spread. Full article

Over the last 40–50 years, ethology has become increasingly quantitative and computational. However, when analysing animal behavioural sequences, researchers often need help finding an adequate model to assess certain characteristics of these sequences while using a relatively small number of parameters. In this review, I demonstrate that the information theory approaches based on Shannon entropy and Kolmogorov complexity can furnish effective tools to analyse and compare animal natural behaviours. In addition to a comparative analysis of stereotypic behavioural sequences, information theory can provide ideas for particular experiments on sophisticated animal communications. In particular, it has made it possible to discover the existence of a developed symbolic “language” in leader-scouting ant species based on the ability of these ants to transfer abstract information about remote events. Full article

The European and national regulations in the decarbonisation path towards 2050 promote district heating in achieving the goals of efficiency, energy sustainability, use of renewables, and reduction of fossil fuel use. Improved management and optimisation, use of RES, and waste heat/cold sources decrease the overall demand for primary energy, a condition that is further supported by building renovations and new construction of under (almost) zero energy buildings, with a foreseeable decrease in the temperature of domestic heating systems. Models for the simulation of efficient thermal networks were implemented and described in this paper, together with results from a real case study in Italy, i.e., University Campus of Parma. Activities include the creation and validation of calculation codes and specific models in the Modelica language (Dymola software), aimed at investigating stationary regimes and dynamic behaviour as well. An indirect heat exchange substation was coupled with a resistive-capacitive model, which describes the building behaviour and the thermal exchanges by the use of thermos-physical parameters. To optimise indoor comfort conditions and minimise consumption, dynamic simulations were carried out for different operating sets: modulating the supply temperature in the plant depending on external conditions (Scenario 4) decreases the supplied thermal energy (−2.34%) and heat losses (−8.91%), even if a lower temperature level results in higher electricity consumption for pumping (+12.96%), the total energy consumption is reduced by 1.41%. A simulation of the entire heating season was performed for the optimised scenario, combining benefits from turning off the supply in the case of no thermal demand (Scenario 3) and from the modulation of the supply temperature (Scenario 4), resulting in lower energy consumption (the thermal energy supplied by the power plant −3.54%, pumping +7.76%), operating costs (−2.40), and emissions (−3.02%). The energy balance ex-ante and ex-post deep renovation in a single user was then assessed, showing how lowering the network operating temperature at 55 °C decreases the supplied thermal energy (−22.38%) and heat losses (−22.11%) with a slightly higher pumping consumption (+3.28%), while maintaining good comfort conditions. These promising results are useful for evaluating the application of low-temperature operations to the existing district heating networks, especially for large interventions of building renovation, and confirm their potential contribution to the energy efficiency targets. Full article

Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%. Full article

Writing requires a suitable and strategic use of language with communicative potential and structural correctness. The use of coherence and cohesion helps create communicative potential and structural correctness in texts. This study aims to investigate the use of cohesive items in the abstracts of Pakistani research articles and thereby determine what type of cohesive items are frequently used by writers. In addition, the study aims to know what functions the said writers achieve through the most frequently used cohesive items. For this purpose, 50 abstracts were retrieved from two famous Pakistani research journals (25 articles per journal), which were developed into a corpus for the study and analysed through AntConc. 3.4.4.0. The results revealed that Pakistani research writers used reference items the most frequently to achieve “directive” as well as “referential” functions. On the basis of these findings, the study concluded that Pakistani research article writers organised information in abstracts using reference items the most frequently and they were mainly concerned with directive as well as referential functions of meaning. The results also showed that the said writers organised texts on a syntactic level only, which implied that they should organise texts on a semantic level also. This would be possible with the use of repetition devices. Full article

The adoption of Sensor Web Enablement (SWE) practices by sensor maintainers is hampered by the inherent complexity of the Sensor Model Language (SensorML), its high expressiveness, and the scarce availability of editing tools. To overcome these issues, the Earth Observation (EO) community often recurs to SensorML profiles narrowing the range of admitted metadata structures and value ranges. Unfortunately, profiles frequently fall short of providing usable editing tools and comprehensive validation criteria, particularly for the difficulty of checking value ranges in the multi-tenanted domain of the Web of Data. In this paper, we provide an updated review of current practices, techniques, and tools for editing SensorML in the perspective of profile support and introduce our solution for effective profile definition. Beside allowing for formalization of a broad range of constraints that concur in defining a metadata profile, our proposal closes the gap between profile definition and actual editing of the corresponding metadata by allowing for ex-ante validation of the metadata that is produced. On this basis, we suggest the notion of Semantic Web SensorML profiles, characterized by a new family of constraints involving Semantic Web sources. We also discuss implementation of SensorML profiles with our tool and pinpoint the benefits with respect to the existing ex-post validation facilities provided by schema definition languages. Full article

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an old area livable, achieve sustainable development, and solve transportation problems. Identifying old areas of metropolises as a research object, this paper puts forth an improved ant colony algorithm and combines it with virtual reality. This paper predicts traffic flow in Yangpu area on the basis of data obtained through Python, a programming language. On comparing the simulation outputs with reality, the results show that the improved model has a better simulation effect, and can take advantage of the allocation of traffic resources, enabling the transport system to achieve comprehensive optimization of time, cost, and accident rates. Subsequently, this paper conducted a robustness test, the results of which show that virtual traffic simulation based on the improved ant colony algorithm can effectively simulate real traffic flow, use vehicle road and signal resources, and alleviate overall traffic congestion. This paper offers suggestions to alleviate traffic congestion in old parts of metropolises. Full article

In this review we integrate results of long term experimental study on ant “language” and intelligence which were fully based on fundamental ideas of Information Theory, such as the Shannon entropy, the Kolmogorov complexity, and the Shannon’s equation connecting the length of a message (l) and its frequency (p), i.e., l = –log p for rational communication systems. This approach enabled us to obtain the following important results on ants’ communication and intelligence: (i) to reveal “distant homing” in ants, that is, their ability to transfer information about remote events; (ii) to estimate the rate of information transmission; (iii) to reveal that ants are able to grasp regularities and to use them for “compression” of information; (iv) to reveal that ants are able to transfer to each other the information about the number of objects; (v) to discover that ants can add and subtract small numbers. The obtained results show that information theory is not only excellent mathematical theory, but many of its results may be considered as Nature laws. Full article