Search Results (2,189)

With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article

The integration of Internet of Things (IoT) systems with social networking concepts has opened new business and social opportunities, particularly by allowing smart objects to autonomously establish social relationships with each other and exchange information. However, these relations must be properly quantified and integrated with trust in order to proliferate the provisioning of IoT composite services. Therefore, this proposed work focuses on quantitatively computing social strength and trust among smart objects in IoT for the purpose of aiding efficient service composition with reasonable accuracy. In particular, we propose a trust-augmented social strength (TASS) management protocol that can cope with the heterogeneity of IoT and demonstrate high scalability and resiliency against various malicious attacks. Afterward, we show how the TASS measurements can be applied to service planning in IoT service composition. Based on the experimental results, we conclude that the proposed protocol is, in fact, capable of exhibiting the above-mentioned characteristics in real-world settings. Full article

Discovering, analyzing, and finding a key to understanding the physiological and biochemical responses that Vitis vinifera L. undertakes against drought stress is of fundamental importance for this profitable crop. Today’s considerable climatic fluctuations force researchers and farmers to focus on this issue with solutions inclined to respect the ecosystem. In this academic work, we focused on describing the drought stress consequences on several parameters of secondary metabolites on Vitis vinifera leaves (quercetins, kaempferol, resveratrol, proline, and xanthophylls) and on some ecophysiological characteristics (e.g., water potential, stomatal conductance, and leaf temperature) to compare the answers that diverse agronomic management techniques (i.e., irrigation with and without zeolite, pure zeolite and no application) could instaurate in the metabolic pathway of this important crop with the aim to find convincing and thought-provoking responses to use this captivating and versatile mineral, the zeolite known as the “magic rock”. Stressed grapevines reached 56.80 mmol/m²s gs at veraison and a more negative stem Ψ (+10.63%) compared to plants with zeolite. Resveratrol, in the hottest season, fluctuated from 0.18–0.19 mg/g in zeolite treatments to 0.37 mg/g in stressed vines. Quercetins were inclined to accumulate in response to drought stress too. In fact, we recorded a peak of quercetin (3-O-glucoside + 3-O-glucuronide) of 11.20 mg/g at veraison in stressed plants. It is interesting to note how the pool of metabolites was often unchanged for plants treated with zeolite and for plants treated with water only, thus elevating this mineral to a “stress reliever”. Full article

The use of a stochastic artificial neural network (SANN) implemented on a Field Programmable Gate Array (FPGA) provides a promising method of performing image recognition on event camera recordings, however, challenges exist due to the fact that event camera data has an inherent unevenness in the timing at which data is sent out of the camera. This paper proposes a sampling system to overcome this challenge, by which all “events” occurring at specific timestamps in an event camera recording are selected (sampled) to be processed and sent to the SANN at regular intervals. This system is implemented on an FPGA in SystemVerilog, and to test it, simulated event camera data is sent to the system from a computer running MATLAB (version 2022+). The sampling system is shown to be functional. Analysis is shown demonstrating its performance regarding data sparsity, time convergence, normalization, repeatability, range, and some characteristics of the hold system. Full article

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

Left ventricular systolic dysfunction (LVSD) is associated with increased mortality and is sometimes reversible when found early. Artificial intelligence (AI)-enabled electrocardiogram (ECG) has emerged as an efficient screening tool for LVSD, but has not been validated in left bundle branch block (LBBB) patients. The clinical significance of developing an AI prediction model for LBBB patients lies in the fact that LBBB can be a cause, consequence, or both of LVSD. This pilot study was designed to develop an AI model for LVSD detection in the LBBB population using a limited dataset. ECG data from 508 patients with sinus rhythm and LBBB were labeled based on an LVSD threshold of 35%. To enhance the performance of a model derived from such a small and skewed dataset, we combined an autoencoder-based anomaly detection model with a convolutional neural network (CNN). We used a lead-wise ensemble technique for the final classification. Experimental results showed an accuracy of 0.81, precision of 0.87, recall of 0.56, and an area under the receiver operating characteristic curve of 0.75 in LVSD prediction among LBBB patients. Despite the limited dataset size, our study findings suggest the potential of deep learning techniques in detecting LVSD in patients with LBBB. Full article

The life cycle of the hepatitis C virus (HCV) is closely linked to lipid metabolism. Recently, the stress defence transcription factor, nuclear factor erythroid 2 related factor-1 (Nrf1), has been described as a cholesterol sensor that protects the liver from excess cholesterol. Nrf1, like its homologue Nrf2, further responds to oxidative stress by binding with small Maf proteins (sMaf) to the promotor antioxidant response element (ARE). Given these facts, investigating the crosstalk between Nrf1 and HCV was a logical next step. In HCV-replicating cells, we observed reduced levels of Nrf1. Furthermore, activation of Nrf1-dependent target genes is impaired due to sMaf sequestration in replicase complexes. This results in a shortage of sMaf proteins in the nucleus, trapping Nrf1 at the replicase complexes and further limiting its function. Weakened Nrf1 activity contributes to impaired cholesterol removal, which occurs alongside an elevated intracellular cholesterol level and inhibited LXRα promoter activation. Furthermore, inhibition of Nrf1 activity correlated with a kinome profile characteristic of steatosis and enhanced inflammation—factors contributing to HCV pathogenesis. Our results indicate that activation of Nrf1-dependent target genes is impaired in HCV-positive cells. This, in turn, favours viral morphogenesis, as evidenced by enhanced replication and increased production of viral progeny. Full article

In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies should be established. Using detailed data from cities at prefecture level and above in China, this study investigates the spatially heterogeneous effects of various factors on road traffic CO₂ emissions. Another theoretical issue is concerned with the analytic method for zonal CO₂ emission modeling. We combine the concepts of geographically weighted regression (GWR) and machine learning for nonparametric regression, proposing a modified random forest (RF) algorithm, named “geographically weighted random forest” (GWRF). Our empirical analysis indicates that, when an appropriate weight parameter is applied, GWRF is able to achieve significantly superior performance compared to both the traditional RF and GWR methods. Moreover, the influences of various explanatory variables on CO₂ emissions differ across cities. These findings suggest that low-carbon transportation strategies should be customized to reflect regional heterogeneity, rather than relying on a unified national policy. Full article

It is widely acknowledged that there is a similarity between Wittgenstein’s later conception of language and a “religious point of view.” An equally well-received view is that there is an essential shift in Wittgenstein’s thought from early to later. However, what both of these contentions ignore is that there is also a similarity between his early philosophy and the “religious point of view”, the negligence of which has led to an exaggeration of the divergence between his early and later philosophy. This paper aims to show that Wittgenstein in fact conducted his early philosophical work from a “religious point of view” and continued to demonstrate such a view in his later writings. I will first identify some essential characteristics of the “religious point of view” in Wittgenstein’s early philosophy by focusing on the mystical. I will then illustrate how these characteristics continue and are developed further in his later thought. The findings of this paper are critical in two ways: on the one hand, they clarify a host of misunderstandings through a comparative investigation into Wittgenstein’s early and later thought; on the other, they provide a more comprehensive overview of Wittgenstein’s “religious point of view,” which will help to deepen our understanding of his philosophy. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

Shaded-pole induction motors are the most frequently used single-phase electric motors in low power applications. Their main advantages are reliability, robustness, low level of noise and vibration, relatively simple manufacturing technology and cost effectiveness. These motors are the driving units of choice in the applications where the variable speed and high starting torque are not of utmost importance, in spite of the fact that they are characterized by inferior efficiency, power factor and starting torque compared to their single-phase counterparts. They are equipped with auxiliary massive copper coils at the stator side, which makes them self-starting, and strongly influence the motor characteristics. This study deals with the numerical modeling and analysis of a shaded-pole induction motor with a C-shaped stator frame. The analysis was performed using 2D finite element-based transient magnetic numerical modeling. The primary objective was to investigate the influence of the number and size of the auxiliary shaded coils on the output torque speed characteristic. We explored the possibility of reducing the amount of material used while preserving the crucial/nominal properties of the motor. Our results have important implications in manufacturing simplification, which may be important for the eco-design of small motors and actuators, including their recycling and/or reuse process. Full article

Russia’s invasion of Ukraine has profoundly altered the global geopolitical landscape. Owing to its geographical proximity, the conflict has had a considerable impact on Europe. Marked by the professionalisation and democratisation of technology, it has underscored the growing significance of hybrid warfare, in which disinformation and propaganda serve as additional instruments of war. Within this context, the aim of this article is to examine the characteristics of false information related to the war between Russia and Ukraine in four European countries between 2022 and 2023. To this end, a content analysis of 297 hoaxes was conducted across eight fact-checking platforms, complemented by ten in-depth interviews with specialised professionals. The findings indicate that disinformation is characterised by viral audiovisual hoaxes, particularly on Facebook and X (formerly Twitter), with a notable surge in disinformation flows at the onset of the invasion. In the early months, misleading content predominantly consisted of decontextualised images of the conflict, whereas a year later, the focus shifted to narratives concerning international support and alliances. The primary objective of this disinformation is to polarise public opinion against a perceived common enemy. The conclusions provide a broader and more nuanced understanding of wartime disinformation within the European context. Full article

Alzheimer’s disease (AD) is one of the main causes of dementia, with an exponential increment in its incidence as years go by. However, since pathophysiological mechanisms are complex and multifactorial, therapeutic strategies remain inconclusive and only provide symptomatic relief to patients. In order to solve this problem, new strategies have been investigated over recent years for AD treatment. This field has been reborn due to epidemiological and preclinical findings that demonstrate the fact that omega-3 polyunsaturated fatty acids (ω-3 PUFAs) can be promising therapeutic agents because of their anti-inflammatory, antioxidant, and neurogenic-promoting activities, thus allowing us to classify these molecules as neuroprotectors. Similarly, ω-3 PUFAs perform important actions in the formation of characteristic AD lesions, amyloid-β plaques (Aβ) and neurofibrillary tangles, reducing the development of these structures. Altogether, the aforementioned actions hinder cognitive decline and possibly reduce AD development. In addition, ω-3 PUFAs modulate the inflammatory response by inhibiting the production of pro-inflammatory molecules and promoting the synthesis of specialised pro-resolving mediators. Consequently, the present review assesses the mechanisms by which ω-3 PUFAs can act as therapeutic molecules and the effectiveness of their use in patients. Clinical evidence so far has shown promising results on ω-3 PUFA effects, both in animal and epidemiological studies, but remains contradictory in clinical trials. More research on these molecules and their neuroprotective effects in AD is needed, as well as the establishment of future guidelines to obtain more reproducible results on this matter. Full article

Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications through users’ voice commands presents significant challenges due to the hardware and software limitations of headset devices. This paper aims to bridge this gap by proposing a methodology to address these issues. In particular, starting from a Mel-Frequency Cepstral Coefficient (MFCC) extraction algorithm able to capture the unique characteristics of the user’s voice, we pass it as input to a Convolutional Neural Network (CNN) model. After that, in order to integrate the CNN model with a VR application running on a standalone headset, such as Oculus Quest, we converted it into an Open Neural Network Exchange (ONNX) format, i.e., a Machine Learning (ML) interoperability open standard format. The proposed system demonstrates good performance and represents a foundation for the development of user-centric, effective computing systems, enhancing accessibility to VR environments through voice-based commands. Experiments demonstrate that a native CNN model developed through TensorFlow presents comparable performances with respect to the corresponding CNN model converted into the ONNX format, paving the way towards the development of VR applications running in headsets controlled through the user’s voice. Full article

Stylistic neutrality is the basis for the stylistic evaluation of maps. Furthermore, the stylistic neutrality of a map as a cartographic text may be related to objectivity. However, what constitutes stylistic neutrality is not clearly stated in the field of cartography. The problem is complicated by the fact that the stylistically neutral image is a hypothetical image. The aim of this research is to investigate stylistic neutrality by exploring the peculiarities of cartographic language functioning in different fields of social activity. The research combines descriptive analysis, stylistic analysis, cartographic and interpretative methods. Firstly, the research reveals the concept of cartographic stylistic neutrality, in line with the cartographic linguistic paradigm. Secondly, an analysis of the characteristics of cartographic language in different fields of social activity from the point of view of stylistic neutrality is carried out. Thirdly, an example is developed to illustrate stylistic cartographic neutrality. Stylistic neutrality is characterised by the stylistic features of cartographic language: clarity, accuracy, conciseness, calmness, abstractness, temperance, neutrality and moderateness. The style of cartographic production for inventory and research activities is closest to stylistic neutrality, while the style of reflective activity is the most expressive and acts as a source of concreteness for stylistic neutrality. Full article