Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel WKNN algorithm for fingerprinting-based fusion of different radio communication technologies for indoor positioning

Indoor Positioning Systems (IPSs), which commonly rely on Wireless Sensor Networks (WSN), are gaining attention in various fields due to the emergence of Industry 4.0. In this paper, a novel Weighted K-Nearest Neighbor (WKNN) -based localization algorithm is proposed for the fusion of different wireless parameters. An experiment was set up for evaluation and measurements were performed in a laboratory. The essence of the novel method is that different wireless communication technologies, namely, 2.4 GHz WiFi Received Signal Strength Indicator (RSSI), 433 MHz CC1101 RSSI, Ultra-Wideband (UWB) RSSI, and UWB Time of Arrival (TOA) were combined for localization purposes. The proposed algorithm has two steps. In the first step the technologies are used separately to calculate the searched position. In the second step the average of the estimated coordinates is taken in all possible combination of the technologies. The original WKNN method considers the data together provided by each technology. The proposed WKNN algorithm provided better results in 5 cases than the original WKNN method. The improvement ranged from 0.7% to 15%. The largest improvement was achieved by using WiFi and CC1101 RSSI together, when the average error was reduced from 1.86 m to 1.58 m. The results show that it is worthwhile to adaptively select the appropriate fusion of technologies, as this can significantly reduce the localization error.
Characterization of Larix decidua Mill. (Pinaceae) oleoresin’s essential oils composition using GC-MS

Introduction Larch oleoresin has been described regarding several biological activities and medicinal applications, such as wound healing and treatment of ulcers, but little is known about its chemical composition. Material and methods Eight oleoresins from Larix decidua Mill. obtained from four companies and one adulterated control were therefore investigated to determine their content of essential oils and to verify possible differences in their composition in relation to the harvest and manufacturing processes. Essential oils (EOs) were isolated by distillation and the yield was analysed. Results and discussion The yield of EO varied among all samples. The yield of the pure larch samples covered a range of 7.8% to 15.5%. A higher yield (19.0%) was observed for adulterated control, which contained oleoresins from different Pinaceae trees. Age of samples had no impact on yield. However, there was a significant statistical variation (p<0.05) in the yields of the mid-summer oleoresins (>10%) compared to early or late summer (<10%), emphasising the importance of the time of collection. Samples were subsequently analysed by GC-MS. EO samples confirmed the presence of various chemical classes, such as monoterpenes, sesquiterpenes, and diterpenes. α-pinene was the compound with the highest concentrations (>50%), followed by β-pinene (>6%), D-limonene (>2.5%), α-terpineol (>0.9%), β-myrcene (>0.2%), and 3-carene (>0.05%). Samples were grouped using multivariate data analysis (MVDA) with respect to the chemical variation between the oleoresins’ EOs. The resulting four clusters were named low (low yield obtained for the samples), mixed (mixed oleoresin from different Pinaceae species, adulteration control), old (old oleoresin kept in the institute), and normal (other oleoresins) samples, each presenting distinct chemical biomarkers. There were considerable differences between site and time of collection. Essential oil yield did not always meet requirements as defined by the German Homeopathic Pharmacopoeia. In addition, adulterated or aged samples could be identified as compared to pure and fresh larch oleoresins. Conclusion We conclude that larch oleoresin used for pharmaceutical applications has to be carefully analysed and standardised to guarantee reproducible product quality.
Processing of Range Cell Migration Correction with Numerical Optimization for Synthetic Aperture Radar

Due to the presence of atmosphere turbulences and the instability of airplane platform, deviations arise between the real track and the nominal one during SAR data collection, which are referred to as motion error (ME). To account for the precision limitation of a navigation system, although roughly compensated by the use of navigation data, the residual motion error (RME) still remains. As far as range cell migration correction (RCMC), the RME makes azimuth frequency-time mapping non-linear, which causes classic RCMC failure as well as the energy spread along range direction. In this paper, an adaptive RCMC (ARCMC) algorithm is proposed. First, the residual target-to-antenna distance caused by RME is extended by polynomials. Then, at each stage, classic RCMC is fast performed under updated coefficients of polynomials until convergence. During the iteration, the sharpness of the RCMC-finished data is chosen as the cost metric. In addition, the optimal coefficients are estimated by Broyden-Fletcher-Goldfarb-Shanno (BFGS) search with adap-tive order selection. Finally, we present the raw data experiment charactering the performance of the proposed method in detail.
Improving the Representativeness of Simulation Intervals for the Cache Memory System

Accurate simulation techniques are indispensable to efficiently propose new memory or architectural organizations. As implementing new hardware concepts in real systems is often not feasible, cycle-accurate simulators employed together with certain benchmarks are commonly used. However, detailed simulators may take too much time to execute these programs until completion. Therefore, several techniques aimed at reducing this time are usually employed. These schemes select fragments of the source code considered as representative of the entire application’s behaviour–mainly in terms of performance, but not plenty considering the behaviour of cache memory levels–and only these intervals are simulated. Our hypothesis is that the different simulation windows currently employed when evaluating microarchitectural proposals, especially those involving the last level cache (LLC), do not reproduce the overall cache behaviour during the entire execution, potentially leading to wrong conclusions on the real performance of the proposals assessed. In this work, we first demonstrate this hypothesis by evaluating different cache replacement policies using various typical simulation approaches. Consequently, we also propose a simulation strategy, based on the applications’ LLC activity, which mimics the overall behaviour of the cache much closer than conventional simulation intervals. Our proposal allows a fairer comparison between cache-related approaches as it reports, on average, a number of changes in the relative order among the policies assessed – with respect to the full simulation – more than 30% lower than that of conventional strategies, maintaining the simulation time largely unchanged and without losing accuracy on performance terms, especially for memory-intensive applications.
Simulation of Eddy Current Losses in Twisted Wires

Twisted wires made of insulated strands, known as stranded conductors or litz wires, are used in various areas where the eddy current loss within the wire needs to be reduced. These areas include induction heating, resonance-based wireless energy transfer, and certain radio frequency devices. Some litz wires consist of thousands of individual conductor strands that are twisted together in multiple stages, creating a hierarchical bundle structure. Computer simulations (typically using finite element analysis) are used in the optimal design of the bundle structure. However, detailed three-dimensional models are computationally demanding. In this work, a two-dimensional finite element model was presented for simulating the eddy current loss in cables made of twisted wires. The key element of the model is considering the bundle structure (generally referred to as 3D configuration conditions) within the cross-sectional model domain. The accuracy of the proposed model is tested against 3D finite element simulations. The new method is shown to be accurate, and its computational cost is by orders of magnitude lower than that of 3D models.
El valor de la evaluación de las relaciones públicas y de la comunicación en las organizaciones: el caso de los hospitales del Sistema Nacional de Salud Español

This article attempts to determine whether the hospitals of the Spanish Health System carry out evaluation activities of their public relations and communication campaigns and to establish their purpose. As secondary objectives, the budget for the evaluation was examined, as well as if the characteristics of the hospital evaluation process were adapted to Besson’s postulates. A large sample of 300 Spanish public hospitals (response rate of 23.33%) was taken. The results indicate that hospitals carry out evaluations for the development of their duties, but that they do not allocate a large budget to evaluate their actions. This suggests that there is a concern to demonstrate the value of public relations practice in hospitals but that they are still at an early stage of a systematic adoption of a concrete form of model for the strategic use of evaluation.
Bem-estar de vacas leiteiras em sistema silvipastoril

In addition to the growing demand for dairy products, recently the population has given greater importance to issues such as sustainability and animal welfare. Thus, producers are adapting to the demands of the consumer market through systems to produce sustainably and quality of life for animals, besides being economically efficient. The silvipastoral system allows combining forestry and livestock activities, and so increasing production efficiency. Among the main benefits of this production system are soil conservation and water resources, the promotion of carbon sequestration and increased biodiversity. In addition, animals raised in this system have a lower risk of thermal stress due to shadow arising from afforestation, favoring animal welfare. Therefore, the silvipastoral system represents a suitable alternative to the production of dairy cattle, once lactating cows need ideal climatic conditions to express their genetic potential, resulting in high production performance. In this way, this study aims to gain a better understanding on the establishment of dairy cattle in silvipastoral system and the influence on their welfare.
Design and Implementation of Service Provider for Integration of Heterogeneous Sensor Networks

Recently, there is a growing IoT(Internet of Things) service using sensors for environmental monitoring and actuators for reacting to environmental changes. Sensor networks have provided us with facilities to collect information and track certain phenomena in the physical environment based on the region of interest. Sensor networks consist of a collection of sensor nodes and a sink node for many applications in different areas, including environmental surveillance, intelligent building, health monitoring, intelligent transportations, etc. In this paper, we present a sensor service provider for integration of heterogeneous large-scale sensor networks. This paper focuses on the design and implementation of the open sensor service provider for the integrated multiple sensor middleware in heterogeneous sensor networks. This provider has various heterogeneous sensor middleware, and each sensor middleware has a sensor networks. We design the sensor service provider using sequence diagram and state diagram, and implement it based on . Net framework. The sensor service provider has the service interface, the sensor content service manager, the sensor service manager, the sensing service manager, and the service controller, etc. The provider supports comprehensive to carry out the context acquisition and provisioning functionality.
SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks

Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.
Toward Improving Ensemble-Based Collaborative Inference at the Edge

Ensemble-based collaborative inference systems, Edge Ensembles, are deep learning edge inference systems that enhance accuracy by aggregating predictions from models deployed on each device. They offer several advantages, including scalability based on task complexity and decentralized functionality without dependency on centralized servers. In general, ensemble methods effectively improve the accuracy of deep learning, and conventional research uses several model integration techniques for deep learning ensembles. Some of these existing integration methods are more effective than those used in previous Edge Ensembles. However, it remains uncertain whether these methods can be directly applied in the context of cooperative inference systems involving multiple edge devices. This study investigates the effectiveness of conventional model integration techniques, including cascade, weighted averaging, and test-time augmentation (TTA), when applied to Edge Ensembles to enhance their performance. Furthermore, we propose enhancements of these techniques tailored for Edge Ensembles. The cascade reduces the number of models required for inference but worsens latency by sequential inference processing. To address this latency issue, we propose <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-parallel cascade, which adjusts the number of models processed simultaneously to <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>. We also propose learning TTA policies and weights for weighted averaging using ensemble prediction labels instead of ground truth labels. In the experiments, we verified the effectiveness of each technique for Edge Ensembles. The proposed <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-parallel cascade achieved a 2.8 times reduction in latency compared to the conventional cascade, even with a 1.06 times increase in computational costs. Additionally, the ensemble label-based learning demonstrated comparable effectiveness to the approach using ground truth labels.
Enhanced phenylpropanoid metabolism underlies resistance to Fusarium oxysporum f. sp. vasinfectum race 4 infection in the cotton cultivar Pima-S6 (Gossypium barbadense L.)

Introduction: Fusarium oxysporum f. sp. vasinfectum (FOV) race 4 (FOV4) is a highly pathogenic soil-borne fungus responsible for Fusarium wilt in cotton (Gossypium spp.) and represents a continuing threat to cotton production in the southwest states of the United States, including California, New Mexico, and Texas. Pima (G. barbadense L.) cotton, which is highly valued for its fiber quality, has been shown to be more susceptible to this pathogen than Upland (G. hirsutum L.) cotton. Still, some Pima cultivars present resistance to FOV4 infection. Methods: To gain insights into the FOV4-resistance mechanism, we performed comparative transcriptional and metabolomic analyses between FOV4-susceptible and FOV4-resistant Pima cotton entries. FOV4-resistant Pima-S6 and FOV4-susceptible Pima S-7 and Pima 3-79 cotton plants were infected with FOV4 in the greenhouse, and the roots harvested 11 days post-infection for further analysis. Results: We found that an enhanced root phenylpropanoid metabolism in the resistant Pima-S6 cultivar determines FOV4-resistance. Gene-ontology enrichment of phenylpropanoid biosynthesis and metabolism categories correlated with the accumulation of secondary metabolites in Pima-S6 roots. Specifically, we found esculetin, a coumarin, an inhibitor of Fusarium’s growth, accumulated in the roots of Pima-S6 even under non-infected conditions. Genes related to the phenylpropanoid biosynthesis and metabolism, including phenylalanine ammonia-lyase 2 (PAL2) and pleiotropic drug resistance 12 (PDR12) transporter, were found to be upregulated in Pima-S6 roots. Discussion: Our results highlight an essential role for the phenylpropanoid synthesis pathway in FOV4 resistance in Pima-S6 cotton. These genes represent attractive research prospects for FOV4-disease resistance and breeding approaches of other cotton cultivars of economic relevance.
Interest Rate Benchmarking for Transfer Pricing Purposes: A Comparison between the Loan and the Bond Approach

This article analyses how an arm’s length interest rate for an intercompany loan can be determined under the two most commonly accepted approaches; the loan approach and the bond approach. In this respect, it first provides some considerations as to performing a loan benchmarking study, followed by some of the main differences and theoretical advantages and disadvantages of the two approaches. As a second step, a case study is presented, for which an arm’s length interest rate is determined under both approaches. The results of the two approaches are then discussed and compared. It is argued that the bond approach yields much more reliable results.
Application of kubeflow as a universal approach for the development and implementation of artificial intelligence systems

An overview of the concept of machine learning and processes (Machine Learning and Operation, MLOps), which is a set of techniques for implementation and automatic continuous integration, as well as delivery to the production environment and model training, is made. The concept of MLOps was considered in terms of Kubeflow tools - a cloud-native open-source system running on the Kubernetes platform. The possibility of using modern MLOps solutions to improve the development processes of machine learning information systems has been studied. The results of the operation of the model in the Kubeflow arsenal have been checked using such improvement factors as speed of development, implementation of changes, reduction of time to search for problems, recovery after global interruptions, and decrease of the number of errors in the model. For practical analysis, a publicly available model was deployed in a Kubeflow cluster using the Seldon Core Serving application manifest. The conducted research showed that Kubeflow consists of a set of various open-source components that have a high level of integration with each other through the Kubernetes platform. At the same time, Kubeflow uses the Kubernetes pattern of operators for machine-learning objects extremely effectively.
The Effects of Environmental Exposure on Epigenetic Modifications in Allergic Diseases

Allergic diseases are one of the most common chronic conditions and their prevalence is on the rise. Environmental exposure, primarily prenatal and early life influences, affect the risk for the development and specific phenotypes of allergic diseases via epigenetic mechanisms. Exposure to pollutants, microorganisms and parasites, tobacco smoke and certain aspects of diet are known to drive epigenetic changes that are essential for immune regulation (e.g., the shift toward T helper 2-Th2 cell polarization and decrease in regulatory T-cell (Treg) differentiation). DNA methylation and histone modifications can modify immune programming related to either pro-allergic interleukin 4 (IL-4), interleukin 13 (IL-13) or counter-regulatory interferon γ (IFN-γ) production. Differential expression of small non-coding RNAs has also been linked to the risk for allergic diseases and associated with air pollution. Certain exposures and associated epigenetic mechanisms play a role in the susceptibility to allergic conditions and specific clinical manifestations of the disease, while others are thought to have a protective role against the development of allergic diseases, such as maternal and early postnatal microbial diversity, maternal helminth infections and dietary supplementation with polyunsaturated fatty acids and vitamin D. Epigenetic mechanisms are also known to be involved in mediating the response to common treatment in allergic diseases, for example, changes in histone acetylation of proinflammatory genes and in the expression of certain microRNAs are associated with the response to inhaled corticosteroids in asthma. Gaining better insight into the epigenetic regulation of allergic diseases may ultimately lead to significant improvements in the management of these conditions, earlier and more precise diagnostics, optimization of current treatment regimes, and the implementation of novel therapeutic options and prevention strategies in the near future.
Cooperation and mental states neurophysiological assessment for pilots’ training and expertise evaluation

The aim of the study was to validate a methodology for professional pilots’ neurophysiological assessment to improve training program tailoring and management. In particular, the assessment focused on quantifying (i) mental workload, (ii) stress level (iii) and the cooperation degree between the two members of the crews. Two groups of pilots were involved in the experiments: Experienced and Unexperienced. Additionally, the Instructor was responsible to provide subjective evaluation about the pilots’ mental states and cooperation. During the entire flight simulations, the pilots’ brain activity was acquired through the Electroencephalography (EEG). The results demonstrated that it is possible to quantify operators’ mental states and degree of cooperation while dealing with flight simulations under realistic settings. Pilots’ mental workload and stress behavioral levels resulted to be positively and significantly correlated with the corresponding neurophysiological measurements (all R > 0.6, all p < 0.05), while cooperation was observed to be higher (p < 0.05) for the Experienced than the Unexperienced. Although the results are preliminary, they show how the capability of quantifying pilots’ mental states and cooperation while dealing with tasks will provide the instructors with additional and objective information. In particular, this information would allow for tailoring the training sessions based on crew’s behavior.