Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Monte Carlo localization in topometric appearance maps

Representing the scene appearance by a global image descriptor (BoW, NetVLAD, etc.) is a widely adopted choice to address Visual Place Recognition (VPR). The main reasons are that appearance descriptors can be effectively provided with radiometric and perspective invariances as well as they can deal with large environments because of their compactness. However, addressing metric localization with such descriptors (a problem called Appearance-based Localization or AbL) achieves much poorer accuracy than those techniques exploiting the observation of 3D landmarks, which represent the standard for visual localization. In this paper, we propose ALLOM (Appearance-based Localization with Local Observation Models) which addresses AbL by leveraging the topological location of a robot within a map to achieve accurate metric estimations. This topology-assisted metric localization is implemented with a sequential Monte Carlo Bayesian filter that applies a specific observation model for each different place of the environment, thus taking advantage of the local correlation between the pose and the appearance descriptor within each region. ALLOM also benefits from the topological structure of the map to detect eventual robot loss-of-tracking and to effectively cope with its relocalization by applying VPR. Our proposal demonstrates superior metric localization capability compared to different state-of-the-art AbL methods under a wide range of situations.
Reference Temperature Sensor for TMOS-Based Thermal Detectors

This paper presents a temperature sensor system providing the reference temperature of TMOS-based thermal detectors, in order to enable contactless absolute temperature measurements. The proposed system directly employs a few pixels of the TMOS detector as sensing element, thus detecting the local TMOS temperature, while the readout circuit is integrated on a separate chip. The readout circuit features a two-phase time-domain architecture, which provides biasing to the sensing element by alternatively switching between two different bias current values in subsequent phases. The temperature reading is converted to the digital domain thanks to a switched-capacitor 1-bit second-order sigma-delta (<inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta $ </tex-math></inline-formula>) analog-to-digital converter (ADC). The proposed readout system was fabricated in a 130-nm CMOS process and extensively characterized through measurements, together with the TMOS detector, realized in a 130-nm CMOS SOI technology and bonded on the same package. The proposed reference temperature sensor system, burning 10.8-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power, features 97-mK resolution when considering a 4096-ms conversion time and <inline-formula> <tex-math notation="LaTeX">$0.13^{\circ} \text{C}$ </tex-math></inline-formula> peak-to-peak inaccuracy after three point calibration in the 15–<inline-formula> <tex-math notation="LaTeX">$40^{\circ} \text{C}$ </tex-math></inline-formula> range, thus satisfying the characteristics for contactless human body temperature measurements.
Active learning methodology, associated to formative assessment, improved cardiac physiology knowledge and decreased pre-test stress and anxiety

Stress and anxiety caused by assessments are often related to the student’s insecurity regarding the knowledge to be evaluated, while teaching strategies that increase effective learning can assist in reducing it. The aim of this study was to evaluate the hypothesis that the use of an active methodology, associated to formative assessment, could reduce students’ anxiety and stress, when compared to the traditional method, by promoting greater learning. New students enrolled in the same discipline of a Dentistry course were invited to participate in the study and were divided into two groups: traditional method and active methodology. The traditional method group received two lectures, delivered orally. The active methodology group received a lecture about cardiac cells and the autonomic control of cardiac function, with home study of the cardiac cycle using a textbook. In the second class, an individual formative assessment was applied. Afterwards, a group activity was performed with an educational game about the cardiac cycle, followed by a group formative assessment. After applying the traditional or active methodology, test 1 was carried out. Immediately before this test, saliva samples were collected for determination of the concentrations of the stress biomarkers cortisol and α-amylase. The students also answered the State-Trait Anxiety Inventory questionnaire, used for anxiety level determination. The score obtained in the test 1 was significantly higher for the active methodology group, compared to the traditional method group. No significant differences between the groups were observed for baseline cortisol and salivary α-amylase concentrations, or for anxiety scores. Before test 1, traditional method group presented higher concentrations of salivary cortisol and α-amylase, compared to the respective baseline values, while the active methodology group showed no difference between the baseline and test 1 levels. Before test 1, there were increases in anxiety levels, relative to the respective baseline values, regardless of the teaching methodology used, but this increase was greater for the traditional method group, compared to the active methodology group. These results showed that the active methodology, associated to formative assessment, decreased test stress and anxiety, with improved student performance in comparison to traditional lectures.
Probability of detection in conventional AOA using ULA and URA arrays : Probabilidade de detecção em AOA convencional usando arranjos ULA e URA

In Massive Multiple Input Multiple Output (MIMO) systems, where a large number of antennas is used, the dependency of the power spectrum on the signal direction allows Angle of Arrival (AoA) estimation with a very high precision. In this work, we derive the probability to correctly detect the angle of arrival using the conventional beamformer. The derivation is presented in an exact manner for Uniform Linear Array (ULA) and Uniform Rectangular Array (URA) geometries. The resulting expressions depend on the noise power, Line of Sight Component (LoS) (modeled as the fading mean), fading variance, number of signal snapshots, number of steering angles, and the number of antennas. We have numerically evaluated the simulated and analytical probability of detection and they have shown a perfect agreement for distinct scenarios framed in the Massive MIMO condition.
Dual-Broadband Circularly Polarized Reconfigurable Antenna Based on Asymmetric U-Slot Patch

A novel dual-broadband circularly polarized (CP) reconfigurable antenna and its 2 × 2 array are presented in this letter. The proposed antenna is realized based on the double asymmetrical U-slots patch, and the dual-band sequential rotation technique is utilized to achieve a dual-broadband 2 × 2 arrays. The dual-band CP (DBCP) radiation characteristic of the patch is discussed based on the mode behaviors predicted by characteristic mode analysis. The switchable DBCP is achieved by asymmetrically controlling the diodes installed on the arms of two U-slots. To further improve the axial ratio (AR) performance, a 2 × 2 array with reconfigurable dual-band feeding networks is designed based on the sequential rotation technique. The measured results show that the array has wide 3 dB AR bandwidths of 28.4% and 16.0%, with wide 3 dB gain bandwidths of 23.8% and 18.2%, respectively.
Ninth Annual DC Public Health Case Challenge Protective Community Environments and
 Their Contribution to Intimate Partner Violence Prevention: The Role of Youth

This discussion paper provides an overview of the Ninth Annual DC Public Health Case Challenge 1 , a student competition held in 2022 by the National Academy of Medicine (NAM) and the Roundtable on Population Health Improvement in the Health and Medicine Division (HMD) of the National Academies of Sciences, Engineering, and Medicine (the National Academies). The Case Challenge, which is both
Tunable Double-Network GelMA/Alginate Hydrogels for Platelet Lysate-Derived Protein Delivery

Hydrogels (gels) are attractive tools for tissue engineering and regenerative medicine due to their potential for drug delivery and ECM-like composition. In this study, we use rheology to characterize GelMA/alginate gels loaded with human platelet lysate (PL). We then characterize these gels from a physicochemical perspective and evaluate their ability to transport PL proteins, their pore size, and their rate of degradation. Finally, their biocompatibility is evaluated. We describe how alginate changes the mechanical behavior of the gels from elastic to viscoelastic after ionic (calcium-mediated) crosslinking. In addition, we report the release of ~90% of PL proteins from the gels and relate it to the degradation profile of the gels. Finally, we evaluated the biocompatibility of the gels. Thus, the developed gels represent attractive substrates for both cell studies and as bioactive materials.
The use of desalinated-dried jellyfish and rice bran for controlling weeds and rice yield

To achieve higher rice production, rice-growing countries have used great amounts of synthetic chemical compounds (chemical fertilizers and pesticides) that can have adverse effects on the environment and humans. Organic products and organic farming technologies are friendlier to the environment and more conducive to sustainable agriculture but require different inputs, knowledge and skills. Weed control is one of the major challenges in organic rice cultivation. The present study proposes and tests the use of desalinated-dried jellyfish chips in the development of sustainable rice production. Vast amounts of jellyfishes have been found in the Sea of Japan (Nomura's jellyfish, Nemopilema nomurai Kishinouye) and Japan inland sea areas (Water jelly, Aurelia aurita (Linne)), and jellyfish populationc can have a negative impact on the fishery industry. In this context, the use of jellyfish in organic agriculture has attracted attention. The present study found that the application of desalinated-dried jellyfish (small pieces of jellyfish which are desalinated and dried) mixed in soil before transplanting can effectively control weeds in rice fields and has a nutrient effect because of the high nitrogen content (12-13%). Desalinated-dried jellyfish has potential as an agricultural material that replaces herbicides and chemical fertilizers. It also contributes to environment-friendly rice production. It was found that both desalinated-dried jellyfish and rice bran effectively controlled rice weeds when mixed in the soil before the transplanting.The grain yields of desalinated-dried jellyfish treatments were consistently higher than the corresponding rice bran treatments. The rice yield from the desalinated-dried jellyfish treatments were comparable to the chemical fertilizer treatment.
Unlocking the Potential of Weight of Evidence and Entity Embedding Encoding for Categorical Data Transformation in Medical Datasets: An Innovative Approach to Enhance Classiﬁcation Accuracy

In the present era, healthcare systems grapple with substantial volumes of medical data. However, a signiﬁcant portion of this data is marked by incompleteness, inconsistency, errors, and unsuitability for training Machine Learning (ML) or Deep Learning (DL) algorithms. This necessitates preprocessing the data to render it amenable to utilization by ML/DL algorithms. Medical datasets predominantly feature two types of attributes: numerical and categorical values. The conversion of categorical features into numerical vectors is a crucial step in preparing the data for ML/DL algorithms, known as Feature Engineering (FE) based categorical encoding. Conventional and straightforward encoding of categorical features, termed one-hot encoding, generates multiple columns, thereby transforming data from a lower-dimensional to a higher-dimensional space. This approach poses challenges, including increased memory requirements due to the proliferation of columns. Considering these issues, this research proposes an encoding technique named “Weight of Evidence with Entity Embedding” (WoEEE). The WoEEE approach bolsters the predictive capabilities of ML/DL algorithms by calculating the weight of evidence and concurrently mitigates dimensionality issues. To empirically validate the proposed method, it is tested on six diverse datasets: Breast Cancer, Hospital Readmission, Vadu, Covid-19, Stroke, and Heartstatlog. Four distinct ML/DL algorithms—Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and a simple Feed-forward Neural Network (NN)— are employed for testing. The re-sults obtained demonstrate that the WoEEE approach yields an average improvement of 11.18%, 10.37%, 5.83%, 7.58%, 7.83%, and 6.83% across all combinations of datasets, classi-ﬁers, and encoding methods. Furthermore, an Anova test is performed to conﬁrm the eﬀectiveness of WoEEE in encoding categorical data, especially for tasks involving binary clas-siﬁcation. This enhances the treatment of categorical data in ML and data analytics scenarios. Overall, WoEEE shows potential as a valuable approach for categorical data encoding, making a positive contribution to the creation of eﬀective techniques for handling this type of data in real-world applications.
A New Ridge Detector Localizing Strong Interference in Multicomponent Signals in the Time-Frequency Plane

In this article, we define a new ridge detector that enables to localize strong interference in multicomponent signals in the time-frequency (TF) plane. Each mode of a multicomponent signal can usually be associated with a ridge in the TF plane, but this is no longer the case when strong interferences occur in the signal. The new ridge detector we propose is thus designed to determine when such situations happen in the TF plane. We show that this knowledge helps to determine an appropriate window length in the definition of the spectrogram, as well as the nature of the strong interference detected. An application of the proposed approach to voice signals concludes the article.
Knowledge of the Business Ecosystem and the Implementation of a Circular Economy

Part of the European Green Deal is the Circular Economy Action Plan. Among other things, this plan formulates priorities for the design of products and production processes so that they are climate-neutral, correspond to a resource-efficient and circular economy (CE) and lead to a reduction of waste. These actions require companies not only to make changes to their organisational and management systems, but also to make investments. For this reason, these changes do not encourage companies to introduce the principles of the circular economy. Such encouragement can come from the business ecosystem. The aim of this article is to answer the question of whether companies gain such knowledge about the ecosystem in which they operate that encourages them to replace harmful technologies with friendly ones, to use their waste in their own production processes and those of other companies, and to neutralise their waste. This includes knowledge relating to the regulatory, economic, social, environmental and technological segments, as well as knowledge gained from buyers, suppliers and also competitors. The research hypothesis of the paper is that the greater this knowledge is, the more companies engage in the introduction of CE principles. In order to verify this hypothesis, an empirical study was conducted in 150 medium and large-sized companies belonging to 19 industrial sectors. Data were obtained using an interview questionnaire and were processed using a Kruskal-Wallis ANOVA and post hoc test. The results obtained allow us to conclude that knowledge of changes occurring in macro-environmental segments is more influential than knowledge obtained from buyers, suppliers and competitors. Companies that have knowledge of the changes taking place in the ecosystem perceive and exploit these changes and actions towards a circular economy as business opportunities. The research results obtained are useful for indicating the practical steps that leaders in the introduction of circular economy principles are taking in order to acquire the necessary knowledge about the ecosystem. They can also provide some contribution to the development of a sustainable product policy legislative initiative by the European Commission using the performance of these leaders.
Animal Health and Productivity of Organic Greek Pig Farms: The Current Situation and Prospects for Sustainability

Simple Summary This article aims to review organic pig production in Greece. Information on the production, development and health status of organic pig farming in Greece and the potential prospects for sustainability and future development. Among the pig breeds reared on organic farms, the indigenous Greek Black Pig is the most common. Respiratory and parasitic infections are the most common health problems, while high piglet mortality rates are the main welfare issue in Greek organic pig farming. Concerns about how farmers and authorities should utilize the demands of modern consumers are discussed. Abstract A review of organic pig production in Greece was carried out. The aim was to present updated information on the production, development and health status of organic pig farming in Greece and potential prospects for sustainability and future development. The indigenous Greek Black Pig is the main breed reared in Greek organic pig farms. All the reasons why Greek Black Pig breeding is ideal for organic farming are mentioned. Furthermore, respiratory and parasitic infections are the most common health problems, while high piglet mortality rates are the main welfare issue in Greek organic pig farming. Concerns about how farmers and authorities should utilize the demands of modern consumers are discussed.
Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images

Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes.
Role of microscopic degrees of freedom in mechanical response of bicrystal nanopillars

This study investigated the high-strain rate deformation of bicrystal Cu nanopillars, using atomistic simulations. Nanopillars with minimum grain boundary energy were deformed to investigate the role of macroscopic degrees of freedom, finding that geometric parameters (Schmid factor) influence the stress–strain response. The deformation of metastable grain boundaries (GBs) revealed that in addition to geometric parameters, the response was also governed by the local atomic arrangement at the boundary, dictating the dislocation-GB interactions. These findings shed light on the response of nanopillars as a function of GBs and show the importance of both macroscopic and microscopic degrees of freedom on the mechanical response. GRAPHICAL ABSTRACT IMPACT STATEMENT Metastable states, an often ignored aspect of GB structure, is shown to have a strong influence on dislocation-GB interactions; shedding new light on mechanical response of realistic GBs.
Validation of the DART Model for Airborne Laser Scanner Simulations on Complex Forest Environments

With the recent progresses in lidar technology for Earth remote sensing, the development of a reliable lidar simulator is becoming central in order to define specifications for new sensors, perform intercomparisons, train machine learning algorithms, and help transferring information from one scale to another. The discrete anisotropic radiative transfer (DART) model includes such a lidar simulator. Although already tested on several virtual scenes, the DART outputs still need to be rigorously evaluated against actual sensor acquisitions, especially on real complex scenes of various forest types, such as dense tropical forests. That is the purpose of the present study. A real airborne laser scanner (ALS) with full-waveform capacity was first radiometrically calibrated on targets of measured reflectance. The properties of the ALS system were then introduced in the DART model, along with a 3-D virtual scene built from terrestrial laser scans and spectroscopic measurements acquired on a forest plot near the calibration site. Finally, an ALS acquisition was simulated and the shape and magnitude of the waveforms were compared with real acquisitions. The comparison between measured and simulated data was performed at different scales by aggregating waveform samples into a 3-D grid with a vertical resolution of 1 m and a horizontal resolution ranging from 2 to 80 m. Results showed a high similarity between simulated and measured waveforms at all scales with R2>0.9 and NRMSE<10%. These promising results open up numerous perspectives for improved spaceborne and airborne lidar data processing and for the development of new systems.
A Hybrid Magnetic Current Sensor With a Dual Differential DC Servo Loop

This article presents a hybrid magnetic current sensor for contactless current measurement. Pick-up coils and Hall plates are employed to sense the high and low-frequency fields, respectively, generated by a current-carrying conductor. Due to the differentiating characteristic of the pick-up coils, a flat frequency response can then be obtained by summing the outputs of the coil and the Hall paths and passing the result through a 1st-order low-pass filter (LPF). For maximum resolution, the LPF corner frequency (2 kHz) is set such that the noise contribution of each path is equal. To suppress the coil-path offset without the use of large ac coupling capacitors, an area-efficient dual dc servo loop (D3SL) is used. This effectively suppresses the coil-path offset, resulting in a total offset of 73 <inline-formula> <tex-math notation="LaTeX">$\mu \text{T}$ </tex-math></inline-formula>, which is mainly dominated by the Hall path. Fabricated in a standard 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, the current sensor occupies 3.9 mm2 and draws 7.1 mA from a 1.8 V supply. It achieves 43 mA resolution in a 5 MHz bandwidth, which is 1.5<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> better than the state-of-the-art hybrid sensors. It also achieves the lowest energy efficiency FoM (3.5<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>) among CMOS magnetic current sensors.