Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainable Food Production Through Vermicomposting – An EPS@ISEP 2021 Project

The European Project Semester (EPS) is a multicultural, multidisciplinary teamwork and project-based learning framework offered to engineering, business and product design undergraduates by a network of European Higher Education institutions, including the Instituto Superior de Engenharia do Porto (ISEP). In the spring of 2021, five EPS@ISEP students from distinct countries and fields of study joined efforts to address the smart and sustainable food production issue. This paper reports their research and development of Wormify, a solution based on vermicomposting. The main goal of the project was to design, simulate, test and build a prototype following ethical and sustainable practices. Wormify aims to minimize the problem of feeding the growing global population, and to prevent food waste from going to landfills. These objectives were pursued by designing a smart modular system for urban rooftops or small balconies. Several modules can be connected to form a place for residents to meet and socialize. The smart system allows monitoring through an app/website. This paper presents the background studies, the concept and design, the development and final results.
Creating assistive technology in disabled communities, five years on

Five years ago, our paper, "Would You Be Mine: Appropriating Minecraft as an Assistive Technology for Youth with Autism" won Best Paper at ASSETS 2016 (Ringland et al. 2016). In that paper, we reported on our ethnographic engagement with a community for autistic youth called "Autcraft. " In Autcraft, we found community members using do-it-yourself (DIY) making activities to transform their Minecraft game into an array of assistive technologies which enhanced their everyday lives. Although centered around the Minecraft game platform, the Autcraft community spans across an array of other social media platforms - such as Twitter, Facebook, YouTube, and Twitch. The creative ethos we found flourishing in Autcraft shares a lineage with past scholarship highlighting how disabled individuals have long adopted, adapted, and appropriated systems in order to serve as assistive devices. Five years on, we take some time here to reflect on what has happened since and what we are looking towards for the future.
Hardware-based simple, fast and accurate measurement of power system frequency using a hybrid system

All digital methods developed for frequency measurement suffer from one or more factors such as superimposed noise, non-linear static characteristics, high-order harmonics and slow response because of complex computations. The measurement speed of conventional analogue methods is constant and depends on the input signal frequency. Additionally, as a result of deviated and/or spurious zero-crossings caused by the DC component, noise and high-order harmonics in the AC waveform, it is hard to attain the desired accuracy with conventional analogue methods. In this study, a phase-locked loop and microcontroller-based hybrid frequency measurement method developed by the modification of conventional zero-crossing is presented. The input signal, with harmonics and noise, and the frequency can be measured in a fast and accurate way with the proposed method, whose real-time application is extremely simple; this is demonstrated in a comparable manner to the studies in the literature. The measurement method presented in this study is fast, cheap, reliable, accurate and suitable for real-time applications.
A new finite element formulation for the forward problem of electro-magnetic source imaging

The voltage measurements on the scalp and magnetic field measurements near the scalp can be used to compute spatio-temporal evolution of electric currents across the cerebral cortex. Such a representation can be called Electro-Magnetic Source Image. In order to interpret the measured fields accurately, a realistic numerical model of the head should be prepared that includes physical properties and geometry of the individual's head. For that purpose, in this study, Finite Element Method (FEM) is used to solve the scalar potential function. A new FEM formulation is derived that assumes constant current density in isoparametric volume elements. A software is prepared by optimizing the code to run in a PC with 128 MB memory. It is shown that, for a current dipole at 2.5 cm from the origin, the potential distribution can be solved within an accuracy of 0.5% in comparison with the exact solutions, with a mesh of 29001 nodes and 7000 elements. In order to solve the magnetic held, the surface integration tools developed in the same FEM code are used. For the same dipole location the radial magnetic fields are calculated within an accuracy of 2.2%. Obviously, the accuracy in results can be increased by increasing the number of nodes.
Laser wave propagation through the atmosphere

Communications with coherent light through the atmosphere bring out new system limitations besides the ones imposed by attenuation and scattering normally associated with incoherent waves. This is so because the laser directivity and coherence responsible for increased channel capacity are easily destroyed or at least degraded by the presence of random thermal turbulences. Simple expressions describing the effects of atmospheric turbulence on laser beam propagation are obtained. Corresponding calculations yield the order of magnitude of such random phenomena as beam scanning, phase variation, beam cross-section change, amplitude and frequency modulation. In particular, the polarization fluctuation is predicted quantitatively in terms of the atmosphere mean square refractive index and its turbulence correlation length.
A Global Ocean Oxygen Database and Atlas for Assessing and Predicting Deoxygenation and Ocean Health in the Open and Coastal Ocean

In this paper, we outline the need for a coordinated international effort toward the building of an open-access Global Ocean Oxygen Database and ATlas (GO2DAT) complying with the FAIR principles (Findable, Accessible, Interoperable, and Reusable). GO2DAT will combine data from the coastal and open ocean, as measured by the chemical Winkler titration method or by sensors (e.g., optodes, electrodes) from Eulerian and Lagrangian platforms (e.g., ships, moorings, profiling floats, gliders, ships of opportunities, marine mammals, cabled observatories). GO2DAT will further adopt a community-agreed, fully documented metadata format and a consistent quality control (QC) procedure and quality flagging (QF) system. GO2DAT will serve to support the development of advanced data analysis and biogeochemical models for improving our mapping, understanding and forecasting capabilities for ocean O2 changes and deoxygenation trends. It will offer the opportunity to develop quality-controlled data synthesis products with unprecedented spatial (vertical and horizontal) and temporal (sub-seasonal to multi-decadal) resolution. These products will support model assessment, improvement and evaluation as well as the development of climate and ocean health indicators. They will further support the decision-making processes associated with the emerging blue economy, the conservation of marine resources and their associated ecosystem services and the development of management tools required by a diverse community of users (e.g., environmental agencies, aquaculture, and fishing sectors). A better knowledge base of the spatial and temporal variations of marine O2 will improve our understanding of the ocean O2 budget, and allow better quantification of the Earth’s carbon and heat budgets. With the ever-increasing need to protect and sustainably manage ocean services, GO2DAT will allow scientists to fully harness the increasing volumes of O2 data already delivered by the expanding global ocean observing system and enable smooth incorporation of much higher quantities of data from autonomous platforms in the open ocean and coastal areas into comprehensive data products in the years to come. This paper aims at engaging the community (e.g., scientists, data managers, policy makers, service users) toward the development of GO2DAT within the framework of the UN Global Ocean Oxygen Decade (GOOD) program recently endorsed by IOC-UNESCO. A roadmap toward GO2DAT is proposed highlighting the efforts needed (e.g., in terms of human resources).
DMSO Concentrations up to 1% are Safe to be Used in the Zebrafish Embryo Developmental Toxicity Assay

Dimethyl sulfoxide (DMSO) is a popular solvent for developmental toxicity testing of chemicals and pharmaceuticals in zebrafish embryos. In general, it is recommended to keep the final DMSO concentration as low as possible for zebrafish embryos, preferably not exceeding 100 μL/L (0.01%). However, higher concentrations of DMSO are often required to dissolve compounds in an aqueous medium. The aim of this study was to determine the highest concentration of DMSO that can be safely used in our standardized Zebrafish Embryo Developmental Toxicity Assay (ZEDTA). In the first part of this study, zebrafish embryos were exposed to different concentrations (0–2%) of DMSO. No increase in lethality or malformations was observed when using DMSO concentrations up to 1%. In a follow-up experiment, we assessed whether compounds that cause no developmental toxicity in the ZEDTA remain negative when dissolved in 1% DMSO, as false positive results due to physiological disturbances by DMSO should be avoided. To this end, zebrafish embryos were exposed to ascorbic acid and hydrochlorothiazide dissolved in 1% DMSO. Negative control groups were also included. No significant increase in malformations or lethality was observed in any of the groups. In conclusion, DMSO concentrations up to 1% can be safely used to dissolve compounds in the ZEDTA.
Embedded Acoustic Scene Classification for Low Power Microcontroller Devices

Automatic sound understanding tasks have been very popular within research community during the last years. The success of deep learning data driven applications in many signal understanding fields is now moving from centralized cloud services to the edge of the network, close to the nodes where raw data are generated from different type of sensors. In this paper we show a complete workflow for a context awareness acoustic scene classification (ASC) application and its effective embedding process into an ultra-low power microcontroller (MCU). It can widen the capabilities of edge AI applications, from environmental and inertial sensors up to acoustic signals, which require more bandwidth and generate more data. In the paper the entire workflow of such development is described in terms of dataset collection, selection and annotations, acoustic features representation, neural net modeling and optimization as well as the efficient embedding step of the whole application into the target low power 32-bit microcontroller device. Moreover, the overall accuracy of the proposed model and the capability to be real time executed together with an audio feature extraction process shows that such kind of audio understanding application can be efficiently deployed on power constrained battery-operated devices.
Designing Optimal Origami Structures by Computational Evolutionary Embryogeny

As the advantages of foldable or deployable structures are being discovered, research into origami engineering has attracted more focus from both artists and engineers. With the aid of modern computer techniques, some computational origami design methods have been developed. Most of these methods focus on the problem of origami crease pattern design — the problem of determining a crease pattern to realize a specified origami final shape, but don’t provide computational solutions to actually developing a shape that meets some design performance criteria. This paper presents a design method that includes the computational design of the finished shape as well as the crease pattern. The origami shape will be designed to satisfy geometric, functional, and foldability requirements. This design method is named computational evolutionary embryogeny for optimal origami design (CEEFOOD), which is an extension of the genetic algorithm (GA) and an original computational evolutionary embryogeny (CEE). Unlike existing origami crease pattern design methods that adopt deductive logic, CEEFOOD implements an abductive approach to progressively evolve an optimal design. This paper presents how CEEFOOD — as a member of the GA family — determines the genetic representation (genotype) of candidate solutions, the formulation of the objective function, and the design of evolutionary operators. This paper gives an origami design problem, which has requirements on the folded-state profile, position of center of mass, and number of creases. Several solutions derived by CEEFOOD are listed and compared to highlight the effectiveness of this abductive design method. © 2014 ASME
Advances in the Progression and Prognosis Biomarkers of Chronic Kidney Disease

Chronic kidney disease (CKD) is one of the increasingly serious public health concerns worldwide; the global burden of CKD is increasingly due to high morbidity and mortality. At present, there are three key problems in the clinical treatment and management of CKD. First, the current diagnostic indicators, such as proteinuria and serum creatinine, are greatly interfered by the physiological conditions of patients, and the changes in the indicator level are not synchronized with renal damage. Second, the established diagnosis of suspected CKD still depends on biopsy, which is not suitable for contraindication patients, is also traumatic, and is not sensitive to early progression. Finally, the prognosis of CKD is affected by many factors; hence, it is ineviatble to develop effective biomarkers to predict CKD prognosis and improve the prognosis through early intervention. Accurate progression monitoring and prognosis improvement of CKD are extremely significant for improving the clinical treatment and management of CKD and reducing the social burden. Therefore, biomarkers reported in recent years, which could play important roles in accurate progression monitoring and prognosis improvement of CKD, were concluded and highlighted in this review article that aims to provide a reference for both the construction of CKD precision therapy system and the pharmaceutical research and development.
Prognostic Factors of Colorectal Cancer: A Comparative Study on Patients With or Without Liver Metastasis

Background Radical or palliative surgery with subsequent adjuvant therapy is the routine treatment for stage II/III colorectal cancer(CRC) and some stage IV CRC patients. This study aimed to clarify the prognostic clinicopathological and genetic factors for these patients. Methods Fifty-five stage II-IV CRC patients undergoing surgery and adjuvant therapy were recruited, including patients without liver metastasis(5 at stage II, 21 at stage III) and with liver metastasis(29 at stage IV). Genetic alterations of the primary cancer tissues were investigated by whole exome sequencing(WES). Patients were followed up to 1652 days(median at 788 days). Results The mutational landscape of primary CRC tissue of patients with or without liver metastasis was largely similar, although the mutational frequency of TRIM77 and TCF7L2 was significantly higher in patients with liver metastasis. Several main driver gene co-mutations, such as TP53-APC, APC-KRAS, APC-FRG1, and exclusive mutations, such as TP53-CREBBP, were found in patients with liver metastasis, but not in patients without liver metastasis. No significant difference was found between the two groups in aberrant pathways. If stage II-IV patients were studied altogether, relapse status, SUPT20HL1 mutations, Amp27_21q22. 3 and Del8_10q23. 2 were independent risk factors(P<0.05). If patients were divided into two groups by metastatic status, surgery types and Amp6_20q13. 33 were independent risk factors for patients without liver metastasis(P<0.05), while TRIM77 mutations were the only independent risk factor for patients with liver metastasis(P<0.05). Conclusions Surgery types and Amp6_20q13. 33 were independent risk factors for CRC patients without liver metastasis, and TRIM77 mutations were the independent risk factor for CRC patients with liver metastasis.
Cyclin-Dependent Kinase Inhibitors and Their Therapeutic Potential in Colorectal Cancer Treatment

Cyclin-dependent kinases (CDKs) are key players in cell cycle regulation. So far, more than ten CDKs have been described. Their direct interaction with cyclins allow progression through G1 phase, transitions to S and G2 phase and finally through mitosis (M). While CDK activation is important in cell renewal, its aberrant expression can lead to the development of malignant tumor cells. Dysregulations in CDK pathways are often encountered in various types of cancer, including all gastrointestinal (GI) tract tumors. This prompted the development of CDK inhibitors as novel therapies for cancer. Currently, CDK inhibitors such as CDK4/6 inhibitors are used in pre-clinical studies for cancer treatment. In this review, we will focus on the therapeutic role of various CDK inhibitors in colorectal cancer, with a special focus on the CDK4/6 inhibitors.
Multiple faults detection using artificial neural network

This thesis investigated issues on the development of efficient fault detection scheme for detection of single and multiple faults due to sensor failure and leakage in the process stream. The proposed scheme consisted of two stage mechanism constructed using artificial neural network (ANN). The first stage was a process estimator that was designed to estimate the normal and unfaulty behaviour of the plant. In order to produce reasonably accurate estimation without including the history data of the output, two types of model have been studied. A group of multi input single output (MISO) Elman network and a multi input multi output (MIMO) Feedforward network have been used, and results revealed that MISO model had better generalisation ability compared to MIMO model. The difference between the actual plant signal and this estimated â€˜normalâ€™ plant behaviour, termed as residual was fed to the second stage for fault classification. In the development of fault classifiers, the MISO models had been proven to be better than MIMO model. The effect of adding input with time delayed signals to the network had also been studied. In both cases, successful implementations were obtained. Finally, the proposed fault detection scheme was applied for detection of sensor faults and stream leakage in the Precut column of a fatty acid fractionation plant. The proposed scheme was successful in detecting both single and multiple faults cases imposed to the process. The strategy was also successful in detecting leakage in the process stream even when the percentage of the leakage was as little as 0.1%. The results obtained in this work proved the potential of neural network in detecting multiple faults and leakage in chemical process plant.
Key Phytochemicals and Biological Functions of Chuanxiong Rhizoma Against Ischemic Stroke: A Network Pharmacology and Experimental Assessment

Presently, the treatment options for ischemic stroke (IS) are limited due to the complicated pathological process of the disease. Chuanxiong Rhizome (CR), also known as Conioselinum anthriscoides “Chuanxiong” (rhizome), is the most widely used traditional Chinese medicine for treating stroke. This study aimed to uncover the key phytochemicals and biological functions of CR against IS through a network pharmacology approach combining with IS pathophysiology analysis. We employed permanent unilateral common carotid artery ligation to construct a mouse model of global cerebral ischemia and found that cerebral ischemia injuries were improved after 7 days of gavage treatment of CR (1,300 mg/kg/day). CR exerts protective effects on neurons mainly by acting on targets related to synaptic structure, synaptic function, neuronal survival and neuronal growth. A total of 18 phytochemicals from CR based on UHPLC-MS/MS that corresponded to 85 anti-IS targets. Coniferyl ferulate, neocnidilide and ferulic acid were identified as the key phytochemicals of CR against IS. Its brain protective effects involve anti-inflammatory, anti-oxidative stress, and anti-cell death activities and improves blood circulation. Additionally, the two most important synergistic effects of CR phytochemicals in treating IS are prevention of infection and regulation of blood pressure. In brain samples of Sham mice, L-tryptophan and vanillin were detected, while L-tryptophan, gallic acid, vanillin and cryptochlorogenic acid were detected in IS mice by UHPLC–MS/MS. Our findings provide a pathophysiology relevant pharmacological basis for further researches on IS therapeutic drugs.
The method of lines for the analysis of rectangular spiral inductors (in MMICs)

An accurate procedure is presented for the investigation of the electrical properties of rectangular spiral inductors. The models of the analyzed spiral inductors are composed of straight and bent multiple-line sections. The line sections are analyzed by the method of lines. For the bends of coupled lines, a new analysis is used. The procedure considers all self- and mutual inductances of the straight multiple-line sections, as well as the electrical characteristics of the bends of coupled lines. >
A Clinical–Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases

Purpose To develop a clinical–radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases. Materials and Methods The MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical–radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC). Results The clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical–radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR. Conclusion We developed a clinical–radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.