Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Affective Evaluative Horizons

A key claim of classical phenomenology is that intentional experiences involve a distinctive kind of implicit intentionality, which accompanies the relevant explicit intentionality. This implicit intentionality is purportedly co-constitutive of the object-presenting phenomenology of those intentional experiences. This implicit intentionality is often framed by Husserl and other classical phenomenologists in terms of horizonal intentionality or intentional horizons. Its most interesting form is labelled the 'inner horizon'. My aim in this paper is to consider whether a case can be made for thinking that affective-evaluative experiences, predominately conscious emotions, exhibit a form of implicit intentionality in terms of an inner horizon. I suggest that one plausible way of motivating this idea is by reference to the normative phenomenology of the relevant experiences, in which particular objects' values are presented as either an ideal 'ought to be' or an ideal 'ought not to be'.
Mammography Images Segmentation via Fuzzy C-mean and K-mean

Breast Cancer is one of the common and dangerous among women at the age of forty, so it is better for woman to have mammography testing as a significant step for the early detection of breast cancer and is diagnosis for treatment; There is an important need to an algorithm is used to determine the boundaries of the tumor in a finite accuracy. In this work, two algorithms were built depending on clustering approach as segmentation method. In the first algorithm has employed (K-mean) method, whilst in the second algorithm has employed fuzzy c-mean method (FCM). In both, the lazy snapping algorithm was used as an additional step to improve the segmentation performance of the detection of abnormal area. The proposed methods have been tested using mini-MIAS database, after assessment the results obtained. it indicates the accuracy of segmentation first algorithm, are 91.18% and accuracy of second algorithm is 94.12%. from results, it concludes that the proposed second algorithm is capable of estimate breast abnormal region boundary at high accuracy because it used fuzzy logic technique.
Opportunities and Challenges of Sensor Networks in Health Care Applications

Healthcare is a promising application of the cloud computing technology. Healthcare network over the cloud has been described in this paper. The existing processes for patients’ vital data collection require a great deal of labor work to collect, input and analyze the information. These processes are usually slow and error prone, introducing a latency that prevents real-time data accessibility. This scenario restrains the clinical diagnostics and monitoring capabilities. A solution has been proposed to automate this process by installing health kiosk and integrate the devices. The information becomes available in the “cloud” from where it can be processed by expert systems and or distributed to medical staff. This design is able to make the system more user-friendly while retaining all the benefits of more complicated processes.
A CNN Sound Classification Mechanism Using Data Augmentation

Sound classification has been widely used in many fields. Unlike traditional signal-processing methods, using deep learning technology for sound classification is one of the most feasible and effective methods. However, limited by the quality of the training dataset, such as cost and resource constraints, data imbalance, and data annotation issues, the classification performance is affected. Therefore, we propose a sound classification mechanism based on convolutional neural networks and use the sound feature extraction method of Mel-Frequency Cepstral Coefficients (MFCCs) to convert sound signals into spectrograms. Spectrograms are suitable as input for CNN models. To provide the function of data augmentation, we can increase the number of spectrograms by setting the number of triangular bandpass filters. The experimental results show that there are 50 semantic categories in the ESC-50 dataset, the types are complex, and the amount of data is insufficient, resulting in a classification accuracy of only 63%. When using the proposed data augmentation method (K = 5), the accuracy is effectively increased to 97%. Furthermore, in the UrbanSound8K dataset, the amount of data is sufficient, so the classification accuracy can reach 90%, and the classification accuracy can be slightly increased to 92% via data augmentation. However, when only 50% of the training dataset is used, along with data augmentation, the establishment of the training model can be accelerated, and the classification accuracy can reach 91%.
Influence of sociodemographic factors and Family Health Strategy coverage on oral health promotion procedures: an analysis of Brazilian municipalities in 2019

Background The aim of the study was to investigate associations between sociodemographic factors and municipal Family Health Strategy (FHS) coverage and oral health promotion (OHP) procedures in Brazil. Material and Methods Data were obtained using public information systems and by direct request to the Ministry of Health. Clinical and collective OHP procedures performed in 2019 were analyzed, and sociodemographic covariates were associated with FHS coverage (population covered by FHS teams [FHST] and oral health teams [OHT]). Negative binomial regression models associated outcomes with covariates and estimated the prevalence ratio (PR) and confidence intervals (95%CI). Results A total of 4,913 municipalities were included. Municipalities with low-income inequality (PR=1.04, 95%CI 1.01 to 1.08), high illiteracy rate (RP=1.06, 95%CI 1.00 to 1.13), and population size of 10,001 to 50,000 inhabitants (PR=1.07, 95%CI 1.02 to 1.12) and 50,001 to 100,000 (PR=1.21, 95%CI 1.12 to 1.30) showed a higher frequency of clinical procedures. In contrast, a low frequency of clinical procedures was associated with reduced vulnerability to poverty (PR=0.83, 95%CI 0.78 to 0.89) and low OHT coverage (PR=0.39, 95%CI 0.33 to 0.45). Regarding collective procedures, the final model showed associations between low frequency and reduced income inequality (PR=0.91, 95%CI 0.87 to 0.95), low per capita income (PR=0.84, 95%CI 0.81 to 0.88), and low (PR=0.53, 95%CI 0.35 to 0.80) and medium Human Development Index (PR=0.79, 95%CI 0.71 to 87). Conclusions Clinical and collective OHP procedures were associated with sociodemographic conditions and OHT coverage in the FHS. Key words:Health Promotion, Oral Health, Social Determinants of Health, Universal Health Coverage.
In Vivo Thermal Ablation of Deep Intrahepatic Targets Using a Super-Convergent MRgHIFU Applicator and a Pseudo-Tumor Model

Simple Summary Magnetic-resonance-guided high-intensity focused ultrasound (MRgHIFU) is a promising technology for ablation of liver tumors not eligible for surgery. This study aimed to demonstrate the workflow feasibility and the spatial accuracy of a novel concept of MRgHIFU transducer dedicated to deep intrahepatic targets on six in vivo pig livers. Before the MRgHIFU ablation, a histological marker mimicking a metastasis was defined in a region considered as challenging to resect. The pseudo-tumor, visible on pre-operative MR images and post-mortem gross pathology, was the target for validation of the entire workflow and the relevancy of the novel concept of HIFU transducer. The presence of the MRgHIFU ablations on gross pathology at the expected locations confirmed the ability to induce transcostal thermal lesions by MRgHIFU in challenging tumors. No relevant side effects on near-field tissues such as the skin or ribs were evidenced. Abstract Background: HIFU ablation of liver malignancies is particularly challenging due to respiratory motion, high tissue perfusion and the presence of the rib cage. Based on our previous development of a super-convergent phased-array transducer, we aimed to further investigate, in vivo, its applicability to deep intrahepatic targets. Methods: In a series of six pigs, a pseudo-tumor model was used as target, visible both on intra-operatory MRI and post-mortem gross pathology. The transcostal MRgHIFU ablation was prescribed coplanar with the pseudo-tumor, either axial or sagittal, but deliberately shifted 7 to 18 mm to the side. No specific means of protection of the ribs were implemented. Post-treatment MRI follow-up was performed at D7, followed by animal necropsy and gross pathology of the liver. Results: The pseudo-tumor was clearly identified on T1w MR imaging and subsequently allowed the MRgHIFU planning. The peak temperature at the focal point ranged from 58–87 °C. Gross pathology confirmed the presence of the pseudo-tumor and the well-delineated MRgHIFU ablation at the expected locations. Conclusions: The specific design of the transducer enabled a reliable workflow. It demonstrated a good safety profile for in vivo transcostal MRgHIFU ablation of deep-liver targets, graded as challenging for standard surgery.
Analisis Rasio Keuangan dan Prediksi Kebangkrutan dengan Model Altman Z-Score pada Perusahaan Semen Tahun 2016-2019

Latar belakang dari penelitian pada karya tulis ini adalah terjadinya kebangkrutan pada Holcim Indonesia akibat strategi pemasaran banting harga yang dilakukan perusahaan semen dari Tiongkok. Penelitian ini bertujuan untuk menjelaskan kinerja keuangan berdasarkan analisis rasio keuangan dan prediksi kebangkrutan terhadap perusahaan semen yang telah terdaftar di BEI. Metode pengumpulan data yang digunakan pada penelitian ini meliputi metode dokumentasi dan metode studi kepustakaan dari berbagai buku, jurnal ilmiah, dan sumber lain, termasuk laporan keuangan dan laporan tahunan dari enam perusahaan semen yang telah terdaftar di BEI tahun 2016 hingga 2019. Seluruh data yang diperoleh diproses menggunakanberbagai rumus rasio keuangan dan prediksi kebangkrutan model Altman Z-Score. Penelitian atas analisis rasio keuangan dan prediksi kebangrutan model Altman Z-Score terhadap enam objek menghasilkan INTP sebagai perusahaan dengan kinerja paling baik karena keunggulannya pada seluruh rasio keuangan dan prediksi kebangkrutan. Sementara perusahaan dengan kinerja paling buruk adalah SMCB karena hampir seluruh penelitian yang dilakukan, SMCB berada pada posisi terbawah dan selalu berada pada zona distress.
Profitable thermal renovation of hotels to combat climate change and depletion of fossil fuels: the case of Cyprus

Hotel buildings were identified as significant energy consumers. Within the theoretical framework of the thesis it was argued that climate change and increasing energy prices due to depletion of fossil fuels would cause pressure on hotel operations in Cyprus by increasing their cooling loads and operational costs. The analysis was techno-socio-economic and the core aim was to evaluate the costeffectiveness of selected energy saving strategies. These were in the form of building envelope upgrades, changes in indoor environmental criteria and introduction of renewable energy technology via solar air-conditioning to be implemented as part of the hotels’ periodic scheduled building and systems upgrades with the aim of lowering their space conditioning energy costs and improving their profitability and competitiveness. The applied methodology consisted of auditing of a case study hotel, energy consumption data collection both from a sample of hotels and industry-wide and a hotel guest survey. Thermal simulation of hotels in present and future climates was used to identify technically viable renovation strategies. Strategies found cost-effective were further checked for sensitivity to energy prices, hotel occupancy and climate. The major findings of the thesis predict over 20% savings in space conditioning energy costs due to strategic building envelope upgrades, over 50% savings if in addition adjustments in thermal comfort criteria are included and over 80% savings if solar AC is added into the above. It was further found that with a minimal increase in room rates hotels could implement thermal renovation while maintaining or even increasing room income. A unique cost-efficiency indicator that measures profitability of the proposed energy saving strategies in terms of hotels’ room income generation potential was developed in this thesis. Finally, if the suggested renovations were implemented on a mass scale, one quarter of Cyprus’ national energy saving and renewable energy targets could be met. Profitable thermal renovation of hotels to combat climate change and depletion of fossil fuels: The case of Cyprus Profitable thermal renovation of hotels to combat climate change and depletion of fossil fuels: The case of Cyprus
A 2-year follow-up of the effects of combined binge drinking and cannabis consumption on academic performance and adjustment in Spanish third-year university students

Introduction The study was based on 2-year follow-up of the effects of binge drinking and cannabis co-consumption on academic performance and adjustment in Spanish Third-Year University Students and to further explore the impact of academic adjustment on this relationship. Methods A total of 144 students (aged 19–20 years) enrolled in the third year of university completed the study. The students were recruited during in first academic year (T1) via a survey that included items regarding the use of alcohol (AUDIT-C), cannabis and other drugs and demographic variables. Then, participants meeting the study criteria were then selected and invited by e-mail to a clinical (face-to face) structured interview. The participants completed a calendar of alcohol consumption during the 6 months prior to the interview (Alcohol Timeline Follow back), and recorded cannabis consumption in 3 months prior to the interview. To examine the effects of alcohol and cannabis co-consumption on the outcome variables, we categorized participants into three consumption groups (i.e., control, BD, and BDCA) based on the number of BD days and cannabis unit scores. Results Binge drinking and cannabis co-consumption in first-year students was significantly associated with poor academic performance and adjustment after 2 years of undergraduate study. Relative to controls, co-consumers (BDCA) reported significantly lower academic and personal-emotional adjustment to university as well as poorer performance. Mediation analysis showed that academic adjustment explains the mechanism by which BDCAs perform less well, mediating the relationship between co-consumption and academic performance, with an indirect effect representing 64.61% of the total effect. Furthermore, the mediating effect of academic adjustment was maintained after controlling for academic adjustment and baseline grade point average (T1). Conclusion This prospective follow-up study helps to further our knowledge of how combined binge drinking and cannabis consumption may affect university adjustment and academic success in Spanish university students Overall, the study results should encourage health professionals, educational psychologists and academic institutions to take ownership of the need for support and involvement in prevention, as well as for provision of guidelines for implementing appropriate intervention strategies.
Integrative analysis of chloroplast genome, chemicals, and illustrations in Bencao literature provides insights into the medicinal value of Peucedanum huangshanense

The genus Peucedanum L. (Apiaceae) is a large group comprising more than 120 species distributed worldwide. Many plants of the genus Peucedanum have been studied and used in traditional Chinese medicine. In 2020, a new species, Peucedanum huangshanense Lu Q. Huang, H. S. Peng & S. S. Chu, was found in the Huangshan Mountains of Anhui Province, China. However, little is known about its medicinal properties. Thus, the objective of this study is to explore the potential medicinal value of P. huangshanense and its relationship with other Peucedanum species. Through textual research on illustrations of Qianhu in Bencao literature, it can be inferred that at least five species of genus Peucedanum have been used in Chinese medicine. Therefore, we chose these five species of Peucedanum and P. huangshanense together for subsequent research. We conducted morphological, chloroplast genome, and chemical analyses of six Peucedanum species, including the newly discovered P. huangshanense. The chloroplast genomes of Peucedanum showed a typical tetrad structure, and the gene structure and content were similar and conservative. There were significant differences in genome size and the expansion of the inverted repeat boundary. Through nucleotide polymorphism analysis, we screened 14 hotspot mutation regions that have the potential to be used as specific molecular markers for the taxonomy of Peucedanum. Our results showed an inversion of the trnD-trnY-trnE gene in the P. huangshanense chloroplast genome, which can be developed as a specific molecular marker for species identification. Phylogenetic analysis showed that the phylogenetic trees had high support and resolution, which strongly supports the view that Peucedanum is not a monophyletic group. P. huangshanense had the closest genetic relationship to P. ampliatum K. T. Fu, followed by P. harry-smithii Fedde ex Wolff. Furthermore, the main coumarins of P. huangshanense were most similar to those of P. japonicum Thunb. and P. harry-smithii. In summary, our research lays a foundation for the systematic classification of Peucedanum and sheds light on the medicinal value of P. huangshanense.
Dynamic Operating Envelope-Based Local Energy Market for Prosumers With Electric Vehicles

This paper presents a new framework for the management of prosumers with electric vehicles (EVs) and photovoltaic (PV) systems in a local energy market (LEM) using the dynamic operating envelope (DOE) that secures the network operation for prosumers’ trading and allows prosumers to manage EVs charge/discharge with PV systems. Prosumers can configure the charge/discharge status of their EVs depending on mobility and determine their net import/export power which will be used to establish DOEs. The proposed framework incorporates a bidding strategy to enable prosumers to participate in the LEM within DOE limits, and peer-to-peer (P2P) energy trading is developed to offer economic benefits to prosumers. The framework’s performance is evaluated with a 100% penetration level of EVs and PV systems on the IEEE 55-node low-voltage European test system. The simulation results demonstrate that DOEs can facilitate prosumers in managing their imports/exports for trading without violating voltage and congestion limits. The proposed framework efficiently manages EV charging/discharging with PV systems and enables prosumers to make optimal bids within DOEs, thereby benefiting from P2P trading in the LEM.
Emerging role of exosome-derived non-coding RNAs in tumor-associated angiogenesis of tumor microenvironment

The tumor microenvironment (TME) is an intricate ecosystem that is actively involved in various stages of cancer occurrence and development. Some characteristics of tumor biological behavior, such as proliferation, migration, invasion, inhibition of apoptosis, immune escape, angiogenesis, and metabolic reprogramming, are affected by TME. Studies have shown that non-coding RNAs, especially long-chain non-coding RNAs and microRNAs in cancer-derived exosomes, facilitate intercellular communication as a mechanism for regulating angiogenesis. They stimulate tumor growth, as well as angiogenesis, metastasis, and reprogramming of the TME. Exploring the relationship between exogenous non-coding RNAs and tumor-associated endothelial cells, as well as their role in angiogenesis, clinicians will gain new insights into treatment as a result.
Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively.
Functional Analysis of Plant Monosaccharide Transporters Using a Simple Growth Complementation Assay in Yeast

The study of genes and their products is an essential prerequisite for fundamental research. Characterization can be achieved by analyzing mutants or overexpression lines or by studying the localization and substrate specificities of the resulting proteins. However, functional analysis of specific proteins in complex eukaryotic organisms can be challenging. To overcome this, the use of heterologous systems to express genes and analyze the resulting proteins can save time and effort. Yeast is a preferred heterologous model organism: it is easy to transform, and tools for genomics, engineering, and metabolomics are already available. Here, we describe a well-established and simple method to analyze the activity of plant monosaccharide transporters in the baker’s yeast, Saccharomyces cerevisiae, using a simple growth complementation assay. We used the famous hexose-transport-deficient yeast strain EBY.VW4000 to express candidate plant monosaccharide transporters and analyzed their transport activity. This assay does not require any radioactive labeling of substrates and can be easily extended for quantitative analysis using growth curves or by analyzing the transport rates of fluorescent substrates like the glucose analog 2-NBDG. Finally, to further simplify the cloning of potential candidate transporters, we provide level 0 modular cloning (MoClo) modules for efficient and simple Golden Gate cloning. This approach provides a convenient tool for the functional analysis of plant monosaccharide transporters in yeast. Key features Comprehensive, simple protocol for analysis of plant monosaccharide transporters in yeast Includes optional MoClo parts for cloning with Golden Gate method Includes protocol for the production and transformation of competent yeast cells Does not require hazardous solutions, radiolabeled substrates, or specialized equipment