Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly supervised histopathological image representation learning based on contrastive dynamic clustering

Feature representations of histopathology whole slide images (WSIs) are crucial to the downstream applications for computer-aided cancer diagnosis, including whole slide image classification, region of interest detection, hash retrieval, prognosis analysis, and other high-level inference tasks. State-of-the-art methods for whole slide image feature extraction generally rely on supervised learning algorithms based on fine-grained manual annotations, unsupervised learning algorithms without annotation, or directly use pre-trained features. At present, there is a lack of research on weakly supervised feature learning methods that only utilize WSI-level labeling. In this paper, we propose a weakly supervised framework that learns the feature representations of various lesion areas from histopathology whole slide images. The proposed framework consists of a contrastive learning network as the backbone and a designed contrastive dynamic clustering (CDC) module to embedding the lesion information into the feature representations. The proposed method was evaluated on a large scale endometrial whole slide image dataset. The experimental results have demonstrated that our method can learn discriminative feature representations for histopathology image classification and the quantitative performance of our method is close to the fully-supervision learning methods. The code is available at https://github.com/junl21/cdc.
Categorization of inorganic crystal structures by Delaunay tetrahedralization

ABSTRACT Crystal structure prediction is demanding in two senses: this is a difficult problem, both experimentally and computationally, yet we want to find new materials with new properties that take unknown crystal structures. Using statistical information on the local environment of already reported stable crystal structures would be helpful when, for instance, finding new crystal structures through visually adding atoms sequentially to a cluster of atoms, which is an intuitive procedure that does not require substantial computational resources. This study conducted hierarchical classification of frequently appearing structure types in the ICSD database by comparing the occurrence ratios of H, B, C, N, O, F, and transition metals (TM) in binary, ternary, and quaternary crystals. The bond angle and solid angle distributions of Delaunay polyhedra with atoms as vertices were considered as descriptors of the local environment for each structure type. Hierarchical clustering of structure types based on bond angle or solid angle distributions revealed that the structure types can be broadly categorized into those favoring TM oxides, having low TM occurrence, and having high TM occurrence. Our results indicate that the anion, or lack of, plays a key role in determining the crystal structure type, and structure types strongly preferred by TM oxides can accommodate a broad range of angles. GRAPHICAL ABSTRACT
Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm

The classification of voltage sag sources is essential for the establishment of controlling scheme and reasonable division of responsibilities in voltage sag-associated accidents. Existing methods for classifying voltage sag sources usually ignore the interpretability of the classification model, and are only dedicated to improving the accuracy of voltage classification, which cannot provide a reliable classification basis for users and power enterprises. Therefore, this article proposes an effective and interpretable voltage sag sources classification method based on sequential trajectory feature learning and Random Forest algorithm. Firstly, to fully consider the interpretable sequential trajectory features of voltage sag signals so as to improve the quality and interpretability of calculation process, the fused lasso generalized eigenvector (FLAG) algorithm is adopted to quickly search for interpretable shapelets sub-sequences from the labeled voltage sag data. After that, the labeled data and samples to-be-classified are subjected to shapelet transformation through the shapelet sub-sequences to obtain the sequential trajectory features. Finally, the random forest is trained on the labeled sequential trajectory feature data to achieve supervised sample classification, which inherits the interpretability of shapelet. To test the feasibility and validity of the proposed voltage sag sources classification method, the simulation cases based on the simulated voltage sag signals were studied. The simulation results show that the proposed method has significant advantages in terms of accuracy and interpretability of voltage sag sources classification.
A Review-based Context-Aware Recommender Systems: Using Custom NER and Factorization Machines

—Recommender Systems depend fundamentally on user feedback to provide recommendation. Classical Recom-menders are based only on historical data and also suffer from several problems linked to the lack of data such as sparsity. Users’ reviews represent a massive amount of valuable and rich knowledge information, but they are still ignored by most of current recommender systems. Information such as users’ pref- erences and contextual data could be extracted from reviews and integrated into Recommender Systems to provide more accurate recommendations. In this paper, we present a Context Aware Recommender System model, based on a Bidirectional Encoder Representations from Transformers (BERT) pretrained model to customize Named Entity Recognition (NER). The model allows to automatically extract contextual information from reviews then insert extracted data into a Contextual Machine Factorization to compte and predict ratings. Empirical results show that our model improves the quality of recommendation and outperforms existing Recommender Systems.
A Conceptual Framework for using Big Data in Egyptian Agriculture

—Agriculture is a typical contributor to the Egyptian economy, which could benefit from the comprehensive capabilities of Big Data (BD). In this work, we review the BD role in the agriculture sector in responding to two main questions: 1) Which technique, frameworks and data types were adopted. 2) Identification of the existing gap associated with the data sources, modeling, and analysis techniques. Therefore, the contribution in this paper can be outlined in three main aspects. 1) Popular BD frameworks were briefed, and a thorough comparison was conducted between them. 2) The potential data sources were described and characterized. 3) A Conceptual framework for Egyptian agriculture practice based on BD analytics was introduced. 4) Challenges and extensive recommendations have been provided, which could guide future development.
Proteomic Analysis of Cr (VI) Reducing Synechocystis sp. AHZ-HB-MK and its Mutant Obtained by Gamma Irradiation

ABSTRACT:A chromium resistant Synechocystis species, AHZ-HB-MK (DQ 381960) was used in this study. It was aimed to characterize this strain at molecular level andto produce certain mutants with better chromate reduction abilities. The strain was irradiated with a 60Co source in a dose range of 0.5, 1, 2, 5, 10 and 20 Gy atdifferent stages of growth (after 5, 15 and 30 days) of incubation. The selected derivatives were characterized morphologically, physiologically and biochemically. Chromium reduction activity and the amount of Cr (VI) accumulated within the cells of all of the selected derivatives were estimated. Protein profiling was done to study the impact of gamma rays on the proteins. Protein profiling proved that in the case of cultures irradiated after 5 days of incubation, maximum protection of proteins to radiation damage was observed at 0.5, 1, 2 and 5 Gy. Maximum expression of stress proteins was observed in the derivative obtained at 2 Gy. This derivative was labeled as Synechocystis MKTR. Identification of these proteins was done by mass-spectrometry. Seventy-four peptides were identified from ten selected protein bands. In these peptides sll0170 a heat shock protein, slr2076 a 60kD chaperonin and sll0416 identified as 60kD chaperonin 2 and GroEL2. Whereas sll0170 was also a heat shock protein 70, slr1198 was identified to be an antioxidant protein and slr1516 was annotated as superoxide dismutase which may be involved in the chromate resistance in this strain.
Biorefinery for the Production of Biodiesel, Hydrogen and Synthesis Gas Integrated with CHP from Oil Palm in Malaysia

Abstract Malaysia is presently the world’s largest exporter of palm oil with total production of 19.22 million tonnes of crude palm oil (CPO) in 2013. Aside CPO, by-products such as empty fruit bunch (EFB), palm kernel shell (PKS), palm kernel oil (PKO), palm kernel cake (PKC) and pressed palm fibres (PPF) are produced from the palm oil mills. These biomasses can be used as potential feedstock for the production of biofuels, biogas and bioelectricity. One of the ways to fully harness the potentials of these biomasses is by employing the biorefinery concepts where all the products and by-products from oil palm are utilized for production of valuable bio-products. In this study, technological feasibility of biorefinery for the production of biodiesel, hydrogen, Fischer-Tropsch liquids (FTLs) integrated with combined heat and power (CHP) generation was investigated. Flowsheet was designed for each of the processes using Aspen HYSYS® v 8.0. Material balance was performed on a palm oil mill processing 250 tonnes per year of fresh fruit palm (FFP). Results from the material balance shows that 45.1 tonnes of refined bleached deodorized palm oil (RDBPO) and 52.4 tonnes of EFB were available for the production of biodiesel, hydrogen, FTLs and the CHP generation. The annual plant capacity of the biodiesel production is estimated to be 26,331.912 tonnes. The overall energy consumption of the whole process was estimated to be 36.0 GJ/h. This energy demand was met with power generated from the CHP which is 792 GJ/h leaving a surplus of 756 GJ/h that can be sold to the grid. The process modelling and simulation of the biorefinery process shows technological feasibility of producing valuable products from oil palm.
Design of EMCCD Driving System for Underwater Low-Light Camera

For the core of underwater low-light camera, i.e. EMCCD, the whole solution of driving system is presented in this paper. The timing signals which meet the driving requirements of EMCCD are produced from FPGA. The high-speed integrated driver chips convert the timing signals into general power driving signals with the amplitude under 12V; a Class A push-pull amplifier circuit built in discrete components transforms the timing signals into high-voltage multiplying signals. Besides, the impedance matching for the circuit optimizes the driving system. The experimental results indicate that the driving system generates the high-voltage multiplying signals, with their high level adjusted from 30V to 48V and frequency raised up to 10MHz. The whole driving system satisfies the requirements of EMCCD, and has the capability to ensure the normal operation of EMCCD.
A Novel Mutual Insurance Model for Hedging Against Cyber Risks in Power Systems Deploying Smart Technologies

In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability.
How Symbols and Social Interaction Influence the Experienced Utility of Sustainable Lifestyle Guiding Policies: Evidence from Eastern China

As the key to mitigating climate change, a sustainable lifestyle has become a focus of environment policy. Past studies have largely neglected the symbols of sustainable lifestyle guiding policies and failed to capture its effect on the experienced utility of sustainable lifestyle guiding policies (EUSLGP). To address this drawback, symbolic value was incorporated into a model consisting of social interaction and the EUSLGP. With data collected from 3257 respondents in Eastern China, ordinary least squares were applied to examine hypotheses and two-stage least squares based on the instrumental variable to verify the results. Results show that symbolic value combines self-expression value, relationship consolidation value, group identification value, and status-showing value, and is positively associated with EUSLGP. Social interaction plays a moderating role in the association between symbolic value and EUSLGP. Moreover, significant regional differences are discovered in the identified relationships. Consequently, policy suggestions, covering symbolic value, social interaction, and regional conditions, are proposed to enhance the EUSLGP for other countries and regions.
An α-Fairness Approach to Balancing the Energy Consumption Among Sensors for UAV–IoT Systems

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.
From Counterculture To Cyberculture Stewart Brand The Whole Earth Network And The Rise Of Digital Utopianism

Thank you very much for reading from counterculture to cyberculture stewart brand the whole earth network and the rise of digital utopianism. As you may know, people have look hundreds times for their favorite readings like this from counterculture to cyberculture stewart brand the whole earth network and the rise of digital utopianism, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.
Assessment of Fracture Resistance of PEEK and Fibre Posts of the Endodontically Treated Teeth

This in vitro study aimed to compare the fracture resistance between pressed custom-made polyetheretherketone (PEEK) post and core and fibre reinforced post and composite core. A total of 40 single rooted extracted teeth were selected, endodontically treated, and prepared to receive the posts. Specimens were randomly divided into two main groups (n=20/group): group 1, (PEEK); group 2, fibre post. PEEK post was manufactured with a computer-aided design/computer-aided manufacturing system further divided into four sub groups (P7, P12, F7, F12) based on lengths 7 and 12 mm at which the posts were cemented. All posts were cemented with self-adhesive resin cement, and specimens were stored in 0.1% thymol solution during the experiment at all the times except during preparation and testing. A universal testing machine was used to measure the fracture resistance. The fracture resistance values (mean and SD, in newtons) were 1255N (P7), 1562N (P12), 1065N (F7), and 1384N (F12). Only P12 exhibited a significant difference (p,0.05). The turkey test showed an association between failure mode and post and- core material. In the remaining groups, most failures were nonrepairable and related to fracture. Customized post-and-cores of PEEK exhibited good mechanical performance. Their fracture resistance was comparable to that observed for fibre posts.
Design and operational issues of 7 full-scale MBRs for municipal wastewater treatment Diagnosis del diseño y operación de 7 BRMs municipales para el tratamiento de aguas residuales

Membrane bioreactors (MBRs) represent an emerging technology for the advanced secondary treatment of municipal wastewater around the world. In this line, seven municipal MBRs facilities recently built, with capacities ranging from 1,100 m 3 ·day -1 to 35,000 m 3 ·day -1 (up to 100,000 m
Polyphenolic Compounds from Lespedeza bicolor Protect Neuronal Cells from Oxidative Stress

Pterocarpans and related polyphenolics are known as promising neuroprotective agents. We used models of rotenone-, paraquat-, and 6-hydroxydopamine-induced neurotoxicity to study the neuroprotective activity of polyphenolic compounds from Lespedeza bicolor and their effects on mitochondrial membrane potential. We isolated 11 polyphenolic compounds: a novel coumestan lespebicoumestan A (10) and a novel stilbenoid 5’-isoprenylbicoloketon (11) as well as three previously known pterocarpans, two pterocarpens, one coumestan, one stilbenoid, and a dimeric flavonoid. Pterocarpans 3 and 6, stilbenoid 5, and dimeric flavonoid 8 significantly increased the percentage of living cells after treatment with paraquat (PQ), but only pterocarpan 6 slightly decreased the ROS level in PQ-treated cells. Pterocarpan 3 and stilbenoid 5 were shown to effectively increase mitochondrial membrane potential in PQ-treated cells. We showed that pterocarpans 2 and 3, containing a 3’-methyl-3’-isohexenylpyran ring; pterocarpens 4 and 9, with a double bond between C-6a and C-11a; and coumestan 10 significantly increased the percentage of living cells by decreasing ROS levels in 6-OHDA-treated cells, which is in accordance with their rather high activity in DPPH• and FRAP tests. Compounds 9 and 10 effectively increased the percentage of living cells after treatment with rotenone but did not significantly decrease ROS levels.
A Real-World Data Study on the Healthcare Resource Use for Uncontrolled Moderate-to-Severe Atopic Dermatitis in Secondary Care in the United Kingdom Prior to the Introduction of Biologic Treatment

Background Whilst there is international evidence around the high healthcare resource utilization (HRU) associated with atopic dermatitis (AD), there is a lack of published data from the United Kingdom (UK). Methods A retrospective, descriptive, observational study was conducted to evaluate the burden of moderate-to-severe AD on the National Health Service (NHS) in an adult UK population treated with traditional standard of care prior to the introduction of biologics. Patients (n=59) were recruited from 6 UK NHS Hospital Trusts and observed over three years. Results 707 dermatology clinic visits were recorded over the observation period, amounting to 6.6 visits per patient-year, most commonly for routine check-ups most of which involved dermatology consultants (n=469, 66%). Physicians were the most consulted healthcare professional (n=652, 92%); emollients were the most common treatment (n=80 courses). 174 flares requiring additional medical advice were recorded in total (1.6 per patient-year). Discussion/Conclusions Complex treatment pathways for adult patients in the UK with moderate-to-severe AD incur considerable HRU, particularly for those patients non-responsive to systemic therapies with broad immunosuppressant action. Recent advances in biologics-based AD management could possibly have a significant positive impact on HRU through significant reduction in the number of NHS touch points identified in this study.