Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain-based multi-domain protein and complex structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 11.8% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly generates models with acceptable quality (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
A genetic algorithm for the three-dimensional bin packing problem with heterogeneous bins

The three-dimensional bin packing problem (3D-BPP) is to select one or more bins from a set of available bins to pack three dimensional, rectangular boxes such that the usage of the bin space is maximized. 3D-BPP finds wide applications in pharmaceutical industry, transportation and packaging system. In the traditional 3D-BPP, the holding bins are of identical size, while the problem considered in this paper addresses the case where bins are heterogeneous, i.e., varying in size. We present a genetic algorithm along with a novel heuristic packing procedure. The packing heuristic procedure converts box packing sequence and container loading sequence encoded in a chromosome into a compact packing solution. The genetic algorithm is used to evolve such sequences. The algorithm is first applied to 12 industrial instances and then tested on randomly generated instances. The results demonstrate that solutions with high quality can be found within reasonable time.
Predicting the timing of ecological phenomena across regions using citizen science data

Spatial predictions of intra-annual ecological variation enhance ecological understanding and inform decision-making. Unfortunately, it is often challenging to use statistical or machine learning techniques to make such predictions, due to the scarcity of systematic, long-term observational data. Conversely, opportunistic time-stamped observation records, supported by highly informative data such as photographs, are increasingly available for diverse ecological phenomena in many regions. However, a general framework for predicting such phenomena using opportunistic data remains elusive. Here, we introduce a novel framework that leverages the concept of relative phenological niche to model observation records as a sample of temporal environmental conditions in which the represented ecological phenomenon occurs. We demonstrate its application using two distinct, management-relevant, ecological events: the emergence of the adult stage of the invasive Japanese beetle (Popillia japonica), and of fruiting bodies of the winter chanterelle mushroom (Craterellus tubaeformis). The framework accounts for spatial and temporal biases in observation data, and it contrasts the temporal environmental conditions (e.g., in temperature, precipitation, wind speed, etc.) associated with the observation of these events to those available in their occurrence locations. To discriminate between the two sets of conditions, we employ machine-learning algorithms (boosted regression trees and random forests). The proposed approach can accurately predict the temporal dynamics of ecological events across large geographical scales. Specifically, it successfully predicted the intra-annual timing of occurrence of adult Japanese beetles and of winter chanterelle mushrooms across Europe and North America. We further validate the approach by successfully predicting the timing of occurrence of adult Japanese beetles in Northern Italy, a recent hotspot of invasion in continental Europe, and the winter chanterelle mushroom in Denmark, a country with a high number of records of this mushroom. These results were also largely insensitive to temporal bias in recording effort. Our results highlight the potential of opportunistic observation data to predict the temporal variation of a wide range of ecological phenomena in near real-time. Furthermore, the conceptual and methodological framework is intuitive and easily applicable for the large number of ecologists already using machine-learning and statistical-based predictive approaches.
REVIEW UTILIZATION OF VARIOUS KINDS OF WATE INTO BIOETHANOL AS AN ALTERNATIVE FUEL

Bioethanol is ethanol or alcohol compounds obtained through the fermentation process of biomass with the help of microorganisms that can be used as alternative fuels, petroleum and other energy derived from fossils. These energy sources are getting depleted if no backup/alternative energy is found. To anticipate the dwindling national energy sources, renewable energy is needed. Some researchers found that a lot of waste is used as an alternative fuel. One way to produce quality bioethanol as a substitute for alternative energy is to have bioethanol levels in accordance with Indonesian National Standards and quality requirements. The purpose of this literature review is to determine the value of the bioethanol content of each type of waste with various stages used. The method used in this scientific work is fermentation, which is a process that involves the performance of microorganisms such as bacteria and yeast, where this process aims to preserve, change the texture to improve sensory attributes. This review was obtained through a literature search paper is a library search through the Google database, Google Scholar and Science Direct using keywords. Journals that have been found are screened with inclusion and exclusion criteria. Based on the results of various searches included in this article, it was found that the best bioethanol content was in the production of bioethanol from tapioca flour waste through the addition of a starter and duration of fermentation. The bioethanol content produced is 92% with a fermentation time of 7 days. This bioethanol is obtained through saccharification, hydrolysis, fermentation, and distillation processes. After passing the 7th day of fermentation.
An Implementation of Interior-Point Methods Based on Kernel Functions for Linear Optimization

In this paper we present the theory and practical aspects of implementing the path following interior point methods for linear optimization, based on kernel functions. We investigate the influence of the choice of kernel functions on the computational behavior of the generic primal-dual algorithm for Linear Optimization. We find that the finite kernel function gives the best results for more than 50 % of the tested problems compared to the standard log-barrier method.
Application of PSO algorithm in pattern synthesisfor antenna arrays

According to the prematurity and easy trapping in local optimum resulting from using Particle Swarm Optimization(PSO) in the pattern synthesis of antenna arrays with sidelobe reduction and nulls control,a mixed optimal algorithm(Chaotic PSO,CPSO) is proposed by fusing the advantages of both chaotic optimal algorithm and PSO algorithm.The new algorithm uses chaotic searching strategy within the neighborhood of the global best position to find a better solution when the swarm traps into stagnancy.The range of the chaotic searching region can be adaptively adjusted.To enhance the diversity of samples,several individuals are picked out as exemplars when the new global best position is updated in each dimension.Simulation results show that the algorithm can lead to a relatively high performance by applying CPSO in the pattern synthesis of antenna arrays.
Zur Differentialgeometrie der Kurven und Flächen.

0,1. Der Gesichtspunkt, unter welchem nachstehend reelle Raumkurven und Flächen betrachtet werden sollen, ist derjenige der sogen. „L-Ordnung" (Abkürzung für „lineare Ordnung'). Sie ist für eine Kurve C gleich der maximalen Mächtigkeit des Durchschnittes von C mit einer Ebene, für eine Fläche F gleich der maximalen Mächtigkeit des Durchschnittes mit einer Geraden. Die Fragen, welche sich beim Operieren mit diesem — wie überhaupt mit jedem derartigen — Ordnungsbegriff einstellen, lassen sich zwanglos unterscheiden in Fragen nach dem Verhalten „im Großen" und in Fragen nach dem Verhalten „im Kleinen", nach dem Verhalten nämlich der betrachteten Gebilde, hier der Kurven und Flächen. Im Folgenden soll es sich um eine Frage im Kleinen handeln. Diese Fragestellung werden wir am besten durch Zurückgehen auf den Fall der ebenen Kurven erläutern können. Von diesen weiß man): Jede Kurve, welche keine Teilstrecken enthält, ist abgeschlossene Hülle einer Summe von „primitiven" Bogen, d. h. von Bogen, deren Teilbogen sämtlich die gleiche L-Ordnung besitzen wie der Bogen selbst; und zwar gibt es primitive Bogen nur von den L-Ordnungen zwei und unendlich. Da die Bogen von der L-Ordnung zwei nichts anderes sind als die Konvexbogen, so kann man auch sagen: Jede ebene Kurve verhält sich im Kleinen wesentlich entweder wie ein Konvexbogen oder wie ein primitiver Bogen unendlicher L-Ordnung. Beschränkt man aber die Betrachtung auf zweimal stetig differenzierbare Kurven, so fehlen die primitiven Bogen unendlicher L-Ordnung, solche Kurven sind daher abgeschlossene Hüllen von Konvexbogensummen; statt dessen kann man auch sagen: Die konvexen Teilbogen liegen auf solchen Kurven überall dicht. 05 2. Dieses zuletzt erwähnte Ergebnis soll nun in den folgenden Zeilen auf Raumkurven und Flächen verallgemeinert werden. Dem Konvexbogen als einzigem primitiven Element in der Ebene entspricht dann im Falle der Raumkurven, wie wir zeigen, der Bogen dritter L-Ordnung ). Wir erhalten infolgedessen den Satz: Jede im wesentlichen dreimal stetig differenzierbare Raumkurve ist abgeschlossene Hülle einer Summe von Bogen dritter L-Ordnung*). Wir haben dabei immer den Fall ausgeschlossen, daß die betrachtete Raumkurve ebene
Close-Grained Classification of Military Aircrafts

Aircraft recognition is a visual skill and crucial for air defence and military surveillance. It is used for intelligence gathering. Recognition techniques generally depend on external appearance, silhouette, and size. Now a days aircraft design has adapted a wide range of structural variations. Automatic recognition from aerial views is not only challenging but may involve a heavy payoff for erogenous results. The major contribution of this work is to evaluate the transfer learning pipeline with SVM and MLP as the final classifier for aircraft recognition. Additionally, the work proposes a novel framework of closed-grained scenarios. A visual image of an aircraft is possible from the ground with the sky as the background or from the air with earth as the background. We call these as flying and resting crafts respectively. Experimentation for resting crafts is carried out on the existing benchmark MTARSI dataset. A dataset consisting of only flying military aircraft is an ancillary contribution to this work. Conventional CNN results are elated by using the state-of-art feature extraction approach followed by the stacking of SVM and MLP classification models. The proposed closed-grained approach is constrained to the stipulation that visualized image is an aircraft and the goal is to identify the category. Closed grain analysis ensures no confusion due to aircraft-like structural isometric objects which never be noticed flying. Results in the form of a confusion matrix, for various combinations of datasets and models, are shown and analysed. A modified hinge loss function is considered to address limitless error possibility, which outperformed the previously mentioned combinations.
Power Saving and Optimization of OLED Displays for Better System Design: A Survey

OLED displays are very popular among current system designs due various advantages like picture quality, response time, lightweight and lower thickness, etc. Still, one of the major design challenges when dealing with these displays is the amount of power consumed. In battery-operated computing devices, the display consumes almost 50% or more of the total system power. Though many methods are available to reduce power consumption like lowering the refresh rate, panel self-refresh, or adaptive dimming, still designers are looking for more room for optimization. As the design philosophy of mobile devices is directed towards extending the battery life, the methods to save power from the OLED display hold great relevance. This paper surveys the latest research and advancements on the topic.
Improved Virtual Synchronous Generator Control Strategy for Synchronous Frequency Resonance

The large amount of distributed energy connected to power grid through inverters tends to cause a decrease in the rotating capacity of the grid, which in turn reduces the stability of the grid. To solve this problem, virtual synchronous generator (VSG) can effectively increase the virtual inertia by simulating synchronous generator (SG). With considering power coupling effect, this paper demonstrated that distributed power generation system (DPGS) under grid connection (GC) mode will cause synchronous frequency resonance (SFR) phenomenon by analyzing the small signal model of VSG for different transmission angle transmission lines during the grid disturbance. Then, an improved VSG strategy with correction link is proposed, which can effectively suppress the generation of synchronous frequency resonance. Finally, this paper verifies the correctness of the small signal analysis, the consistent response of VSG and SG, and the suppression effect of the improved strategy on the synchronous resonance through the simulations of the small signal model, the VSG model and the SG model.
Covering-Based Reduction of Property-Oriented Concept Lattices

A method to reduce the property-oriented concept lattice of a formal context directly is proposed in this paper.The similarity degree between object sets and the similarity degree between attribute sets are firstly introduced,and then,object neighborhood and attribute neighborhood are created accordingly.The sizes of object neighborhood or the attribute neighborhood are adjusted by the similarity degrees,thus,the number of the property-oriented concepts can be controlled and the property-oriented concept lattice is compressed.Using this method,we can compress the property-oriented concept lattice from the viewpoints of object covering and attribute covering,and the most important is that the reduced lattice is a subset of the original one.
Learning-Based Predictive Uncertainty Estimation of Magnetic Flux Leakage Data for Parametric Defect Classification

Magnetic flux leakage (MFL), one of the most popular electromagnetic-based Nondestructive Evaluation (NDE) methods, is an important inspection technique for pipeline safety to prevent long-term failures. In real-life field testing or inspection scenario, there exist lots of associated uncertainties that will affect damage condition-based decision-making, therefore, it is vital to address and quantify the involved uncertainty for ensuring the reliability of inspection. This paper investigates the effect of uncertainties in the dynamic magnetization process due to the relative motion of a magnetic flux leakage (MFL) sensor and the material under test in the axial and circumferential directions. During the inspection, the roughness of the surface of the measured material is a main source to affect sensor liftoff and is considered as one of the important uncertainty sources affecting the inspection results. Therefore, in this work, the uncertainties from sensing liftoff are investigated, which is propagated throughout the sensing system to affect the output data. Considering the complexity of describing the forward uncertainty propagation process, Deep Ensemble, a learning-based non-Bayesian uncertainty estimation method, is applied to address the input uncertainty from the response MFL data. For performance evaluation, a three-dimensional finite element method (FEM) based model is used to generate simulation data for MFL based defect depth classification, while experiment data are validated in MFL based defect size classification. Prediction accuracy and uncertainty with calibration are conducted which is valuable in assessing the prediction performance and quantifying uncertainties. Further, an autoencoding method is applied for tackling the lack of experimental data for training model, which is extended to address the bottleneck of insufficient experimental data in generalized NDE problems.
Optimization of Cell Individual Offset for Handover of Flying Base Stations and Users

To ensure a seamless mobility of users in the scenario with flying base stations (FlyBSs) and static ground base stations (GBSs), an efficient handover mechanism is required. In this paper, we introduce new framework simultaneously managing cell individual offset (CIO) for handover of both FlyBSs and mobile users. Our objective is to maximize capacity of the mobile users while considering also a cost of handover to reflect potential excessive signaling and energy consumption due to redundant handovers. This problem is of a very high complexity for conventional optimization methods and optimal solution would require knowledge of information commonly not available to the mobile network. Hence, we adjust the CIO of FlyBSs and GBSs via reinforcement learning. First, we adopt Q- learning to solve the problem. Due to practical limitations implied by a large Q-table, we also propose Q- learning with approximated Q-table. Still, for larger networks, even the approximated Q-table can require a large storage and computation time. Therefore, we apply also actor-critic-based deep reinforcement learning. Simulation results demonstrate that all three proposed algorithms converge promptly and increase the communication capacity by dozens of percent while the handover failure ratio and the handover ping-pong ratio are reduced multiple times compared to state-of-the-art.
OpenCV and Python for Emotion Analysis of Face Expressions

If someone showed you a picture of themselves and asked you to describe how they feel, you'd probably have a good idea. Think about how useful it would be if your computer could do that! But what if you could enhance the things you have? It seems like a completely absurd idea. In the past, it was easy to infer a person's emotional state simply by observing their face. However, it is much more challenging for a computer to perform this task. Emotion recognition in photographs is now feasible with the help of machine learning and computer vision. Facial expression recognition is a growing subset of the field of facial recognition. Despite the fact that there are methods that use machine learning and artificial intelligence to accomplish the same goals, this work attempts to use the OpenCV approach to recognise expressions and classify the expressions based on the photos.
EVM-iPPG: Design and Evaluation of a Vital Monitoring System in NICU

The vital monitoring system is a critical component of a Neonatal Intensive Care unit. Despite recent advances in intensive care technology, conventional techniques are obtrusive and may cause skin abrasion and discomfort during movement in the neonate. Traditional approaches have recently undergone adjustments, such as the use of wireless equipment to replace wired systems, but these modifications have not made the approach less unsafe for the skin of neonates. To address the limitations of previous techniques, this study introduces a novel approach for developing a non-invasive, real-time, low-cost vital monitoring system employing video processing technology. The purpose of this research is to determine how different areas of interest, specifically the forehead and chest, influence heart rate and respiration rate when measured with a webcam. The model uses eulerian video magnification to enlarge the region of interest observed by the haar cascade detector, followed by the use of an imaging photoplethysmogram to detect changes in skin color to estimate heart rate and respiration rate in a situational setting. Using the proposed EVM-iPPG approach, we were able to obtain results that were compared with ground truth acquired from the hospital’s vital monitoring system, with mean absolute error of 5.097 for the forehead and 6.494 for the chest when measured for heart rate, and mean absolute error of 3.523 for the forehead and 4.902 for the chest when measured for respiration rate. The forehead showed lower errors than the chest, indicating that it should be the region of focus for vitals estimation.
Coordinated Management for Off-grid Photovoltaic-Electrolyzer Systems with Battery Storage Facilities

The production of green hydrogen from renewable energy sources using water electrolyzer (EL) devices is a promising approach to achieving a low carbon economy. This paper presents a coordinated management strategy for an off-grid Photovoltaic (PV)-EL system considering energy consumption by both hydrogen production and PV array reconfiguration. Firstly, a dynamic array reconfiguration (DAR) method is proposed to mitigate the mismatch losses and thus reduce the output fluctuation of the PV array under the mismatch conditions. Then, an optimal energy management scheme is proposed to coordinate the PV array, the battery and the EL to minimize the daily operation and maintenance (O&M) cost and degradation cost of EL and batteries. The proposed strategy is assessed through a test PV-EL system, and the numerical results confirm its validity. The proposed DAR method can improve the maximum power generation of the PV array under different mismatch conditions, and the proposed optimal energy management scheme can effectively utilize batteries to coordinate energy consumption by both hydrogen production and PV array reconfiguration.
Prediction Model of Submarine Cable Burial Depth Trend Based on CNN-LSTM

In the process of submarine cable working, the fault caused by the change of buried depth becomes more and more prominent. However, because the change of submarine cable burial depth is difficult to detect, a prediction method of submarine cable burial depth change trend based on convolutional neural network and long short-term memory unit combined neural network is proposed. First established considering dynamic characteristics of submarine cable material thermal resistance, thermal road model, according to the cable core temperature data, calculate the submarine cable burial depth data sets, then using convolution neural network mining cable buried depth data set that is associated with the time node characteristic vector and the input results into long short-term memory in the network optimization training. Thus, the development trend of submarine cable buried depth can be predicted. Finally, the feasibility and effectiveness of the proposed method are verified by the prediction trend analysis of submarine cables buried in an offshore oil production platform. Compared with single convolutional neural network model and single long and short term memory neural network model, this method has higher prediction accuracy and higher prediction efficiency.