Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Tumor Microenvironment and DNA Methylation-Related Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses in Cervical Cancer

Background: Tumor microenvironment (TME) has been reported to have a strong association with tumor progression and therapeutic outcome, and epigenetic modifications such as DNA methylation can affect TMB and play an indispensable role in tumorigenesis. However, the potential mechanisms of TME and DNA methylation remain unclear in cervical cancer (CC). Methods: The immune and stromal scores of TME were generated by the ESTIMATE algorithm for CC patients in The Cancer Genome Atlas (TCGA) database. The TME and DNA methylation-related genes were identified by the integrative analysis of DNA promoter methylation and gene expression. The least absolute shrinkage and selection operator (LASSO) Cox regression was performed 1,000 times to further identify a nine-gene TME and DNA methylation-related prognostic signature. The signature was further validated in Gene Expression Omnibus (GEO) dataset. Then, the identified signature was integrated with the Federation International of Gynecology and Obstetrics (FIGO) stage to establish a composite prognostic nomogram. Results: CC patients with high immunity levels have better survival than those with low immunity levels. Both in the training and validation datasets, the risk score of the signature was an independent prognosis factor. The composite nomogram showed higher accuracy of prognosis and greater net benefits than the FIGO stage and the signature. The high-risk group had a significantly higher fraction of genome altered than the low-risk group. Eleven genes were significantly different in mutation frequencies between the high- and low-risk groups. Interestingly, patients with mutant TTN had better overall survival (OS) than those with wild type. Patients in the low-risk group had significantly higher tumor mutational burden (TMB) than those in the high-risk group. Taken together, the results of TMB, immunophenoscore (IPS), and tumor immune dysfunction and exclusion (TIDE) score suggested that patients in the low-risk group may have greater immunotherapy benefits. Finally, four drugs (panobinostat, lenvatinib, everolimus, and temsirolimus) were found to have potential therapeutic implications for patients with a high-risk score. Conclusions: Our findings highlight that the TME and DNA methylation-related prognostic signature can accurately predict the prognosis of CC and may be important for stratified management of patients and precision targeted therapy.
A display evaluation methodology applied to vertical situation displays

An approach to display evaluation based on the optimal-control or state-variable model of the human operator is described. The approach evolved over the past several years, and is believed to have significant advantages for display evaluation. The foundations of the methodology are briefly described, and results are presented of its application to the analysis of vertical situation displays for STOL approach. This analysis includes the effects of both status and command displays on pilot workload and system performance.
A Systematic Approach for Mining Quality of Services: A methodology and a case study

Web services are used to provide different software functionalities to Web service users. Web services can be further classified according to their quality. Mining and visualizing quality attributes could help Web service selectors to find the appropriate Web service based on visual figures representing the quality instead of looking at different values and numbers. Moreover, it provides stakeholders in the service computing domain a comprehensive view of the Quality of Service (QoS) performance of a group of Web services. This research proposes a systematic methodology for mining Web services' qualities. It studies the aspects of using clustering regarding Web service quality. A case study was conducted using K-mean clustering to divide a Web services pool based on quality criteria into subgroups.
Real time driver drowsiness detection using computer vision techniques

: A system used to detect a drowsiness state of a driver based on a nonintrusive approach. This system is fully focused to consistently monitor the state of the person who is driving a vehicle. In this way we can determine whether the person is in drowsiness state or not. This system will help to reduce down fatal accident caused by drowsiness while driving. An alarm system is used to warn if the driver has gone in the state of drowsiness. In our proposed design, system will identify face and eyes, and localize pupil and iris boundary, examine the condition whether both eyes are open or close and system will take drowsiness decision based on eyes state. This system requires no training data and it gives high accuracy with normal computer camera.
Consumer Preference Towards Layer Chicken Eggs and Native Chicken Eggs in Yogyakarta

Protein needs in the body can be met by consuming chicken eggs. Native chicken eggs were the first to be consumed and produced in Indonesia, but layer chicken eggs are more widely consumed and produced than native chicken eggs. The study aimed to determine consumer attitudes and consumer preferences towards chicken eggs and find factors that affected consumer preferences for chicken eggs. This study involved 100 respondents. Fishbein multi-attribute and conjoint analysis were used to determine consumer attitudes and consumer preferences, while binary logistic regression analysis was used to determine factors affecting preferences. The results revealed that consumers had a more positive attitude towards layer chicken eggs than native chicken eggs. The ideal characteristic of layer egg was one with large size, clean shell, dark brown shell color, and price of Rp 20,000-22,000/kg (approximately 15 eggs/kg), while the characteristics for native chicken eggs are large size, clean shell, white shell color, and a price of Rp 2,600-3,000/item. Factors that increase consumer preference for native chicken eggs are age, education, and perception that native chicken eggs are more nutritious, while the factor that increases the probability of consumer preference for layer chicken eggs is the perception of shell color.
CHANCE CONSTRAINED ORIENTED DISPATCHING RULES FOR FLEXIBLE JOB-SHOP SCHEDULING

a Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel, and Department of Industrial Engineering and Management, Academic College of Judea and Samaria, Ariel, 44837, Israel, E-Mail: dimitri@bgumail.bgu. ac.il b Department of Industrial Engineering and Management, Negev Academic College of Engineering, Beer Sheva, 84100, Israel, E-Mail: zohar@nace.ac.il
Scalable Predictive Control and Optimization for Grid Integration of Large-scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.
Implementation of a digital Player Monitoring System: pmSys

Today football matches are often settled by the smallest margin possible. Teams will therefore try to find ways of gaining even the smallest of advantages over their opponent. Success and failure is often determined by how a team can keep their players healthy and rejuvenated throughout the course of a long season, which includes training matches, league matches and, for the top teams, tournament matches in Europe. Although it is impossible to predict whether a player will break a leg, injuries relating to overuse can be detected, and in many cases avoided. If we monitor a player daily, and build a strong foundation of data, it is possible to track the players condition so that we can maximize his performance, avoid fatigue and overtraining, while also prevent the player for getting injured as a result of overuse. In this thesis, we will discuss ways to design and develop a tailor-made system that will work as a supplement for trainers and coaches so that they can monitor their players. By giving football teams a better alternative for monitoring their players, rather than using a pen and paper, or even improving the already existing applications and systems, we hope to provide them with the edge that is often needed to succeed. The system is today deployed to the Norwegian national football team and several Norwegian Top Division teams. It is used daily by the coaches to monitor and supervise their players.
Design of intelligent load frequency control strategy using optimal fuzzy-PID controller

This paper proposes a robust control strategy involving a novel optimised fuzzy-PID controller tuning by particle swarm optimisation (PSO) algorithm. The proposed control strategy was suggested to design an intelligent load frequency control (LFC) scheme in multi-area interconnected power system. The PSO algorithm was employed to optimise the fuzzy-PID controller parameters including the scaling factors of fuzzy logic and the PID controller gains for minimisation of both system frequency deviation and tie-line power changes during load disturbances using the integral time multiply absolute error (ITAE) as objective function. To demonstrate the effectiveness of the proposed control strategy, the three-area 9-unit interconnected power system was used for the simulation. The superiority of the proposed approach was shown by comparing the obtained results to other strategies available in literature. Initially, the simulation was performed using the same controllers in each area, and then was extended with different controllers in each area. The comparative study demonstrates the potential of the proposed control strategy and shows its robustness to enhance frequency stability.
Parameter variation effects in sensorless vector controlled induction machines.

Vector control of induction machines has established itself during the last two decades as the most suitable way of achieving high performance in induction motor drives. In its basic form, vector control relies on the existence of the speed sensor. This is at present considered to be the major drawback and enormous efforts have been put in development of so-called sensorless vector controlled drives during the last decade. Vector control principles require utilisation of the mathematical model of an induction machine within the controller. Furthermore, most of the schemes, used for speed estimation in sensorless drives, are based on the model of the machine as well. The standard model regards all the machine parameters as constant and ignores the existence of the iron loss. Hence any variation of the actual machine parameters leads to detuned operation and deterioration of the performance takes place. The objectives of this project are to investigate detuning in the sensorless indirect feed-forward rotor flux orientated induction motor drive, to develop improved sensorless vector control schemes with compensation of iron loss and main flux saturation, and to perform experimental investigation with the aim of confirming some of the theoretical studies. A detailed investigation to quantitatively assess detuning effects in the sensorless vector-controlled drive, that utilises MRAC based speed estimator of rotor flux type, is undertaken. Detuning due to parameter variations (stator and rotor resistance, stator and rotor leakage inductance), iron loss and main flux saturation is evaluated for steadystate and transient operation. Simulation studies show that parameter variations, iron loss and main flux saturation lead to deterioration in performance of the sensorless induction motor drive. It is therefore necessary to compensate the detuning effects online. Based on advanced induction machine models, several improved sensorless vector control schemes are developed. The first scheme provides compensation of iron loss. The second scheme provides compensation of main flux saturation. The third one, obtained by combining the previous two schemes, provides simultaneous compensation of both the iron loss and main flux saturation. By employing the novel sensorless schemes, detuning effects are reduced significantly. The improved performance of the novel sensorless vector controlled induction motor drives is confirmed by simulation studies. Experimental investigation is performed to confirm the theoretical findings. Speed estimator is operated at all times in parallel with an existing sensored vector controlled drive. Detuning due to parameter variations is at first investigated. Next, improved performance of one of the developed modified MRAC speed estimators, that compensates for the main flux saturation, is confirmed experimentally.
Sentiment analysis system using hybrid word embeddings with convolutional recurrent neural network

There have been wide ranges of innovations in sentiment analysis in recent past, with most effective ones involving use of various word embeddings methods for analysis of sentiments. GloVe and Word2Vec are acclaimed to be two most frequently used. A common problem with simple pre-trained embedding methods is that these ignore information related to sentiments of input texts and further depend on large text corpus for training purpose and generation of relevant vectors which is hindrance to researches involving smaller sized corpuses. The aim of proposed study is to propose a novel methodology for sentiment analysis that uses hybrid embeddings with a target to enhance features of available pre-trained embedding. Proposed hybrid embeddings use Part of Speech (POS) tagging and word2position vector over fastText with varied assortments of attached vectors to the pre-trained embedding vectors. The resultant form of hybrid embeddings is fed to our ensemble network-Convolutional Recurrent Neural Network (CRNN). The methodology has been tested for accuracy via different Ensemble models of deep learning and standard sentiment dataset with accuracy value of 90.21 using Movie Review (MVR) Dataset V2. Results show that proposed methodology is effective for sentiment analysis and is capable of incorporating even more linguistic knowledge-based techniques to further improve results of sentiment analysis.
Effect of Allium cepa loaded polyacrylonitrile and polyvinyl pyrrolidone nanofibers on Candida albicans growth and the expression of CDR1 and CDR2 genes

Background and Purpose: This study aimed to assess the effect of Allium cepa ethanolic extract (ACE) loaded polyacrylonitrile (PAN) and polyvinyl pyrrolidone (PVP) nanofibers on Candida albicans (C. albicans) CDR1 and CDR2 genes expression. Materials and Methods: The minimum inhibitory concentrations (MICs) of ACE against C. albicans ATCC 10231 and clinical fluconazole (FLC)-resistant C. albicans PFCC 93-902 were determined using the Clinical and Laboratory Standards Institute (CLSI) protocol (M27-Ed4) at a concentration range of 45.3-5800 µg/ml. The nanofibers containing ACE (60 wt%) were fabricated using the electrospinning technique. The expression of the CDR1 and CDR2 genes was studied in the fungus exposed to ACE-loaded nanofibers and 0.5×MIC concentration of FLC using the real-time polymerase chain reaction. Results: MIC50 and MIC90 of ACE against FLC-resistant C. albicans were 725 and 1450 μg/mL, respectively. The expression of CDR1 (4.5-fold) and CDR2 (6.3-fold) were down-regulated after the exposure of FLC-resistant C. albicans to ACE-loaded nanofibers (P<0.05). Furthermore, the expression of CDR1 (2.8-fold) and CDR2 (3.2-fold) were up-regulated in FLC-treated C. albicans (P<0.05). Conclusion: The results revealed that nanofibers containing ACE interact with drug-resistant genes expressed in C. albicans. Further studies are recommended to investigate the mode of action and other biological activities of ACE-loaded nanofibers against C. albicans and other pathogenic fungi.
An Application of The PROMETHEE Method To Select The Best Response for Carbon Fibre Reinforced Plastic Drilling in Machining Operations

The manufacture of carbon fibre reinforced plastic (CFRP) composites from carbon fibres fused with epoxy matrix, deploying the hand layup method has raised considerable attention. Within this research domain, drilling with various tools such as coated and uncoated drills is of great significance. Unfortunately, the use of intuition and experience to select the best parameter in the drilling operation has been known to be less efficient, causing the inadequate distribution of drilling resources to actualize the effectiveness of drilling parameters. Energy wastages are also associated with the present practice of intuition in drilling process. In this study, a novel approach of PROMETHEE I and II are presented to avoid ineffectiveness in drilling resource distribution and select the best drilling operations parameters. The proposed method utilizes experimental data from the literature to verify the method's performance. This study helps in reducing waste due to the inadequate distribution of drilling operations sources. PROMETHEE analyses the drilling parameters of the CFRP composites using preference functions that map the differences among alternatives during machining judgments. Out of the six responses examined, the best response is exit delamination with a weight of 0.059, surface roughness with a weight of 0.031emerged as the second position, torque weighing 0.003 took the 3rd position while the last position is entry delamination, weighing -0.102.
Bilevel Neighborhood Search Hybrid Algorithm for the Flexible Job Shop Scheduling Problem

： For the flexible job shop scheduling problem (FJSP), in order to optimize the maximum completion time, a hybrid algorithm mixed with bilevel neighborhood search and genetic algorithm is proposed. The neighborhood structure is constructed by using machine idle time to reduce the maximum completion time. In order to improve the current solution, critical operations of the critical path are moved to achieve neighborhood search. The method of bilevel neighborhood search is designed according to the characteristics of FJSP. The first level neighborhood search is the cross-machine moving operation, and the operation is moved to other optional machines in addition to current processing machine. The second level neighborhood search is the same-machine moving operation, and the operation is moved on current processing machine. Operation moving conditions corresponding to the bilevel neighborhood search are given to ensure feasible solutions. Both of global search ability and local search ability of FJSP solving algorithm are considered, and to use genetic algorithm to achieve global search, bilevel neighborhood search to achieve local search. The internationally accepted FJSP benchmark examples are adopted to test the validity of the proposed method.
Short-term Reactive Power Reserve Optimization Based on Trajectory Sensitivity

– An increasing concern is paid to short-term voltage stability with the growth of penetration of induction motor loads. Reactive power reserve(RPR) of power system is critical to improve voltage stability. A definition of short-term voltage stability-related RPR(SVRPR) is proposed. Generators vary their contributions to voltage stability with their location and system condition, etc. Voltage support coefficient based on the second-order trace sensitivity method is proposed to evaluate SVRPR’s contribution to short-term voltage stability. The evaluation method can account for the generator’s reactive support in transient process and the contingency severity. Then an optimization model to improve short-term voltage stability is built. To deal with multiple contingencies, contingency weight taking into account both its probability and severity is proposed. The optimization problem is solved by primal dual interior point method. Testing on IEEE_39 bus system, it is indicated that the method proposed is effective. Short-term voltage stability is improved significantly by the way of SVRPR optimization. Hence, the approach can be used to prevent the happening of voltage collapse during system’s contingency.
Properties and physical interpretation of the dynamic interactions between voltage source converters and grid: electrical oscillation and its stability control

Voltage source converters (VSCs) play an important role in the power conversion of renewable power generation (RPG) systems. Conventional power systems are considerably affected by power electronic devices in systems with a high percentage of RPG. Various abnormal interactions in the form of oscillations between VSCs and grid have been reported, whereas the mechanism at the core is still lacking understanding. In this study, the properties of interactions between the VSC and grid are investigated, and through an analytical model the mechanisms of electrical oscillations are revealed. First, a simple resistance-inductance-capacitance (RLC) equivalent to the small-signal model of VSC-grid system was derived based on the knowledge of virtual passive element effects of VSC. Then, an intrinsic oscillatory point in the RLC circuit was identified, the damping characteristics of the current controller and phase-locked-loop (PLL) at this point were analysed. Subsequently, a critical stability criterion for the determination of PLL bandwidth that may trigger oscillations was established. For the improvement of the overall damping, a VSC stabiliser was proposed. Finally, the mechanism analysis and analytical criteria were verified by time-domain simulations in PSCAD/EMTDC.
The effect of Cr alloying with Pt/C as an electrocatalyst for low temperature PEM fuel cell

ABSTRACT A sonochemical or ultrasound-assisted method was proposed to synthesize bimetallic Pt-Cr nanoparticles. Later, these Pt-Cr nanoparticles were sonochemically supported on a carbon surface (Pt-Cr/C) to produce an electrocatalyst. The obtained electrocatalyst was coated on the cathode electrode to evaluate its catalytic activity in polymer electrolyte membrane (PEM) fuel cell operation. The series of combinations and effects of bimetallic electrocatalysts (Pt:Cr atomic ratio = 83:17, 75:25, and 50:50) were assessed for the PEM fuel cell application. The morphology, size, and alloy formation of as-synthesized Pt-Cr/C electrocatalyst were analyzed by TEM, PSD, and XRD. The electrochemical characteristics, such as open-circuit potential, oxygen reduction reaction, the electrochemical active surface area (EASA) of the Pt-Cr/C electrocatalyst, were determined using CV and LSV. The performances of as-prepared Pt-Cr/C cathode MEA’s were compared with commercial 40 wt. % Pt/C cathode MEA in PEM fuel cell. Among them Pt83-Cr17 coated cathode exhibits higher power density of 0.138 W/cm2 at 0.38 V.