Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the design, operation, and economy of the seawater reverse osmosis plant in Al-Jubail region, Saudi Arabia

The aim of this study is to conduct performance evaluation of a seawater reverse osmosis plant located in Jubail, Arabian Gulf, via the development of theoretical and computational models. A simulation package was used to validate the simulated data against the real plant data, and resulted in good agreement with the actual plant data. A parametric analysis was also performed to study the effect of various parameters for a broad range of design considerations. Most of the previous studies involved the modeling of single-stage RO process; however, in the current work, we investigated both singleand two-stage RO systems. Besides, detailed economic assessment was also conducted in terms of standardization in design and cost analysis by utilizing a theoretical and computational model. Results indicated that with enhancing recovery ratio from 25% to 40%, maximum flux of 34.13 lmh was obtained for single-stage RO system. The economic analysis predicted that fuel ratio corresponded to power utilization factor constitute about 62% of the total cost of the plant. Another important aspect is to calculate the effect of membrane life on TDS values annually and it was concluded that the most stable values were achieved during month of April.
Performance of self-cleaning cotton textiles coated with TiO2, TiO2-SiO2 and TiO2-SiO2-HY in removing Rhodamine B and Reactive Red 120 dyes from aqueous solutions

In the present study composites based on TiO2 nanoparticles, SiO2 and HY zeolite were synthesized by using a standard sol-gel methodology and analyzed by Fourier-transform infrared spectroscopy, X-ray diffraction and scanning electron microscopy-energy-dispersive X-ray spectroscopy techniques. The photocatalysts were impregnated on cotton textiles and their photocatalytic abilities were examined by degradation of Rhodamine B (RhB) and Reactive Red 120 (RR120) dyes in aqueous solution under similar solar irradiation. RhB removal efficiencies from cotton textiles were 87%, 72% and 50% for TiO2, TiO2-SiO2-HY zeolite and TiO2-SiO2 composites, respectively and achieved after 3 h of light irradiation. However, for the RR120 dye, the reached result was different as the photodegradation for this dye (65% efficiency) was only observed in bare TiO2 functionalized textile. In addition, for the treatment with TiO2-SiO2-HY zeolite, it was found that for initial alkaline conditions at pH of the RhB dye solution, the photodegradation rate decreased.
A SURVEY ON ANALYSIS, EVALUATION AND IMPROVEMENT OF GRAPH MINING ALGORITHMS USING DUPLICATE REDUCTION TECHNIQUE.

: At the center of graph mining lies free extension of bases where a base or subgraph autonomously develops into various bigger bases in every cycle. Such a free development, perpetually, prompts the age of copies. Within the sight of chart segments, copies are created both inside and across allotments. Wiping out these copies brings about age and capacity cost as well as extra calculation for its end. Essential point is to plan methods to decrease creating copy foundations as we show that they can't be dispensed with. Proposed philosophy presents three requirement based enhancement strategies, each essentially improving the general mining cost by diminishing the quantity of copies produced. These options give adaptability to pick the correct procedure dependent on chart properties. Proposed philosophy tests show noteworthy advantages of these imperatives regarding capacity, calculation, and correspondence cost (explicit to divided methodologies) across graphs with differed qualities.
HIGH GAIN AND WIDEBAND MULTI-STACK MULTILAYER ANISOTROPIC DIELECTRIC ANTENNA

A multi-stack anisotropic cylindrical dielectric resonator antenna with high gain and wide bandwidth is reported. This antenna is designed with three different stacks, and each stack consists of a multilayer dielectric structure to emulate uniaxial anisotropy. Multi-stack, multilayer structure is responsible for producing wide bandwidth and high gain. In addition, the antenna is surrounded by cylindrical metallic cavity to increase directivity in broadside direction. Similar simulated and measured results indicate a wide impedance bandwidth of 37% along with a maximum gain 9.25 dB.
Modeling and optimizing of coagulation-flocculation process by response surface methodology for rehabilitation of tannery wastewater treatment plant

This work aims to optimize coagulation–flocculation operational parameters using chitosan as a biocoagulant to reach a satisfying reduction rate of chemical oxygen demand (COD), chloride concentration and suspended materials in an alkaline pH. This process is suggested as pretreatment before the biological step and a comparison study with aluminium sulphate is conducted. In the first part, nine factors were screened by Plackett–Burman experimental design to identify the most significant, that is, for coagulant: weight percentage of chitosan mixed to aluminium sulphate, dosage, mixing rate and stirring time, for flocculent: dosage, stirring time, mixing rate also initial pH and settling time. In the second part, modelling and optimization of the chosen responses were established by Doehlert design. The target responses, that is, pH: 8.6, COD: 592 mg O2/L, suspended matter (SM): 12.8 mg/L and [Cl]: 2,889 mg/L were obtained with the following optimal conditions: dosage of chitosan = 612.8 mg/L, a stirring speed of 6 rpm at an initial pH of 8.8, and 5.6 mg/L of flocculant with a stirring speed of 43 rpm followed by a settling time of 22 min. The most significant abatements obtained for COD, SM and [Cl] are 68.90%, 95.24% and 39.72%, respectively. Chitosan was found to be an efficient eco-friendly coagulant, forming less sludge than aluminium sulphate and allowing the industry to reduce treatment costs.
Automated Fault-Detection for Small Satellite Pointing Control Systems using One-Sided Learning

Figure 1: Attitude Dynamics Control System for a Spacecraft [2] INTRODUCTION As the size and prevalence of small satellite constellations grow, so does the interest in Prognostic and Health Management (PHM). In keeping with the small-satellite philosophy to maintain low design, manufacturing and operating costs, the small-satellite community is interested in efficient ground operations and fault management that does not require excessive labor from trained space systems experts [1]. The expanding scale of small-satellite constellations has posed a significant challenge for ground operations: how to find a sustainable way to monitor and manage a large amount of satellites efficiently with minimal cost? To overcome this challenge, this research introduces an autonomous, ground-based faultdetection system that was trained using only nominal data, without requiring any prior expert knowledge of the spacecraft systems. By observing nominal data during the commissioning phase of the satellite, the fault-detection algorithm learned how to differentiate normal data from abnormal data without a labelled set of abnormal data. Training and testing results are presented to show how this one-sided learning method of fault-detection could detect untrained failures related to reaction wheel performance. Specifically, this research demonstrates the utility of one-sided learning methods by autonomously detecting faults in reaction wheel bearing friction and wheel speed measurement, without any prior exposure to the failures.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization

Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML. Despite recent progress, pipeline optimization remains a challenging problem, due to potentially many combinations to consider as well as slow training and validation. We present the BLDS algorithm for optimized algorithm selection (ML operations) in a fixed ML pipeline structure. BLDS performs multi-fidelity optimization for selecting ML algorithms trained with smaller computational overhead, while controlling its pipeline search based on multi-armed bandit and limited discrepancy search. Our experiments on well-known classification benchmarks show that BLDS is superior to competing algorithms. We also combine BLDS with hyperparameter optimization, empirically showing the advantage of BLDS.
Obtaining the daily actual evapotranspiration through remote sensing techniques application in Brazilian Semiarid

Large volumes of water are released to the atmosphere through evaporation from soil and transpiration from vegetation, constituting evapotranspiration (ET). Estimating the water consumption in vegetated areas is important for the management and rational use of this resource. For this study were processed orbital images which correspond to Quixeré-CE, with interest at the Frutacor Farm, where there is predominance the banana crop. The main objective of this study was to assess the accuracy and applicability of S-SEBI and SSEB algorithms with regard to SEBAL to estimate the actual daily evapotranspiration ETa) of a semi-arid region of Northeast Brazil, containing areas of banana orchard, native vegetation (caatinga) and bare soil. S-SEBI. The SSEB and SSEB algorithms showed strong correlation (r > 0.93) with statistical significance of 5%. The S-SEBI exhibited errors less than 12% in the orchard and caatinga and SSEB exhibited greater errors at 22%, though for the bare soil, both models showed large discrepancies when compared with SEBAL, with errors greater than 36%. Therefore, among the two algorithms compared with SEBAL, S-SEBI had a better performance in ETa estimation with lower deviations.
DUAL-BAND COMPLEMENTARY SPLIT-RING RESONATOR ENGRAVED RECTANGULAR MONOPOLE FOR GSM AND WLAN/WIMAX/5G SUB-6 GHZ BAND (NEW RADIO BAND)

In this paper, a rectangular monopole antenna engraved with a complementary split-ring resonator is proposed for dual-band operation. The proposed antenna is fabricated on an FR4 substrate with a dimension of 20 × 34 × 1.6 mm3. The entire simulation is done using CST EM studio software. The proposed antenna exhibits dual-band operation from 1.78 GHz to 1.90 GHz and from 3.45 GHz to 6.58 GHz. The band from 1.78 GHz to 1.90 GHz is due to the inclusion of CSRR, and its corresponding bandwidth is 120 MHz. It is validated with the quasi-static analysis. The permittivity characteristics of the proposed CSRR are retrieved using the NRW method and presented. The resonant frequency of the band created by the CSRR is 1.83 GHz with −37.68 dB as its return loss values. The second wider band is due to the combination of the mode created by the CSRR along with the radiating patch from 3.45 GHz to 6.58 GHz with 3132 MHz which has dual resonances at 3.65 GHz and 5.59 GHz with return losses of −30.23 dB and −29.80 dB. The optimal values are chosen with the help of parametric analysis. The designed antenna is fabricated and measured. The measured results of return loss, gain, E-plane, and H-plane are compared with simulated ones, and they comply with each other. The dual-band operation, compact size, stable radiation pattern along with gain above 2.3 dBi in the whole resonating band make it suitable for the GSM and WLAN/WiMAX/5G Sub-6GHz band (new radio band).
A HIGH THROUGHPUT DOUBLE MAC FOR HARDWARE ACCELERATION IN DEEP LEARNING

: Convolutional Neural Networks which is a class of deep learning have an significant workload which increases demand for high-performance hardware acceleration. This paper is proposed to increase the rate of computation of accelerators by enclosing double multiply and accumulate (MAC) operations into single DSP block of offshelf FPGAs (called Double MAC). The method is validated synthesis by FPGAs and verilog code simulation. The distinguishing feature of Deepelearning it can withstand tiny mathematical errors and works extremely fine with hardware which are less accurate. This paper presents a technique that can increase the rate of computation of a CNN layer by twice.In this paper we implement a Double Multiply Accumulate (MAC) unit based on a SIMD multiplication and 8-bit Ripple carry adder which is again replaced by 8-bit Kogge-Stone adder (KSA) for a DSP module in order to achieve high throughput. The evaluation results shows that Double MAC approach not only can increase the computation throughput of a CNN layer by twice but also has essentially reduced area, delay and power
DESIGN OF A RECONFIGURABLE BAND-NOTCH SWB ANTENNA

This paper introduces a novel planar super-wideband (SWB) antenna with reconfigurable band-notch characteristic. The antenna can work in band-notch mode or band-notch free mode. A good impedance matching is responsible for the SWB characteristic of the proposed antenna by adopting a gradient ground, a gradient feeder line, and a gradient radiating patch. Furthermore, to achieve a reconfigurable notched band function, a 0.3 mmn deep slot which is 16 mm in length and 8 mm in width is dug near the antenna feeder for the placement of dielectric plates etched with different sizes of split ring resonator (SRR). The designed antenna has a size of 200 mm × 109 mm × 0.79 mm, and the measured frequency band of bandwidth covers 0.8–26 GHz with a reconfigurable band-rejection characteristic. The dielectric plates with different SRRs reject the part of WLAN band (5.44–5.55 GHz), X-band satellite downlink band (7.65 GHz–7.82 GHz), and 6.33 GHz–6.59 GHz. A good agreement is achieved within the super-wideband frequency range between simulated and measured results.
On Generalised Quasi-ideals in Ordered Ternary Semigroups

In this paper, we introduce generalised quasi-ideals in ordered ternary semigroups. Also, we define ordered m-right ideals, ordered (p, q)-lateral ideals and ordered n-left ideals in ordered ternary semigroups and studied the relation between them. Some intersection properties of ordered (m, (p, q), n)-quasi ideals are examined. We also characterize these notions in terms of minimal ordered (m, (p, q), n)-quasi-ideals in ordered ternary semigroups. Moreover, m-right simple, (p, q)-lateral simple, n-left simple, and (m, (p, q), n)-quasi simple ordered ternary semigroups are defined and some properties of them are studied.
Exploring Datasets via Cell-Centric Indexing

We present a novel approach to dataset search and exploration. Cell-centric indexing is a unique indexing strategy that enables a powerful, new interface. The strategy treats individual cells of a table as the indexed unit, and combining this with a number of structure-speci � c � elds enables queries that cannot be answered by a traditional indexing approach. Our interface provides users with an overview of a dataset repository, and allows them to e � ciently use various facets to explore the collection and identify datasets that match their interests.
Experimental test and sensitivity analysis of performance parameters of a solar humidification-dehumidification system

Sun-powered desalination technologies, such as humidification–dehumidification (HDH), are an attractive alternative to provide freshwater in remote high-radiation climate conditions. In this work, an experimental and numerical study is presented for a solar HDH unit operating with a closed air-open water configuration with water-heated solar collectors. The numerical model shows a good agreement with experimental data for both temperature and distillate production. A new performance parameter, so-called distillate-to-irradiance ratio (DIR), is introduced, which is specifically designed to characterise solar thermal desalination systems. This parameter takes into account both solar collection and desalination processes and could be complemented with the conventional gained output ratio (GOR = Md l/Qu), which involves the distillate Md with the heat required to distillate Qu. The validated model is used to determine the relevant operational parameters and how they influence the DIR throughout a sensitivity analysis. Feedwater to air mass flow rate ratio (MR) and solar radiation are the key parameters to predict DIR and distillate production. For the specific conditions, a maximum of DIR = 0.44 kg/kWh for a global radiation of 1,000 W/m2 and a MR = 2 was found. Finally, shortand long-term correlations based on environmental conditions were developed to easily estimate the desalination potential in specific locations.
Doing Gender and Reshaping the Self: The Rehabilitation Narratives of TBI Survivors

This article provides a review of selected studies conducted in recent years on the relationship between gender identity and recovery following traumatic brain injury (TBI) with the goal of determining whether gender constructions play a role in the lived experience of TBI survivors. The studies reviewed show that psychosocial issues resulting from perceived difficulties in doing gender can be a burden to successful recovery and that addressing these constructions can have a positive influence on long-term results. Consequently, the author argues that gender should be considered a critical element in the aetiology and management of psychosocial issues following the injury and that a combination of factors should be addressed when looking at gendered aspects of post-TBI recovery, including attitudes toward care, motivation and satisfaction with rehabilitation outcomes. In particular, stronger collaboration between the medical field and social sciences is encouraged, with the aim of exploring individual perspectives and experiences especially on a larger scale than what has currently been achieved.
CIA_A_289479 187..202

Department of Osteonecrosis and Joint Reconstruction, Honghui Hospital Xian Jiao Tong University Health Science Center, Xian, Shaanxi 710068, People’s Republic of China Purpose: Osteonecrosis of the femoral head is a common disease of the hip that leads to severe pain or joint disability. We aimed to identify potential differentially expressed miRNAs and mRNAs in osteonecrosis of the femoral head. Methods: The data of miRNA and mRNA were firstly downloaded from the database. Secondly, the regulatory network of miRNAs–mRNAs was constructed, followed by function annotation of mRNAs. Thirdly, an in vitro experiment was applied to validate the expression of miRNAs and targeted mRNAs. Finally, GSE123568 dataset was used for electronic validation and diagnostic analysis of targeted mRNAs. Results: Several regulatory interaction pairs between miRNA and mRNAs were identified, such as hsa-miR-378c-WNT3A/DACT1/CSF1, hsa-let-7a-5p-RCAN2/IL9R, hsa-miR-28-5pRELA, hsa-miR-3200-5p-RELN, and hsa-miR-532-5p-CLDN18/CLDN10. Interestingly, CLDN10, CLDN18, CSF1, DACT1, IL9R, RCAN2, RELN, and WNT3A had the diagnostic value for osteonecrosis of the femoral head. Wnt signaling pathway (involved WNT3A), chemokine signaling pathway (involved RELA), focal adhesion and ECM-receptor interaction (involved RELN), cell adhesion molecules (CAMs) (involved CLDN18 and CLDN10), cytokine–cytokine receptor interaction, and hematopoietic cell lineage (involved CSF1 and IL9R) were identified. Conclusion: The identified differentially expressed miRNAs and mRNAs may be involved in the pathology of osteonecrosis of the femoral head.
Visual Scene Graphs for Audio Source Separation

State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to better characterize the sources, especially when the same object class may produce varied sounds from distinct interactions. To address this challenging problem, we propose Audio Visual Scene Graph Segmenter (AVSGS), a novel deep learning model that embeds the visual structure of the scene as a graph and segments this graph into subgraphs, each subgraph being associated with a unique sound obtained by co-segmenting the audio spectrogram. At its core, AVSGS uses a recursive neural network that emits mutually-orthogonal sub-graph embeddings of the visual graph using multi-head attention. These embeddings are used for conditioning an audio encoder-decoder towards source separation. Our pipeline is trained end-to-end via a self-supervised task consisting of separating audio sources using the visual graph from artificially mixed sounds.In this paper, we also introduce an “in the wild” video dataset for sound source separation that contains multiple non-musical sources, which we call Audio Separation in the Wild (ASIW). This dataset is adapted from the AudioCaps dataset, and provides a challenging, natural, and daily-life setting for source separation. Thorough experiments on the proposed ASIW and the standard MUSIC datasets demonstrate state-of-the-art sound separation performance of our method against recent prior approaches.
Breaking Google reCaptcha v 2 with Transfer Learning 1

In this section we evaluate our method with a manually collected reCaptcha v2 dataset. The dataset consists of 2,000 challenges, divided into 1,223 image select challenges, and 777 tile select challenges. As there are multiple types of queries with few examples, we perform experiments only on four types of queries: (i) traffic lights; (ii) cars; (iii) palm trees; and (iv) bridges, resulting in a dataset of 1,315 challenges. This dataset is split into train, validation, and test sets with the proportion of 70%/15%/15% respectively. In our experiments, we consider three types of CNN architectures: (i) vgg19; (ii) resnet50; and (iii) inceptionV3. We also compare a baseline (network trained from scratch), with two different forms of transfer learning: (i) feature extraction, where the convolutional layers of the network are frozen; and (ii) fine tuning, where no layer is frozen, but the network is initialized with pre-trained weights. At the end of each convolutional network, we drop the existing fully connected layers and append two fully connected layers and a layer for classification. We treat each query type as a binary classification problem, where the true examples are the selected images or tiles, and the false examples are the remaining images or tiles in each challenge. We perform grid search on the validation set based on the macro average f-measure score, for each type of query, for each architecture, for each method, varying the number of neurons at each fully connected layer in range [128, 256, 512, 1024], and varying dropout after each dense layer in range [0.3, 0.5], resulting in 288 variations. We set the batch size = 64, the learning rate = 0.001, and train the networks with Adam for at most 50 epochs. The training set is randomly augmented with rotation [0, 15], zoom [0, 0.15], width and height shift [0, 0.15] and shear [0, 0.15]. 4. CONCLUSION
A Study on Optimal PID Controller Design Ensure the Absolute Stability

In this paper, an optimal controller design that guarantees absolute stability is proposed. The order of application of the thesis determines whether the delay time is included, and if the delay time is included, the delay time is approximated through the Pade approximation method. Then, the open loop transfer function for the process model and the controller transfer function is obtained, and the absolute stability interval is calculated by the Routh-Hurwitz discrimination method. In the last step, the optimal Proportional and Integral and Derivative(PID) control parameter value is calculated using a genetic algorithm using the interval obtained in the previous step. As a result, it was confirmed that the proposed method guarantees stability and is superior to the existing method in performance index by designing an optimal controller. If we study the compensation method for the delay time in the future, it is judged that better performance indicators will be obtained.