Direct Selection Method Research Articles

Genome-wide CRISPRi screens for high-throughput fitness quantification and identification of determinants for dalbavancin susceptibility in Staphylococcus aureus.

Antibiotic resistance and tolerance remain a major problem for the treatment of staphylococcal infections. Identifying genes that influence antibiotic susceptibility could open the door to novel antimicrobial strategies, including targets for new synergistic drug combinations. Here, we developed a genome-wide CRISPR interference library for Staphylococcus aureus, demonstrated its use by quantifying gene fitness in different strains through CRISPRi-seq, and used it to identify genes that modulate susceptibility to the lipoglycopeptide dalbavancin. By exposing the library to sublethal concentrations of dalbavancin using both CRISPRi-seq and direct selection methods, we not only found genes previously reported to be involved in antibiotic susceptibility but also identified genes thus far unknown to affect antibiotic tolerance. Importantly, some of these genes could not have been detected by more conventional transposon-based knockout approaches because they are essential for growth, stressing the complementary value of CRISPRi-based methods. Notably, knockdown of a gene encoding the uncharacterized protein KapB specifically sensitizes the cells to dalbavancin, but not to other antibiotics of the same class, whereas knockdown of the Shikimate pathway showed the opposite effect. The results presented here demonstrate the promise of CRISPRi-seq screens to identify genes and pathways involved in antibiotic susceptibility and pave the way to explore alternative antimicrobial treatments through these insights.IMPORTANCEAntibiotic resistance is a challenge for treating staphylococcal infections. Identifying genes that affect how antibiotics work could help create new treatments. In our study, we made a CRISPR interference library for Staphylococcus aureus and used this to find which genes are critical for growth and also mapped genes that are important for antibiotic sensitivity, focusing on the lipoglycopeptide antibiotic dalbavancin. With this method, we identified genes that altered the sensitivity to dalbavancin upon knockdown, including genes involved in different cellular functions. CRISPRi-seq offers a means to uncover untapped antibiotic targets, including those that conventional screens would disregard due to their essentiality. This paves the way for the discovery of new ways to fight infections.

Penerapan Metode Weighted Product Pada Sistem Pendukung Keputusan Pemilihan Calon Dosen Pembimbing Skripsi

Decision Support System is a computer system that processes data into information to make decisions regarding specific semi-structured problems. It is specially developed to support solutions for unstructured management issues to enhance the quality of decision-making. The selection of potential thesis advisors at Hamzanwadi University's Informatics Engineering program is currently done by directly choosing an advisor while considering individual criteria or by seeking advice from peers. However, this direct selection method sometimes overlooks crucial aspects that should be prioritized. This research aims to construct a web-based Decision Support System (DSS) to assist final-semester students in the Faculty of Engineering at Hamzanwadi University in selecting their thesis advisors. The methodology employed in this Decision Support System is the Weighted Product method, which enables the calculation and comparison of multiple factors during the decision-making process. Factors analyzed in this study encompass the instructors' competency areas and the number of students they supervise. The utilization of this Decision Support System is expected to aid students in choosing an appropriate and fitting thesis advisor according to their needs. Moreover, the Decision Support System is anticipated to aid the faculty in providing information about potential thesis advisors before students finalize their choices. This research is expected to contribute to and benefit the development of information technology in the education sector

Treatment of Refractory Adenovirus (ADV) Infections Using Adv-Specific Cytotoxic T-Lymphocytes (CTLs) in Children, Adolescents and Young Adults (CAYA) Post Allogeneic Hematopoietic Stem Cell Transplantation (AlloHSCT), Solid Organ Transplantation (SOT), or with Primary Immunodeficiency (PID)

Background: AlloHSCT induces long-term, cure in CAYA with malignant and non-malignant disorders, including those with primary immunodeficiencies (PID). However, post-transplant complications secondary to immunosuppression and slow or delayed immune reconstitution remains a cause of morbidity and mortality amongst AlloHSCT recipients (Satwani/Cairo, BBMT, 2009). Major complications include refractory viral infections, most importantly adenovirus (ADV), which is secondary to absent or decreased adenovirus specific T-cell immunity which occurs due to delayed immune reconstitution and/or continued immunosuppression (George/Cairo, BJH, 2012). The use of virus-specific (vs) CTLs in immunocompromised patients offer an opportunity to restore temporary virus specific immunity. From the Viral Cytotoxic T-Lymphocyte Consortium (VIRCTLC), we present safety and efficacy results on immune compromised, AlloHSCT, or SOT patients treated with familial ADV specific CTLs. Objective: Determine the safety and efficacy of familial ADV specific CTLs for the treatment of refractory systemic ADV infections. Design/Methods: Patient were deemed eligible if they had a history of AlloHSCT, SOT, or PID and had evidence of ADV infections refractory to anti-viral agents. Refractory ADV infection was defined by increasing serum RT-PCR DNA (by 1 log) after 7 days or persistent quantitative RT-PCR DNA copies after 14 days of appropriate anti-viral therapy, and/or known resistance/intolerance to anti-viral agents. Related donors were required to be matched ≥3 HLA (A, B, or DRB1) loci and have an adequate T-cell response to viral specific MACS ADV PepTivators®. We chose a direct selection method to facilitate more rapid generation of cellular products in a point of care setting (and with 14 hrs eligibility determination). Peripheral blood mononuclear cells were collected from eligible family donors using non-mobilized apheresis. vsCTLs were isolated using the CliniMACS ® Prodigy following stimulation with viral specific MACS® GMP PepTivator® AdV5 Hexon, generously provided by Miltenyi Biotec®. ADV specific CTLs were enriched using a Cytokine Capture System (CCS). CD4+ and CD8+ vsCTLs were quantified by flow cytometry (Feuchtinger et al, Blood, 2010). The target cell dose was 0.5x10 4 CD3+ cells/kg (recipient weight) for haploidentical related donors and 2.5x10 4 CD3+ cells/kg for matched related donors. Repeated doses were permitted every 2 weeks to a maximum of doses in the absence of a complete response (CR) and adverse events. The following were used to define response: CR - undetected ADV PCR, partial response (PR) - at least one log decrease from baseline, progressive disease (PD) - at least one log increase from baseline, and patients with stable disease. Results: Fifteen patients were enrolled: 10 females and 5 males; aged 1-19 years. All patients were post-AlloHSCT. All vsCTL donors were haploidentical: 8 were the original HSCT donors and 7 were third party donors (5 maternal, 2 paternal). The mean number of ADV CTL infusions was 3.7 (range: 1-16). One patient with underlying aplastic anemia had progressive disease complicated by increased adenoviral load despite 5 infusions of vsCTLs. Patient received FDA approval for additional CTLs, totaling 16 doses. Thirteen patients achieved CR as defined and 2 achieved PR. The overall response (OR) was 100% and the CR was 88%. The average time to OR was 33 days (range 6-112 days) and time to CR was 34 days (range 6-112 days). Day 100 and 365 overall survival (OS) post-HSCT was 86.1% (CI95: 49.4-95.7) and 70.5% (CI95: 38.9-87.8), respectively (Figure 1). For patients who achieved CR, the Day 100 OS post AlloHSCT was 83.9% (CI95: 55.0-94.3) and Day 365 post AlloHSCT was 74.6% (CI95: 39.8-91.1). Day 100 and 365 probability of ADV related mortality was 0% (Figure 2). One patient developed acute grade 2 skin GVHD possibly related to infusion, which resolved. Conclusion: Our preliminary data demonstrate that haploidentical ADV specific CTLs manufactured by direct selections using the CCS with the CliniMACS® Prodigy and ADV specific peptivators are safe, well-tolerated, and efficacious in patients with refractory/persistent ADV infections after AlloHSCT. Manufacturing is rapid, reproducible, and effective. Accrual is ongoing. This research is supported by FDA RO1006301A1.

Comparing Three Methods of Selecting Training Samples in Supervised Classification of Multispectral Remote Sensing Images.

Selecting training samples is crucial in remote sensing image classification. In this paper, we selected three images-Sentinel-2, GF-1, and Landsat 8-and employed three methods for selecting training samples: grouping selection, entropy-based selection, and direct selection. We then used the selected training samples to train three supervised classification models-random forest (RF), support-vector machine (SVM), and k-nearest neighbor (KNN)-and evaluated the classification results of the three images. According to the experimental results, the three classification models performed similarly. Compared with the entropy-based method, the grouping selection method achieved higher classification accuracy using fewer samples. In addition, the grouping selection method outperformed the direct selection method with the same number of samples. Therefore, the grouping selection method performed the best. When using the grouping selection method, the image classification accuracy increased with the increase in the number of samples within a certain sample size range.

Efficiency of index-based selection for potential yield in durum wheat [<em>Triticum turgidum</em> (L.) ssp. <em>turgidum convar. durum</em> (Desf.) Mackey] lines

Wheat is a socioeconomically important crop in Algeria. Improving genetic gain of quantitative traits through selection is at the core of every successful breeding program. Selection is usually performed on grain yield, but other agronomically related characteristics can also help increase genetic gain through indirect or multi-trait selection. The objective of this work was to quantify genetic parameters and compare the efficiency of direct, indirect and simultaneous selection methods in terms of predicted genetic values of wheat progenies. For this purpose, 418 F4-derived lines were evaluated for six agronomic traits including heading date, flag leaf area, plant height, number of spikes, thousand kernel weight and grain yield in an augmented block design with three check varieties. Wide genetic variation with moderately high broad-sense heritability were observed for the recorded traits, except for heading date. The results from genetic gain revealed variation in gains for assessed traits and breeding methods employed. The classic index of Smith and Hazel (SHI) demonstrated a similar genetic gain in grain yield compared to gain from direct selection. Generally, the selection-based index showed the highest responses considering all traits simultaneously with a slight inferiority of the SHI index. The coincidence rates among the evaluated indices were higher than those obtained between the measured traits. Based on the comparisons between the selected lines, the SHI index and the selection base index of Williams were similar to grain yield and can reach up to 79.51% coincidence of breeding lines identified by these selection criteria. Breeding lines L252, L34, L24, L130 and L413 were the most common individuals identified according to number of coincidences from the different selection methods used. Of these, L34 and L24, and to a lesser extent L15 can be considered promising wheat lines for improving grain yield.

Methods for estimation of genetic parameters in soybeans: an alternative to adjust residual variability

Selection practices are maximized when plant breeders have the availability of consolidated parameters, which will guide direct and indirect selection methods. This study aimed to apply a biometric alternative to minimize residual variance and maximize selection parameters by parent-progeny regression, interim controls, and mixed linear models intrinsic to breeding. The obtained data were subjected to the assumptions of the statistical model, which identified the normality and homogeneity of the residual variances and model additivity. Subsequently, two analysis scenarios were created. The first preserved all information obtained in the experiment, both from segregating families and pure-line cultivars, and was called original scenario. The other scenario preserved progeny data, but the residual variability of controls was restricted using as criterion observations contained between the interval of the first sample standard deviation. Thereby, an acceptable residue limit could be obtained. Both scenarios were submitted to three consolidated frequentist methods (genitor-progeny regression; sum of squares of augmented block design with interim controls; and mixed linear models, wherein random genetic effects are taken as weighted genetic parameters by the genealogical matrix). Restricting residual variation in parents or controls can maximize genetic parameters and genetic gains in soybean breeding. Significant heritability estimate gains were obtained in the augmented blocks with interim control approach. Mixed linear models with random genetic effects can be considered a great tool to obtain genetic parameters in experiments with a high magnitude of common and regular treatments.

SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs.

Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a and speedup over the state-of-the-art graph processing frameworks Gunrock and Enterprise, respectively.

High-Rate Underwater Acoustic Localization Based on the Decision Tree

Underwater acoustic localization (UWAL) is widely applied in ocean exploration, and a key issue for the high-rate time-of-arrival (TOA)-based UWAL method is to select the direct signal in the multipath acoustic channel. However, the existing direct signal selection methods suffer from a deterioration of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high-rate direct signal selection algorithm in this article. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Second, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments, including simulation, tank experiment, and sea trial, are implemented to verify the effectiveness of the proposed method.

Pre-analytical phase impact in the differences in natremia obtained by direct and indirect ion selective electrodes methods

Natremia is an important biological parameter providing information on the hydration state of patient's intracellular sector. Its measurement can be carried out either by multiparametric laboratory analyser (indirect potentiometry) or delocalized biology analyser (direct potentiometry). The main problem is that for a same patient, these two analysers can give quite different results, hence inducing interpretation problems for clinician. Two one-week study periods comparing the variations in blood sodium levels produced by these automatic analysers were carried out in two intensive care units of Clermont-Ferrand University Hospital. During the second study period, a protocol for collecting blood samples was applied in order to improve the pre-analytical conditions. Between the two weeks of studies, the median of the differences in natremia was significantly reduced, going from 4mmol/L to 2mmol/L (p <0.001), as was the proportion of patients with large differences in sodium levels (strictly higher than 3mmol/L) going from 51% to 24.8% (p <0.001). The patients still presenting large variations in sodium had a median of proteins significantly lower than patients with deviations less than or equal to 3mmol/L: 58.1g/L against 62.25g/L respectively (p <0.001) leading to pseudo-hypernatremia (indirect potentiometry). Despite a significant reduction in differences linked to the application of good preanalytical practices, some patients nonetheless presented a major difference in natremia due to the difference of technique (variations in the lipidoprotein phase of the plasma of intensive care patients) and to the measurement uncertainties.

Estimates of additive and non-additive genetic effects on growth traits in a diallel cross of three strains of pearl oyster (Pinctada fucata)

Several strains of pearl oysters (Pinctada fucata) are distributed in the coastal areas of South China. To explore strategies for genetic improvement, additive genetic, heterotic, and strain reciprocal effects on growth traits were estimated by performing a full 3 × 3 diallel cross of three pearl oyster strains that are local to Beihai, Guangxi Province; Xuwen, Guangdong Province; and Sanya, Hainan Province. The simultaneous production of progeny for all crosses was achieved using mass spawning in Lingshui, Hainan Province. For body weight, the Beihai strain showed the highest additive genetic effect (+ 5.7%) and the Sanya strain showed the lowest (− 4.3%), with similar additive genetic effect patterns for shell length, shell height, and shell width. The averaged heterotic outcomes were significant (P < 0.01) for all growth traits, varying from + 6.1% for shell length to + 19.1% for body weight. Reciprocal cross effects were determined for growth traits, with a relatively high correlation for all traits between strain additive performance and total performance (r2 = 0.72 to 0.85). These findings reveal the potential to exploit strain additive variation to improve P. fucata culture stock using direct selection methods for increased productivity of pearl oyster cultivation in China.

Genetic variability studies in cluster bean [Cyamopsis tetragonoloba (L.) Taub.] for growth, yield and quality parameters

The study on genetic parameters of 74 cluster bean genotypes collected from different parts of the country revealed, high estimates of variation for number of branches (90 DAS), number of clusters per plant, number of pods per cluster, pod length, pod weight, vegetable pod yield per plant, pod yield per plot, pod yield per hectare, dry pod yield per plant, weight of ten dry pods, seed yield per plant, seed yield per plot and gum content of seed endosperm The high estimates of heritability coupled with high values of genetic advance over mean (GAM) were noticed for traits like, plant height (cm) (90 DAS), number of branches (90 DAS), number of clusters per plant, pod length, pod weight, pubescence, seed yield per plant, seed yield per plot and seed gum content. These outcomes showed prevalence of additive constituents for these characters and hence for improving these traits by direct selection method would be more effective.

Relay selection for multi-source network-coded D2D multicast communications in heterogeneous networks

Multicast service is considered as a useful transmission mode for future mobile social services to deliver the traffic to multiple mobile users simultaneously. Device-to-device (D2D) multicast communication enables multiple proximate users to share the contents of their common interests directly. This paper aims to present a relay selection scheme for multi-source network-coded D2D multicast communication underlying heterogeneous cellular network which consists of macrocells and femtocells. We propose two methods for selecting efficient relays from the set of femtocell base stations (FBSs). The first method, named destination-blind relay selection, identifies those idle FBSs that can correctly decode all source messages. Since all identified relays may not be necessary, this method reduces the average transmission rate. Hence, in the second method named destination-aware relay selection, we find the minimum number of required relays by maximizing the spectral efficiency to make sure that all destinations can correctly decode all source messages. In contrast to the first method, the second method requires information about destinations. We present efficient solutions for the proposed methods and obtain their performance in terms of outage probability and spectral efficiency. We also derive analytical expressions for the outage probability to verify simulation results. It is shown that the introduced methods improve the spectral efficiency compared to the direct D2D multicast, random relay selection, and coordinated multi-point transmission. Further, the presented methods achieve lower outage probability than direct D2D multicast and random relay selection methods. The proposed methods reduce the average outage probability about 17 times and increase the average spectral efficiency about 16 times in comparison with direct multi-source D2D multicast, which indicate their efficiency.

Comparison of direct and indirect perceptual head-related transfer function selection methods.

When a personalized set of head-related transfer functions (HRTFs) is not available, a common solution is identifying a perceptually appropriate substitute from a database. There are various approaches to this selection process whether based on localization cues, subjective evaluations, or anthropomorphic similarities. This study investigates whether HRTF rankings that stem from different selection methods yield comparable results. A perceptual study was carried out using a basic source localization method and a subjective quality judgment method for a common set of eight HRTFs. HRTF rankings were determined according to different metrics from each method for each subject and the respective results were compared. Results indicate a significant and positive mean correlation between certain metrics. The best HRTFs selected according to one method had significant above-average rating scores according to metrics in the second method.

Campylobacter in Street Vended Poultry Meat Meals and its Public Health Hazard

In this study, a total of (250) samples were collected from different ready to eat poultry meat products, (50) samples from each of (Chicken Bannae, Chicken Shawarma, Chicken Phaheta, Chicken Shesh kabab, Chicken Zinger) from vending sites and restaurants of different localities of different level of hygienic measures in Cairo governorate to detect and identify the Campylobacter species bacteriologically by direct plating and selective enrichment methods and by PCR. As regards to the Bacteriological examination of (250) collected ready to eat poultry products meal, the obtained results revealed the isolation of Campylobacter spp. from (8) samples with a prevalence rate reached (3.2%) by studding the morphological identification on the selective culture media and microscopically identification as well as the biochemical reactions. On the other hand, the all isolated strains proved to be C. jejuni by molecular identification (PCR assay) using specific primers. For experimental challenge, the isolated strains of Campylobacter jejuni were cultured to investigate the role of cross - contamination in disseminating campylobacter from raw poultry within a food service operation specializing in poultry dishes. Samples were collected weekly from environmental surfaces and various kitchen surfaces used at different stages of the preparation process from a retail poultry food service operation. The obtained results revealed that with use of an inoculum of 107 cells, viable C. jejuni could not be recovered from the stainless steel surface after 1 hrs. of drying. Also, Campylobacter spp. were not detected in any of the 125 restaurant samples collected The results indicated that as few as 100 Campylobacter cells could be detected if sampling was done within 45 min of inoculation; however, despite the reported high prevalence of Campylobacter spp. in ready to eat poultry meal, this organism was not detected on surfaces within a kitchen of a restaurant specializing in Poultry dishes. This inability to detect Campylobacters may be related to routine cleaning of preparation surfaces, adequate preparation and storage of foodstuffs, good worker hygiene, and physical separation of raw foods from cooked foods, limiting cross-contamination. It is also likely that the lack of Campylobacters in samples from this high-volume retail food service operation may also be attributed to the sensitivity of Campylobacter spp. to drying conditions.

Estimates of phenotypic diversity and genetic parameters of &lt;i&gt;Hibiscus cannabinus&lt;/i&gt; L. grown in Burkina Faso

Hibiscus cannabinus is a traditional leafy-vegetable consumed more and more in Burkina Faso. Its leaves and fruits are some good food supplement against nutritional deficiencies. Its fibers are also a source of income for households. Despite of its nutritional an economic potential, this species is underdeveloped in Burkina Faso. The study aims to determine genetic variability and level of heritability among agronomic interest traits of H. cannabinus using agro-morphological descriptors. This enable to identify outstanding accessions which could be involved in further breeding programs. A total of 70 accessions of H. cannabinus were characterized according randomized incomplete block, using 5 qualitative characters and 15 quantitative characters. The study showed great variability for most of the qualitative and quantitative characteristics studied. Several correlations were observed between characters related to the biomass, the fruits and the fibers production. These correlations are particularly interesting because the improvement of only one character induces improvement of the others. The study, moreover, shows high broad sense heritability (H 2 ) and weak differences between the phenotypic (PCV) and genotypic (GCV) coefficients of variation. These results indicate that the characters expression is under genetic control. So a direct selection method of H. cannabinus improvement is possible for these characters. © 2019 International Formulae Group. All rights reserved Keywords: Agromorphological, characterization, Hibiscus, selection, Burkina Faso

Sparsity-regularized feature selection for multi-class remote sensing image classification

Remote sensing image classification plays an important role in a wide range of applications and has caused widely concerns. During the last few years, great efforts have been made to develop a number of scene classification methods for remote sensing images. However, the existing remote sensing image classification methods do not perform satisfactorily in dealing with multi-class classification problems and rely heavily on the quality of data sets. These disadvantages seriously restrict the application of remote sensing image, including industrial research, analysis and calculation of land use and land coverage. To this end, this paper proposes a remote sensing image classification algorithm based on the sparse regularized feature learning method. Specifically, after constructing bag of features by using speeded up robust features extraction algorithm, direct sparsity optimization-based feature selection method is applied for selecting discriminative features, which is used for constructing support vector machine classifier model. The proposed algorithm has been evaluated and compared with other advanced feature selection methods on four public remote sensing image data sets. The experimental results demonstrate the effectiveness of our proposed image classification algorithm, which has been successfully applied to remote sensing image classification tasks.

Intracellular selection of trans-cleaving hammerhead ribozymes.

Hammerhead ribozyme is the smallest and best characterized catalytic RNA-cleaving ribozyme. It has been reported as potential therapeutic tools to manipulate the expression of target genes. However, most of naturally occurring hammerhead ribozymes process self-cleavage rather than cleave substrate RNA in trans, and its high intracellular activity relies on the tertiary interaction of Loop II and steam I bulge, resulting in decreased performance as applied in gene silencing. We described a direct intracellular selection method to evolve hammerhead variants based on trans-cleavage mode via using a toxin gene as the reporter. And a dual fluorescence proteins system has also been established to quantitatively evaluate the efficiency of selected ribozymes in the cell. Based on this selection strategy, we obtained three mutants with enhanced intracellular cleaving activity compared to wide type hammerhead ribozyme. The best one, TX-2 was revealed to possess better and consistent gene knockdown ability at different positions on diverse targeted mRNA either in prokaryotic or eukaryotic cells than wild-type hammerhead ribozyme. These observations imply the efficiency of the intracellular selection method of the trans-acting ribozyme and the potentials of improved ribozyme variants for research and therapeutic purposes.

Top-N-Targets-Balanced Recommendation Based on Attentional Sequence-to-Sequence Learning

User's behaviors and preferences alter with the temporal evolution dynamically, which leads to low performance, such as the Hit Rate and Normalized Discounted Cumulative Gain (NDCG). Understanding the dynamics of users' behaviors and preferences can improve the performance of recommendation system. In this paper, we propose a Top-N-targets-balanced recommendation based on attentional sequence-to-sequence (Seq2Seq) learning to capture the users' transient interests. The attentional Seq2Seq learning is introduced to largely exploit the coherence of users' sequential behaviors and preferences in the out module of our sequential recommendation. We use two methods to get the Top-N outputs as inputs of the attentional Seq2Seq learning. The direct selection method is choosing the k items with high probability. The simulated generation method is an improvement of the network by simulating the output. We balance the loss between Top-N outputs and the sequence targets to train the neural networks, which include Long Short-Term Memory and attentional Seq2Seq learning. Besides, we modify the recommendation list generation method to further improve the performance. Experimental results demonstrate that our methods outperform existing algorithms including state-of-the-art NCF, ItemPop, ItemKNN, BPR, eAls, and SVD++ on the performance of HR and NDCG. In the best case, the NDCG of our proposal is 14.72% higher than that of NCF.

An AUV Adaptive Sampling Path Planning Method Based On Online Model Prediction

Aiming at the problem of rapid observation of coastal marine environment, an adaptive sampling method based on Gaussian Process Regression (GPR) for small Autonomous Underwater Vehicle (AUV) is proposed. GPR analysis is used to predict the environmental data of unobserved areas based on the realtime observation data from the AUV, and the AUV is guided to implement online path planning by calculating the regional gradient extremes and the forecasting uncertainty. Based on this, an AUV observation direction selection method based on the global estimation of the boundary gravity matrix after data exchange is designed. Finally, this method is used to simulate the regional environmental observation with different feature distributions. Results show that this method can obtain the estimation of low-error feature distribution of the observed area more efficiently than the conventional method, and obtain the hot spot monitor of the observed area more quickly and show more adaptable of the different regional characteristics observation.

AUTOMATION OF THE EQUIPMENT FOR MOLECULAR PHYSICS LABORATORY PRACTICE USING THE ARDUINO BOARD

The article deals with the problem of developing a modern computer Interface to traditional installations for laboratory work on physics and search for new, active forms, methods and learning tools that would fit modern trends in the development of education and facilitate training highly professional physics teachers. Modern facilities for research isoprocesses in gases consist of a vessel, the volume of which can be changed, pressure and temperature sensors, device for processing signal from sensors. But the price for such installation is too high for educational institutions. The results of implementation of the method of use of the platform Arduino in a laboratory workshop on molecular physics as a simple and, at the same time, an effective alternative to factory equipment for the study of gas laws are given in the article. Arduino is a hardware computer platform for amateur design, basic components of which is a microcontroller board with I / O elements and Processing development environment. The article contains a description of the hardware platform Arduino Nano and pressure and temperature sensors BMP180, MS5611-01BA03, DS18B20, which are used in the installation for the study of gas laws. An important methodological aspect of laboratory work with the use of Computer and Arduino is the data processing of the experiment. Need experimentally establish a functional relationship between independent measured gas parameters: pressure, volume and temperature. In the laboratory works on the study of gas laws is proposed to use methods of statistical analysis: direct selection method functions and the method of linearization. The isochoric or isochloric process is thermodynamic process, which occurs at a constant volume. In its gases to carry out simply - it is enough to heat (cool) substance in a vessel that does not change its volume. The article describes how to explore isochoric process with the help of the developed device. The given software is a code for working with pressure and temperature sensor. Proposed methodology proved the feasibility of practical use of hardware and computer platform Arduino at the Laboratory for Molecular Physics.