Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons, the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this paper, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images are collected from the Internet and several warehouses, and objects are labeled using per-instance segmentation for precise localization. There are total of 250,000 instance masks from 16,136 images. Naturelly, a suite of benchmarks are established with several popular detectors. In addition, we design a carton detector based on RetinaNet by embedding our proposed Offset Prediction between Classification and Localization module (OPCL) and Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality which boosts AP by 3.1%∼4.7% on SCD at the model level while BGS guides the detector to pay more attention to boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL to other datasets, we conduct extensive experiments on MS COCO and PASCAL VOC. The improvements of AP on MS COCO and PASCAL VOC are 1.8%∼2.2% and 3.4%∼4.3% respectively. Source dataset is available here.

Atherosclerosis (AS) is a chronic progressive disease caused by various factors, and causes various cerebrovascular and cardiovascular diseases (CVDs). Reducing the plasma levels of low-density lipoprotein-cholesterol (LDL-C) is the primary goal in preventing and treating AS. Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) plays a crucial role in regulating LDL-C metabolism. Panax notoginseng has potent lipid-reducing effects and protects against CVDs, and its saponins induce vascular dilatation, inhibit thrombus formation, and are used in treating CVDs. However, the anti-AS effect of the secondary metabolite, 20(S) -protopanaxatriol (20(S)-PPT), remains unclear. In this study, the anti-AS effect and molecular mechanism of 20(S)-PPT were investigated in vivo and in vitro by western blotting, real time-polymerase chain reaction (RT-PCR), Enzyme-linked Immunosorbent Assay (ELISA), immunofluorescence staining, and other assays. The in vitro experiments revealed that 20(S)-PPT reduced the levels of PCSK9 in the supernatant of HepG2 cells, upregulated low density lipoprotein receptor (LDLR) protein levels, promoted LDL uptake by HepG2 cells, and reduced PCSK9 mRNA transcription by upregulating the levels of FoxO3 protein and mRNA and decreasing the levels of HNF1α protein and mRNA. The in vivo experiments revealed that 20(S)-PPT upregulated aortic αSMA expression, increased the stability of atherosclerotic plaques, and reduced aortic plaque formation induced by a high-cholesterol fed in ApoE-/- mice (HCF group). Additionally, 20(S)-PPT reduced the aortic expression of CD68, reduced inflammation in the aortic root, and alleviated the hepatic lesions in the HCF group. The study revealed that 20(S)-PPT inhibited LDLR degradation via PCSK9 to alleviate AS.

Lower Back Pain (LBP) is a disease that needs immediate attention. Person with back pain shall go immediately to doctor for treatment. Injury, excessive works and some medical conditions are result of back pain. Back pain is common to any age of human for different reasons. Due to factors such as previous occupation and degenerative disk disease the chance of developing lower back pain increases for older people. It hampers the working condition of people common reason for seeking medical treatment. The result is absence from work and is unable to normal due to pain. It creates uncomfortable and debilitating situations. Hence, detecting this disease at an early stage will assist the medical field experts to suggest counter measures to the patients. Detection of lower back pain is implemented in this paper by applying ensemble machine learning technique. This paper proposes Stacking ensemble classifier as an automated tool that will predict lower back pain tendency of a patient. Experimental result implies that the proposed method reaches an accuracy of 76.34%, f1-score of 0.76 and MSE of 0.34.

Animal behavior analysis is a crucial tasks for the industrial farming. In an indoor farm setting, extracting Key joints of animal is essential for tracking the animal for longer period of time. In this paper, we proposed a deep network that exploit transfer learning to trained the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on hourglass stacked dense-net. In order to train the network, key frames are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.

Naturally fermented dairy products are an important component of the human diet. They are a valuable source of nutrients as well as vitamins and minerals. Their importance as a source of probiotic bacterial strains should not be overlooked. A number of studies highlights the positive effects of species of the Lactobacillacea on the intestinal microbiota, the overall homeostasis of the body, as well as a complementary treatment for some diseases. However, data on the effects on intestinal epithelial cells of postmetabolites released by bacteria are incomplete. This is likely due to the fact that these effects are species and strain specific. In the present study, we investigated the effects of postmetabolites produced by a pre-selected candidate probiotic strain Limosilactobacillus fermentum on HT-29 intestinal epithelial cells. Our data showed a pronounced proliferative effect, evaluated by flow cytometry, quantification of cell population, and determination of mitotic index. This was accompanied by stabilization of the cell monolayer, measured by increase of TEER (trans epithelial electric resistance) and reorganization of actin filaments. The data obtained are a clear indication of the positive effects, that the products secreted by L. fermentum strain 53 have on the intestinal epithelial cells.

Seeds from several Ribes taxa were surveyed for phenolic compounds and in vitro antiproliferative activity against HT-29 colorectal cancer cells. Total phenolic compounds were analysed through the Folin-Ciocalteu procedure, while phenolic profiles were analysed by LC coupled to a single mass spectrometer Orbitrap using an electrospray interface (ESI). Antitumor effects were established using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Total phenolics ranged from 11.4 in R. alpinum to 94.8 mg of caffeic acid equivalents (CAE)/g in R. nigrum 'Koksa'. Concerning phenolic compounds, four were hydroxylated benzoic acids, four cinnamic acid derivatives, eight flavonoids, and nine flavonoid glycosides. The growth inhibition against HT-29 cancer cells was exercised much better by R. nigrum 'Koksa' and Ribes 'Erkeeni' (GI50 37 and 42 µg/ml). All Ribes extracts, except R. nigrum 'Hara katarlik', showed higher activity than that of R. rubrum (GI50 at 72 h: 99 µg/ml). Interestingly, the highly bioactive extract from Ribes 'Erkeeni' contains all detected phenolics, while R. nigrum 'Koksa' lacks only populnin. Therefore, the high bioactivity found for such extracts could be due to a synergy due to all detected compounds. This work constitutes a comprehensive action for expanding knowledge on the phenolic profiles and antitumor activity of GLA-rich Ribes seeds.

This study focuses on the potential medicinal properties of Campsis radicans L. extracts and its aim is to investigate the antioxidant and antiproliferative effects of these extracts on the human colon adenocarcinoma cell line (HT-29). Cytotoxic activity in the HT-29 cell line was evaluated, and the 3-dimensional structures of phenolic compounds were obtained using the GC-MS method. Docking simulations were performed with AutoDock Vina, targeting proteins that are crucial in the apoptotic pathway (Bax, Bcl-2, and Caspase-3). Drug similarities and ADMET estimates of the phenolic compounds were obtained from databases. The highest antioxidant activity of Campsis radicans L. plant was observed in ethanol and water extracts, with notable inhibition capacities. While the total phenolic content was highest in ethanol, water, and ether extracts respectively the total flavonoid content was highest in ethanol, then ether, and water extracts. Certain elements were undetectable in mineral determination, and others exceeded the toxic doses for plants. The water extract exhibited dose-dependent cytotoxic activity in the HT-29 cell line, with an IC50 value of 4.47 mg/mL. Molecular docking results revealed the binding of phenolic molecules to the active sites of apoptotic pathway proteins. The findings suggest that Campsis radicans L. plant possesses cytotoxic properties, significant phenolic and flavonoid content, and a high antioxidant capacity. These properties make it a potential candidate for treatment use, emphasizing its potential therapeutic value in the HT-29 cell line. Further research and exploration on the identified phenolic compounds could contribute to the development of novel therapeutic agents.

Breast Cancer diagnosis is one of the most studied problems in the medical domain. In the medical domain, cancer diagnosis has been studied extensively which instantiates the need of early prediction of cancer disease. For obtaining advance prediction, health records are exploited and given as input to an automated system. This paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining possibility of being affected by breast cancer. Proposed model is compared against other baseline classifiers such as stacked Simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that stacked GRU-LSTM-BRNN yield better classification performance for predictions related to breast cancer disease.

Endotoxins are toxic substances that widely exist in the environment and can enter the intestine with food and other substances. Intestinal epithelial cells are protected by a mucus layer that contains MUC2 as its main structural component. However, a detailed understanding of the mechanisms involved in the function of the mucus barrier in endotoxin penetration is lacking. Here, we established the most suitable proportion of Caco-2/HT-29 co-culture cells as a powerful tool to evaluate the intestinal mucus layer. Our findings significantly advance current knowledge as focal adhesion and ECM-receptor interaction were identified as the two most significantly implicated pathways in MUC2 small interfering RNA (siRNA)-transfected Caco-2/HT-29 co-culture cells after 24 h of LPS stimulation. When the mucus layer was not intact, LPS was found to damage the tight junctions of Caco-2/HT29 co-cultured cells. Furthermore, LPS was demonstrated to inhibit the integrin-mediated focal adhesion structure and damage the matrix network structure of the extracellular and actin microfilament skeletons. Ultimately, LPS inhibited the interactive communication between the extracellular matrix and the cytoskeleton for 24 h in the siMUC2 group compared with the LPS(+) and LPS(-) groups. Overall, we recognized the potential of MUC2 as a tool for barrier function in several intestinal bacterial diseases.

To date, radiation adaptation response has been reported as a low-dose related phenomenon and has been associated with radiation hormesis. Well-known cancers are caused by non-radiation active reactants, in addition to radiation. A model of suppression for radiation-specific cancers was previously reported, but the model did not target radiation-nonspecific cancers. In this paper, we describe kinetic models of radiation-induced suppressors for general radiation non-specific cancers, estimating the dose that induces the maximum hormesis effect while satisfying the condition that the risk is approximately proportional to dose above 100 mSv. It was found that the radiation hormesis effect is maximal at about 20 mSv when the rate constant for generation is the same as the rate constant for decomposition. When the two rate constants are different, the dose at which the radiation hormesis effect is maximized depends on both rate constants, but the dose increases as the two rate constants approach each other, reaching a maximum of about 20 mSv. This conclusion may lead to the discovery of unknown cancer suppressors at low doses and the development of cancer suppression methods in the future.

Keywords: Wall-like RC columns; Strengthening; FRP; NSM rebars; Steel plates; Testing; FE modeling; Preloading.

The strategy of any organization is based on the growth of its customer base, and one of 5 its principles is that selling a product to an existing customer is much more profitable than acquiring 6 a new customer. However, this approach has several opportunities for improvement, since it usu- 7 ally has a totally reactive approach, which does not give opportunity to the areas specialized in 8 customer experience and recovery, to give an effective response for that moment, since the customer 9 is gone at the time of the intervention. This happens because usually a diagnostic analysis of cus- 10 tomers who have stopped buying products or services in a defined period, commonly three (3) pe- 11 riods or months, is performed. This thesis work challenges the way to face this problem, and pro- 12 poses the development of a complete solution, which does not focus exclusively on the prediction 13 of churn, as is usually done in the state of the art research, but to intervene in different interactions 14 that can be carried out with customers. The above focused not only to prevent customer churn, but 15 to generate an added value of continuous improvement in sales processes, increase customer pene- 16 tration, leading to an improvement in customer experience and consequently, an increase in cus- 17 tomer loyalty.

The strategy of any organization is based on the growth of its customer base, and one of 5 its principles is that selling a product to an existing customer is much more profitable than acquiring 6 a new customer. However, this approach has several opportunities for improvement, since it usu- 7 ally has a totally reactive approach, which does not give opportunity to the areas specialized in 8 customer experience and recovery, to give an effective response for that moment, since the customer 9 is gone at the time of the intervention. This happens because usually a diagnostic analysis of cus- 10 tomers who have stopped buying products or services in a defined period, commonly three (3) pe- 11 riods or months, is performed. This paper challenges the way to face this problem, and proposes 12 the development of a complete solution, which does not focus exclusively on the prediction of 13 churn, as is usually done in the state of the art research, but to intervene in different interactions 14 that can be carried out with customers. The above focused not only to prevent customer churn, but 15 to generate an added value of continuous improvement in sales processes, increase customer pene- 16 tration, leading to an improvement in customer experience and consequently, an increase in cus- 17 tomer loyalty.

In a complex working environment, the fault signal of the gearbox is greatly affected by the outside world, and fault feature recognition is difficult, so the fault diagnosis accuracy is difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the Particle Swarm Optimization algorithm in Adaptive Weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that, compared with existing commonly used methods of fault diagnosis, the fault diagnosis model proposed in this paper can reduce the influence of noise in the original signal can better learn the deep-level features in the original signal and has higher diagnosis accuracy, faster diagnosis speed and good generalization in fault diagnosis.

Запропонована методика спрямована на визначення та прогнозування технічного стану елементів мосту, що може слугувати прогресивним інженерним інструментом для оцінки надійності та довговічності. Розроблено на основі фундаментальних досліджень, які узагальнюють досвід вивчення фізико-механічних і фізико-хімічних властивостей матеріалів мостових конструкцій, що експлуатуються в реальних умовах. Теоретичною основою методики є модель надійності та прогнозування залишкового ресурсу елементів мосту на основі теорії Маркова. Розроблена методика призначена для оцінки технічного стану окремих елементів мосту з подальшою комплексною оцінкою всієї конструкції. За показник технічного стану прийнята надійність при експлуатації. Цей кількісний показник надійності в моделі є критерієм для: оцінки рівня безпеки елементів мосту; ранжування елементів мосту за необхідністю для конкретних видів ремонту, реконструкції або заміни; стратегічне планування витрат на ремонт або реконструкцію в умовах обмеженого фінансування; прогнозування залишкового ресурсу елементів. Для застосування розробленої методики запропоновано алгоритм оцінки та прогнозування технічного стану мостів. Було проведено математичний експеримент розробленої методики, який підтвердив адекватність висунутої гіпотези, тобто використання моделі надійності та прогнозування залишкового ресурсу елементів мосту. По-перше, запропоновано триступеневий механізм уточнення технічного стану мосту, суттєво підвищення точності розрахунків. Тому розроблена методика має практичне значення і може слугувати основою для інформаційно-аналітичних систем управління станом мостів.

The timely diagnosis of defects at their incipient stage of formation is crucial to extend the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damages, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of Acoustic Emission (AE)-based inspection techniques through the computation of the Time of Arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor Signal-to-Noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative Deep Learning methods are proposed for ToA retrieval, namely a Dilated Convolutional Neural Network (DilCNN) and a Capsule Neural Network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two novel methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations.

During the last trimester of gestation and for the first 18 months after birth, both docosahexaenoic acid,22:6n-3 (DHA) and arachidonic acid,20:4n-6 (ARA) are preferentially deposited within the cerebral cortex at a rapid rate. Although, the structural and functional roles of DHA in brain development are well investigated, similar roles of ARA are not well documented. The mode of action of these two fatty acids and their derivatives at different structural-functional roles and their levels in the gene expression and signaling pathways of the brain have been continuously emanating. In addition to DHA, importance of ARA has been much discussed in recent years for fetal and postnatal brain development and the maternal supply of ARA and DHA. These fatty acids are also involved in various brain developmental processes; however, their mechanistic cross talks are not clearly known yet. This review describes the importance of ARA, in addition to DHA to support the optimal brain development and growth and functional roles in the brain.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

Thirteen benzylethylenearyl ureas and one carbamate have been designed, synthesized and biologically evaluated as regards their antiproliferative activity on tumor cell lines HEK-293 and A-549 and on the immune and endothelial Jurkat T cells and HMEC-1, respectively. A relationship between the structure of the synthetic compounds and their antiproliferative activity has been established. Compounds C.1, C.3, C.12 and C.14 were chosen for further biological studies in order to determine their potential as oncoimmunomodulating agents. Some compounds exhibited significant inhibitory effects on both total PD-L1 and VEGFR-2 in HT-29 cell line, showing urea C.12 active against both targets. Some compounds could reduce more than 50% of cancer living cells, compared to non-treated ones, when assessed in co-cultures using HT-29 and THP-1 cells. In addition, these compounds showed no effect on the immune cell viability and significantly reduce CD11b expression, which is a promising target for immune modulation in anti-cancer therapies.

The first detection of gypsum (CaSO4.2H2O) by the Mars Science Laboratory (MSL) rover Curiosity in the Gale Crater, Mars created a profound impact on planetary science and exploration. The unique capability of plasma spectroscopy involving in situ elemental analysis in extraterrestrial environments, suggesting the presence of water in the red planet based on phase characterization and providing a clue to Martian paleoclimate. The key to gypsum as an ideal paleoclimate proxy lies in its textural variants, and in this study terrestrial gypsum samples from varied locations and textural types have been analyzed by Laser Induced Breakdown Spectroscopy (LIBS) technique. Petrographic, sub-microscopic and powder X-ray diffraction characterizations confirm the presence of gypsum (hydrated calcium sulphate; CaSO4.2H2O), bassanite (semi-hydrated calcium sulphate; CaSO4.1/2H2O) and anhydrite (anhydrous calcium sulphate; CaSO4) along with accessory phases (quartz and jarosite). The principal component analysis of LIBS spectra from texturally varied gypsums can be differentiated from one another because of the chemical variability in their elemental concentrations. The concentration of gypsum is determined from the partial least-square regressions model. Rapid characterization of gypsum samples with LIBS is expected to work well in extraterrestrial environments.

Delay in data transmission is one of key performance indicators (KPIs) of a network. The planning and project value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in an Long Term Evolution (LTE) network using three ML techniques - Neural Networks (NN), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that, in all three optimization approaches, the k-NN model is selected as the best solution. For the RReliefF optimization algorithm, the best model has 6 inputs and minimum relative error (RE), RE=0.109; for the Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and RE=0.041; and for the Pareto 80/20 rule, the best model has 11 inputs and RE= 0.049. The comparative analysis of the results concludes that according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

This contribution is an attempt to reconstruct the favorable atmosphere that prevailed in Lau-sanne in 1986-87 and provided the backdrop of our invention of two-dimensional ion cyclotron resonance mass spectroscopy (2D ICR-MS). To avoid a self-centered histoire d’ancien combattant, we shall try to emphasize the context, the contributions of key players within our nascent research group at UNIL and the established group of Tino Gäumann at EPFL, the role of external speakers, and the open atmosphere that was not yet polluted by bibliometrics, obsessive concern with im-pact factors, and top-down management of research. We shall also explain why the idea of 2D ICR-MS has been ignored for many years and still has a limited impact: different scientific cul-tures in the ICR and NMR communities, different concerns with fundamental vs. applied research, different status of theory and numerical simulations, different levels of commitment of instrument manufacturers, not to mention many theoretical problems that appear to be at least as challenging in ICR as in NMR.

This paper presents work done as part of a transformation effort towards a greener and more sustainable Aluminium manufacturing plant. The effort includes reducing the carbon footprint by minimising waste and increasing operational efficiency. The contribution of this work includes the reduction of waste through the implementation of autonomous, real-time quality measurement and classification at an Aluminium casthouse. Data is collected from the MV20/20 which uses ultrasound pulses to detect molten Aluminium inclusions, which degrade the quality of the metal and cause subsequent metal waste. The sensor measures cleanliness, inclusion counts and distributions from 20 - 160 microns. The contribution of this work is in the development of business analytics to implement condition-based monitoring through anomaly detection, and to classify inclusion types for samples that failed. For anomaly detection, multivariate K-Means and DBSCAN algorithms are compared as they have been proven to work in a wide range of datasets. For classification, a two-stage classifier is implemented. The first stage classifies the success or failure of the sample, while the second stage classifies the inclusion responsible for the failed sample. The algorithms considered include logistic regression, support vector machine, multi-layer perceptron and radial basis function network. The multi-layer perceptron offers the best performance using k-fold cross-validation, and is further tuned using grid search to explore the possibility of an even better performance. The results reveal that the model has achieved a global maximum in performance. Recommendations include the integration of additional sensor systems and the improvements in quality assurance practices.

Hexakis(2-alkoxy-1,5-phenyleneimine) macrocycles were synthesized using a simple one−pot procedure through by precipitation−driven cyclization. The acetal−protected AB−type monomers, 2-alkoxy-5-aminobenzaldehyde diethyl acetals, underwent polycondensation in water or acid−containing tetrahydrofuran. The precipitation−driven cyclization based on imine dynamic covalent chemistry and π−stacked columnar aggregation played a decisive role in the one−pot synthesis. The progress of the reaction was analyzed by MALDI−TOF mass spectrometry. The macrocycles with alkoxy chains were soluble in specific organic solvents such as chloroform, allowing their structures to be analyzed by NMR. The shape−anisotropic, nearly planar, and persistent macrocycles aggregated into columnar assemblies in polymerization solvents, driven by aromatic π−stacking. The octyloxylated macrocycle OcO−Cm6 exhibited an enantiotropic columnar liquid crystal−like mesophase between 165°C and 197°C. In the SEM image of (S)-(−)-3,7-dimethyloctyloxylated macrocycle (−)BCO-Cm6, columnar substances with a diameter of 100−200 nm were observed. The polymerization solution for 2-(2-methoxyethoxy)ethoxy)ethoxylated macrocycle (TEGO−Cm6) gelled and showed thixotropic properties by forming a hydrogen bond network.

This study investigates the spatio-temporal variability of the onset, offset, and Length of the rainy season in Senegal over the 1981-2018 period. The onset, offset dates, and length of the rainy season are crucial parameters for agricultural planning in West Africa, which exhibits high interannual and spatial variability in rainfall. The objective is to detect and spatially classify these phenological parameters across Senegal using different approaches. Daily precipitation data from the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) dataset over the study period and ERA5 reanalysis were used. The method of was applied to detect the onset, offset dates, and length of the rainy season. The Kohonen algorithm was then used to spatially classify these parameters on topological maps. A hierarchical ascendant classification was also performed to reduce the number of classes obtained. Finally, a composite analysis of moisture transport before the onset and end of the 1984 and 2007 seasons was conducted with ERA5 data. The results show a meridional gradient of the start of the season, progressively later from southeast to northwest, while the end follows a north-south gradient. The duration of the rainy season ranges from 45 days in northern Senegal to 150 days in the south. The maximum number of dry days also decreases from north to south. The Kohonen self-organizing maps allow spatial prediction of the onset, end date, and duration of the rainy season across Senegal. The dynamics analysis reveals that early onset years are associated with stronger and earlier northward moisture transport over West Africa, underscoring the role of this parameter in triggering the rainy season. These promising results can provide an early warning to policymakers and farmers on the start and end dates of the season in each area of Senegal.

Pathogenicity is unknown for the majority of human gene variants. For prioritization of sequenced somatic and germline mutation variants, in silico approaches can be utilized. In this study, 84 million non-synonymous Single Nucleotide Variants (SNVs) in the human coding genome were annotated using consensus Variant Effect Prediction (cVEP) method. An algorithm, implemented as a stacked ensemble of supervised learners, performed combination of the 39 functional, conservation mutation impact scores from dbNSFP4.0. Adding gene indispensability score, accounting for differences in the pathogenicities of the variants in the essential and the mutation-tolerant genes, improved the predictions. For each SNV the consensus combination gives either a continuous-value pathogenicity score, or a categorical score in five classes: pathogenic, likely pathogenic, uncertain significance, likely benign, benign. The provided class database is aimed for direct use in clinical practice. The trained prediction models were 5-fold cross-validated on the evidence-based categorical annotations from the ClinVar database. The rankings of the scores based on their ability to predict pathogenicity were obtained. A two-step strategy using the rankings, scores and class annotations is suggested for filtering and prioritization of the human exome mutations in clinical and biological applications of NGS technology.

In this paper, we study the effect of parasitic channel on electrical characteristics and power consumptions of vertically stacked gate-all-around (GAA) silicon nanosheet (NS) MOSFETs of the bulk and silicon-on-insulator (SOI) substrates. Through deliberate control of the bottom parasitic channel coverage, we systematically assess its influence on DC, AC, and RF characteristics including power consumption. The observed findings highlight a significant differentiation among the bottom parasitic channels: GAA (i.e., control sample with 100% coverage), omega-shaped gate NS (70% and 80% coverages), and FinFET (60% coverage). Compared with the device with a 60% coverage channel, the device with a 100% coverage channel possesses 3520% and 8030% normalized leakage current improvement for both NFET and PFET under the bulk substrate, respectively. Compared with bulk-substrate devices, the SOI-substrate devices further significantly improve the leakage current. Due to enhanced off-state current leakages in the 100% covered bulk-substrate device, the static power demonstrates a notable improvement (about 98.1%), compared to 60% covered devices. Hence, the utilization of a 60% covered channel will result in a degradation of both device and circuit performance. The observation of GAA NS MOSFET indicates that an increase in the bottom parasitic channel coverage ratio leads to a positive impact on power consumption and leakage currents. The examination presented here is useful in the fabrication of stacked GAA NS MOSFETs.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

Multi-junction solar cells comprised of stacked III-V semiconductor junctions represent the highest-efficiency photovoltaic technology, with recent demonstrations exceeding 47% efficiency . Optimizing the design and thickness of each junction is critical for maximizing performance . This work utilizes Silvaco TCAD tools to systematically optimize a 5-junction cell based on AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge similar to recent record cells . The junction thicknesses are varied using a predictive profiler to sample the parameter space . For each combination, the spectral absorption and I-V characteristics are simulated to determine the efficiency . Statistical analysis identifies the optimal thickness set that maximizes performance to 26% under 1 sun illumination .

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.