The paper investigates the theory of operation of a passive millimeter-wave seeker sensor using fast electronic sequential-lobing technique and the experimental validation obtained through laboratory trials. The paper analyzes in detail the theoretical performance of a difference channel sensor and a pseudo-monopulse sensor deriving agile formulas for the estimation of target angular tracking accuracy and the subsequent experimental validation.

Sequential reactions of aminoalkynes represent a powerful tool to easily assembly biologically important polyfunctionalized nitrogen heterocyclic scaffolds. Metal catalysis often plays a key role in terms of selectivity, efficiency, atom economy and green chemistry of these sequential approaches. This review examines the existing literature on the applications of reactions of aminoalkynes with carbonyls which are emerging for their synthetic potential. Aspect concerning the features of the starting reagents, the catalytic systems, alternative reaction conditions and the pathways as well as the possible intermediates are provided.

The depletion and environmental problems associated with fossil fuels have encouraged us to look for alternative feedstock that do not compromise food security and the environment. Sorghum is a fast-growing crop that can be harvested twice a year and produces both food and straw that can be utilized in the production of bio-based fuels. In this study, the production of bioethanol and the effects of fermentation parameters on ethanol yield are presented. A sequential pretreatment method was employed, using dilute sulfuric acid (1%) at 125 °C in the first stage and dilute sodium hydroxide (1.25 %) at 90 °C in the second stage. The residues left after the sequential pretreatment stage were hydrolyzed using acid hydrolysis. The sugar concentration of the hydrolysates was determined using the phenol sulfuric acid method, and three hydrolysates having sugar levels of 30.42 g/L, 31.79 g/L, and 32.9875 g/L were selected for fermentation. The ethanol yield was determined after 72 hours of fermentation at 30 °C with varying inoculum sizes (5%, 10%, and 15%) and pH (4.5, 5, and 5.5). With a maximum ethanol yield of 0.617 mL/g (48.742%) produced at a sugar content of 32.9875 g/L, pH of 5, and inoculum size of 15%, statistical analysis showed that all three independent parameters affected ethanol yield. According to these findings, while raising sugar content, inoculum amount, and pH all initially result in higher ethanol yields, doing so further reduces yield. So, in order to increase ethanol yield, fermentation conditions must be carefully managed while producing ethanol from sequential acidic-alkaline pretreated sorghum straw. The strategy followed by using sequential acidic-alkaline pretreatment of sorghum straw provides prospects for efficient and effective production of biofuels from alternative feedstock.

Sequential hypothesis test and change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. We show that for such problems, detection procedures based on sequential likelihood ratios with simple one-sample update estimates such as online mirror descent are nearly second-order optimal. This means that the upper bound for the algorithm performance meets the lower bound asymptotically up to a log-log factor in the false-alarm rate when it tends to zero. This is a blessing, since although the generalized likelihood ratio (GLR) statistics are optimal theoretically, but they cannot be computed recursively, and their exact computation usually requires infinite memory of historical data. We prove the nearly second-order optimality by making a connection between sequential analysis and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

Several studies in recent years have shown that the use of non-Saccharomyces yeasts, used both in single and in mixed fermentations with Saccharomyces cerevisiae can help produce craft beers with distinctive compositional characteristics. The aim of this study was to evaluate the suitability of three Metschnikowia pulcherrima strains, isolated from Albanian vineyards, to be used as starters in the brewing process. Due to its specific enzymatic activities (protease, β-glucosidase and β-lyase) and its low production of hydrogen sulfide, M. pulcherrima 62 was selected as a starter culture for the production of craft beer. Specifically, the aptitude of this yeast to be used in sequential inoculation with S. cerevisiae S04 for the production of an American IPA-style beer and the main volatile compounds produced during fermentation were evaluated. Results showed significant differences in glycerol, isoamyl alcohol and isoamyl acetate content in beer obtained with sequential inoculum of M. pulcherrima 62 with S. cerevisiae S04 compared to beer obtained using S. cerevisiae S04 as a single starter. Therefore, these preliminary data support a candidacy of M. pulcherrima 62 as a new starter in the brewing process.

Up to now in breast cancer (BC) treatment, adjuvant chemotherapy (A-CT) precedes adjuvant radiotherapy (A-RT). It’s beyond question that A-RT improves local control while A-CT improves the Disease Free Survival (DFS) due to the reduction of metastatic distant recurrences. Taken togheter these two approaches impact favourably on the reduction of breast cancer mortality. In the last twenty years, the adjuvant treatment of BC has evolved quickly due to a better knowledge of its molecular biology , genetic prohile and its α/β ratio 3 / 4 Gy for the tumor and normal tissues radiosensitivity. Thus new schedules with hypofractionated radiotherapy have been tested and third generation of A-CT have been introduced leading to a better survival rate but a further delay in A-RT starting, raising the question to rethink the sequencing between these two approaches. However the sequencing timing between these two approaches still remains unchanged with A-CT before A-RT as the standard of care. Many attempts have been provided in order to optimize this sequencing. This comprehensive review is a journey among randomised, retrospective and prospective studies which highlights the past, the current and the novelties on timing sequencing proposals between these two modalities to assess the state of art. Sequential, concomitant and sandwitch modalities of chemotherapy with conventional or hypofractionated RT schedules from the most important studies will be analysed in this review.

In the last decade, the application of non-Saccharomyces yeasts in oenology as a natural tool to obtain wine diversification and higher quality has aroused great interest. In this work, three M. pulcherrima strains, isolated from vineyard, were characterized through the evaluation of their main technological properties, antimicrobial activity and specific enzymatic activities (β-glucosidase, β-lyase, polygalacturonase and protease). The M. pulcherrima strains did not produce any inhibition against S. cerevisiae, while they were able to exert an antimicrobial action against some unwanted bacteria and yeasts frequently present in grape must and potentially causing alteration of wines. After this preliminary screening, M. pulcherrima AS3C1 has been selected to be used in the winemaking of red grape Vitis vinifera cv. Aglianico on a pilot scale. The effect of sequential inoculation of M. pulcherrima AS3C1 with a commercial strain of Saccharomyces cerevisiae was verified using for comparison a single inoculum with S. cerevisiae and a spontaneous fermentation. Our results showed a higher concentration of anthocyanins and catechins in wines obtained by sequential inoculation of M. pulcherrima AS3C1 and S. cerevisiae. On the basis of the data obtained, M. pulcherrima AS3C1 possesses an enzymatic profile and some oenological properties that could contribute positively to the definition of the chemical composition of wines, suggesting its possible use for red winemaking processes.

In this paper, a study was conducted on the Ramsey interference and spectral line locking techniques based on rubidium-87 cold atoms. Using sequential control technique, the timing design of a three-dimensional magneto-optical trap (MOT), cold atoms’ free fall, Ramsey microwave interference, and two-level fluorescence detection was realized, achieving the state preparation of cold atoms and clock transitions. By designing an automatic peak-finding algorithm, numerical integration was performed on the time-of-flying (TOF) signal, and narrow linewidth cold atom Ramsey interference fringes were obtained. Based on this, a frequency-hopping method was used to lock the frequency of the 10MHz oven-controlled crystal oscillator (OCXO) in a closed loop, achieving a linewidth of 38Hz. This preliminary result verifies the feasibility of the system design.

Azadirachta indica A. Juss (neem) extracts have been used in pharmaceutical applications as antitumor agents, due to their terpenes and phenolic compounds. To obtain extracts from neem leaves with potential antiproliferative effect, a sequential process of pressurized liquid extraction was carried out in a fixed bed extractor at 25 ºC and 100 bar, using hexane (SH), ethyl acetate (SEA) and then ethanol (SE) as solvents. An extraction using only ethanol (EE) was also conducted to compare the characteristics of the fractionated extracts. The results obtained by liquid chromatography-electrospray ionization mass spectrometry suggested the highest concentration of terpenes for SEA extract in comparison to SH, SE and EE extracts. Therefore, antiproliferative activity showed SEA extracts were the most efficient inhibitors to human tumor cells MCF-7, NCI-H460, HeLa, and HepG2 between all other extracts studied. However, hepatocellular normal cells were more resistant to SH, SEA, SE, and EE compared to malignant cells of breast, lung, hepatocellular, and cervical. Neem fractioned extracts obtained in the present study seem to be more selective for malignant cells compared to the normal cells.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

This article examines varying viewpoints on the single-submission policy in scientific publishing, which has recently come under attack. The rule permits the sequential, rather than simultaneous, submission of a manuscript to more than one journal and dictates that an author(s) must wait for a response from one journal before resubmitting the same work to another for consideration. A corollary is that legitimising multiple-submissions would create more problems than it could solve.

Oscillating Sequential Universes Model (OSUM), an innovative cosmological model describing the universe's evolution through the interplay of dark energy and dark matter is presented in this study. The model proposes a series of expansions and contractions driven by dark energy and dark matter interactions, challenging the conventional view of a singular Big Bang event followed by continuous expansion. The necessity for model refinements, incorporating new observational constraints, reassessing dark energy-dark matter interaction assumptions, and investigating alternative mathematical formulations for the oscillation mechanism have been discussed in this study. The OSUM offers a comprehensive framework for future research and potential discoveries in astrophysics, cosmology, and fundamental physics, raising new questions and challenging prevailing assumptions about the cosmos.

As an effective active remote sensing (ARS) technology for shallow underground targets, ground penetrating radar (GPR) is a detection method to obtain the characteristic information of underground targets by transmitting electromagnetic wave from the antenna and analyzing the propagation law of the electromagnetic wave underground. Due to the high frequency(1MHz-3GHz) of GPR, the depth of geological exploration is shallow(0.1m-30m). In order to remote sensing the deep earth, it is necessary to increase the size of the radiation source in order to reduce the radiation frequency. At the same time, for most separated GPRs, a single dipole antenna (SD) is used as the radiation source and another antenna device placed along the electromagnetic wave propagation direction in the far field as a remote sensing sensor (RSS), both of which are horizontally linearly polarized (LP) antennas. In some cases, such a design is apt to cause problems such as multipath effect (ME) and polarization mismatch (PM). When GPR in ARS of deep earth is performed, it often results in increased errors, signal attenuation during data reception and processing. In contrast, at the radiation source, with the use of large aperture multiple-dipole antennas (MD) and multi-channel sequential rotational excitation, the electromagnetic wave can radiate outward in the form of circular polarization at a low frequency. At the RSS, the trouble caused by ME and PM can be reduced even if the LP antennas are used. A novel sequential rotationally excited (SRE) circularly polarized (CP) MD array for separated GPR in ARS of deep earth is proposed in this paper, which uses a large aperture CP MD array instead of a small size LP SD. The analysis and simulation results demonstrate that under the premise of the same transmitting power, comparing circular polarization and linear polarization, by using SRE CP MD antennas array radiation source, a significant enhancement (about 7dB) of the Signal to Noise Ratio (SNR) will occur by collecting the radiant energy at the RSS. More importantly, by reducing the exploration frequency to 10KHz, the exploration depth will also be greatly increased by about 10 times.

In this paper, the lamellar structural evolution and microvoids variations of β-iPP during the processing of two different stretching methods, sequential biaxial stretching and simultaneous biaxial stretching, were investigated in detail. It was found that different stretching methods led to significantly different lamellae deformation modes, and the microporous membranes obtained from the simultaneous biaxial stretching exhibited better mechanical properties. For the sequential biaxial stretching, abundant coarse fibers originated from the tight accumulation of the lamellae parallel to the longitudinal stretching direction, whereas the lamellae perpendicular to the stretching direction were easily deformed and separated. Those coarse fibers were difficult to be separated to form micropores during the subsequent transverse stretching process, resulting in a poor micropores distribution. However, for the simultaneous biaxial stretching, the β crystal had the same deformation mode, that is, the lamellae distributed in different directions were all destroyed, forming abundant microvoids and little coarse fibers formation.

Studies on the structure of economic systems are, most frequently, carried out by the methods of informational statistics. These methods, often accompanied by a wide range of indicators (Shannon entropy, Balassa coefficient, Herfindahl specialty index, Gini coefficient, Theil index etc.) around which a wide literature has been created over time, have a major disadvantage. Such weakness is related to the imposition of the system condition, therefore the need to know all the components of the system (as absolute values or as weights). This restriction is difficult to accomplish in some situations, and in others, this knowledge may be irrelevant, especially when there is an interest in structural changes only in some of the components of the economic system (either we refer to the typology of economic activities - NACE or of territorial units – NUTS). This article presents a procedure for characterizing the structure of a system and for comparing its evolution over time, in the case of incomplete information, thus eliminating the restriction existent in the classical methods. The proposed methodological alternative uses a parametric distribution, with subunit values for the variable. The application refers to Gross Domestic Product values for five of the 28 European Union countries, with annual values of over 1,000 billion Euros (Germany, Spain, France, Italy and United Kingdom) for the years 2003 and 2015. A form of the Wald sequential test is applied to measure changes in the structure of this group of countries, between the years compared. The results of this application validate the proposed method.

In the first part of this article, we discuss and generalize the complete convergence introduced by Hsu and Robbins (1947) to the r-complete convergence introduced by Tartakovsky (1998). We also establish its relation to the r-quick convergence first introduced by Strassen (1967) and extensively studied by Lai (1976). Our work is motivated by various statistical problems, mostly in sequential analysis. As we show in the second part, generalizing and studying these convergence modes is important not only in probability theory but also to solve challenging statistical problems in hypothesis testing and changepoint detection for general stochastic non-i.i.d. models.

Methyl Salicylate (MS), the principal constituent of Wintergreen oil (WO) was obtained from acetyl salicylic acid (ASA) by sequential transesterification-esterification reaction promoted by NbCl5for the first time. The reagents were added simultaneously, and the reaction process involved the transesterification and esterification reactions, which were accompanied by thin layer chromatography and gas chromatography. The conversion rate via GC was 100%, and the MS yield was 94%. A cytotoxicity of 50% and 64% for cultured S. aureus and metastatic melanoma cells, respectively, was observed for a concentration of 0.6 mg/mL, whereas no cytotoxicity for non-tumor cells was observed for this concentration, and it is considered to be the optimum concentration.

Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. Recently, Arikan presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. At short blocklengths, such codes offer a dramatic improvement in performance as compared to CRC-aided list decoding of conventional polar codes. PAC codes are based primarily upon the following main ideas: replacing CRC codes with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. Simulation results show that PAC codes, resulting from the combination of these ideas, are close to finite-length lower bounds on the performance of any code under ML decoding. One of our main goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size $L$ is moderately large (say, $L \geq 128$). List decoding has distinct advantages over sequential decoding in certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, and use these estimates to approximate the union bound on their performance. These results indicate that PAC codes are superior to both polar codes and Reed-Muller codes. We also consider random time-varying convolutional precoding for PAC codes, and observe that this scheme achieves the same superior performance with constraint length as low as $\nu = 2$.

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user-item rating matrix input of collaborative filtering. This reviews focuses on algorithmic techniques of existing E-commerce recommendation systems that are sequential pattern based such as ChoRec05, ChenRec09, HuangRec09, LiuRec09, ChoiRec12, Hybrid Model RecSys16, Product RecSys16, SainiRec17, HPCRec18 and HSPCRec19. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potentials for solving more important problems in this domain. The review showed that integrating sequential pattern mining of historical purchase and/or click sequences into user-item matrix for collaborative filtering (i) improved recommendation accuracy (ii) reduced user-item rating data sparsity (iii) increased novelty rate of recommendations and (iv) improved scalability of the recommendation system.

Bimetallic (or multimetallic) catalysis has emerged as a powerful tool in modern chemical synthesis, offering improved reaction control and versatility. This review focuses on the recent de-velopments in bimetallic sequential catalysis for the synthesis of nitrogen heterocycles, essential building blocks in pharmaceuticals and fine chemicals. The cooperative action of two (and sometimes more) different metal catalysts enables intricate control over reaction pathways, enhancing selectivity and efficiency of N-heterocyclic compounds synthesis. By activating less reactive substrates, this multimetal catalytic strategy opens new synthetic possibilities for challenging compounds. The use of catalytic materials in bimetallic systems reduces waste and improves atom efficiency, aligning with green chemistry principles. With a diverse range of metal combinations and reaction conditions, bimetallic catalysis provides access to a broad array of N-heterocyclic compounds with various functionalities. This paper highlights the significant progress made in the past decade in this topic, emphasizing the promising potential of bimetallic catalysis in drug discovery and the fine chemical industries.

This paper studies sequential quantum games under the assumption that the moves of the players are drawn from groups and not just plain sets. The extra group structure makes possible to easily derive some very general results, which, to the best of our knowledge, are stated in this generality for the first time in the literature. The main conclusion of this paper is that the specific rules of a game are absolutely critical. The slightest variation may have important impact on the outcome of the game. It is the combination of two factors that determine who wins: (i) the sets of admissible moves for each player, and (ii) the order of moves, i.e., whether the same player makes the first and the last move. Quantum strategies do not a priori prevail over classical strategies. By carefully designing the rules of the game the advantage of either player can be established. Alternatively, the fairness of the game can also be guaranteed.

Presepsin is a diagnostic and prognostic biomarker of sepsis; however, elevated presepsin levels have also been documented without sepsis. This study aims to retrospectively analyze the laboratory parameters and Sequential Organ Failure Assessment (SOFA) score affecting presepsin levels in 567 patients. Some patients with elevated presepsin levels exhibited renal dysfunction or elevation of biliary enzymes despite a low SOFA score. The univariate regression analysis revealed a close correlation between presepsin levels and SOFA score, serum creatinine (CRE), blood urea nitrogen, and biliary enzymes. In addition, a multivariate regression analysis revealed that SOFA score, alkaline phosphatase (ALP), and CRE independently affected presepsin levels significantly. The analysis of covariance (ANCOVA) revealed that presepsin levels were significantly higher in patients with hepatobiliary disease. Besides, we found that patients who presented with the dilatation of intra- or extrahepatic bile ducts and the elevation of ALP or total bilirubin exhibited remarkable high presepsin levels in the bile. Furthermore, the presepsin production in the liver’s Kupffer cells was established by immunostaining in patients who received surgical liver resection. Overall, this study elucidates that biliary enzymes’ elevation affects presepsin levels, presepsin exists in high concentrations in the bile, and is positive in Kupffer cells.

In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).

This paper reviews the pioneering work related to the generation of a sequence of prompts in Pretrained Language Models (PLMs) to automatically complete a complex writing task. The paper identifies the main categories of automated multi-step prompting and discusses their advantages and disadvantages. The paper also proposes an additional approach for automated multi-step prompting with iterative four main phases: in the first phase a sequence of prompts is geared to elicit the structure of the requested task. This is done by identifying the task’s main parts, the general contents of the different parts and their precedence relations. The structure of each main part is iteratively explored to find secondary and tertiary parts if they exist. A second phase is followed by a sequence of prompts that fill in the primary, secondary, and tertiary parts, and seek to fine-tune the result. The third phase reviews the generated text and criticizes it. Based on this criticism a revision is prepared. The fourth phase is fine-tuning the document, with emphasis on section length and text tone. Future research could deal with the system identification of points where the system should initiate interaction with the user for desired feedback and guidance.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

In engineering practice, one can often encounter issues related to optimization where the goal is to minimize material consumption, minimize stresses or deflections of the structure. In most cases, these issues are addressed with finite element analysis software and simple optimization algorithms. However, in the case of optimization of certain structures, it is not co straightforward. An example of such constructions are bubble deck ceilings, where in order to reduce the dead weight, air cavities are used, which are regularly arranged over the entire surface of the ceiling. In the case of these slabs, the flexural stiffness is not constant in all its cross-sections, which means that the use of structural finite elements (plate or shell) for static calculations is not possible, and therefore the optimization process becomes more difficult. The paper presents a minimization procedure of the weight of bubble deck slabs using numerical homogenization and sequential quadratic programming with constraints. Homogenization allows to determine the effective stiffnesses of the floor, which in the next step are sequentially corrected by changing the geometrical parameters of the floor and voids in order to achieve the assumed deflection. The presented procedure allows to minimize the use of material in a quick and effective way by automatically determining the optimal parameters describing the geometry of the bubble deck floor cross-section.

N-acetyl-L-cysteine (NAC), a sulfhydryl-containing compound, is mainly used as a mucolytic and as an antidote for acetaminophen overdose. Flow injection and sequential injection systems were designed and optimized with the aim of providing precise, accurate and reliable flow methods for NAC determination in pharmaceuticals with very low sample and reagent consumption. Proposed methods are based on a redox reaction wherein NAC reduces a complex of Cu(II) and bathocuproine disulfonate (BCS) to orange [Cu(BCS)2]3– complex, which absorption was measured at λmax = 483 nm. The optimized FIA and SIA configuration yielded a linear calibration curve with correlation coefficients (R2 = 0.9999 and R2 = 0.9996) in the concentration range of 3.0 × 10–7 – 3.0 × 10–5 mol L–1 and analytical frequency of 120 h–1 for the FIA method and 4.0 × 10–7 – 4.0 × 10–5 mol L–1, at sampling rate 60 h–1 for the SIA method. The proposed flow methods were successfully applied for the determination of NAC in pharmaceutical products, as the results showed good agreement with the standard method prescribed by Pharmacopoeia. Recoveries were in the range from 98.4% to 101.9% for the FIA method and from 97.2% to 101.8% for the SIA method.

In 1989, we first reported the utilisation of the radial artery as a secondary conduit for coronary artery bypass grafting. However, subsequent randomised trials have reported discrepancies in clinical outcomes between the radial artery and alternative grafts. As we have now accumulated over 50 years of experience with radial artery grafting, we sought to identify the second-best target vessel for coronary operations by conducting a review of the literature. The radial artery has been demonstrated to be a consistently effective second target conduit for coronary artery bypass grafting. The results indicated improved outcomes and patency rates in comparison to those observed for saphenous vein grafts. Furthermore, it has been demonstrated to be a safe and effective conduit in the territory of the right coronary artery. The lack of available literature and the scarcity of similar case series restrict the application of the gastroepiploic artery. After five decades of utilisation, it can be unequivocally stated that the radial artery is the optimal conduit for coronary bypass surgery following the left internal thoracic artery to the left anterior descending artery.

In order to explore more spectrum resources to support sensors and its related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensors paradigm is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attack from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attack, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. Finally, the numerical simulation results demonstrate that compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability, and average number of samples under various attack ratios and probabilities.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

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.

The development of universal influenza vaccines, i.e. vaccines that can provide broad protection against seasonal and potentially pandemic influenza viruses, has been a priority for more than 20 years. Several approaches have been proposed that redirect the adaptive immune responses from immunodominant hypervariable regions to low-immunogenic but highly conserved regions of viral proteins. Here we induced broadly reactive anti-hemagglutinin (HA) stalk antibody by sequential immunizations with live attenuated influenza vaccines (LAIVs) expressing chimeric HA (cHA). These vaccines contained the HA stalk domain from H1N1pdm09 virus but antigenically unrelated globular head domains from avian influenza viruses H5N1, H8N4 and H9N2. In addition, the source of the viral nucleoprotein (NP) of the LAIV strains was changed from A/Leningrad/17 master donor virus (MDV) to wild-type (WT) H1N1pdm09 virus, in order to induce CD8 T-cell immune responses more relevant to current infections. To avoid any difference in protective effect of the various anti-neuraminidase (NA) antibodies, all LAIVs were engineered to contain the NA gene of Len/17 MDV. Naïve ferrets were immunized with three doses of (i) classical LAIVs containing non-chimeric HA and NP from MDV (LAIVs (NP-MDV)); (ii) cHA-based LAIVs containing NP from MDV (cHA LAIVs (NP-MDV)); and (iii) cHA-based LAIVs containing NP from H1N1pdm09 virus (cHA LAIVs (NP-WT)). A high-dose challenge with H1N1pdm09 virus induced significant pathology in the control, non-immunized ferrets, including high virus titers in respiratory tissues, clinical signs of disease and histopathological changes in nasal turbinates and lung tissues. All three vaccination regimens protected animals from clinical manifestations of disease: immunized ferrets did not lose weight or show clinical symptoms, and their fever was significantly lower than in the control group. Further analysis of virological and pathological data revealed the following hierarchy in the cross-protective efficacy of the vaccines: cHA LAIVs (NP-WT) > cHA LAIVs (NP-MDV) > LAIVs (NP-MDV). This ferret study showed that prototype universal LAIVs that combine the two approaches of inducing anti-HA stalk antibody and more relevant CD8 T-cell immune responses are highly promising candidates for further clinical development.

Sequential properties can violate Bell-type inequalities without direct influence on each other, but only if detected in pairs. This is demonstrated below with theoretical arguments, and with a straightforward toy model. Rotating arrows can have transient qualities that produce maximal Bell violations with predetermined values. Any non-signaling phenomenon (classical, or non-classical) can also be interpreted as local.

Up to 50% of patients with high-risk myeloid malignancies die of relapse after allogeneic stem cell transplantation. Current sequential conditioning regimens like the FLAMSA protocol combine intensive induction therapy with TBI or alkylators. Venetoclax has synergistic effects to chemotherapy. In a retrospective survey among German transplant centers, we identified 61 patients with myeloid malignancies that had received FLAMSA based sequential conditioning with Venetoclax between 2018 and 2022 as an individualized treatment approach. Sixty patients (98%) had active disease at transplant and 74% had genetic high-risk features. Patients received allografts from matched unrelated, matched related or mismatched donors. Tumor lysis syndrome occurred in two patients but no significant non-hematologic toxicity related to Venetoclax was observed. At day +30, 55 patients (90%) were in complete remission. Acute GvHD II°-IV° occurred in 17 (28%) and moderate/severe chronic GvHD in 7 patients (12%). Event-free survival and overall survival were 64% and 80% at 1 year as well as 57% and 75% at 2 years, respectively. The combination of sequential FLAMSA-RIC with Venetoclax appears to be safe and highly effective. To further validate these insights and enhance the idea of smart conditioning, a controlled prospective clinical trial has been initiated in 07/2023.

Several specific types of generalized compactness of generalized topological spaces have been defined, investigated and related to compactness in ordinary topological spaces from time to time in the literature of topological spaces. Our recent research in the field of a new class of generalized compactness in a generalized topological space is reported herein as a starting point for more generalized classes.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Continuous research into the availability of phosphorus (P) in forest soil is critical for sustainable management of forest ecosystems. In this study, we used sequential chemical extraction and 31P-nuclear magnetic resonance spectroscopy (31P-NMR) to evaluate the form and distribution of inorganic P (Pi) and organic P (Po) in Casuarina forest soils of a subtropical coastal sand dune at Houlong in Taiwan. The soil samples were collected from humic (+2-0 cm) and mineral layers (mineral-I: 0-10, mineral-II: 10-20 cm) at two topographic locations (upland and lowland) by elevation. Sequential chemical extraction revealed that the NaOH-Po fraction, as moderately recalcitrant P, was the dominant form in humic and mineral-I layers in both upland and lowland soils, whereas the cHCl-Pi fraction was the dominant form in the mineral-II layer. Resistant P content, including NaOH-Pi, HCl-Pi, cHCl-Pi, and cHCl-Po fractions, was higher in the upland than lowland in the corresponding layers; however, labile P content, NaHCO3-Po, showed the opposite pattern. Content of resistant Pi (NaOH-Pi, HCl-Pi, and cHCl-Pi) increased significantly with depth, but that of labile Pi (resin-Pi and NaHCO3-Pi) and recalcitrant Po (NaHCO3-Po, NaOH-Po, and cHCl-Po) decreased significantly with depth at both locations. 31P-NMR spectroscopy revealed inorganic orthophosphate and monoesters-P as the major forms in this area. The proportions of Pi and Po evaluated by sequential chemical extraction and 31P-NMR spectroscopy were basically consistent. The results indicated that the soils were in weathered conditions. Furthermore, the P distribution and forms significantly differed between the upland and lowland by variation in elevation and eolian aggradation effects in this coastal sand dune landscape.

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

Detailed evaluation is provided for the likelihood statistics intrinsic to interlocking Sequential Bayes analysis, which allows estimation of evidential support for dendrograms charting the macroevolution of taxa. It involves complexity functions, such as fractal evolution, to generate well-supported evolutionary trees. Required are data on trait changes from ancestral species to descendant species, which is facilitated by reduction of large genera to monothetic groups (one ancestral species each), most conveniently named as separate genera. Assigning each species one Shannon informational bit per new trait, then adding the traits as sequential Bayesian analysis (each posterior probability used as the prior for the next iteration of Bayes Formula), then interpretation with an odds chart, provides a posterior probability that the ancestor-descendant order is correct, that is, entirely follows evolutionary theory involving adaptation and rarity of total trait reversals. The key fact is that the most recently acquired traits of the ancestral species are selectively inviolate and passed on without change to each descendant species. The details of sequential Bayesian analysis were clarified by calculation of likelihood ratios and Bayes factors that compare optimal models with the next most likely model. Such analysis demonstrated that the optimum arrangement of ancestor and descendant species leads to high support values for fitting evolutionary theory, comparable to statistical support levels reported for molecular evolutionary trees. The fact that phylogenetic analysis does not offer an ancestor-descendant model in is found to be conducive of precision at the expense of accuracy, and leads to wrong ancestor-free models with likelihoods explaining data at high credible levels. The number of advanced traits in the outgroup as a Bayesian prior greatly enhances posterior probability. The method is simple, free of special computer analysis, and well-suited to standard taxonomic practice.

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

This article develops algorithms for the characterization and the visualization of micro-scale features by using a small number of sample points, and with a goal to mitigate for the measurement shortcomings which are often destructive or time consuming. We implement the algorithms to rapidly examine the microscopic features of a Microelectromechanical System (MEMS) surface. Such images are highly dense; therefore, traditional image processing techniques might be computationally expensive. The contribution of this research include first, we develop local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives by using low resolution images. Second, we obtain a bending energy algorithm from our modified TPS model, and use it to filter out image defects. Finally, we develop a computationally efficient Windowing technique, which combines TPS and Linear Sequential Estimation (LSE), to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20 x 20 m convex and non-convex environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment and at lower computational complexity per action. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12

This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.

Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.