The production of butanol, acetone and ethanol by Clostridium acetobutylicum is a biphasic fer-mentation process. In the first phase the carbohydrate substrate is metabolized to acetic and bu-tyric acid, in the following second phase the product spectrum is shifted towards the economi-cally interesting solvents. Here we present a cascade of six continuous stirred tank reactors (CCSTR), which allows performing the time dependent metabolic phases of an ace-tone-butanol-ethanol (ABE) batch fermentation in a spatial domain. Experimental data of steady states under four operating conditions - with variations of the pH in the first bioreactor between 4.3 and 5.6 as well as the total dilution rate between 0.042 1/h and 0.092 1/h - were used to optimize and validate a corresponding mathematical model. Beyond a residence time distribution representation and substrate, biomass and product kinetics this model also includes the differen-tiation of cells between the metabolic states. Model simulations predict a final butanol product concentration of 8.2 g/L and a butanol productivity of 0.75 g/(L h) in the CCSTR operated at a pH in bioreactor 1 of 4.3 and D = 0.092 1/h, while 31 % of the cells are differentiated to the solventogenic state. Aiming at an enrichment of solvent-producing cells, a feedback loop was introduced into the cascade - sending cells from a later state of the process (bioreactor 4) back to an early stage of the process (bioreactor 2). In agreement with the experimental observations, the model accurately predicted an increase of butanol formation rate in bioreactor stages 2 and 3, resulting in an overall butanol productivity of 0.76 g/(L h) for the feedback loop cascade. The here presented CCSTR and the validated model will serve to investigate further ABE fermentation strategies for a controlled metabolic switch.

The potential yield of annual crops is affected by management practices and water and energy availabilities throughout the crop season. The current work aimed to assess the effects of plant population and soil covering on yield components of maize. Field experiments were carried out during 2014-15 and 2015-16 growing seasons at areas grown with oat straw, voluntary plants and bare soil, considering five different plant populations (40,000, 60,000, 80,000, 100,000 and 120,000 plants ha-1) and three sowing dates (15 Sep., 30 Oct., 15 Dec.) for the hybrid P30F53YH in Ponta Grossa, State of Parana, Brazil. Non-impacts of soil covering or plant population on plant height at the flowering phenological stage were observed. Significant effects of soil covering on crop physiological and yield components responses throughout the 2014-15 season were detected. Influence of plant populations on yield components was evidenced, suggesting that from 80,000 plants ha-1 the P30F53YH hybrid performs a compensatory effect among assessed yield components in such a way as to not compromise productivity insofar as plant population increases up to 120,000 plants ha-1. It was noticed a positive trend of yield components and crop final yield as a function of plant density increments.

A photovoltaic-thermal (PVT) collector is a solar-based micro-cogeneration system which generates simultaneously heat and power for buildings. The novelty of this paper is to conduct energy and exergy analysis on PVT collector performance under two different European climate conditions. The performance of the PVT collector is compared to a PV panel. Finally, the PVT design is optimized in terms of thermal and electrical exergy efficiencies. The optimized PVT designs are compared to the PV panel performance as well. The main focus is to find out if the PVT is still competitive with the PV panel electrical output, after maximizing its thermal exergy efficiency. The PVT collector is modelled into Matlab/Simulink to evaluate its performance under varying weather conditions. The PV panel is modelled with the CARNOT toolbox library. The optimization is conducted using Matlab gamultiobj-function based on Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The results indicated 7.7% higher annual energy production in Strasbourg. However, the exergy analysis revealed a better quality of thermal energy in Tampere with 72.9% higher thermal exergy production. The electrical output of the PVT is higher than from the PV during the summer months. The thermal exergy- driven PVT design is still competitive compared to the PV panel electrical output.

Nowadays, many political campaigns are using social influence (SI) in order to convince voters to support/oppose a specific candidate/party. In election control via SI problem, an attacker tries to find a set of limited influencers to start disseminating a political message in a social network of voters. A voter will change his opinion when he receives and accepts the message. In constructive case, the goal is to maximize the number of votes/winners of a target candidate/party, while in destructive case, the attacker tries to minimize them. Recent works considered the problem in different models and presented some hardness and approximation results. In this work, we consider multi-winner election control through SI on different graph structures and diffusion models, and our goal is to maximize/minimize the number of winners in our target party. We show that the problem is hard to approximate when voters' connections form a graph, and the diffusion model is the linear threshold model. We also prove the same result considering an arborescence under independent cascade model. Moreover, we present a dynamic programming algorithm for the cases that the voting system is a variation of straight-party voting, and voters form a tree.

Acetyleugenol is a phytochemical compound with broad effect against infectious diseases and tumors. Here, we extracted, characterized and elucidated the structure of acetyeugenol, for the first time, from the leaves of Acacia nilotica (L.)―a well-known medicinal plant. The broad antibacterial potential of acetyleugenol was first confirmed against seven bacterial pathogenic isolates with best activity against Proteus sp., Salmonella typhi, Staphylococcu aureus, and Streptococcus pneumonia, which showed similar or better zone of inhibition to that of the control amoxicillin. To further investigate its effect against Mycobacterium tuberculosis, acetyleugenol and its indole and phenyl analogs were subjected to molecular docking experiments against two potential tuberculosis drug targets―MtPknE and MtPknB Ser/Thr protein kinases. The results reveal that all of the analogs have improved docking scores comparing to the acetyleugenol. The indole analogs EUG-1 and EUG-3 were more effective with better docking scores for MtPknE with –11.08 and –10.05 kcal/mol, respectively. Similar results were obtained for the MtPknB. In contrast, only the EUG-2 phenyl analog has given rise to similar docking scores for both targets. This opens the door for further comprehensive studies on these acetyleugenol analogs with in vitro and in vivo experiments to validate and get more insights into their mechanisms of action.

Diabetes mellitus (DM) is one of the major risk factors for COVID-19 complications as it is one of the chronic immune-compromising conditions especially if patients have uncontrolled diabetes, poor HbA1c &/or irregular blood glucose levels. Diabetic patient’s mortality rates with COVID-19 are higher than cardiovascular or cancer patients. Recently Bacillus Calmette–Guérin (BCG) has shown successful results in reversing diabetes in both rats and clinical trials based on different mechanisms from aerobic glycolysis to Beta cells regeneration. BCG is a multi-face vaccine that has been used extensively in protection from TB and leprosy and has been repositioned for treatment of bladder cancer, diabetes & multiple sclerosis. Recently, the COVID-19 epidemiological study confirmed that universal BCG vaccination reduced morbidity and mortality in certain geographical areas. Countries without universal policies of BCG vaccination (Italy, Nederland, USA) have been more severely affected compared to countries with universal and long-standing BCG policies that have shown low numbers of reported COVID-19 cases. Some countries have started clinical trials that included a single dose BCG vaccine as prophylaxis from COVID-19 or an attempt to minimize its side effects. This proposed research aims to use BCG vaccine as a double-edged weapon countering both COVID-19 & diabetes, not only as protection but also as therapeutic vaccination. The work includes a case study of regenerated pancreatic beta cells based on improved C-peptide & PCPRI laboratory findings after BCG vaccination for a 9 years’ patient. The patient was re-vaccinated based on a negative tuberculin test & no scar at the site of injection of the 1st BCG vaccination at birth. Furthermore, the authors in the present article described a prospective BCG multi-dose clinical study in full details that they will apply in case of acceptance of their submitted grant & the ethical committee approval. The aim of the clinical study is to check if double dose BCG (4 weeks apart) will show a significant difference in the protection of health care professionals in Egypt. The authors suggest and invite the scientific community to take into consideration the concept of direct BCG re-vaccination (after 4 weeks) because of the reported gene expressions & exaggerated innate immunity consequently. As the diabetic MODY-5 patient (mutation of HNF1B, Val2Leu) was on low dose Riomet® while eliminating insulin gradually, a simple analytical method for metformin assay was recommended to ensure its concentration before use as it is not approved yet by the Egyptian QC labs.

The need to understand the process by which particles, including solar wind and coronal ions as well as pickup ions, are accelerated to high energies (ultimately to become anomalous cosmic rays) motivate a multi-fluid shock wave model which includes kinetic effects (e.g. ion acceleration) in an electromagnetically self-consistent framework. Particle reflection at the cross-shock potential leads to ion acceleration in the motional electric field and thus anisotropic heating and pressure in the shock layer, with important consequences for the multi-fluid dynamics. This motivates development of a multi-fluid model of solar wind electrons and ions treated as fluid, coupled self-consistently with a small population of kinetically treated ions (e.g. pickup ions.) Consideration of both the time dependent and steady state regimes, indicate that such a multi-fluid approach is necessary for resolving the, Debye scale, particle reflecting cross-shock potential and subsequent dynamics. To study charge separation effects in narrow, supersonic wave layers we consider a reduction of the system to the steady state for cold ions and hot electrons and find two types of solitary waves inherent to the reduced two-fluid system in this limiting case.

Industry 4.0 is having a great impact in all smart efforts. This is not a single product, but is composed of several technologies, being one of them Industrial Internet of Things (IIoT). Currently, there are very varied implementation options offered by several companies, and this imposes a new challenge to companies that want to implement IoT in their processes. This challenge suggests to use multi-criteria analysis to make a repeatable and justified decision, requiring a set of alternatives and criteria. This paper proposes a new methodology and comprehensive criteria to help organizations to take an educated decision by applying multi-criteria analysis. Here, we suggest a new original use of PROMETHEE-II with full example from weight calculation up to IoT platform selection, showing this methodology as an effective study for other organizations interested to select an IoT platform. The criteria proposed outstands from previous work by including not only technical aspects, but economic and social criteria, providing a full view of the problem analyzed. A case of study was used to prove this proposed methodology.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. IoT has introduced various services and daily human life depends on its reliable and accessible operation. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routs with separate links. One of such protocols is AOMDV routing protocol. AOMDV protocol is a multi-path protocol which uses multiple different paths for sending information in order to maintain the network traffic balance, manage and control node energy, decrease latency, etc. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named GSTMPR-IoT is introduced which chooses the routs with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed GSTMPR-IoT method is compared to the EECRP and AOMDV approaches with regard to throughput, packet delivery rate, end to end delay, average residual energy, and network lifetime. The results demonstrate the superior performance of the proposed GSTMPR-IoT compared to the EECRP and AOMDV approaches.

The immunomodulatory behaviour of the CCR6/CCL20 axis in multi -system pathophysiology and molecular signalling was investigated at two clinically significant time points, using a Ccr6 - deficient mouse model of spontaneous colitis. Four groups of mice, (C57BL/6J, Ccr6^-/- of C57BL/6J, Winnie x Ccr6 ^-/- and Winnie) were utilized and (I) colonic clinical parameters (2) histology of colon, spleen, kidney and liver (3) T and B lymphocyte distribution in the spleen and MLN by flowcytometry (5) colonic CCL20, phosphorylated PI3K and phosphorylated Akt expression by immunohistochemistry and (6) colonic cytokine expression by RT-PCR were evaluated. CCR6 deficiency was shown to attenuate inflammation in the spleen, liver and gut while renal histology remained unaffected. Marked focal lobular inflammation with reactive nuclear features were observed in hepatocytes and a significant neutrophil infiltration in red pulp with extra medullary hemopoiesis in the spleen existed in Winnie. These changes were considerably reduced in Winnie x Ccr6^-/- with elevated goblet cell numbers and mucus production in the colonic epithelium. Results indicate that Ccr6- deficiency in the colitis model contributes towards resolution of disease. Our findings demonstrate an intricate networking role for CCR6 in immune activation, which is downregulated by Ccr6 deficiency, and could provide newer clinical therapies in colitis.

In this paper we show how to efficiently implement parallel discrete simulations on Multi-Core and GPU architectures through a real example of application: a cellular automata model of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with48cores and also an NVIDIA Tesla6V100 GPU, both running on Amazon Web Server (AWS) Cloud, and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results are compared and analysed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current Multi-Core CPUs with large core numbers can bring a performance very near to that of GPUs, even similar in some cases.

This study presents a path planning method for a mobile robot to be effectively operated through a multi-objective decision-making problem. Specifically, the proposed Fuzzy analytic hierarchy process (FAHP) determines an optimal position as a sub-goal within the multi-objective boundary. The key features of the proposed FAHP are evaluating the candidates according to the fuzzified relative importance among objectives to select an optimal solution. In order to incorporate FAHP into path planning, an AHP framework is defined, which includes the highest level (goal), middle level (objectives), and the lowest level (alternatives). The distance to the target, robot’s rotation, and safety against collision between obstacles are considered as objective functions. Comparative results obtained from the artificial potential field and AHP/FAHP simulations show that FAHP is much preferable for the mobile robot’s path planning than typical AHP.

Two desirable functions of healthy soil are nutrient cycling and pest suppression. We review recent literature on the contributions of soil microarthropods to soil health through their intersecting roles in decomposition and nutrient cycling and direct and indirect suppression of plant pests. Microarthropods can impact soil and plant health directly by feeding on pest organisms or serving as alternate prey for larger predatory arthropods, and indirectly, by mediating the ability of crop plants to resist or tolerate insect pests and diseases through interactions with the decomposition food web in support of plant nutrition. Soil fauna, including microarthropods, are key regulators of decomposition at local scales but their role at larger scales is unresolved. Future research priorities include the incorporation of multi-channel omnivory into food web modeling and understanding the vulnerability of our soil carbon to increased global temperatures.

This paper details the evolution of access network sharing models from legacy DSL to the most recent fibre-based technology and the main challenges faced from a technical and business perspectives. We first give an overview of existing access sharing models, that span physical local loop unbundling and virtual unbundled local access. We then describe different types of optical access technologies and highlight how they support network sharing. Next, we examine how the concept of SDN and network virtualization has been pivotal in enabling the idea of “true multi-tenancy”, through the use of programmability, flexible architecture and resource isolation. We give examples of recent developments of cloud central office and OLT virtualization. Finally, we provide an insight into the role that novel business models, such as blockchain and smart contract technology, could play in 5G networks. We discuss how these might evolve, to provide flexibility and dynamic operations that are needed in the data and control planes.

The growth of demand for metallic minerals has faced with the need for new techniques and improving technologies for all mine life cycle operations. Nowadays, the exploitation of old tailings and mine wastes facilities could represent a solution to this demand, with economic and environmental advantages. W-Sn Panasqueira Mine has been operating for more than 100 years. Its first processing plant “Rio” was located near Zêrere river being the mineral processing residues deposited on the top hillside on the margin of this river in Cabeço do Pião tailings dam. The lack of maintenance and monitoring of this enormous structure in the last twenty years represents high risks to the environment and population of the surrounding region. The re-mining of the tailings by hydrometallurgical methods was considered, in order to satisfy these two enounced conditions - metals demand and environmental risk, aiming for the sale of the metal to pay the environmental intervention. Field samples campaign allowed collecting data and results from laboratory tests driving to use regression optimization. The re-mining solution was studied, taking into account the technical, economic, social, and environmental aspects.

The combination of new-generation information technology and manufacturing technology has resulted in major and profound impact on future development paradigm of manufacturing. It is challenging for existing methods to conduct a multidimensional trend exploration related to machine tool domain, which is the basis of virtually everything in manufacturing. In this paper, we proposed an integrating approach framework combined topic models, bibliometric, trend analysis and patent analysis to mine insightful information about future development from multi-source data related to machine tool, such as papers, grants, patents and news. Specifically, papers and grants provided two different perspectives to explore the current focuses and future trends in machine tool research. Furthermore, the future technology development of machine tool was investigated through patents analysis. Finally, news related to machine tool industry in recent years was analyzed to analyze future machine tool business mode. The integration of the above various analytical methods and the multi-dimensional mining of literatures enabled the analysis of the future development of machine tool domain systematically from multi-perspectives which include research, technology development and industry. The conclusions obtained in this paper is beneficial to different communities of machine tool in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

In this paper, we propose a novel approach of video skimming by exploiting the fusion of video temporal information and keyword information representation extracted from multi-model video information including audio, text and visual indices. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-friendly content in the video skim. In the experiment by using the videos from YouTube-8M dataset, we have proved that the semantic conservation in the video skim from the proposed approach highly outperforms the approaches by only partial information of the video in conserving the semantic content of the video.

The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms, like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, like the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies an efficient sparse inference algorithm to model the signal using the learnt dictionary. As a result, our method is much more efficient from a computational point of view than other existing methods, thus becoming amenable to deal with long recordings from multiple patients. Regarding the dictionary construction, we locate first all the QRS complexes in the training database, then we compute a single average waveform per patient, and finally we select the most representative waveforms (using a correlation-based approach) as the basic atoms that will be resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.

The paper analyses how between 1956 and 2009 the agrarian metabolism of the Barcelona Metropolitan Region (BMR) has become less functional, losing circularity in biomass flows and in relationship to its landscape. We do so by adopting a Multi-EROI and flow-fund (MuSIASEM) analyses and its nexus with landscape functional structure. The study of agricultural flows of Final Produce, Biomass Reused and External Inputs is integrated with that of land use, livestock, power capacity and population changes between 1956 (at the beginning of the Green Revolution) and 2009 (fully industrialized agriculture). A multi-scale analysis is conducted at the landscape scale (seven districts within the Barcelona metropolitan region) as well as for the functions deployed, within an agroecosystem, by the mutual interactions between its funds (land-uses, livestock and farming population). A complex nexus between land, livestock, dietary patterns and energy needs is shown; we conclude that from the perspective of the circular bioeconomy the agrarian sector has gone worse hand in hand with the landscape functional structure. Therefore, a novel perspective in landscape agroecology is opened

This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from atmospheric reanalysis over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM, as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation, as compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy.

Physical frailty and sarcopenia (PF&S) are hallmarks of aging that share a common pathogenic background. Perturbations in protein/amino acid metabolism may play a role in the development of PF&S. In this preliminary study, 68 community-dwellers aged 70 years and older, 38 with PF&S and 30 non-sarcopenic, non-frail controls (nonPF&S), were enrolled. A panel of 37 serum amino acids and derivatives was assayed by UPLC-MS. Partial Least Squares Discriminant Analysis (PLS-DA) was used to characterize the amino acid profile of PF&S. The optimal complexity of the PLS-DA model was found to be three latent variables. The proportion of correct classification was 76.6 ± 3.9% (75.1 ± 4.6% for enrollees with PF&S; 78.5 ± 6.0% for controls). Older adults with PF&S were characterized by higher levels of asparagine, aspartic acid, citrulline, ethanolamine, glutamic acid, sarcosine, and taurine. The profile of nonPF&S individuals was defined by higher levels of α-aminobutyric acid and methionine. Distinct profiles of circulating amino acids and derivatives characterize older individuals with PF&S. The dissection of these patterns may provide novel insights into the role played by protein/amino acid perturbations in the disabling cascade and possible new targets for interventions.

Wireless Sensor Networks (WSN) is designed to collect information across a large number of limited battery sensor nodes. Therefore, it is important to minimize the energy consumption of each node, which leads to the extension of the network life. Our goal is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent has been migrated to each node in order to process the information and to cooperate with these neighboring nodes while Mobile Agents (MA) can be used to reduce information between nodes and send those to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the Multi Agent System (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the nodes of the network have been grouped into sector. As for each MA, we have established an optimal itinerary, consuming a minimum amount of energy with the data aggregation efficiency in a minimum time. Successive simulations in large scale wireless sensor networks through the SINALGO simulator show the performance of our proposal, in terms of energy consumption and package delivery rate.

The activity of prioritizing software requirements should be done as efficiently as possible. Selecting the most stable requirements for the most important customers for the development company can be a positive factor when we consider that the available resource does not always encompass the implementation of all requirements. Quantitative methods for reaching software prioritization in releases are many in the field of Search-Based Software Engineering (SBSE). However, we show that it is possible to use qualitative Verbal Decision Analysis (VDA) methods to solve this same type of problem. Moreover, we will use the ZAPROS III-i methods to prioritize requirements considering the opinion of the decision-maker, who will participate in this process. Finally, the results obtained in the VDA structured methods were quite satisfactory when compared to the methods using SBSE. A comparison of results between quantitative and qualitative methods will be made and discussed later.

Landing is one of the most dangerous tasks in all the operations on the aircraft carrier, and the landing safety is very important to the pilot and the flight deck operation. The problem of landing path selection is studied in this paper as there several candidates corresponding to different situations. A fuzzy path selection strategy is proposed to solve the problem considering the fuzziness of environmental information and human judgment, and the goal is to provide the pilot with more reasonable decision. The strategy is based on Fuzzy Multi-attribute Group Decision Making (FMAGDM), which has been widely used in industry. Firstly, the background of the path selection problem is given. Then the essential elements of the problem are abstracted to build the conceptual model. A group decision-making method is applied to denote the preference of each decision maker for each alternative route, and the optimal landing path under the current environment is determined taking into account the knowledge and the weight of both decision makers. Experimental studies under different setups, i.e., different environments, are carried out. The results demonstrate that the proposed path selection strategy is validated in different environments, and the optimal landing paths corresponding to different environments can be determined.

The eddy current loss should be optimized to be as less as possible for the stability of permanent magnet in high speed permanent magnet synchronous motor (HSPMSM) rotor and ensure the high efficiency and low temperature of the motor. This paper analyzes the eddy current distribution in rotor, with consideration of the conflict of the thickness of sleeve and diameter of the rotor, calculating the eddy current loss (ECL) and the thermal distribution via Separation of variables method for solving Maxwell's equations with analytical hieratical model of ECL constructed. The optimization result of ECL of the HSPMSM whose power and rated speed is 30kw 48000r/min can be got by multi-objective optimization method, combined weighting coefficient method and traversal algorithm based on chaotic local search particle swarm optimization (CLSPSO), utilizing ECL analytical model and other analytical constraints. Related experiment and measurement has been implemented with new approach of loss separation.

The planning of rural land depends to a large extent on the agrarian and forestry development model agreed upon for it. Within the framework of agrarian multifunctionality, to evaluate different development alternatives and their effect on territorial planning, its multiple dimensions must be considered as well as the interests and needs of the stakeholders. This work thus addresses the comprehensive evaluation of development alternatives for the rural area of a municipality of the Basque Country (Spain) through the implementation of a multi-criteria evaluation method with social participation. The results show, firstly, the existence of trade-offs between the functions to be promoted in the various development models evaluated. Secondly, the results indicate the need to reverse recent trends in the sector and the convenience of moving towards an agro-livestock model of greater ecological and local character, independently of the followed forest model. In fact, the evaluation carried out also reveals, thirdly, the existence of more or less latent conflicts, in particular with regards to forest policy.

This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment Problem (QAP) implemented on the OpenCL platform. Our work was motivated by results of time efficiency tests performed for single-swarm algorithm implementation that showed clearly that the benefits of a parallel execution platform can be fully exploited provided the processed population is large. The described algorithm can be executed in two modes: with independent swarms or with migration. We discuss the algorithm construction as well as we report results of tests performed on several problem instances from the QAPLIB library. During the experiments the algorithm was configured to process large populations. This allowed us to collect statistical data related to values of goal function reached by individual particles. We use them to demonstrate on two test cases that although single particles seem to behave chaotically during the optimization process, when the whole population is analyzed, the probability that a particle will select a near-optimal solution grows.

Surface damage on honeycomb aircraft panels is often measured manually, and is therefore subject to variation based on inspection personnel. Eddy current testing (ECT) is sensitive to variations in probe-to-specimen spacing, or lift-off, and is thus promising for high resolution profiling of surface damage on aluminum panels. Lower frequency testing also allows inspection through the face sheet, an advantage over optical 3D scanning methods. This paper presents results from the ECT inspection of surface damage on an approximately flat aluminum honeycomb aircraft panel, and compares the measurements to those taken using optical 3D scanning technology. An ECT C-Scan of the dented panel surface was obtained by attaching the probe to a robotic scanning apparatus. Data was taken simultaneously at four frequencies of 25, 100, 400 and 1,600 kHz. A reference surface was then defined that approximated the original, undamaged panel surface, which also compensated for the effects of specimen tilt and thermal drift within the ECT instrument. Data was converted to lift-off using height calibration curves developed for each probe frequency. A damage region of 22,550 mm² area with dents ranging in depth from 0.13-1.01 mm was analyzed. The method was accurate at 1,600 kHz to within 0.05 mm (2s) when compared with 231 measurements taken via optical 3D scanning. Testing at 25 kHz revealed a 3.2 mm cell size within the honeycomb core, which was confirmed via destructive evaluation. As a result, ECT demonstrates potential for implementation as a method for rapid in-field aircraft panel surface damage assessment.

Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spectral bands than a general image, thereby permitting the multi-spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-spectral analysis and explores the middle-level statistics of spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-spectral analysis firstly learns the multi-spectral prototypes (codebook) for representing any pixel-wise spectral data, and then based on the learned codebook, a sparse coded spectral vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded spectral vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is much more accurate than even the deep learning framework for spatial analysis.

This paper deals with a generic methodology to evaluate the magnetic parameters of contactless power transfer systems. Neumann's integral has been used to create a matrix method that can model the magnetics of single coils (circle, square, rectangle). The principle of superposition has been utilised to extend the theory to multi-coil geometries such as double circular, double rectangle and double rectangle quadrature assuming linearity of magnetics. Numerical and experimental validation has been performed to validate the analytical models developed. A rigorous application of the analysis has been carried out to study misalignment and hence the efficacy of various geometries to misalignment tolerance. Comparison of single-coil and multi-coil shapes considering coupling variation with misalignment, power transferred and maximum efficiency is carried out.

Integration of multi-view datasets which are comprised of heterogeneous sources or different representations is challenging to understand the subtle and complex relationship in data. Such data integration methods attempt to combine efficiently the complementary information of multiple data types to construct a comprehensive view of underlying data. Nonnegative matrix factorization (NMF), an approach that can be used for signal compression and noise reduction, has aroused widespread attention in the last two decades. The Kullback–Leibler divergence (or relative entropy) information distance can be used to measure the loss function of NMF. In this article, we propose a fast and robust framework (RSNMF) based on symmetric nonnegative matrix factorization (SNMF) and similarity network fusion (SNF) for clustering human microbiome data including functional, metabolic and phylogenetic profiles. Many existing methods typically utilize all the information provided by each view to create a consensus representation, which often suffers a lot from noise in data and cannot provide a precise representation of the latent data structures. In contrast, RSNMF combines the strength of SNMF and the advantage of SNF to form a robust clustering indicator matrix thus can reduce the noise influence. We conduct experiments on one synthetic and two real dataset (microbiome data, text data) and the results show that the proposed RSNMF has better performance over the baseline and the state-of-art methods, which demonstrates the potential application of RSNMF for microbiome data analysis.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

The study of different forms of preconditioners for solving a system of nonlinear equations, by using Newton’s method, is presented. The preconditioners provide numerical stability and rapid convergence with reasonable computation cost, whenever chosen accurately. Different families of iterative methods can be constructed by using a different kind of preconditioners. The multi-step iterative method consists of a base method and multi-step part. The convergence order of base method is quadratic and each multi-step add an additive factor of one in the previously achieved convergence order. Hence the convergence of order of an m-step iterative method is m + 1. Numerical simulations confirm the claimed convergence order by calculating the computational order of convergence. Finally, the numerical results clearly show the benefit of preconditioning for solving system of nonlinear equations.

We present information theoretic search strategies for single and multi-robot teams to localize the source of biochemical contaminants in turbulent flows. The robots synthesize the information provided by sporadic and intermittent sensor readings to optimize their exploration strategy. By leveraging the spatio-temporal sensing capabilities of a mobile sensing network, our strategies result in control actions that maximize the information gained by the team while optimizing the time spent localizing the position of the biochemical source. By leveraging the team's ability to obtain simultaneous measurements at different locations, we show how a multi-robot team is able to speed up the search process resulting in a collaborative information theoretic search strategy. We validate our proposed strategies in both simulations and experiments.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

Predicting electricity prices is a very important issue in modern society, because the associated decision process under uncertainty requires accurate forecasts for the economic agents involved. In this paper, we apply the decision tree extension of Random Forests to the prediction of electricity prices in Spain, but with the novelty of modeling prices jointly with demand, with the purpose of achieving greater accuracy than with univariate response Random Forests, particularly in price prediction, as well as understanding the effect of the input variables (lagged values of price and demand, current production levels of available energy sources) on the joint of the two outputs. The results are very encouraging, providing significant increase in price prediction accuracy. Also, interesting methodological challenges appear as far as the appropriate choice of the relative weights of price and demand in the joint modeling is concerned and a new procedure to provide the importance variable ranking is proposed. The partykit (package of R software) library allowing for multivariate Random Forests has been used.

Modification of canopy architecture is fundamental to productivity in grapevine. This research focused on evaluating the impact of canopy openness on photosynthetically active radiation, photosynthetic activity, and the physicochemical properties of fruit in Cabernet Sauvignon grapevines. A completely randomised design was used to compare open and closed canopies, with ten replicates per treatment (20 trees in total), during the vegetative growth period and after harvest. Key measurements included photosynthetically active radiation (PAR), daily light inte-gral (DLI), photosynthetic rate (PR), stomatal conductance (SC), intercellular CO2 concentration (IC), leaf area (LA), transpiration, ambient CO2 concentration, and temperature. Additionally, we assessed fruit quality parameters such as total soluble solids (TSS), glucose-fructose, total titratable acidity (TTA), pH, TSS/TTA, and total phenols (TP). significantly (p≤0.05) increased PAR, DLI, PR, IC, and LA, while after harvest, only PR and IC showed variation. Closed canopies increased CO2/H2O by 62.5%, while H2O/CO2 and temperature were higher in open canopies. Canopy openness increased contacts, gaps and visible sky and reduced leaf area index. Open canopy fruits showed higher TSS, glucose-fructose, pH, TSS/TTA and TP contents. Canopy openness is essential for improving light interception, photosynthetic efficiency and fruit quality in Cabernet Sauvi-gnon grapevine cultivated in northern Mexico.

This paper intoduces a non-standard research technique to clarify how complex phenomena, such as those that are abundantly present in human physiology, can be faithfully described using fractal dynamical models with and without stochastic forces. This method for conducting research involves tracing the his- torical evolution of understaning an empirical medical process faciltated by the fractal calculus perspective. Herein we trace the analysis of the time series for heart rate varability (HRV) developed for diagnosing the cardiovascular health of a patient. This is done herein by introducing four (one empirical which en- tails three theoretical fractal models) distinct but related fractal models each one introduced to solve a particular problem arising from a fundamental defect in the previous model but in generalizing a model at one stage to resolve the problem associated with the defect another is invariably introduced by the re- placement model. It is through the utilization of the fractional calculus that the necessity for rethinking how to systematically incorporate additonal layers of complexity are revealed ultimately resulting in a 'complete' description of its empirical dynamics in fractal terms.

Microplastic pollution, particularly particles smaller than 5 mm, poses significant environmental and health risks due to their potential for long-range transport and inhalation. This study provides the first long-term assessment of airborne microplastics in Porto, Portugal, over 18 months (September 2022 to March 2024). Bi-weekly samples were collected using a Microplastic Collector NILU, which were size-fractionated into five categories (>125 μm, 125-63 μm, 63-25 μm, 25-12 μm, and 12-1.2 μm), and quantified via optical microscopy. Microplastics (26 to 1,484 MPs/day/m²) and fibers (14 to 646 fibers/day/m²) showed increasing pollution trends. With a focus on the 12-1.2 μm size-range due to its classification as PM10 and PM2.5, the highest microplastic concentrations were 164 MPs/day/m² (12-1.2 μm) and 534 MPs/day/m² (25-12 μm). Recovery rates of methodology varied among polymers, with PP, PE-HD, and ABS showing high accuracy (75.9%) and PES significantly lower (26.5%). The study highlights significant temporal variability in airborne microplastic pollution, increasing trends, and the need for ongoing monitoring and targeted mitigation strategies to address associated health risks.

Neural network regularized learning has garnered significant attention in recent years. We give a systematic investigation on the performance of regularized regression associated zonal translation networks. We propose the concept of Marcinkiewicz-Zygmund inequality Setting (MZIS) for the scattered nodes collected from the unit sphere. We show that, under the MZIS, the corresponding convolutional zonal translation network has reproducing property. Based on these facts,we propose a kind of kernel regularized regression learning framework and provide upper bound estimate for the learning rate with the kernel approach. We also give proof for the density of the zonal translation network with spherical Fourier analysis.We provide the approximation error with a K-functional.

Monte Carlo simulations were conducted to investigate the interaction between nucleation and the rates of chemical reduction in Au/Ag, Au/Pt and Au/Pd nanoparticles prepared in microemulsions using a one-pot method. The impact of nucleation on final nanostructure depends on the critical nucleus size value: at a high critical nucleus size, nucleation becomes the main factor in determining the final nanostructure, even with very large difference in reduction rates, as seen in the Au/Pd pair. However, when the critical nucleus size is small, the difference in reduction rates of the two metals becomes the key parameter determining the final nanostructure. Furthermore, the relevance of heteroatomic nucleation on the mechanism of nanoparticle formation depends on the difference between the reduction rates of the two metals. Smaller differences, such as in the Au/Ag or Au/Pt pairs, result in a greater impact of heteroatomic nucleation on the final nanostructure. In contrast, in the Au/Pd pair, heteroatomic nucleation becomes less important due to the low availability of Pd until late stages of synthesis. This study provides deeper insight into the complex mechanisms that govern reactions in microemulsions.

Increasing nitrogen use efficiency (NUE) by improving agricultural practices, soil knowledge and implementing precision agriculture, is essential to reduce the overuse of fertilisers and increase nutrient retention. This study aimed to optimise N management in agriculture by establishing a critical N dilution curve (CNDC) and analysing variations in NUE and N nutrition index (NNI) among different crops under various treatments. Using a Bayesian model, the CNDC was determined as %Nc = 3.63 * PDM-0.71. The results presented that plant dry matter (PDM) and plant N content (PNC) varied significantly with crop type and sampling moments. Strong positive correlations are presented by PDM with N uptake (NUp) (0.89) and NNI (0.88), along with an inverse correlation with critical N concentration (-0.95). The study found that crops under irrigation conditions had higher NUp and higher NNI. This study provides valuable insights into the influence of key management decisions on the N nutritional status of annual ryegrass-based forage crops. The results highlight the critical role of accurate and conscious decision-making in improving NUE and crop yields, emphasising the complex interactions between biomass production and N dynamics in crops. The conclusions allow significant benefits to be realised, contributing to the sustainability of agricultural systems.

The common house mosquito (Culex pipiens) is a native vector for West Nile virus (WNV). Invasive species like the tiger mosquito (Aedes albopictus) and Asian bush mosquito (Aedes japonicus) are rapidly spreading through Europe, posing a major threat as vectors for dengue, chikungunya (CHIKV), and Japanese Encephalitis virus (JEV). These mosquitoes share a similar ecological niche as larvae, but the carry-over effects of aquatic larval interactions to terrestrial adult stage remain largely unknown and their medical relevance requires further investigation. This study examines the context-dependency of larval interactions among Aedes albopictus, Aedes japonicus and Culex pipiens. The survival, development time, growth, and energetic storage were measured in different European populations within density-response experiments (intraspecific) and replacement experiments (interspecific) at 20°C and 26°C. Overall, Ae. japonicus was the weakest competitor, while competition between Ae. albopictus and Cx. pipiens varied with temperature. Culex pipiens was infected with WNV, Ae. albopictus with CHIKV and Ae. japonicus with JEV. While no JEV infection was observed, mosquitoes experiencing interspecific interactions during their larval stages exhibited higher infection rates and viral RNA titers for CHIKV and WNV. This increased susceptibility for viral infection after larval competition suggests a higher risk of arbovirus transmission in co-occurring populations.

Global stress is widespread in todays post-pandemic world of political and economic uncertainty. Vibroacoustic technology is a vibrotactile intervention with multiple uses, and its impact on stress lacks interpretation. This research assessed if the vibroacoustic technology of a Vibroacoustic Sound Massage (VSM) can reduce psychological, physiological and cognitive stress. The Perceived Stress Scale (PSS-10), and electrocardiogram (ECG) and electroencephalogram (EEG) biosignals were used to quantify results. Participants were divided into Low Stress and High Stress groups. ECG results show VSM increased parasympathetic activity for all participants, with the Low Stress group more effected. EEG results indicate increased concentration (TBR), reduced arousal and increased relaxation (BAR), and non-significant effects on participant well-being (FAA), though variability in this metric was homogenised after VSM. Together, these results validate VSM as an effective support tool for stress management and further research is required.

We present a laser-plasma electron accelerator module designed to be driven by high repetition rate lasers for industrial applications of laser-driven electron beams. It consists of a single vacuum chamber containing all the necessary components for producing, optimizing, and monitoring electron beams generated via laser wakefield acceleration in a gas jet. The core methods involve comprehensive metrological assessments, including rigorous temporal laser pulse characterization and contrast measurements, supplemented by detailed spatio-temporal distribution analyses of the laser system. Results demonstrate the good stability and reproducibility of the laser system, confirming its suitability for advanced scientific and industrial applications. We demonstrate the full functionality of the laser-plasma accelerator module and perform target density and laser-plasma characterizations. Our work is a first step towards the wide dissemination of fully integrated laser-plasma accelerator technology.

First, a recently developed eigenfunction expansion technique, based in part on separation of the thickness-variable and partly utilizing a modified Frobenius type series expansion technique in conjunction with the Eshelby-Stroh formalism, is employed to derive three-dimensional singular stress fields in the vicinity of the front of an interfacial crack weakening an infinite bicrystalline superlattice plate, made of orthorhombic (cubic, hexagonal and tetragonal serving as special cases) phases, of finite thickness and subjected to the far-field extension/bending, in-plane shear/twisting and anti-plane shear loadings, distributed through the thickness. Crack-face boundary and interface contact conditions as well as those that are prescribed on the top and bottom surfaces of the bicrystalline superlattice plate are exactly satisfied. It also extends a recently developed concept of lattice crack deflection (LCD) barrier to a superlattice, christened superlattice crack deflection (SCD) energy barrier for studying interfacial crack path instability, which can explain crack deflection from a difficult interface to an easier neighboring cleavage system. Additionally, the relationships of the nature (easy/easy, easy/difficult or difficult/difficult) interfacial cleavage systems based on the present solutions with the structural chemistry aspects of the component phases (such as orthorhombic, tetragonal, hexagonal as well as FCC (face centered cubic) transition metals and perovskites) of the superlattice are also investigated. Finally, results pertaining to the through-thickness variations of mode I/II/III stress intensity factors and energy release rates for symmetric hyperbolic sine distributed load and their skew-symmetric counterparts that also satisfy the boundary conditions on the top and bottom surfaces of the bicrystalline superlattice plate under investigation, also form an important part of the present investigation.

This study aimed to investigate the relationship between sleep duration, dementia severity, age, and ANS regulation. This cross-sectional pilot study included 27 older patients with probable Alzheimer’s disease, hospitalized at a psychiatric center. We measured heart rate variability to assess ANS regulation at night, evaluated dementia severity using the Clinical Dementia Rating scale, and obtained sleep duration data from sleep diaries maintained by psychiatric nurses. The data were analyzed using repeated-measures generalized linear models with age, sex, dementia severity, and medication (antipsychotics) as covariates. A sleep duration of 7–8 h per night was associated with better ANS regulation than other sleep durations, as indicated by sympathovagal balance (p = 0.005). ANS dysregulation was characterized by a 20.7% increase in sympathetic nervous system activity and a 14.8% decrease in parasympathetic nervous system (PSNS) activity. We found a significant correlation between dementia severity and PSNS activity (p = 0.04) and observed an inverted U-shaped association between sleep duration and ANS regulation. This pilot study demonstrated that a sleep duration of 7–8 h per night may be critical in maintaining optimal ANS regulation. Bidirectional associations between sleep duration, inadequate sleep duration, aging, and ANS regulation warrant further investigation.

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control tools. In statistical process control, control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. In the control chart, the mean value is taken as the center line, and 3σ limits are placed on either side of the center line. In CPV, manufactured lots are summarized, trended, and discussed at a cross-functional level at pre-defined intervals (either weekly, bi-weekly, or even monthly) based on the batch run rate. A cross-functional team assesses details regarding 1) violation frequency, 2) statistical strength of the signal, 3) violation magnitude and its impact on process or quality, and 4) any potential root causes for out-of-expectation or out-of-trend signals. The cross-functional team must analyze whether any signal identified by the control chart is reliable and must determine whether corrective action should be taken and justify the decision in the CPV meeting minutes based on statistical strength and reliability of the signal. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

Agarwood, a highly valuable fragrant wood of Aquilaria spp. has been widely used in perfumes, traditional medicine, religious rites and cultural activities. Due to the presence of aromatic and medicinal properties, the Agarwood has gained its economic importance in recent years. It has been providing livelihood and employment facilities to many people who are cultivating those. Owing to high demand and profitability, the people of Assam has started to cultivate Agarwood in a commercial manner in recent days. This study was conducted to examine the economic viability of Agarwood cultivation in Assam and to understand the problems of the cultivators to consider Agarwood cultivation as the primary source of income. The primary data were collected during the months of May and June, 2022 from 210 households using structured questionnaire. The cost-benefit analysis revealed that INR 100 of initial investment yields a net benefit of INR 623 which indicates that the return from the Agarwood cultivation among the cultivators are very high. Thus, the present study argues that the government should encourage the cultivators by providing the necessary support in the form of a legalized market, credit requirement, creating the awareness about the demand of Agarwood and making provision of training.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

The increasing number of caesarean section (CS) is a major challenge in modern obstetrics. This rise is not supported by better neonatal and maternal outcomes. The prevention of unnecessary primary CS is a global priority. This study focuses on effectiveness and safety of yoga practice during pregnancy and it’s connection to perinatal outcome. We conducted a single-blinded, randomized controlled clinical trial, with healthy singleton primiparas, randomized at first trimester of pregnancy. A total of 214 participants were randomized, 106 in yoga and 108 in controls. The study group performed yoga exercise while the controls received standard antenatal care. The yoga exercises were performed once a week, lasted 90 minutes, under the supervision of a certified yoga teacher. All pregnant women from study group completed 12 yoga exercises. The results showed that the yoga group had a lower rate of urgent intrapartum CS (p-0.004) and more frequent spontaneous vaginal delivery (p-0.009), while the control group had a higher rate of induced labour (p-0.032). Conclusion: Yoga practice in pregnancy is a promising intervention that has a potential to decrease intrapartum urgent CS rates and assures natural and spontaneous vaginal birth; it’s highly recommendable to healthy pregnant women.

Transport infrastructure’s asphalt pavement deteriorates under the influence of destructive factors. Damages which have been occurred during its exploitation period are repaired, and when their further rehabilitation is economically and technically irrational, the asphalt pavement is recycled. The material from the asphalt pavement layer that has reached its limit state is milled out or broken and crushed and then is repeatedly used in the production of hot-mix asphalt (HMA) or warm-mix asphalt (WMA) mixtures. In this paper, the dynamics of the percentage recycling ratio (RR) of old asphalt pavement material was investigated. RR represents the quantity of reclaimed asphalt pavement (RAP) used in the production of HMA and WMA mixtures in Europe and the USA, divided by the total amount of RAP prepared in the country. Factors and goals affecting it are analyzed. An original system of 10 criteria that increase the RR country has been created. By applying different multiple criteria decision making (MCDM) methods and using the importance given to these criteria by 14 experts, the normalized subjective weights of the criteria were determined. Analytic Hierarchy Process (AHP), rank correlation, Average Rank Transformation into Weight - Linear (ARTIW-L) and Non-linear (ARTIW-N), Direct Percentage Weight (DPW) methods were used in the study. The results display that the RAP recycle rate is close to 100% in countries with a sustainable economic background. In the Baltic countries, it is mostly increased by the adequacy of regulatory documents, the strategy promoting asphalt recycling in the country, and the homogeneity and classifying of RAP. The number and capacity of RAP stocks, the number and productivity of asphalt milling equipment and the wear and tear of the asphalt pavement have the least influence on the increase of RR. The opinions of experts in assessing the significance of all criteria are consistent. The averages of the weights of criteria determined by four MCDM methods (AHP, ARTIW-L, ARTIW-N, DPW) made it possible to obtain more reliable results. These results can be used to make strategic decisions and to create a plan of practical actions to increase the RAP recycling rate in the developing countries.

An overview of the link between nonequilibrium thermodynamics and complexity theory is offered. It is shown how the rate of entropy production can be quantified through the spectrum of the Lyapunov exponents. It was shown how the entropy production per unit of time meets the necessary and sufficient conditions to be a Lyapunov function and constitutes per se an extremal principle. The entropy production fractal dimension conjecture was established. It is shown how the rate of entropy production as a non-extremal criterion represents an alternative way of sensitivity analysis of differential equations. Finally, an extension to biophysical-chemical systems, on the one hand, the use of the dissipation function is shown, as a thermodynamic potential out of equilibrium, in the characterization of biological phase transitions. On the other hand, it was evidenced how the rate of entropy production represents a physical quantity to evaluate the complexity and robustness of cancer.

FLASH-RT represents a novel therapeutic radiation modality that holds remarkable potential for mitigating radiation therapy’s adverse side effects. This cutting-edge technology allows for the sparing of healthy tissue while precisely targeting cancerous cells by administering an ultra-high dose-rates of typically between 10 and 30 Gy in less than a few hundred milliseconds. FLASH-RT has demonstrated impressive results in small-animal models, prompting scientists to adapt and advance existing technologies to make it a viable treatment option for humans. However, producing the ultra-high-dose-rate radiation required for the therapy remains a significant challenge. Several radiation sources, such as very high energy electrons (VHEEs), low energy electrons, x-rays, and protons, have been studied for their ability to deliver the necessary dose. Among them, FLASH-x-ray has gained the most attention owing to its capacity to penetrate deeply seated tumours. Despite the complexity of the process, the potential advantages of FLASH-RT make it an exciting area of research. To achieve the FLASH effect, high-frequency, pulsed irradiated accelerator technology can be employed. Sparing healthy tissue, it may allow for more aggressive and effective cancer treatments, leading to a better quality of life for patients. Ongoing research and development will be necessary to refine and optimize this approach to radiation therapy.

Chlorhexidine digluconate (CHX) has shown the ability to significantly reduce inflammation and marginal bone loss (MBL) at 1-year follow-up but limited data exists regarding its long-term efficacy in peri implant stability. The objective was to compare the long-term effects of a placebo gel (16 patients in Group A) or a 0.20% CHX gel (15 patients in Group B) used at each previous surgical and prosthetic stage. Follow-ups were conducted in 2022, investigating by biological, radiological, clinical, mechanical evaluation the long-term effects. Data were statistically analyzed. The research achieved a 96.7% implant success rate over five years, but 41.9% of patients didn't attend annual oral hygiene check-ups. The average MBL was 1.04 ± 0.39 mm, with no significant differences between the two groups. Notably, patients who attended regular periodontal check-ups experienced significantly less MBL than those who didn't (p <0.05). At five years, the effects of CHX were reduced, with both groups showing moderate bone loss. The results suggest early disinfection could enhance both short and long-term outcomes. In fact, patient with initial minor MBL preserve this advantage also at 5 years of follow-up. Additionally, the data underscore the importance of annual check-ups in early detection and management of biological and mechanical complications.

Generalized Bayes is a Bayesian approach based on the learning rate parameter η. In this study, we examine the effect of parameter η on the estimation results considering joint type-I and type-II hybrid censored samples from k exponential populations. In addition to the learning rate parameter, we consider two loss functions, the Linex and general entropy loss functions in the Bayesian approach. Monte Carlo simulations are performed to compare the performances of the estimation results under losses and different values of η. An illustrative example is performed to study the effect of the learning rate parameter and the different losses with different parameters.

Information-Centric Networking (ICN) is a new paradigm of network architectures that focuses on content, rather than hosts, as first-class citizens of the network. As part of these architectures, in-network storage devices are essential to provide end users with close copies of popular content, to reduce latency and improve overall experience for the user, but also to reduce network congestion and load on the content producers. To be effective, in-network storage devices, such as content storage routers should maintain copies of the most popular content objects. Adversaries that wish to reduce this effectiveness can launch cache pollution attacks to eliminate the benefit of the in-network storage device caches. Therefore, it is crucial to protect these devices and ensure the highest hit rate possible. This paper demonstrates per-face popularity approaches to reducing the effects of cache pollution and improving hit rates by normalizing assessed popularity across all faces of content storage routers. The mechanisms that were developed prevent the consumers, whether legitimate or malicious, on any single face or small number of faces from overwhelmingly influencing the content objects that remain in the cache. The results demonstrate that per-face approaches generally have much better hit rates than currently used cache replacement techniques.

A 36-year-old professional marathon runner reported sudden heart palpitations occurring during competitions, with heart rates (HR) of 230 bpm recorded on sports HR monitor (HRM) over 4 years. These episodes subsided upon the cessation of exercise. Electrocardiograms, echocardiography, and cardiac magnetic resonance imaging results were borderline for athlete's heart. Because an electrophysiology study and standard exercise tests provoked no arrhythmia, doctors suspected Munchhausen syndrome. Ultimately, an exercise test that simulated the physical effort during a competition provoked tachyarrhythmia consistent with the HRM readings. This case demonstrates the diagnostic difficulties related to exercise-induced arrhythmia and the diagnostic usefulness of sports HRMs.

Purpose: The purpose of this study was to evaluate the dosimetric impact of multiple channels compared to a single channel in high dose rate (HDR) vaginal cuff brachytherapy (VCBT). Methods and materials: Sample plans of HDR VCBT from both single and multiple channel cylinders at 2.5, 3.0 and 3.5 cm in diameter were created and analyzed. The doses were prescribed either at the 0.5 cm depth with 5.5 Gy for 4 fractions or at the cylinder surface with 8.8 Gy for 4 frac¬tions, in various treatment lengths ranging from 3 to 8 cm. A 0.5 cm ring target volume called PTV_Eval was contoured for each plan and served as the target volume for dosimetric evaluation. Mean doses and the doses covering 90% (D90), 10% (D10), and 5% (D5) of PTV_Eval were calculated and compared. The same treatment lengths, same prescription regimens, same target volume contouring and same plan evaluation methods were repeated for the similar size single channel and multiple channel cylinders. Results: The mean doses of both regimens with the multi-channel cases (6.3 to 7.1 Gy) were comparable to single channel ones (6.4 and 7.5 Gy). The ranges of D90, D10 and D5 were from 3.654 to 5.04 Gy, 8.44 to 10.08 Gy and 8.84 to 10.86 Gy in the multi-channel cases, but from 5.36 to 5.76 Gy, 7.40 to 9.97 Gy and 8.16 to 11.93 Gy in the single channel cases, respectively. Conclusion: The single channel and multiple-channel cylinder deliver a similar mean dose, but with marked heterogeneity in D90, D5 and D10 for the same prescription dose and target volume. Multi-channel cylinders adequately cover the target volume, but given the heterogeneity, increased treatment time and cost are best suited for asymmetric target volumes.

FMD is a highly contagious animal disease that occurs in cloven-hoofed animals including pigs. To prevent FMD, vaccines and adjuvants are used to induce an immune response, but it is not sufficient to show the effect of preventing viral infection. In this study, we conducted to evaluate the increasing effect of FMD vaccine SP antibody by administrating Zn-ASP to 100 pigs from three test pig farms with feed. FMD vaccine antibody titer and immunological index were analyzed using an enzyme-linked immunosorbent assay (ELISA) kit, and hematological and blood biochemical parameters were analyzed using an automatic blood analyzer. The titer of FMD vaccine SP antibody in the 0.2% Zn-ASP-administered group significantly increased compared to that in the positive control group only injected with FMD vaccine at 4 weeks after the first vaccination and at 4, 8 and 16 weeks after the second vaccination, respectively (p<0.05). The FMD vaccine SP antibody positive rate was 100% until shipment. IFN-γ and IgA were significantly increased by Zn-ASP administration 4 weeks after the first vaccination and 4 weeks after the second vaccination (p<0.05). On the other hand, serum AST, and CPK (p<0.001) were significantly decreased by Zn-ASP. Our results show that the administration of Zn-ASP is effective in enhancing the antibody titer and immunity of the FMD vaccine by FMD vaccination, and it is thought that it can be used as an oral adjuvant (OrAd) to prevent viral diseases such as FMD.

Nutrient release from marine sediments in Osaka Bay has a significant impact on nutrient concentrations in seawater. A tsunami induced by the Nankai Trough earthquake may disturb marine sediments in the inner part of Osaka Bay. An incubation experiment to estimate the release rates of NH4-N and PO4-P was conducted to understand the present conditions and to quantify the changes caused by tsunamis. Two types of cores were created: a "control core" representing the current sediment, and a "redeposition core" representing the redeposition after the tsunami. The release rates have been decreasing since the year 2000 and have remained low. The experimental results suggest that the release rate after exposure to an aerobic environment by a tsunami may decrease to approximately 70% for NH4-N and 60% for PO4-P of the current level. In the past, the release rates were values experienced in the inner part of Osaka Bay. However, the reduction in the release rate by tsunamis may be more limiting for primary production under the current situation where the contribution of release for nutrients in seawater is significant.

The use of active video games or exergames has been booming recently, as they are motivating and promote exercise and healthy lifestyle habits for their users, and these immersive virtual reality (IVR) exercise programs could be compared to traditional physical exercise programs. A starting point for this would be to explore the physiological responses that can be generated by its use. Therefore, the main objective of this study was to evaluate the response across certain physiological parameters (heart rate, blood pressure and stress) after an IVR exergame exposure in a sample of healthy adults, and secondarily to explore its feasibility and usefulness as a tool to facilitate physical exercise. 37 healthy adults (22-54 years, 54.1% women) participated in the study. They carried out one exergaming session with the HTC Vive ProTM hardware. The primary outcome was evaluated using the concentration of cortisol, heart rate and blood pressure. Secondary outcomes were evaluated with safety using the Simulator Sickness Questionnaire (SSQ); usability evaluated with the System Usability Scale (SUS); personal experiences evaluated with Game Experience Questionnaire (GEQ) and perceived exertion through the Borg perceived effort scale. All participants completed the IVR session without significant adverse effects, and all physiological parameters analysed increased significantly in relation to pre-intervention levels. Perceived exertion corresponded to a moderate to intense exercise (6.30±0.50/10 on the modified Borg scale). All the sample considered the experience as good or very good and would recommend it. These findings support that our IVR session can be compared to a moderate to intense physical activity, as it involved similar perceived exertion with significant increases in heart rate, blood pressure and salivary cortisol levels.

In this study, we surveyed over 600 participants to determine: a) major causes to mental stress during the pandemic and its future impacts, and b) diversity in public perception and acceptance (specifically for children) of Covid-19 vaccination. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity, mental stress causes, vaccination perception, and Covid-19 infections. For instance, statistical results indicate significant dependence between mental stress due to Covid-19 and gender (p = 1.7e-05). Over 25% of males indicated work related stress comparing 35% in females however, females indicated more stressed (17%) due to relationships comparing to males (12%). Around 30% of Asian/Arabic participants don’t feel vaccination being safe as compared to 8% of white-British and 22% of white-European indicating significant dependence (p = 1.8e-08) with ethnicity. More specifically, vaccination acceptance for children is significantly dependent to ethnicity (p = 3.7e-05) where only 47% participants show willingness towards children’s vaccination. Primary dataset in this study along with experimental outcomes identifying sociodemographic information diversity with respect to public perception and acceptance of vaccination to children and potential stress factors might be useful for public and policy makers to be better prepared for future epidemics as well as working globally to combat mental health issues and running more effective vaccination campaigns.

We construct a novel family of summation-integral type hybrid operators in terms of shape parameter $\alpha\in \lbrack 0,1]$ in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre's K-functional, Lipschitz class, and second-order modulus of smoothness.

Creep deformation behaviour in single phase γ-TiAl alloy has been an extensively studied topic since the late 1970s. A lot of literatures have reported creep behaviour of γ-TiAl alloys, manufactured using different processing techniques [1]–[7]. The present discussion revisits the original work on understanding the tensile creep deformation behaviour of wrought single-phase γ-TiAl alloy by Hayes et al. [8] and is aimed to develop an understanding of steady state creep, through strain vs strain rate and strain vs ln(strain rate) plots. Besides, it also attempts to study the variation of stress exponent with temperature between 760-900⁰C and also, to determine activation energies using the two most common approaches, namely: Zener-Hollomon (Z-H) [9] and Sherby-Dorn (S-D, temperature compensated time approach) [10] for stress levels of 69 and 103.4 MPa between 760-900⁰C.

Understanding of the decisive role of the interaction mechanism and kinetics in the combustion processes is highly relevant for the elaboration of optimal conditions for obtaining Mo-Cu composite powders. From this perspective, the efficient delivery of the reduction mechanism of copper and molybdenum oxides with combined Mg + C reducing agents at high heating rates is crucial to develop a valuable approach for the combustion synthesis of Mo-Cu composite powders. Herein, we shed light on the mechanism of the reactions in all the studied binary, ternary and quaternary systems contemporaneously demonstrating the effect of the heating rate on the conversion degree. The combination of two highly exothermic and speedy reactions (MoO3+3Mg and CuO+Mg vs MoO3+CuO+4Mg) led to a slow interaction with weak self-heating (dysynergistic effect) due to a change in the reaction mechanism. On the other hand, it has been shown that during the simultaneous utilization of the Mg and C reducing agents, the process begins exclusively with carbothermic reduction, and at relatively high temperatures it continues with magnesiothermic one. The effective activation energy values of the magnesiothermic stages of the studied reactions were determined by Kissinger isoconversional method.

This study aligns with the 2030 United Nations Sustainable Development Goals- 3 which aim to “ensure healthy lives and promote well-being for all at all ages”. It contributes to the nascent literature stream on energy-health dynamics by introducing a holistic theoretical model to empirically examine the mediation effect of carbon emissions on the relationship between nonrenewable energy and infant mortality rates. Using an unbalanced panel data on 42 Asia and the Pacific countries from 2005 to 2015 and deploying the structural equation modeling approach, the empirical results are surmised as follows: (i) in regard to the full sample of countries, nonrenewable energy indirectly increases infant mortality rates through increasing carbon emissions. In other words, carbon emissions play a partial mediation role between nonrenewable energy and infant mortality rates; and (ii) for the different income groups, carbon emissions show varying mediation effects. For example, the mediation effects of carbon emissions in lower-middle and upper-middle income countries are found to be similar to those of the full sample of countries. Therefore, based on these findings, we conclude that nonrenewable energy is an essential determinant of infant mortality rates. Policy recommendations are put forward.

Air change rate is an important parameter for quantification of ventilation heat losses and also affects the indoor climate of buildings. Indoor air quality is significantly associated with ventilation. If air change isn't sufficient, trapped allergens, pollutants and irritants can degrade the indoor air quality and affect the well-being of a building's occupants. Many studies on ventilation and health have concluded that lower air change rates can have a negative effect on people’s health and low ventilation may result in an increase in allergic diseases. Quantification of air change rate is complicated, since it is affected by a number of parameters, of which the one of the most variable is the air-wind flow. This study aims to determination and comparison of values of the air change rate in two methods - by quantifying of aerodynamic coefficient Cp = Cpe - Cpi – so called aerodynamic quantification of the building and the methodology based on experimental measurements of carbon dioxide in the selected reference room in apartment building.

Management practices must be developed to improve yam production sustainability. Image-based phenotyping techniques could help developing such practices based on non-destructive analyses of important plant traits. Our objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in glasshouse and field. We took plant and leaf pictures with consumer cameras. We used the numbers of image pixels to derive the shoot biomass and the total leaf surface and calculated the ‘triangular greenness index’ (TGI) which is an indicator of the plant nitrogen (N) nutritional status. Under glasshouse conditions, the number of pixels obtained from nadir view (image taken top down) was positively correlated to the shoot biomass, and the total leaf surface, while the TGI was negatively correlated to the N content of diagnostic leaves. Under field conditions, pictures taken from the nadir view showed an increase in soil surface cover and a decrease in TGI with time. TGI was negatively correlated to SPAD measured on specific leaves but was not correlated to the N content of these leaves. In conclusion, these phenotyping techniques deliver relevant results but need to be further developed and validated for application in yam.

I propose a dynamic evolution hypothesis regarding the evolution of organisms by incorporating both diminished fitness returns and mutation rate tuning during adaptation to a constant environment. Basically, accumulating evidence from life history studies conducted over the past 70 years suggests that the evolution of individual fitness is subject to ecological constraints, leading to the evolutionary existence of an upper limit of individual fitness (ULIF). Given the existence of the ULIF, organismal evolution, which might initially have relatively great fitness returns through primarily Darwinian evolution, will eventually be subject to diminished fitness returns towards zero. With the diminished fitness return, Darwinian selection strength may eventually become smaller than the power of random genetic drift, leading to the occurrence of neutral evolution at both phenotypic and molecular levels. Meanwhile, mutation rates may change from an initial increase, due to the relatively strong fitness return, to subsequent decreases, due to both the diminished fitness return of beneficial mutations and the cost of deleterious mutations. The diminished fitness returns with subsequently reduced mutation rates are two potential evolution barriers leading to eventual evolutionary stasis. These findings provide important insights for understanding the conditions for the occurrences of different evolutionary patterns. Darwinian evolution theory, neutral evolution theory and punctuated equilibrium theory can be unified in the context of the dynamic evolution hypothesis formulated in this study.

This paper considers a hybrid relay network consisting of the source, the amplify-and-forward (AF) relay, the decode-and-forward (DF) relay, and the destination. We propose the optimal power allocation schemes between two different relays which maximize the achievable rate under a sum relay power constraint for given channel gains and transmit power from source. By solving the optimization problem to maximize the achievable rate for each relay network, the transmit power values in closed-form are derived. When the channel gains are the same, the optimal power allocation scheme for AF-DF relay network proves that a more power should be allocated at the first relay to maximize the achievable rate. In case of the DF-AF relay network, we derive the optimal power allocation scheme for the possible four cases. Under the same SNR condition at the first hop, we show that the achievable rate of AF-DF relay network is greater than that of DF-AF relay network when the channel gain between two relays is higher than that between the second relay and destination. Simulation results show that the proposed power allocation schemes provide a higher achievable rate than the equal power allocation schemes.

To evaluate the 2-year efficacy and safety of ab interno trabeculectomy with the Trabectome in Chinese primary open-angle glaucoma (POAG) patients. This was a multicenter, retrospective study and included POAG patients with or without visually-significant cataracts. The primary outcome was intraocular pressure (IOP) reduction. Secondary outcomes included reduction of glaucoma medications, surgical complications, and success at 2-years. Success was defined as: 1) IOP < 21 mmHg and at least 20% IOP reduction from baseline after 3 months at any two consecutive visits, and 2) no additional glaucoma surgery required. A total of 42 patients were enrolled; thirteen had a history of failed glaucoma surgery. Twelve patients underwent Trabectome surgery combined with phacoemulsification while the remainder underwent Trabectome surgery alone. Overall, the mean preoperative IOP was 21.4±1.23 mmHg. The Trabectome lowered IOP to 18.5± 0.94 mmHg at 2 years (P<0.05). The number of glaucoma medications also decreased from a baseline of 2.0±0.9 to 1.2± 0.21 at 2 years (P=0.04). The overall success was 78%, with patients undergoing combined surgery having a higher success than those with Trabectome surgery alone (100% vs. 76%). No major complications were seen. The Trabectome appears to be an efficient and safe procedure in Chinese POAG patients in the long-term.

Predicting the growth response of seedlings from the environmental responses of photosynthesis and metabolism may be improved by considering the dynamics of non-structural carbohydrate, NSC, over a diurnal cycle. Attenuation of growth metabolism when NSC content is low could explain why some NSC is conserved through the night. A dynamic model, incorporating diurnal variation in NSC, was developed to simulate growth of seedlings hour-by-hour. I compared predictions of this model to published growth and NSC data for seedlings that varied according to temperature, light, day length, or CO₂. Prolonged-darkness experiments showed a temperature dependent upper limit on the respiration capacity. Respiration was attenuated as NSC was depleted. Furthermore, when NSC was high at dawn, inhibition of photosynthesis could attenuate the accumulation of NSC under low temperature, or high light, or high CO₂. These concepts were used to simulate plant metabolism and growth rates and diurnal variation of NSC in tomato seedlings under two light levels and various temperatures. Comparison of other results using the same model parameters showed the dynamic model could predict results for starch and starch-less plants, and when growth was affected by CO₂ enrichment and day length.

Urban areas are centres of business and innovation. Freight transport is indispensable for the proper functioning of any modern urban society. Urban areas can’t function without an appropriate freight transport system, due to the need to replenish stocks of food and other goods in retail shops. The complexity of the decisions concerning implementation of measures to improve the movement of goods in the city requires tools designed to support this process. The purpose of this article is to introduce the possibility of applying the Analytic Hierarchy Process (AHP) as well as the Decision Making Trial and Evaluation Laboratory Method (DEMATEL) in choosing a set of measures and in analysing in the field of distribution logistics, which will help to solve the delivery problems and streamline the cargo flows in Szczecin, in the context of sustainable development.

In the dynamic landscape of online shopping, predicting user behaviors such as clicks, cart additions, and orders is crucial for optimizing sales strategies. Traditional recommendation systems often focus on a single objective, limiting their effectiveness in a multifaceted e-commerce environment. This article proposes a multi-objective recommendation system that leverages previous events in user sessions to forecast these key metrics, addressing challenges such as imbalanced positive and negative samples, varying session lengths, and the need for effective sampling techniques. Our approach integrates LightGBM, XgBoost, and Recbole GRU4Rec through ensemble learning, combining the strengths of these models to enhance prediction performance. Extensive evaluations demonstrate that our model outperforms existing methods, offering significant improvements in accuracy and robustness. This work provides a comprehensive solution for online retailers to better predict user behaviors and optimize their sales strategies, ultimately enhancing customer satisfaction and business outcomes.

The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.

Large language models (LLMs) are essential to the field of natural language processing, and as their applications expand, security risks have become increasingly prominent. This paper introduces a novel benchmark for evaluating LLMs security, termed CS-Eval, designed to effectively assess the models' ability to address vulnerabilities. CS-Eval targets seven key security risks: ethical dilemmas, marginal topics, error detection, detailed event handling, cognitive bias, logical reasoning, and privacy identification, and establishes a Multi-Security Hazard Dataset (MSHD). The evaluated models include GPT-4o, Llama-3-70B, Claude-3-Opus, ERNIE-4.0, Abab-6.5, Qwen1.5-110B, Gemini-1.5-Pro, Doubao-Pro, SenseChat-V5, and GLM-4. We analyzed each model's performance in relation to these security risks and provided recommendations for improvement. Experimental results demonstrate varying levels of effectiveness across models, with GPT-4o exhibiting the best overall performance. Moreover, the relationship between security enhancement and model capa-bility is nonlinear, indicating that improving safety requires a multifaceted approach, considering various factors in both development and application.

Under the wave of technological advancement and industrial upgrading, new productivity is deeply influencing the field of vocational education and promoting fundamental changes in teachers' roles. Modern teachers must not only keep pace with technological development but also master innovative teaching modes and guide students to form correct values. They are gradually evolving from mere knowledge transmitters to guides for learning, stimulators of interest, explorers of in-depth research, and promoters of comprehensive growth. Through interdisciplinary knowledge structures and innovative abilities, combined with advanced tools such as information technology and artificial intelligence, teachers innovate teaching methods to meet students' diverse learning needs and focus on cultivating students' professional skills, comprehensive qualities, and innovative spirits. This multi-dimensional reshaping of roles is not only an inevitable choice for the development of vocational education but also provides important insights for educational reform.

Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenge of viral infections and their growing resistance to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised Multi-Query Similarity Search Models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models' robustness and reliability. The top-performing model, M13+, demonstrated impressive results with an accuracy of 0.969 and a Matthew’s correlation coeffi-cient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine learning and deep learning AVP predictors. The MQSSMs outper-formed these predictors, highlighting their efficiency in terms of resource demand and public ac-cessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date.

The Zero Trust Architecture (ZTA) security system follows the "never trust, always verify" principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation is time-consuming and difficult. Azure's extensive services and customizations require careful design and implementation. Azure administrators struggle to navigate and change configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTA's comprehensive policy definitions, multi-factor and pass-wordless authentication, and other advanced features are tested in a mid-size business scenario. These configuration changes require a solid grasp of Azure's architecture and Zero Trust. Implementing an Azure ZTA reduces vulnerabilities and restricts authorized users' access to critical resources. Implementation is time-consuming and expensive, requires a significant commitment to changing IT procedures, and can be confusing because the same features are available in multiple places. Azure ZTA has shown great potential for both hybrid and cloud-native companies.

While advances in information and communication technology have increased the efficiency of information distribution, it has also brought several challenges to the database industry. One of the main challenges is ensuring the real-time availability of data, which is critical. In addition, downtime in centralized database systems can cause significant failures. Therefore this research applies database replication, especially the multi-master method, is used to increase availability and reduce the possibility of failure. For distributed transaction distribution, this research uses the auto-promote master method and web services for PostgreSQL database replication. The focus of the research is on real-world college site selection transactions, where speed, scalability, and high data availability are critical. The results show that combining these methods can improve system performance and protect data availability. With fast response time, high throughput, and low error rate, the API performs well with various loads. As a testament to scalability and reliability, the system can handle large volumes of traffic without experiencing significant crashes. For containerization, Docker supports efficient and stable performance and ensures consistency and manageability. Future recommendations include improving the data security mechanism and further efforts to optimize the distribution system architecture.

The presence of cracks reduces egg quality and safety, which are easy to cause food safety hazards to consumers. Machine vision-based methods for crack egg detection have made significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches which rely on improving network structures or increasing training data volumes, can not effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown testing egg data. Firstly, multi-domain egg data is built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust crack egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 model trained by proposed multi-domain trained method with NSFE-MMD has a detection mAP of 86.6\% on the unknown test domain 4, and the YOLOV8 model has a detection mAP of 88.8\% on domain 4, which is an increase of 8\% and 4.4\% compared to the best performance of models trained on a single domain, and an increase of 4.7\% and 3.7\% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain trained method has a detection mAP of 87.9\% on egg data of the unknown testing domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain trained method, which can be more suitable for the large quantity and variety of egg detection production.

Previous research has proven that one of the fundamental requirements for ensuring increased profitability and economic competitiveness of offshore-based projects is co-locating different technologies within the same marine space. This paper presents a number of techno-feasibility analyses for floating offshore technologies for the Maltese Islands, located in the Central Mediterranean Sea. The first part compares the feasibility of offshore-based solar photovoltaic prices with those onshore, taking into consideration Malta’s land prices. The second part considers the use of a novel floating breakwater design that integrates energy storage. The breakwater is used to create a sheltered water area in deep waters for a multi-use marine park. Different revenue streams for the floating break water are considered, including renting the sheltered marine space out to operators of floating solar farms, aquaculture cages and vessel berthing facilities, and the provision of energy storage services. It is found that the combined income from the multiple revenue streams is still insufficient to justify the investment and that financially support from Government is essential to render the floating breakwaters viable.

Maternal obesity and over/undernutrition can have a long-lasting impact on offspring health during critical periods in the first 1000 days of life. Children born to mothers with obesity have reduced immune responses to stimuli which increase susceptibility to infections. Recently, maternal western-style diets (WSD), high in fat and simple sugars, have been associated with skewing neonatal immune cell development, and recent evidence suggests that dysregulation of innate immunity in early life has long-term consequences on metabolic diseases and behavioral disorders in later life. Several factors contribute to abnormal innate immune tolerance or trained immunity, including changes in gut microbiota, metabolites, and epigenetic modifications. Critical knowledge gaps remain regarding the mechanisms whereby these factors impact fetal and postnatal immune cell development, especially in precursor stem cells in bone marrow and fetal liver. Components of the maternal microbiota that are transferred from mothers consuming a WSD to their offspring are understudied and identifying cause and effect on neonatal innate and adaptive immune development needs to be refined. Tools including single-cell RNA-sequencing, epigenetic analysis, and spatial location of specific immune cells in liver and bone marrow are critical for understanding immune system programming. Considering the vital role of immune function plays in offspring health, it will be important to understand how maternal diets can control developmental programming of innate and adaptive immunity.

Pharmacists must acquire the necessary knowledge to provide better drug therapy to patients. In other words, educational programs need to facilitate behavioral changes among pharmacists. Therefore, this study aimed to evaluate how the behavior of pharmacists changed after attending a palliative care educational program. We conducted a web-based questionnaire survey examining the behavior of pharmacists regarding palliative care before participating in the program, two months after participating in the program, and eight months after participating in the program, to determine the behavior and changes over time. For all questions, scores were higher at two and eight months after attending the program than before attending the program (P &lt;0.05). In addition, no significant difference was observed between two and eight months after attending the program for all questions (P =0.504-1.000). The knowledge gained from the educational program was used to repeatedly intervene with patients with cancer, to address the various symptoms they experienced and maintain their behavior. The proven effectiveness of this program serves as a stepping stone for a nationwide rollout across Japan’s 47 prefectures.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

In recent years, humanity has faced a growing challenge posed by the rise of antibiotic-resistant bacteria, resulting in an alarming surge in fatalities and the emergence of increasingly hard-to-manage diseases. Among these resilient pathogens is Acinetobacter baumannii, due to its escalating prevalence in hospitals, elevated mortality rates, and resistance to treatment. Despite its clinical significance, our understanding of this pathogen in non-hospital settings and its virulence factors remains limited. Therefore, this review aims to shed light on the latest research regarding its ecological niche, microbiological traits, and antibiotic resistance profiles. Recent investigations have expanded its detection to various environmental niches, including rivers, treatment plants, and soils. Moreover, this bacterium has been discovered in diverse food sources such as meat and vegetables, as well as in farm animals and household pets like dogs and cats. This broader presence of Acinetobacter baumannii outside of hospital environments underscores a significant risk of environmental contamination, necessitating heightened awareness and preventive measures to address potential public health implications.

In this paper, a distributed algorithm is proposed to solve a consensus convex optimization problem. It is a Jacobi-proximal alternating direction method of multipliers with a damping parameter $\gamma$ in the iteration of multiplier. Compared with existing algorithms, it has the following nice properties: (1) The restriction on proximal matrix is relaxed substantively, thus alleviating the weight of the proximal term. Therefore, the algorithm has a faster convergence speed. (2) The convergence analysis of the algorithm is established for any damping parameter $\gamma\in(0,2]$, which is larger ones in the literature. In addition, some numerical experiments and an application to a logistic regression problem are provided to validate the effectiveness and the characteristics of the proposed algorithm.

The variety of risks due to climate change with a potential impact on Cultural Heritage CH requires a multidisciplinary approach and a preliminary overview of the existing methods for risk analysis and assessment in different fields. Thus, the starting point of the research conducted by the Sapienza PNRR working group consists in a systematic literature review of risk assessment approaches, methods and indicators. As a first outcome, the research has designed a multidisciplinary comprehensive framework consisting of the definition of risk categorization, the framework represented by a measurable index and the main target, according to the classification of Cultural Heritage based on the harmonization of European vocabularies definitions and protocols. The risk assessment framework sets the methodological core of a future multi criteria matrix and digital platform for decision focused on the historic built environment and it is functional to the definition of specific operational guidelines connected to each risk for elaborating descriptive/predictive Architectural Heritage’s transformation scenarios to support the development of mitigation and adaptation strategies.

Current methods for assessing individual well-being in team collaboration at the workplace rely often on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal is to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We are focusing on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to find which features are predictors of self-reported satisfaction scores. We apply the toolchain to a case study where we collected videos of 22 teams with 56 participants collaborating over a four-day period in a team project. Our audiovisual speaker diarization extracts individual speech features in a noisy environment. As the dependent variable team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores have been predicted with speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain is able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

The availability of multi-rotor UAVs with lifting capacities of several kilograms, allows for a new paradigm in atmospheric measurement techniques, i.e. the integration of research-grade sonic anemometers for airborne turbulence measurements. With their ability to hover and move very slowly, this approach yields an unrevealed flexibility compared to mast-based sonic anemometers, for a wide range of boundary layer investigations that require an accurate characterization of the turbulent flow. For an optimized sensor placement, potential disturbances by the propeller-induced flow (PIF), as well as the potential impact of the sensor weight and angular momentum on the flight performance in the case of a boom-mounted sensor, have to be considered.

The meticulously engineered powertrain mounting system of hybrid-electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data-mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid-electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—assessing their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

The field of program classification, a critical aspect of software engineering, facilitates understanding and categorization of code across various applications, including anomaly detection, and code quality assessment. The advancement of cross-language program classification opens up avenues for efficient code translation among different programming languages, significantly aiding developers in rapid coding and reducing development cycles. Existing research primarily focuses on semantic code analysis, with limited emphasis on cross-linguistic challenges. To address this, we introduce an innovative neural network model, CodeSemanticsNN, which leverages a refined Syntax Structure (SS) approach. This model consists of two integral mechanisms: first, it adopts a novel SS representation that combines sequential and graph-based SS structures, enhancing semantic feature capture. Second, it employs a 'unified vocabulary' strategy to bridge the gap between diverse programming languages, facilitating efficient cross-language classification. Additionally, we have compiled a comprehensive dataset of 20,000 files spanning five programming languages, serving as a benchmark for cross-language classification. Our experiments on this dataset demonstrate that CodeSemanticsNN surpasses existing models in four key metrics: Precision, Recall, F1-score, and Accuracy.

We introduce and discuss a generalization of the classical multi-objective optimization to pairs of functions. This procedure is referred to as bi-multi-objective optimization. A justification of this general optimization procedure is presented, related both to multi-objective optimization under ambiguity concerning individual preferences and to Pareto optimality for a family of preferences with nontransitive indifference. Incidentally, the binary relation naturally associated to a bi-multi-objective optimization problem is represented by a finite bi-multi-utility, which generalizes to the nontransitive case the classical finite multi-utility representation. An important application is presented to Markowitz portfolio selection under ambiguity concerning both the vector of returns and the covariance matrix.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

This study aimed to overcome the difficulty of conducting the horizontal-well cavity leaching test in the field and to investigate the long-term stability of the horizontal-well salt-cavity natural gas storage. The simulation test design is combined with the similarity theory to study the cavity expansion characteristics and the influence law of cavity leaching parameters. Through the design of a rubber hose connection, an integrated closed test system for multi-stage horizontal-well cavity leaching and brine drainage was built. The test system also realises the repeatable backward movement of the injection well during the test. A similarity simulation of the test design was carried out, and the test platform was constructed to carry out multi-stage horizontal-well leaching tests with a nitrogen cushion. In the horizontal-well leaching tests with a nitrogen cushion, the influence laws of the well spacing, flow rate and liquid level position on cavity expansion were investigated. Based on the morphological characteristics of the horizontal-well cavity, a numerical model of the horizontal-well salt cavity was developed, which reflects the real cavity morphology leached in the test. The long-term stability of the horizontal-well salt-cavity natural gas storage under different internal pressures was investigated through numerical simulation.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

In this work, Al-Mo-B(CN) thin coatings were deposited by magnetron sputtering using composite metal-ceramic Al-Mo-B4C target. A metal-ceramic composite target for magnetron sputtering was fabricated by a robotic complex for detonation spraying of coatings equipped with a multi-chamber detonation accelerator. The powder composition (30Mo-30Al-40B4C) was sprayed onto the copper plate base of the composite target-cathode. The obtained cathode target with Al-Mo-B4C coating (thickness 280-300 μm) was used to deposit the Al-Mo-B(CN) coating (DC mode) on flat specimens of AISI 316 steels and silicon using equipment for magnetron sputtering UNICOAT 200. The Al-Mo-B4C coating has a lamella-type structure with inclusions of boron car-bide particles the elemental composition is evenly distributed in the material. The structure and morphology of coatings were studied using methods of optical, scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray analysis (XRD), and X-ray photoelectron spectros-copy (XPS). Mechanical and tribological properties of the Al-Mo-B(CN) thin coatings were determined using methods of nanoindentation, scratch testing, and tribological testing under fluid-free friction regime at room temperature. The Al-Mo-B(CN) coating (thickness ~ 1 μm) exhibited a dense uniform fine-grained structure with no columnar elements and were well-adherent to the substrates after deposition. Al-Mo-B(CN) coating has a amorphous structure. XPS analysis confirmed the formation of the MoB2 and AlN phase with an admixture of oxygen in the form of aluminum oxide, molybdenum oxide and boron oxide. Al-Mo-B(CN) coating possess hardness of 13 GPa, elasticity modulus of 114 GPa, elastic recovery of 45%, and friction coefficient of 0.8 against steel 100 Cr6 ball. The failure mode occurring in the tested coatings was fatigue and abrasive. The adhesion strength of Al-Mo-B(CN) coating amounted to about 11 N, and the failure mode was cohesive associated with plastic deformation and formation of fatigue cracks in the coating material.

In recent years, the emergence of spatiotemporal big data has made the transition of functional identification from the physical dimension to socioeconomic or human activities becoming more common. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Using taxi trajectory data and point of interest (POI) data as the main data source, this study proposes a multi-scale recursive identification method for urban functional areas based on cross-validation. First, used the dynamic time warping (DTW) algorithm generates a time series similarity matrix, the CA-RFM model combines the clustering algorithm and random forest model is constructed, the model uses a clustering algorithm (K-MEDOIDS) to extract sig-nificant feature regions as input, which are imported into the random forest model for UFZ identification. Then, to overcome the shortcomings of single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method, obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area for the case study, the results show that the com-bination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the research shows the superiority of the multi-scale recursive identification method that fuses multi-source data and performs cross-validation from two aspects, that is, the division speed of urban functional area identification results is accelerated and the accuracy is improved.