Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

Rabies remains a significant global threat, yet accurate estimations of its impact are hindered by the lack of confirmatory diagnoses. Postmortem confirmation of rabies traditionally involves invasive brain tissue testing, a process met with resistance from deceased patients' families, impeding consent. This paper presents and evaluates an innovative yet unpublished transnasal approach for postmortem brain tissue collection, offering a minimally invasive, easier, faster, and safer method. This method preserves the cadaver's integrity, potentially easing family reluctance towards autopsies. The limited testing of both human and animal rabies in Ghana highlights the challenges in diagnosing this fatal disease. Scarce diagnostic resources and the complexity of obtaining brain tissue samples exacerbate the issue. Cultural and religious beliefs surrounding autopsies contribute to familial hesitation, viewing these procedures as disruptive and disfiguring, further complicating consent. The transnasal technique involves approaching the brain tissue through the nostrils and cribriform plate without any superficial manipulation of the patient's head and face, thereby preserving aesthetics and natural features of the person. Technological advancements and seamless One Health collaboration among governmental, non-governmental, and research entities locally and globally culminated in Ghana's first confirmed rabies diagnosis using this method of brain tissue collection. This success emphasizes the efficiency and feasibility of transnasal brain collection approach and the invaluable role of the One Health approach and collaborative efforts in overcoming diagnostic challenges in rabies control.

Dust storms play a crucial role in the climate system and the space environment of Mars and have a great impact on human’s Mars exploration activities. The Martian dust storms exhibit significant regional, seasonal and interannual variations due to various controlling factors such as the large-scale atmospheric circulation, varying solar radiation forcing, and Martial orbital and rotational motions and their coupling to the atmospheric dynamics. This paper focuses on the review of cur-rent understandings on Martian dust storms. This paper begins by elucidating the basic properties of dust storms, and their driving mechanisms and impacts on atmospheric dynamics, atmospheric electric property, space environment, topography, and Mars explorations. Then it introduces the observation methods on different platforms including orbiters and landers/rovers, and the da-tasets constructed based on these historical observations for Martian dust storms. Finally, we pro-posed the dust storm monitoring and forecasting for the upcoming Chinese Tianwen-3 Mars sam-pling returning mission. It concludes by depicting the future research topics to systematically un-derstand the Martian dust storms.

The aim of this paper is to establish a valid procedure for better understanding all the phenomena associated with the high-speed machining of Glass Fibre Reinforced Plastic (GFRP) composites. Both rectangular and circular specimens were machined at high speeds (up to 50 m/min) in order to understand what occurred for all values of fiber orientation angles during machining operations. An innovative testing methodology was proposed and studied to investigate the phenomenon of burr formation and thus understand how to avoid it during machining operations. To this end, the forces arising during the machining process and the roughness of the resulting surface, were carefully studied and correlated with the cutting angle. Additionally, the cutting surface and chip morphology formed during cutting tests were examined using a high-speed camera. Close correlations are found between the variations of the cutting forces' signals and the trends of the roughness and the morphology of the machined surface.

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

The systematic sampling is used as a method to get the quantitative results from the tissues and the radiological images. Systematic sampling on real line (R) is a very attractive method within which the biomedical imaging is consulted by the practitioners. For the systematic sampling on R, the measurement function (MF) is occurred by slicing the three-dimensional object equidistant systematically. The currently used covariogram model in variance approximation proposed by [28,29] is tested for the different measurement functions in a class to see the performance on the variance estimation of systematically sampled R. This study is an extension of [17], and an exact calculation method is proposed to calculate the constant λ(q,N) of confidence interval in the systematic sampling. The exact value of constant λ(q,N) is examined for the different measurement functions as well. As a result, it is observed from the simulation that the proposed MF should be used to check the performances of the variance approximation and the constant λ(q,N). Synthetic data can support the results of real data.

This study introduces a novel computational approach for testing conditional independence (BB CI test) within causal Directed Acyclic Graphs (DAGs), leveraging Bayesian non-parametric bootstrap and machine learning techniques. Our method offers an alternative for validating the assumptions underpinning causal DAGs. Through simulation studies and an industrial case analysis, we demonstrate the test procedure in accurately assessing conditional independence, comparing it with the Generalized Covariance Measure (GCM) test. Our findings suggest that the BB CI test is advantageous in scenarios where existing methods may falter due to violations of model assumptions. This research contributes to the causal inference literature by providing a computational tool for researchers and practitioners to validate causal models.

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the Peak Signal-to-Noise Ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance.

Wastewater-based surveillance is emerging as an important tool for COVID-19 pandemic trending. Current methods of wastewater collection, such as grab and auto-composite sampling, have drawbacks that impede effective surveillance, especially from small catchments with limited accessibility. Passive samplers, which are more cost-effective and require fewer resources to process, are promising candidates for monitoring wastewater for SARS-CoV-2. Here, we compared traditional auto sampling with passive sampling for SARS-CoV-2 detection in wastewater. Torpedo-style 3D printed passive sampler device containing both cotton swabs and electronegative filter membranes was used. Between April and June 2021, fifteen passive samplers were placed at a local hospital wastewater outflow alongside an auto sampler. Reverse transcription and quantitative polymerase chain reaction (RT-qPCR) was used to detect SARS-CoV-2 in the samples after processing and RNA extraction. The swab and membrane of the passive sampler showed similar detection rates and Ct values for SARS-CoV-2 RNA for the N1 and N2 gene targets. The passive method performed as well as the grab/auto sampling, with no significant differences between N1 and N2 Ct values. There were discrepant results on two days with negative grab/auto samples and positive passive samples, which might be related to the longer duration of passive sampling in the study. Overall, the passive sampler was rapid, reliable and cost-effective, and could be used as an alternative sampling method for the detection of SARS-CoV-2 in wastewater.

The oral microbiome, studied by sampling the saliva or oral rinse, has been long thought to have diagnostic capacity for head and neck cancers (HNC). However, previous reports on the HNC oral microbiome provide inconsistent results. The aim of this study is to consolidate these datasets and determine the oral microbial composition between HNC patients to healthy and premalignant individuals. We analyzed 16 published head and neck cancer (HNC) short-read 16S rRNA sequencing datasets, specifically targeting the V3V4, V4 and V4V5 regions. These datasets included saliva and oral rinse samples from donors with HNC, as well as from healthy and premalignant donors. Differences in diversities and microbial abundance were determined. HNC saliva displayed lower alpha diversity than healthy donors. In contrast, the opposite trend was observed for oral rinse samples. Beta diversity were largely similar across different patient types. Similar oral phyla were detected for all samples, but proportions were largely dependent on sample type (i.e. saliva or oral rinse) and primer set utilised for 16S rRNA sequencing. Neisseria, Leptotrichia and Megasphaera were elevated in healthy saliva, while Mycoplasma was elevated in HNC saliva. Oral rinse and saliva displayed similar enrichment for Fusobacterium, while Veillonella, Alloprevotella, and Campylobacter had conflicting results. The sparse partial least squares discriminant analysis model performed effectively in discriminating HNC from healthy or premalignant patients using V3V4 saliva (AUC = 0.888), and V3V4 oral rinse (AUC = 0.928), while poor discriminative capacity was observed for V4 saliva (AUC = 0.688). In conclusion, our meta-analysis highlighted the limitations of 16S rRNA sequencing, particularly due to variations across study batches, primer sets (i.e. V3V4, V4), and sample types. Hence, caution should be exercised when interpreting 16S rRNA sequencing results across studies, especially when different primer sets and sample types are used.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

Microplastic pollution (MP) in marine environments around the globe is severe and enough precautions have not been taken so far for its prevention. The focus of this study is to quantify MPs from beach sediment and seawater samples and to identify their distribution and types along the western coast of Sri Lanka from the Kelani River estuary to the Mahaoya estuary. Nine sites along this 42 km stretch were selected and random sampling was employed to collect a minimum of 8 sediment samples from each site between October and December 2021. Water samples were also collected, parallel to the sediments, from the ocean surface. FTIR analysis revealed that most of the MPs found were polyethylene(PE), polypropylene(PP), polystyrene(PS), polyethylene terephthalate(PET), and phenol formaldehyde resin. The mean abundance of MPs varied from 2.0 ± 0.6 items/L to 161.0 ± 15.7 items/L in water samples and, from 3.0 ± 0.3 items/m2 to 656.0 ± 34.5 items/m2 in sediment samples. The MPs found were identified in different shapes as fragments (80.2%), pellets (14.9%), fibers (2.7%) and foams (2.5%). Analysis revealed that the beach sediments were contaminated with PS, phenol formaldehyde resin, PET, PP and PE, while surface seawater is dominated by phenol formaldehyde resin, PS, PP and PE.

The present paper reports an alternative solution of the measurement problem in quantum theory. The measurement problem can be characterized as that the U-procedure verses the R-procedure. The R-procedure is tested by the Yes/No measurement originally proposed by von Neuman and discussed in details by Penrose. We propose a novel stochastic sampling method to tackle the measurement paradox. Each testing sample produces a pair of Yes-number c and No-number d, which in turn generates a sample phase with respect to the exponential form of (c+id). All the sample phases forms a group G_R. Taking G_R as the sampling potential, combined with the dynamic phase group G_U，we establish a unified model of U-procedure and R-procedure for the evolution of wavefunction. Based on the Born rule, the probability of a sample is given by the squared magnitude of （c+id）. Some metaproperties of the new U-R model, such as natural transformation, consistency, and completeness, are presented. The present work provides a new picture of quantum mechanics and alike. The measurement problem widely exists in dynamic analysis, higher cognition, and large data modeling. Thus, the work reported here has a wide range of applications. Keywords: The measurement problem; U-R procedures; the Yes/No type measurement; stochastic sampling; sample phase; Born probability.

The question whether a given set of test items can be considered “identical” is often addressed in terms of the homogeneity of the test material from which said items were taken. However, for some types of matrices – in particular, for matrices consisting of minute separate particles, only some of which carry the analyte under consideration – even in the case of homogenous test material, an irreducible source of variability between test items may remain: the fundamental variability. In this paper, the concept of fundamental variability is explained, and procedures for reducing and characterizing it are described.

Divergences have become a very useful tool for measuring similarity (or dissimilarity) between probability distributions. Depending on the field of application a more appropriate measure may be necessary. In this paper we introduce a family of divergences we call gamma-divergences. They are based on the convexity property of the functions that generate them. We demonstrate that these divergences verify all the usually required properties, and we extend them to weighted probability distribution. We investigate their properties in the context of kernel theory. Finally, we apply our findings to the analysis of simulated and real time series.

We derive a concentration inequality for the uncertainty in the mean computed by stratified random sampling, and provide an online sampling method based on this inequality. Our concentration inequality is versatile and considers a range of factors including: the data ranges, weights, sizes of the strata, the number of samples taken, the estimated sample variances, and whether strata are sampled with or without replacement. Sequentially choosing samples to minimize this inequality leads to a online method for choosing samples from a stratified population. We evaluate and compare the effectiveness of our method against others for synthetic data sets, and also in approximating the Shapley value of cooperative games. Results show that our method is competitive with the performance of Neyman sampling with perfect variance information, even without having prior information on strata variances. We also provide a multidimensional extension of our inequality and discuss future applications.

Current sampling methods for detecting the presence of the invasive freshwater hydrozoan Craspedacusta sowerbii rely mainly on visual confirmation of the medusa stage. Confirming the presence of the polyp stage is equally important to observing medusae since typical late summer/early fall occurrences or observations of medusae are sporadic though becoming more frequent. The polyp stage is important as it is the organism’s primary stage and is present throughout the year depending on water temperatures. Therefore, sampling methods for the polyp stage are commonly the collection of substrates such as rocks, plants or pieces of wood in a given body of water and can be cumbersome to examine. Based on preliminary culturing of the polyp stage on glass and plastic microscope slides in the lab, we designed a novel sampling methodology based on submerging 4 substrate types (small glass petri dishes, Hester-Dendy plates, plastic and glass microscope slides) to confirm the presence of C. sowerbii polyps in the field. We tested this method in various lakes in the Illinois-Indiana region (USA). The sampling method we designed was effective in that C. sowerbii polyps (and sometimes colonies) were found on both plastic and glass slides. While this method can be sufficient for detection of the polyp stage, it also shows significant potential for improvement; we highlight water flow and competition and predation from other organisms as significant factors influencing the collection of C. sowerbii polyps for future methodologies.

Although the behavioral mechanism studying is a difficult and complex task, it can produce an important systems optimization impact. In this work, we use, in this paper, inference system approach to represent reasoning mechanism and the operations of dynamic systems to extract mental representations from traveling salesman and convert them into cognitive structures. For this we develop an extraction automatic method to create knowledge bases and, later, data are stocked into structure based on transition maps and the performances of these created maps get improved through combining the reinforcement learning thus augment traveller's deciding capacity from historical data. These transition maps help to find best actions for obtaining useful new policies. Generated intermediate transition maps are gathered to give a global map called main map whose advantage is to improve the learning process. The main idea of this approach is to improve learning capability by using a reinforcement learning technique as exploration and exploitation strategy of the metaheuristique GRASP method and the use of the fuzze logical rules mechanism allowing concepts model to have more variability of states. The results obtained after simulation as presented in this paper are very encouraging.

: This article was not intended to be a complete report of a standard clinical trial. It is a report of the outcomes of preliminary data for validation of the CMECD procedure (Coletti Method of EMG ChemoDenervation) protocol for the treatment of chronic pain resulting from chronic muscle spasm. Methods are here detailed on how to approach the patient with chronic pain, identify the presence of chronic muscle spasm, undertake the treatment protocol and how to perform the follow up process to confirm that chronic pain secondary to chronic muscle spasm was the accurate diagnosis. Furthermore, this article presents the results of a cohort of more than 90 patients treated by the CMECD procedure regarding location and duration of prior pain, prior treatment strategies, degree of success in resolving pain and duration of relief. Outcome data consisting of patient and staff reporting of specific situations in which the chronic pain treatment was successful has been included to help establish the “believability” of outcome successes and to elucidate the potential life altering effects of successful treatment of chronic pain secondary to chronic muscle spasm. This article will hopefully increase the interest in this treatment protocol and increase the chance that a classical international clinical trial will be undertaken.

Distributed tracing is a cutting-edge technology for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and direct storage and utilization of every trace are impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as simplifying application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of the sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

This article examines air sampling as an environmental surveillance strategy for early pathogen detection and offers a preliminary comparison of air and wastewater surveillance. Air sampling involves collecting bioaerosols to analyze the microbial composition of the air, utilizing techniques like PCR and metagenomic sequencing. Air sampling appears especially promising for detecting respiratory pathogens and highly transmissible pandemic agents, as these spread primarily via aerosols and droplets. The article details various air sampling techniques, their efficiency, and practical considerations, categorizing them into filtration, impaction, impingement, electrostatic precipitation, and condensation methods. It also explores the composition of airborne microbes, emphasizing the low concentration of viral nucleic acids and the challenges this poses for metagenomic sequencing. The selection of sampling locations, including indoor environments and airplanes, is discussed as critical for effective pathogen detection. Finally, the article contrasts air and wastewater sampling, concluding that while both methods should ideally be explored as layered defenses, air sampling does not collectively appear more promising than wastewater sampling.

Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well-suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection will be discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays will be discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

(1) Background: This study aimed to evaluate whether the implementation of a modified blood sampling protocol, which focused on need-based laboratory testing and minimized venous sampling by replacing it with point-of-care testing (POCT) via capillary puncture, successfully reduced iatrogenic blood loss, incidence of anemia, and frequency of blood transfusion in extremely low birth weight infants (ELBWIs) without negatively affecting neonatal outcomes. (2) Methods: A retrospective analysis was conducted on 313 ELBWIs of gestational age (GA) between 23 and 28 weeks, born between 2013 and 2019. The infants were divided into two groups: before (period I) and after (period II) the implementation of the modified blood sampling protocol in January 2016. Propensity score matching was conducted to minimize selection bias. Clinical data, including the frequency and amount of blood sampling, frequency and volume of blood transfusion, and clinical characteristics, such as gestational age, birth weight, and neonatal outcome data, were collected and compared between the two groups. (3) Results: No significant differences in the GA or birth weight between the two periods were observed. The total sampling volume during a month after birth (16.7 ± 4.1 mL vs. 15.6 ± 4.4 mL, P=0.03) and total sampling volume during hospital days (51.4 ± 29.7 mL vs. 44.3 ± 27.5 mL, P=0.04) in period II were significantly lower than that in period I. There were no differences in the mortality or morbidity between the two periods. Although the transfusion frequency and amount did not have significant differences between the periods, we observed a positive correlation between the transfusion frequency and sampling volume (coefficient: 0.09, 95% CI: 0.08–0.11). (4) Conclusions: The modified blood sampling protocol effectively reduced the iatrogenic blood loss without negatively affecting the neonatal outcomes.

Fatigue performance and self-repairing activity of asphalt binders are two properties that highly influence the fatigue cracking response of asphalt pavement. There are still numerous gaps of knowledge to fill linked with these two characteristics. For instance, current parameters fail to fully accommodate these two bitumen phenomena. This study aims to propose a new procedure to address this issue utilizing the linear amplitude sweep (LAS) test, LAS with rest period (RP) (LASH) test, and simplified viscoelastic continuum damage (S-VECD) model. This research work used four different types of asphalt binders: neat asphalt (NA), self-healing thermoplastic polyurethane (STPU)-modified bitumen (STPB), self-healing poly (dimethyl siloxane) crosslinked with urea bond (IPA1w)-modified bitumen (IPAB), and styrene–butadiene–styrene (SBS)-modified bitumen (SBSB). Before the testing process, all the materials were subjected to short-term and long-term aging. The new procedure showed a superior capacity to analyze and accommodate all bitumen fatigue performances and self-repairing activities compared to the current method. Another finding proved that asphalt binders with higher self-restoration behavior failed to show better fatigue performance. A breakthrough finding demonstrated that asphalt binder fatigue response augmented when the RP was applied at a higher damage intensity (S) value. STPBs and IPABs reached their highest increments of fatigue response, containing 1.0% of STPU and 0.5% of IPA1w, respectively. Those augmentations were 207.54% and 232.64%, respectively.

Ranked Set Sampling (RSS) is a useful technique for improving the estimator of population mean when the sampling units in a study can be easily ranked than the actual measurement. RSS performs better than simple random sampling (SRS) when the mean of units corresponding to each rank is used. The performance of RSS can be increased further by assigning weights to the ranked observations. In this paper, we propose weighted RSS procedures to estimate the population mean of positively skew distributions. It is shown that the gain in the relative precisions of the population mean for chosen distributions are uniformly higher than those based on RSS. The gains in relative precisions are substantially high. Further, the relative precisions of our estimator are slightly higher than the ones based on Neyman’s optimal allocation model for small sample sizes. Moreover, it is shown that, the performance of the proposed estimator increases as the skewness increases by using the example of lognormal family of distributions.

There is growing interest in characterizing the viromes of diverse mammalian species, particularly in the context of disease emergence. However, little is known about virome diversity in aquatic mammals, in part due to difficulties in sampling. We characterized the virome of the exhaled breath (or blow) of the Eastern Australian humpback whale (Megaptera novaeangliae). To achieve an unbiased survey of virome diversity a meta-transcriptomic analysis was performed on 19 pooled whale blow samples collected via a purpose-built Unmanned Aerial Vehicle (UAV, or drone) approximately 3km off the coast of Sydney, Australia during the 2017 winter annual northward migration from Antarctica to northern Australia. To our knowledge, this is the first time that UAVs have been used to sample viruses. Despite the relatively small number of animals surveyed in this initial study, we identified six novel virus species from five viral families. This work demonstrates the potential of UAVs in studies of virus disease, diversity, and evolution.

On the eve of the Brexit process, in the context of a rising Euroscepticism that fuels the modest confidence of European citizens in their national and European institutions, the article assesses the efficiency and effectiveness of the European Parliament within the framework of the ordinary legislative procedure (co-decision). After defining and formulating the main indicators, the paper analyses the micro- and macro-performance of the European Parliament within the decision-making process from a quantitative-qualitative and a qualitative-quantitative perspective, highlighting the relativizing factors and the responsiveness of the European decision-making process to the Europeans’ needs.

Although several protocols for genetic transformation of citrus have been published, it is highly desirable to further improve its efficiency. Here we report treatments of Agrobacterium cells and citrus explants prior to and during co-cultivation process to enhance transformation efficiency using a commercially used rootstock 'Carrizo' Citrange as a model plant. We pre-cultured Agrobacterium cells in a 1/10 MS, 0.5 g/L 2-(N-morpholino) ethanesulfonic acid (MES) and 100 µM acetosyringone liquid medium for 6 hours at 25 ℃ before used to infect citrus explants. We incubated epicotyl segments in an MS liquid medium containing 13.2 µM 6-BA, 4.5 µM 2,4-D, 0.5 µM NAA for 3 hours at 25℃ prior to Agrobacterium infection. In the co-cultivation medium, we added 30 µM paclobutrazol and 10 µM lipoic acid. Each of these three treatments significantly increased the efficiencies of transformation. When the three treatments were combined, we observed that the transformation efficiency was enhanced from 11.5% to 52.3%. The improvement of genetic transformation efficiency mediated by these three simple treatments may facilitate more efficient applications of transgenic and gene editing technologies for functional characterization of citrus genes and for genetic improvement of cultivated citrus varieties.

A paper impregnated with silver nanoparticles (AgNPs) has been prepared. For the preparation of the substrates, aqueous suspensions of pulp fines, a side product from the paper production, have been mixed with Ag nanoparticles (AgNP) suspensions. The nanoparticle synthesis thereof was carried out via laser ablation of pure Ag in water. After the sheet formation process, the leaching of the AgNPs was determined to be low while the sheets exhibited antimicrobial activity towards E. Coli.

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can reduce the dimensionality of the decision space and therefore reduce computational resources by employing different sampling strategies. While these approaches reduce computational consumption, they may miss an optimal pixel pair when the number of available high-quality pixel pairs is limited. To address this shortcoming, we propose a novel multi-criterion sampling strategy that avoids missing high-quality pixel pairs by incorporating multi-range pixel pair sampling and high-quality samples selection method. This strategy is employed to develop a multi-criterion matting algorithm via Gaussian process, which searches for the optimal pixel pair by using the Gaussian process fitting model instead of solving the original pixel pair objective function. Experimental results demonstrate that our proposed algorithm outperforms other methods even with 1% computing resources, and achieves alpha matte results comparable to those yielded by the state-of-the-art optimization algorithms.

Human papillomavirus (HPV) self-sampling (Self-HPV) is a promising strategy to improve cervical cancer screening coverage in low-income countries. However, issues associated with women who prefer conventional HPV clinical-sampling over HPV self-sampling may affect screening participation. To address this issue, our study assessed factors associated with women’s preferences related to Self-HPV. This study was embedded in a large clinical trial recruiting women aged 30–49 years in a primary HPV-based study termed “3T-Approach” (for Test-Triage-Treatment), launched in 2018 at Dschang District Hospital, West Cameroon. Participants were invited to perform a Self-HPV. After the sampling and before receiving the results, participants completed a questionnaire about cervical cancer screening and their preferences and perceptions around Self-HPV. The median age of the 2201 participants was 40.6 (IQR 35–45) years. Most (1693 (76.9%)) preferred HPV self-sampling or had no preference for either method and 508 (23.1%) preferred clinician-sampling. Factors associated with an increased likelihood of reporting a clinician-sampling preference were tertiary educational level (14.4% CI: 12.8–16.1 vs 29.5% CI: 25.6–33.6) and being an employee with higher grade professional or managerial occupations (5.5% CI: 3.8–7.9 vs 2.6% CI: 2.3–2.8). The main reported reason for women preferring clinician-sampling was a lack of “self-expertise”. Most women (>99%) would agree to repeat HPV self-sampling and would recommend it to their relatives. HPV self-sampling in the cultural context of central Africa was well accepted by participants, but some participants would prefer to undergo clinician sampling. Health systems should support well-educated women to increase self-confidence in using HPV self-sampling.

This paper studies the pilot control strategy and workload of tilt-rotor aircraft dynamic conversion procedure between helicopter mode and fixed-wing mode. A nonlinear flight dynamics model of tilt-rotor aircraft with full flight modes is established. On this basis, a nonlinear optimal control model of dynamic conversion is constructed, considering factors such as conversion corridor limitations, pilot control, flight attitude, engine rated power, and wing stall effects. To assess pilot workload, an analytical method based on wavelet transform is proposed, which examines the mapping relationship between pilot control input's amplitude, constituent frequencies, and control tasks. By integrating the nonlinear optimal control model and the pilot workload evaluation method, the analysis of pilot control strategy and workload during the conversion procedure is conducted, leading to the identification of strategies to reduce pilot workload. The results indicate that incorporating the item of pilot workload in the performance index results in a notable reduction in the magnitude of collective stick inputs and longitudinal stick inputs. Moreover, it facilitates smoother adjustments in altitude and pitch attitude. Additionally, the conversion of the engine nacelle can be achieved at a lower and constant angular velocity. In summary, the conversion and reconversion procedures are estimated to have a low workload (Level 1 ~ Level 2), with relatively simple and easy manipulation for the pilot.

Abstract: Mass spectrometry research laboratories reported multiple probes for ambient ionization in the last years. Combining them with a mechanical moving stage enables automated sampling and imaging applications. We developed a robotic platform, which is based on RepRap 3D-printer components, and therefore easy to reproduce and to adopt for custom prototypes. The minimal step width of the Open LabBot is 12.5 μm, and the sampling dimensions (x, y, z) are 18 × 15 × 20 cm. Adjustable rails in an aluminium frame construction facilitate the mounting of additional parts such as sensors, probes, or optical components. The Open LabBot uses industry-standard G-code for its control. The simple syntax facilitates the programming of the movement. We developed two programs: 1) LABI-Imaging, for direct control via a USB connection and the synchronization with MS data acquisition. 2) RmsiGUI, which integrates all steps of mass spectrometry imaging: The creation of G-code for robot control, the assembly of imzML files from raw data and the analysis of imzML files. We proved the functionality of the system by the automated sampling and classification of essential oils with a PlasmaChip probe. Further, we performed an ambient ionization mass spectrometry imaging (AIMSI) experiment of a lime slice with laser desorption low-temperature plasma (LD-LTP) ionization, demonstrating the integration of the complete workflow in RmsiGUI. The design of the Open LabBot and the software are released under open licenses to promote their use and adoption in the instrument developers’ community.

From an industrial point of view, the milling of 2.5D cavities is a frequent operation, 1 consuming time and presenting optimization potential, especially through a judicious choice of 2 the tool trajectory. Among the different types of trajectories, some have a general spiral-like aspect 3 and can potentially offer a reduced machining time. They are called curvilinear trajectories and are 4 obtained by interpolation between structure curves which are the numerical solutions of a partial 5 differential equation. In this case, the machine tool will connect points and the trajectory will be made 6 up of small segments. Even if, macroscopically, these trajectories have all the qualities to allow the 7 tool to move quickly, on a small scale, the discontinuities in tangency, inherent in the discretization, 8 significantly increase machining time. This article suggests to enhancing the continuity level of the 9 toolpath by rebuilding structure curves with a set of Hermite quartic spline patches connected in 10 tangency and curvature. Thanks to this, the tool will machine at an average feedrate closer to the 11 programmed one and will, de facto, reduce traveling time. 12 This article shows that the proposed method increases, on the two tested cavities, toolpath quality 13 indicators, reduces milling time from 10% to 18% for a curvilinear method without a filter and make 14 it possible to generalize the Bieterman and Sandström method for all non convex pockets.

Cyclic voltammetry can be applied as the reliable method for the determination of chloride ions in a range from several to couple hundred ppm. Since the standard potential of chloride ion/gaseous chlorine is 1.36 V vs NHE the efficient oxidation of Cl- ion occurs at very positive electrode potentials, usually higher than + 1.7 V vs NHE electrode. It is possible to observe this phenomenon only at noble-metal or inert carbon electrodes. Many other solutes, usually organic compounds, are often oxidized at this potential. This is the reason why determination of Cl- content directly from increase of the oxidation current is not reliable and can lead to overestimated values. However, gaseous chlorine, generated at positive potential dissolves in the analyzed solution and can be reduced in the reverse scan of cyclic voltammetric curve. Optimization of the experimental procedure using the statistical tools allowed for the development of the improved method for the direct quantification of chloride ions in acid copper electroplating baths. Under the proposed experimental conditions, the calibration curve (Cl2 voltammetric reduction current vs. chloride ions concentration) is represented by the linear model for the concentration of chlorides ranging from 10 to 200 mg/dm3.

The bond index is an indicator of the grindability of the material and is widely used in the preparation of mineral raw materials and the cement industry. Paper offers new, abbreviated and simplified procedure to determine Bond work index that relies on first-order kinetics law and can be performed with any number of grinding cycles, depending on the desired accuracy of the required data. The parameters G and P80 of each grinding cycle are multiplied by the newly founded coefficients giDT and piDT to obtain values approximately equal to these parameters of the last grinding cycle when the equilibrium state is reached in the standard test. The paper presents comparative results obtained by standard Bond and new shortened procedure on individual samples of andesite, limestone, copper-ore and smelter slag and on composite samples of andesite from limestone and copper-ore with smelter slag in different mass ratios. As the number of grinding cycles increases, the precision of the shortened procedure increases, and the mean square error decrease 3.59%, 2.61% and 1.74% for two, three and four grinding cycles.

Winding design methods have been a subject of research for many years of the past century. Many methods have been developed, each one characterized by some advantages and some drawbacks. Nowadays, the star of slots is the most widespread design tool for electrical machine windings. In this context, this paper presents a simple and effective procedure to determine the distribution of the EMF stars and of the winding configuration in all possible typologies of electrical machines equipped with symmetrical windings. Moreover, this procedure can also be easily implemented in a computer program in order to perform automated winding designs for rotating electrical machines. Several examples are provided in order to validate the proposed procedure.

Aquatic macroinvertebrates play a crucial role in freshwater ecosystems, serving as reliable indicators of water quality and ecosystem health. To effectively monitor and assess these ecosystems, it is essential to employ appropriate sampling methods that capture the diversity and abundance of macroinvertebrate communities. The objective of this study was to provide a comprehensive overview and comparative analysis of different sampling methods for aquatic macroinvertebrates, highlighting their advantages, limitations, and applicability in ecological research was carried out. The results showed that the most commonly used methods include Kick-net Sampling, Surber Sampler, D-frame Net, Bottle Sampling, and Pitfall Traps. Researchers should select methods based on study goals, environmental conditions, and target organisms. Integrating multiple techniques enhances understanding of macroinvertebrate communities and their responses to environmental changes, contributing to improved assessments of water quality, ecosystem health, and conservation in freshwater ecosystems.

Micro-nano-plastics (MNPs) are an important constituent of atmospheric aerosol. However, there is still no standard procedure for their sampling and size fractionation, which is an obstacle to the aggregation and critical analysis of results obtained by different research groups. This review focuses on the sampling and fractionation methodologies used for MNPs. Moreover, a straightforward optimized methodology for the sampling and fractionation is proposed.

We advance binational link-tracing sampling design, an innovative data collection methodology for sampling from transnational social fields, i.e., transnational networks embedding migrants and non-migrants. This paper shows the practical challenges of such a design, the representativeness of the samples and the qualities of the resulted networks. We performed 303 face-to-face structured interviews on sociodemographic variables, migration trajectories and personal networks of people living in a Romanian migration sending community (Dâmbovița) and in a migration receiving Spanish town (Castellón), simultaneously in both sites. Inter-connecting the personal networks, we built a multi-layered complex network structure embedding 4,855 nominated people, 5,477 directed ties (nominations) and 2,540 edges. Results indicate that the participants’ unique identification is a particularly difficult challenge, the representativeness of the data is not optimal (homophily on observed attributes was detected in the nomination patterns), and the relational and attribute data allow to explore the social organization of the Romanian migrant enclave in Castellón, as well as its connectivity to other places. Furthermore, we provide methodological suggestions for improving link-tracing sampling from transnational networks of migration. Our research contributes to the emerging efforts of applying social network analysis to the study of international migration.

Malignant tumors behave dynamically as cell communities governed by ecological principles. Massive sequencing tools are unveiling the true dimension of the heterogeneity of these communities along their evolution in most human neoplasms, clear cell renal cell carcinomas (CCRCC) included. Although initially thought to be purely stochastic processes, very recent genomic analyses have shown that temporal tumor evolution in CCRCC may follow some deterministic pathways that give rise to different clones and sub-clones randomly spatially distributed across the tumor. This fact makes each case unique, unrepeatable and unpredictable. Precise and complete molecular information is crucial for patients with cancer since it may help in establishing a personalized therapy. Intratumor heterogeneity (ITH) detection relies on the correctness of tumor sampling and this is part of the pathologist’s daily work. International protocols for tumor sampling are insufficient today. They were conceived decades ago, when ITH was not an issue, and have remained unchanged until now. Noteworthy, an alternative and more efficient sampling method for detecting ITH has been developed recently. This new method, called multisite tumor sampling (MSTS), is specifically addressed to large tumors that are impossible to be totally sampled, and represent an opportunity to improve ITH detection without extra costs.

This study aimed to evaluate the effect of convective and microwave drying on the content of bioactive compounds of blackberry (Rubus fruticosus), drying parameters and energy consumption. The fruit was dehydrated in a convective dehydrator at a temperature of 50 °C and 70 °C and a microwave oven at power levels of 90 V, 180 V and 240 V. The highest amount of anthocyanins, polyphenols and antioxidant capacity were obtained in microwave dried blackberry fruits at 90 V and 180 V. It turned out that microwave dehydration shortened the processing time and lower the energy consumption compared to the convective drying. Blackberry fruits dehydrated at 240 V showed the shortest dehydration time, minimal energy consumption and the most efficient diffusion.

Ranunculus asiaticus L. is an ornamental geophyte, mainly propagated by rehydrated tuberous roots. Vernalization before planting is a common practice to overcome the natural dormancy of tuberous roots, however little is known about the mechanisms underlying the plant response to low temperature. We investigated the influence of two hybrids, MBO and MDR, and three preparation procedures of tuberous roots, only rehydration (control, C), and rehydration plus vernalization at 3.5 °C for 2 weeks (V2) and for 4 weeks (V4), on plant growth, leaf photosynthesis, flowering and metabolism in plants grown in pot in unheated greenhouse. We reported the responses observed in the aerial part previously in this journal (Fusco et al., 2023). In this paper, we show changes in carbohydrate, amino acids, polyphenols and protein levels throughout the growing cycle, in the different plant stages: pre-planting, vegetative phase and flowering.

Patients in intensive care unit often lose a considerable fraction of their gut microbiome due to exposure of broad-spectrum antibiotics and other reasons. Dysbiosis often results in prolonged diarrhea and increase occurrence of multi-drug resistant pathogens in the colon with clinical consequences not yet well understood. Restoring the microbioma by faecal microbial transplantation (FMT) is a plausible therapeutic possibility, so far only documented in case reports and case series using very heterogeneous methodologies. Before FMT in critically ill can be tested in randomised controlled trials, there is a burning need to describe a standardized operating procedure (SOP) of the whole process, respecting the specifics of critically ill population, such as the risk of disrubted intestinal barrier and time critical nature of the procedure. We describe the SOP that has been developed for experimental use in critically ill patients by a multidisciplinary team of intensivists, gastroenterologist and microbiologist based on feedback from regulatory authority (State Institue of Drug Control of the Czech Republic). The hallmarks of these SOPs are multi-donor freshly frozen transplantate quaranteeded for 3 months consisting of 7 aliqutes from 7 unrelated healthy donors, and administered by rectal tube. In this paper we discuss the rationale for this SOP and the process of its development in details and release the full proposed SOP is in the form of online appendix.

The use of different sources to energize a load is convenient in many applications, particularly those where two or more renewable energy sources are employed as: energy harvesting, hybrid vehicles, and off-grid systems. In these cases, a multi-input converter able to admit sources with different characteristics and, if necessary, select the output power of each source. Several topologies of multi-input converters have been proposed to this aim, however, most of them are based on multi-stage designs, which decreases efficiency and increases control complexity, particularly when more than two sources are used. In this work, a three-input step-up converter easy to control in open loop condition is analyzed. A designed procedure is described, and experimental results are presented for a 1 kW power converter. The implemented converter results in a higher voltage gain, less storage element keeping high efficiency compared to similar topologies. Using the procedure here proposed, this converter that was initially proposed for photovoltaic applications is enabled to be used in medium and high-power applications, for example when renewable energy sources are used.

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper a CFD (Computer Fluid Dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and a 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.

Afterthe series of earthquakes in August 1953, most of Kefalonia’s building stock totally collapsed. The buildings that emerged as a result of a collective effort are commonly referred to as «arogi» buildings, with the term arogimeaning help or assistance. In this way, merely referring to these structures is a direct mention to the circumstances under which they were constructed. The reconstruction of the building stock of the island was based in a series of building types proposed from the authorities, and proceeded replacing the richness of the architectural forms that stood before the earthquake, with austere but necessary settlements. Nevertheless, it is these buildings that constitute today’s image of the island. This study wishes to introduce us to “arogi” buildings structural system, as it was applied in Kefalonia after 1953 earthquake, with reference to recent bibliography and the recent experience of 2014 earthquake. The purpose of setting the grounds for such a research would be to highlight the effectiveness of this structural system. Moreover, to emphasize the fact that “arogi” buildings and their construction procedure incorporate Kefalonia’s recent history and have eventually produced today’s available “traditional” architecture of the island.

Malassezia is lipid-dependent commensal yeast of the human skin. The different culture media and skin sampling methods used to grow these fastidious yeasts are a source of heterogeneity in culture-based epidemiological study results. This study aimed to compare the performances of three methods of skin sampling, and two culture media for the detection of Malassezia yeasts by culture from the human skin. Three skin sampling methods, namely sterile gauze, dry swab and TranswabTM with transport medium, were applied on 10 healthy volunteers. Each sample was further inoculated onto either the novel FastFung medium or the reference Dixon agar for the detection of Malassezia spp. by culture. At least one colony of Malassezia spp. grew on 93/300 (31%) of the cultures, corresponding to 150 samplings. The positive culture rate was 67%, 18%, and 15% (P < 10-3), for samples collected with sterile gauze, TranswabTM, and dry swab, respectively. The positive culture rate was 62% and 38% (P < 0.003) by using the FastFung and the Dixon media, respectively. Our results showed that sterile gauze rubbing skin sampling followed by inoculation on FastFung medium should be implemented in the routine clinical laboratory procedure for Malassezia spp. cultivation.

Objective: In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than ten years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods: We performed a systematic review and identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Findings: Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, many surveys interviewed all households in each cluster or segment, though some sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions: For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

Nowadays a recognized need for accurate observations of atmospheric aerosols (AEs) and reactive gases (RGs) exists in the framework of regional, national and global near-surface networks based on permanent or mobile measurement stations. In this context, a paramount and not-trivial issue is related to the correct execution of continuous sampling of ambient air and its subsequent distribution to measurement analyzers hosted inside the stations. Sampling artifacts must be minimized for obtaining reliable pictures of ambient air composition. To respond to this need, a suite of novel “smart” and relatively low-cost systems for the continuous sampling of ambient air was developed in the framework of the Project I-AMICA (2012 – 2015, www.i-amica.eu). These systems were designed to execute AE and RG measurements according with WMO/GAW and ACTRIS recommendations and standard operation procedures. A particular attention was dedicated to the stabilization and control of the sampling flow rates and temperatures. The analysis of one full year of operations at the WMO/GAW regional station of Capo Granitola (GAW ID: CGR, Italy), allowed to conclude that these systems are effective in meeting the technical requirements for correct execution of AE and RG measurements

A new formulation for a proposed solution to the 3D Navier-Stokes Equations in cylindrical co-ordinates coupled to the continuity and level set convection equation is presented in terms of an additive solution of the three principle directions in the radial, azimuthal and z directions of flow and a connection between the level set function and composite velocity vector for the additive solution is shown. For the case of a vertical tube configuration with small inclination angle, results are obtained for the level set function defining the interface in both the radial and azimuthal directions. It is found that the curvature dependent part of the problem alone induces sinusoidal azimuthal interfacial waves wheras when the curvature together with the equation for the composite velocity is considered oscillating radial interfacial waves occur. The implications of two extremes indicate the importance of looking at the full equations including curvature.

Recently concluded, large-scale cancer genomics studies involving multiregion sequencing of primary tumors and paired metastases appear to indicate that many or most cancer patients have one or more “clonal" mutations in their tumors. Clonal mutations are those that are present in all of a patient’s cancer cells. Achilles Therapeutics is currently the only company specifically targeting clonal mutations. However, they are doing so with tumor-derived T cells. To address the potential limitations of immunotherapy, I have devised another approach for exploiting clonal mutations, which I call “Oncolytic Vector Efficient Replication Contingent on Omnipresent Mutation Engagement” (OVERCOME). The ideal version of OVERCOME would likely employ a bioengineered facultative intracellular bacterium. The bacterium would initially be attenuated, but (transiently) reverse its attenuation upon clonal mutation detection.

To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, Hybrid Monte Carlo on Hilbert spaces [A. Beskos, F.J. Pinski, J.-M. Sanz-Serna and A.M. Stuart, Stoch. Proc. Applic. 121, 2201 - 2230 (2011); doi:10.1016/j.spa.2011.06.003] that provides finite-dimensional approximations of measures π which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method which is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous method, the phase space π was augmented with Brownian bridges. With the new choice for the mass operator, π is augmented with Ornstein-Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the Metropolis-Hasting acceptance rate. This contrasts with the covariance of OU bridges which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally the Metropolis-Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier.

In this paper the problem of sampling from uniform probability distributions is approached by means of space-filling curves (SFCs), a topological concept that has found a number of important applications in recent years. Departing from the theoretical fact that they are surjective but not necessarilly injective, the investigation focused upon the structure of the distributions obtained when their domain is swept in a uniform and discrete manner, and the corresponding values used to build histograms, that are approximations of their true PDFs. This work concentrates on the real interval [0,1], and the Sierpinski space-filling curve was chosen because of its favorable computational properties. In order to validate the results, the Kullback-Leibler distance is used when comparing the obtained distributions in several levels of granularity with other already established sampling methods. In truth, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. In this fashion, sequences resulting from this sort of process are not truly random.

We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of the watermark. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of segment-based host audio in a diagonal matrix multiply by the over-complete dictionary producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information but also compressing the audio host. The application of the proposed method is a broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in range 729-5292 bps with compression ratio 1.47-4.84 and perfect detected watermark.

Volatile compounds, abundant in breath, can be used to accurately diagnose and monitor a range of medical conditions. This offers a non-invasive, low-cost approach with screening applications; however, uptake of this diagnostic approach has been limited by conflicting published outcomes. Most published reports rely on large scale screening of the public, at single time points and without reference to ambient air. Here, we present a novel approach to volatile sampling from cellular headspace and mouse breath that incorporates multi-time point analysis and ambient air subtraction revealing compound flux as an effective proxy of active metabolism. This approach to investigating breath volatiles offers a new avenue for disease biomarker discovery and diagnosis. Using gas chromatography mass spectrometry (GC/MS), we focus on low molecular weight, metabolic substrate/by-product compounds and demonstrate that this non-invasive technique is sensitive (reproducible at ~1 µg cellular protein, or ~500,000 cells) and capable of precisely determining cell type, status and treatment. Isolated cellular models represent components of larger mammalian systems and we show that stress- and pathology-indicative compounds are detectable in mice, supporting further investigation using this methodology as a tool to identify volatile targets in human patients.

The modifications in planting structure and continuous land use adjustments have significant implications for the structure, diversity, and functioning of arthropod communities in farmland landscapes, particularly regarding the biological control of pest insects by natural enemy species. Therefore, there is an urgent need to comprehensively and accurately assess insect community diversity in croplands. In this study conducted between 2022-2023, we utilized Malaise traps and sweep netting to collect insects from three different double-cropping paddy fields. We extracted DNA from preservative ethanol of Malaise traps and tissue samples of selected parasitoid wasps, followed by amplification using DNA barcoding primers to prepare high-throughput sequencing libraries. As a result, we annotated a total of 4956 operational taxonomic units (OTUs) belonging to 174 genera and 32 families of parasitoid wasps. Our findings demonstrate that the ethanol filter method efficiently captures a wide range of information diversity. However, it demonstrates a lower resolution and may result in a reduction of species abundance. Furthermore, we have identified additional insect species in the parasitoid samples. This demonstrates that high throughput sequencing from adult parasitoid wasps can effectively detect host specie, enabling a comprehensive understanding of host species and providing novel insights into food web construction.

The analysis of microscopy images has always been an important yet time consuming process in in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad-hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points.

Welding of dissimilar aluminium alloys has been a challenge for a long period until the discovery of the solid state welding technique called friction stir welding (FSW). The discovery of this technique encouraged different research interests revolving around the optimization of this technique. This involves the welding parameters optimization and this optimization is categorized into two classes i.e. similar alloys and dissimilar alloys. This paper reports about the mechanical properties of the friction stir welded dissimilar AA1050-H14 and AA5083-H111 joint. The main focus is to compare the mechanical properties of specimens extracted from different locations of the welds i.e. the beginning, middle and the end of the weld. The specimen extracted at the beginning of the weld showed low tensile properties compared to specimens extracted from different locations of the weld. There was no certain trend noted through the bending results. All three specimens showed dimpled fracture which is the characterization of the ductile fracture.

The basis of modern teeth whitening systems is the use of a whitening gel, usually containing hydrogen peroxide or carbamide peroxide. The purpose of our study was to evaluate the effect of chemical and modified office teeth whitening systems on the mineral metabolism of oral fluid and to determine a safer method. The study covered 81 patients aged 22 to 35 years of both sexes with teeth color A2 and darker on the Vita Classic scale. Determination of the concentration of calcium and phosphates in the oral fluid was carried out on a spectrophotometer using ready-made Human kits. Chemical tooth whitening was performed using a gel based on 40% hydrogen peroxide. After the procedure, remineralization therapy with enamel-sealing liquid was performed for 14 days. The obtained results indicate that the most pronounced changes in the content of calcium (+74.4%, p <0.001) and рhosphates (phosphorus) (-23.07%, p <0.001) were observed when using a chemical bleaching system. After the teeth whitening procedure using a modified technique, less pronounced changes in the concentrations of calcium (+29.07, p <0.001) and phosphorus (-14%, p <0.001) were observed. The use of remineralizing agents led to a faster recovery of the initial levels of calcium and phosphates in the oral fluid. Both bleaching systems caused similar changes in the chemical composition of the oral fluid.

Perceptual and statistical evidence has underlined voice characteristics of individuals affected by genetic syndromes different from that of normophonic subjects. In this paper we propose a procedure for the systematic collection of such pathological voices and the development of AI-based automatic tools to support differential diagnosis. Guidelines are provided concerning most suitable recording devices, vocal tasks and acoustical parameters, in order to simplify, speed up and make the whole procedure homogenous and replicable. The proposed procedure was applied to a set of 56 subjects, affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS) and Smith-Magenis syndrome (SMS). The whole database has been divided into three groups, respectively called: paediatric subjects (PS, individuals < 12 years of age), female adults (FA) and male adults (MA) subjects. In line with literature results, the Kruskal-Wallis test and post-hoc analysis with Dunn-Bonferroni test highlighted several significant differences in acoustical features not only between healthy subjects and patients, but also across syndromes within PS, FA and MA groups. Machine learning provided for the PS group a k-nearest neighbour classifier with 75% accuracy, for the FA group a support vector machine (SVM) model with 84% accuracy and for the MA group a SVM model with 97% accuracy. These preliminary results suggest that the proposed procedure, based on acoustical analysis and AI, might be helpful for an effective non-invasive automatic characterization of genetic syndromes.

The beginning of industrialization human being has observed a variety of environmental troubles in the world. This industrialization has not only brought growth and affluence but ultimately troubled the ecosystem. One of the crashes is visible, in form of water contamination. Here the current study heavy metal contamination of water body has been discussed. Effluents from a great number of industries viz., tannery, textile, pigment & dyes, paint, wood processing, petroleum refining, electroplating, leather etc., have a major amount of heavy metals in their wastewater. The conventional technique of handling heavy metal pollution includes chemical oxidation, chemical precipitation, ion exchange, reverse osmosis, membrane separation, electrodialysis etc. These processes are expensive, energy intensive and frequently related with generation of poisonous by-product. Therefore, the adsorption has been examined as a cost-efficient technique of elimination of heavy metals from wastewater. In the current study different low-cost adsorbent has been a review as an abatement of heavy metal contamination from wastewater. These adsorbent comprise materials of natural origin like peat moss, zeolites, clay, and chitin are found to be an effective agent for removal of deadly heavy metals like Pb, Cd, Zn, Cu, Ni, Hg, Cr etc. Separately from these, a variety of agricultural wastes like rice husk, waste tea, neem bark, black gram; Turkish coffee, walnut shell etc. were also known as a powerful adsorbent for heavy metal removal. at the side of that low-cost industrial byproduct like fly ash, lignin, iron (III) hydroxide and red mud, coffee husks, Areca waste, tea factory waste, sugar beet pulp, battery industry waste, blast furnace sludge, waste slurry, sea nodule remains and grape stalk wastes have been discovered for their technical possibility to eliminate toxic heavy metals from impure water.

Recently, the focus of entity linking research has centered on the zero-shot scenario, where the entity mention to be labeled at the time of testing was never observed during the training phase or may belong to a different domain than the source domain. Current studies have used BERT as the base encoder as it effectively establishes distributional links between source and target domains. The currently available negative sampling methods all use an extractive approach, which makes it difficult for the models to learn diverse and more challenging negative samples. To address this problem, we propose a generative negative sampling method, Adaptive_mixup_hard, which generates more difficult negative entities by fusing the features of both positive and negative samples on top of hard negative sampling and introduces a transformable adaptive parameter W to increase the diversity of negative samples. Next, we fuse our method with the Biencoder architecture and evaluate its performance under three different score functions. Ultimately, experimental results on the standard benchmark dataset Zeshel demonstrate the effectiveness of our method.

This work addresses the problem of sampling from Gaussian probability distributions by means of uniform samples obtained deterministically and directly from space-filling curves (SFCs), a purely topological concept. To that end, the well-known inverse cumulative distribution function method is used, with the help of the probit function,which is the inverse of the cumulative distribution function of the standard normal distribution. Mainly due to the central limit theorem, the Gaussian distribution plays a fundamental role in probability theory and related areas, and that is why it has been chosen to be studied in the present paper. Numerical distributions (histograms) obtained with the proposed method, and in several levels of granularity, are compared to the theoretical normal PDF, along with other already established sampling methods, all using the cited probit function. Final results are validated with the Kullback-Leibler and two other divergence measures, and it will be possible to draw conclusions about the adequacy of the presented paradigm. As is amply known, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. That said, sequences resulting from this kind of procedure are not truly random. Even so, and to be coherent with the literature, the expression ”random number” will be used along the text to mean ”pseudo-random number”.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

Electronic-health applications rely on large computer networks to facilitate patients' information access and to communicate various types of medical data. To examine the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of the information carrying packets, examining each packet's transmission parameters individually is not practical, especially when a real time operation is needed. Sampling allows a subset of packets that accurately represents the original traffic to be formed. In this study an adaptive sampling method based on regression and fuzzy inference system was developed. It dynamically updates the number of packets sampled by responding to the traffic variations. Its performance was found to be superior to the conventional non-adaptive sampling methods.

The viral nervous necrosis virus (VNNV) is the causative agent of an important disease affecting fish species cultured worldwide. Early and accurate diagnosis is at present the most effective control and prevention tool, and the molecular techniques have been strongly introduced and accepted by official organizations. Among those, real time quantitative polymerase chain reaction (rt-qPCR) is nowadays displacing other molecular techniques. However, another PCR-based technology, the droplet digital PCR (ddPCR), is on the increase. It has many advantages over qPCR, such as higher sensitivity and more reliability of the quantification. Therefore, we decided to design and validate a protocol for diagnosis and quantification of SJ and RG type VNNV, using reverse transcription-ddPCR (RT-ddPCR). We obtained an extremely low limit of detection, 10 to 100-folds lower than with RT-qPCR. Quantification by RT-ddPCR, with a dynamic range of 6.8 – 6.8 x 104 (SJ type) or 1.04 x 101 – 1.04 x 105 (RG) cps/rctn, was more reliable than with RT-qPCR. The procedure was tested and validated in field samples, providing the high clinical sensitivity and negative predictive values. In conclusion, we propose this method to substitute RT-qPCR protocols because it exceeds the expectations of qPCR in the diagnosis and quantification of VNNV.

Objectives: Minimally invasive direct coronary artery bypass (MIDCAB) is an alternative for revascularisation of isolated left anterior descending (LAD) or as multi-vessel (MV) procedure for diagonal branch (RD) or circumflex artery (RCX) region. Methods: From 2021 to 2022 91 patients underwent MIDCAB procedure in our heartcenter. 80% were male. Eighty percent of the patients had two or three vessel coronary artery disease. Mean age was 65±10 years. The left internal mammary artery (LIMA) was anastomosed to the left anterior descending via left minithoracotomy approach in all patients. Fourteen patients received multi-vessel MIDCAB (15%) with two or three anastomoses a saphenous vein graft (as T-graft to LIMA) was anastomosed to RD or RCX. Results: Mean operation time was 2.6±0.8 hours. The 30-day mortality was 0. Mean required packed red blood cells (pRBC) was 0.4±1.2 unit. The intensive care unit stay (ICU) was 1.5±1.6 days mean. We observed two patients (2%) with wound infections postoperatively. Four patients underwent rethoracotomy because of bleeding. Mean follow-up time was 1.5±0.5 years. Seventy-eight patients (86%) were in New York Heart Association (NYHA) class 1, 11 patients (12%) were in NYHA class 2. One patient received percutaneous coronary intervention due to de novo stenosis of the RCA. Late mortality was 2%. The Kaplan-Meier survival rate was 98.8% at 1 and 2 years. Conclusion: MIDCAB is technically demanding. Our postoperative and short-term clinical results demonstrate that this procedure is safe and feasible. Optimal patient selection and an experienced surgical team are mandatory.

One of the main challenges in geodetic deformation analysis is to infer whether the geometry of some engineering structures or zones of natural hazards has changed from their initial state or not. The pillar that supports such an analysis is tightly based on the fundamentals of statistical hypothesis testing. The null hypothesis model indicates that no displacement has occurred. It is tested against a class of alternative models, which stipulate different displacement patterns. In this contribution, we present an innovative geodetic displacement detection which integrates combinatorial analysis and likelihood ratio tests into a sequential procedure for the case where the differences between observations of two epochs are in play. This framework is applied and investigated to two test scenarios: a synthetic and a real simulated trilateration network. From a statistical point of view, our approach is rigorous, because the alternative model can identify simultaneously more than one unstable point. In addition, the relationship between the unknown parameters and the observations is always linear, even if the problem manifests itself as non-linear. Consequently, we avoid a potential loss of statistical test power due to the model linearization. In addition, the proposed method controls the false positive rate efficiently. One of the problems that arise here is also related to the selection of the maximum number of points to be considered in the test procedure ( ). Here, we provide an innovative methodology based on rank computation of the design matrices to define , which can even be extended to the problem of outlier detection. Determining avoids the problem of having non-separable models in identifying unstable points. The algorithms and data are available in the repository.

Congenital breast asymmetry represents a particular challenge to the classical techniques of plastic surgery due to a young group of patients. This study compares traditional breast augmentation using silicone implants to the more innovative lipograft technique regarding long-term results. To achieve this, we not only captured subjective parameters like satisfaction with outcome and symmetry, but also objective parameters such as breast volume and anthropometric measurements. Objective examination was performed manually and by using the Vectra® H2 photogrammetry scanning system. Patients who underwent implant augmentation and lipograft both showed no significant differences in patient´s satisfaction with surgical outcome (p = 0.55) and symmetry (p = 0.69). Furthermore, a breast symmetry of 93 % in both groups was reported. Likewise, no statistically significant volume difference between left and right breast was observed in both groups (p<0.41). However, on average, lipograft patients needed 1.3 procedures more until the desired result was achieved. In contrast, patients treated with implant-based breast augmentation usually need several implant changes during their life. In conclusion, both methods should be considered for patients with congenital breast asymmetry.

Evidence-based canine decontamination protocols are underrepresented in the veterinary literature. Aerosolized microbiological and chemical contaminants can pose a risk in deployment environments highlighting the need for improved canine field decontamination strategies. Prior work has established the efficacy of traditional, water-intensive methods on contaminant removal from the coat of the working canine; however, it is not known if similar reductions can be achieved with simple field expedient methods when resources are limited. The objective of this study was to measure the reduction of aerosolized contamination via a practical “wipe-down” procedure performed on working canine coats contaminated with a fluorescent, non-toxic, water-based aerosol. Disposable, lint-free towels were saturated with one of three treatments: water, 2% chlorhexidine gluconate scrub (CHX), or 7.5% povidone-iodine scrub (PVD). Both CHX and PVD were diluted at a 1:4 ratio. Treatments were randomly assigned to one of three quadrants established across the shoulders and back of commonly utilized working dog breeds (Labrador retrievers, n = 16; German shepherds, n = 16). The fourth quadrant remained unwiped, serving as a control. Reduction in fluorescent marker contamination was measured and compared across all quadrants. PVD demonstrated greater marker reduction compared to CHX or water in both breeds (P < 0.0001). Reduction was similar between CHX or water in Labradors (P = 0.86) and shepherds (P = 0.06). Effective wipe-down strategies using common veterinary cleansers should be further investigated and incorporated into decontamination practices to safeguard working canine health and prevent cross-contamination of human personnel working with these animals.

Patients with chronic renal failure and vascular diseases require the vascular access for hemodialysis procedure to be performed with the most possible comfort for the patient. Native vein graft has a longer-lasting term in the patients’ limb, lower risk of graft infection and lower price. Native vein graft is constructed from the great saphenous vein, if the diameter of the vessel is smaller than the diameters of vessels (brachial artery, cephalic vein) it is connected to in the cubital fossa region due to the risk of graft folding, that might occur, if the graft diameter is bigger than the diameter of one or both vessels, on which anastomosis is made. The most important sizes, that were taken before graft placement, are the length of the forearm compartment, the distance between the brachial artery and cephalic vein in the cubital fossa region, distance from an expected incision in the brachial artery to the middle of the forearm compartment, the distance between incision in cephalic vein to the middle of the forearm compartment, length of the great saphenous vein (the graft) and diameters of blood vessels, used in the procedure. Finally, the right position of the graft should be determined for the successful outcome of anastomosis creating procedure.

Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are rec-ognized as devices that can effectively manage problems such as voltage segment, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the dis-advantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular structured memory filter. The proposed circular structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system was built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.

Glioblastoma, a deadly brain tumor, shows limited response to standard therapies like te-mozolomide (TMZ). Recent findings from the REGOMA trial underscore a significant survival improvement offered by regorafenib (REGO) in recurrent glioblastoma. Our study aimed to propose a 3D ex vivo drug response precision medicine approach to investigate recurrent glio-blastoma sensitivity to REGO and elucidate the underlying molecular mechanisms involved in tumor resistance or responsiveness to treatment. 3D glioblastoma organoids (GB-EXPs) obtained from 18 patients’ resected recurrent glioblastoma tumors were treated with TMZ and REGO. Drug responses were evaluated using NAD(P)H FLIM, stratifying tumors as responders (Resp) or non-responders (NR). Whole-exome sequencing was performed on 16 tissue samples, and whole-transcriptome analysis on 13 GB-EXPs treated and untreated. We found 35% (n=9) and 77% (n=20) of tumors responded to TMZ and REGO, respectively, with no instances of TMZ-Resp be-ing REGO-NR. Exome analysis revealed a unique mutational profile in REGO-Resp tumors compared to NR tumors. Transcriptome analysis identified distinct expression patterns in Resp and NR tumors, impacting Rho GTPase and NOTCH signaling, known to be involved in drug response. In conclusion, recurrent glioblastoma tumors were more responsive to REGO com-pared to TMZ treatment. Importantly, our approach enables a comprehensive longitudinal ex-ploration of the molecular changes induced by treatment, unveiling promising biomarkers in-dicative of drug response.

Fungal infection of grape berries (Vitis vinifera) by Botrytis cinerea frequently coincides with harvest, impacting both the yield and quality of grape and wine products. A rapid and non-destructive method for identifying B. cinerea infection in grapes at an early stage prior to harvest is critical to manage loss. In this study, zeolitic imidazolate framework-8 (ZIF-8) crystal was applied as an absorbent material for volatile extraction from B. cinerea-infected and healthy grapes in a vineyard followed by thermal desorption gas chromatography-mass spectrometry. The performance of ZIF-8 to absorb and trap targeted volatiles was evaluated with a standard solution of compounds and with a whole bunch of grapes enclosed in a glass container to maintain standard sampling conditions. Results from sampling methods were then correlated to B. cinerea infection in grapes as measured and determined by Genus specific antigen quantification. Trace levels of targeted compounds reported as markers of grape B. cinerea infection were successfully detected with in-field sampling. Peak area counts for volatiles 3-octanone, 1-octen-3-one, 3-octanol, and 1-octen-3-ol extracted using ZIF-8 were significantly higher than values achieved using Tenax®-TA from field testing and demonstrated good correlation with B. cinerea infection severities determined by B. cinerea antigen detection.

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters depth, that are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016-2018 period the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using high resolution SSS simulations, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity are observed in regions with large mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly considered. We find that 1) the sampling mismatch can explain most of the observed differences between Argo and CCI data, especially for monthly products and in dynamical regions (river plumes, fronts), 2) overall, the uncertainties are well estimated in CCI version 3, much better compared to CCI version 2. There are a few dynamical regions where discrepancies remain, and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

In most industrialized countries, screening programs for cervical cancer have shifted from cytology (Pap smear or ThinPrep) alone on clinician-obtained samples to the addition of screening for human papillomavirus (HPV), its main causative agent. For HPV testing, self-sampling instead of clinician-sampling has proven to be equally accurate, in particular for assays that use nucleic acid amplification techniques. In addition, HPV testing of self-collected samples in combination with a follow-up Pap smear in case of a positive result is more effective in detecting precancerous lesions than a Pap smear alone. Self-sampling for HPV testing has already been adopted by some countries, while others have started trials to evaluate its incorporation into national cervical cancer screening programs. Self-sampling may result in more individuals willing to participate in cervical cancer screening, because it removes many of the barriers that prevent women, especially those in low socioeconomic and minority populations, from participating in regular screening programs. Several studies have shown that the majority of women who have been underscreened but who tested HPV-positive in a self-obtained sample, will visit a clinic for follow-up diagnosis and management. Additionally, a self-collected sample can also be used for vaginal microbiome analysis, which can provide additional information about HPV infection persistence as well as vaginal health in general.

Diffuse cavernous hemangioma of the rectum (DCHR) is a rare benign lesion of vascular malformation. According to previous studies, the most reported major symptoms of DCHR are recurrent painless rectal bleeding or iron deficiency anemia[1][2][3][4][5][6]. We present a case of an 8Y11M boy who was diagnosed with DCHR by MRI. The patient underwent Laparoscopic-assisted transanal distal sigmoidectomy and sigmoido-anal anastomosis（Laparoscopic Swenson Procedure）, and recovered well. After the operation, the child had short-term fecal incontinence and loose stools, which returned to normal after half a year. Regardless of age and symptoms, early and correct diagnosis and treatment of DCHR and postoperative restoration of normal anal function are necessary. Laparoscopic Swenson procedure may be a good option for the treatment of DCHR.

The KID (Koopmans in DFT) procedure usually applies in organic molecules of the closed–shell type. We used the KID procedure in an open–shell system for the first time to choose the most suitable density functional to compute global and local reactivity descriptors coming from the Conceptual Density–Functional Theory. From a set of 18 density functionals spread from the second until the fourth rung of the Jacob’s ladder: BP86, B97-D, BLYP, CAM-B3LYP, M06-L, M11-L, MN12-L, B3LYP, PBE0, N12-SX, M06-2X, M11, MN12-SX, CAM-B3LYP, LC-ωHPBE, ωB97X-D, APFD, MN15 and MN15-L, we concluded that CAM-B3LYP provides the best outcome.

Purpose To alleviate the data imbalance problem caused by subjective and objective reasons, scholars have developed different data preprocessing algorithms, among which undersampling algorithms are widely used because of their fast and efficient performance. However, when the number of samples of some categories in a multi-classification dataset is too small to be processed by sampling, or the number of minority class samples is only 1 to 2, the traditional undersampling algorithms will be weakened. Methods This study selects 9 multi-classification time series datasets with extremely few samples as the objects, fully considers the characteristics of time series data, and uses a three-stage algorithm to alleviate the data imbalance problem. Stage one: Random oversampling with disturbance items increases the number of sample points; Stage two: On this basis, SMOTE (Synthetic Minority Oversampling Technique) oversampling; Stage three: Using dynamic time warping distance to calculate the distance between sample points, identify the sample points of Tomek Links at the boundary, and clean up the boundary noise.Results This study proposes a new sampling algorithm. In the 9 multi-classification time series datasets with extremely few samples, the new sampling algorithm is compared with four classic undersampling algorithms, ENN (Edited Nearest Neighbours), NCR (Neighborhood Cleaning Rule), OSS (One Side Selection) and RENN (Repeated Edited Nearest Neighbours), based on macro accuracy, recall rate and F1-score evaluation indicators. The results show that: In the 9 datasets selected, the dataset with the most categories and the least number of minority class samples, FiftyWords, the accuracy of the new sampling algorithm is 0.7156, far beyond ENN, RENN, OSS and NCR; its recall rate is also better than the four undersampling algorithms used for comparison, at 0.7261; its F1-score is increased by 200.71%, 188.74%, 155.29% and 85.61%, respectively, relative to ENN, RENN, OSS, and NCR; In the other 8 datasets, this new sampling algorithm also shows good indicator scores.Conclusion The new algorithm proposed in this study can effectively alleviate the data imbalance problem of multi-classification time series datasets with many categories and few minority class samples, and at the same time clean up the boundary noise data between classes.

This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF considering all the potential changes in the boundary conditions of a water body. A two-dimensional contaminant transport simulation model (RMA4) was implemented to estimate the distribution patterns of the total dissolved solids (TDS) within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 model to determine the minimum SF of the monitoring stations with an accuracy level of detectable change in TDS concentration (ALC) of 5%, 10% and 15%. The proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. Additionally, increasing the ALC to 10% and 15% increase the minimum SF for some MSs by approximately 18% and 21%, respectively. However, the proposed methodology includes all the potential values and cases of boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, we adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Our evaluation employed standard metrics such as Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). Regarding the F1-Score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization.

In this paper, the spatial distribution of stony desertification characteristics and its influencing factors in Karst areas in different sampling scales are studied using a grid sampling method based on geographic information system (GIS) technology and geo-statistics, with the rock bareness rate obtained through sampling with 150m × 150m grids in the Houzhai River Basin being taken as the original data and five grid scales (300m × 300m, 450m × 450m, 600m × 600m, 750m × 750m, and 900m × 900m) as the subsample sets. The results show that the rock bareness rate does not vary much from one sampling scale to another while average values of the five sub-samples all fluctuate around the average value of the entire set. As the sampling scale is expanding, the maximum value and the average value of rock bareness rate are decreasing gradually, with a gradual increase in the coefficient of variability. In the scale of 150m × 150m, the areas of minor stony desertification, medium stony desertification, and major stony desertification in the Houzhai River Basin are 7.81 km2, 4.50 km2, and 1.87 km2, respectively. The spatial variability of stony desertification on small scales is influenced by many factors, and that on medium scales is jointly influenced by gradient, rock contents, and rock bareness rate. On large scales, the spatial variability of stony desertification is mainly influenced by soil thickness and rock bareness rate.

Mesoporous MgAl2O4+ LnFe0.7Ni0.3O3 (Ln=La, Pr) nanocomposites were prepared by inexpensive one-pot procedure with Pluronic P123 copolymer, Ni+Ru active component was supported by wet impregnation. The real structure of samples was studied by XRD and TEM with EDX, surface properties by FTIRS of adsorbed CO, reactivity by H2 –TPR, catalytic activity was tested in ethanol steam reforming. Disordering of the real structure of nanocomposite supports due to incorporation of transition metal cations into MgAl2O4 results in developed metal-support interface and domination of single surface metal centers. This provides a high catalytic activity in reaction of ethanol steam reforming in the intermediate temperature range ~550 oC close to that of the best known catalysts, and stability to coking. A higher activity for Pr-containing catalyst is provided by a high reactivity of surface oxygen species bound with Pr cations.

Diabetes is a serious health problem throughout the world, including in Indonesia. The International Diabetes Federation (IDF) reports that the number of adults with diabetes is increasing every year. The Behavioral Risk Factor Surveillance System (BRFSS) is a survey conducted by the Centers for Disease Control and Prevention (CDC) in the United States. Classification methods in data mining techniques are used to classify diabetics and non-diabetics. The data mining process is carried out by preprocessing, feature selection, and dataset classification stages. In the preprocessing stage, data cleaning, data formatting, and data oversampling are carried out using the Synthetic Minority Over-sampling Technique (SMOTE). Next, the feature selection stage is carried out using the Particle Swarm Optimization (PSO) algorithm to find the best attributes. The dataset classification stage is carried out using the CART Model Decision Tree algorithm. The results of the performance evaluation of the CART algorithm are calculated using the confusion matrix and the MAE value, the results obtained for the CART algorithm without SMOTE and PSO obtained the best accuracy of 75.34% and the MAE value of 0.2466, while the CART algorithm using SMOTE and PSO can increase accuracy by 10 .94% to 86.28% and an MAE value of 0.1372.

Collapse calderas often experience large up and down movements, which rarely give rise to eruptions. The pattern of vertical deformation has generally an elliptical geometry, very close to a circular one. A simple and powerful method to explore second order anomalies of the volcanic caldera deformation patterns can enlighten important structural features. This method is here presented and applied to the case study of Campi Flegrei caldera ground deformation, representing a typical example of such a quasi-circular geometry, with dominant ground uplift occurring since 1950. Despite a markedly radial dependence of the vertical displacement, with a very constant shape in the time, we explore in the paper the second order anomalies, with respect to a purely radial behavior. To this aim, we have used a procedure based on the polynomial fit of the vertical displacement data, assuming they only depend on the distance from the maximum uplift point (i.e. deformation center); then, we have obtained anomalies map by subtracting from the theoretical deformation so determined the true data. We then obtain very peculiar results, which put in evidence a sharp separation between a less uplifted zone and a more uplifted one than expected, along a NE-SW alignment which approximately define the most seismically active area, in which most of the largest magnitude earthquakes occur. This very peculiar feature is likely to represent a main volcano-tectonic structure in the area, which can be of leading importance for evaluating both the highest seismic hazard area and the most likely zone of possible eruptive vent opening. The results obtained here indicate that the proposed method can be useful to analyze unrest related hazards.

Critical aortic valve stenosis (CAVS) is a life-threatening cardiac disease of the newborn requiring urgent treatment. First-line therapy is still controversial. The aim of our study was (1) to analyze retrospectively the patients of our institution who underwent balloon aortic valvuloplasty (BAV) due to CAVS, and (2) to describe techniques for improved feasibility of the intervention using microcatheters and retrieval loops. Twelve patients underwent 23 BAVs: 1 BAV was performed in 3 patients, 2 BAVs in 7 patients, and 3 BAVs in 2 patients. Trans-valvular pressure gradient (Δp) and left ventricular shortening fraction (LVSF) improved significantly in the first two interventions; first BAV: Δp from 73.7 ±34.5 mmHg to 39.8 ±11.9 mmHg (p=0.003) and LVSF from 22.3 ± 13.5 % to 31.6 ±10.2 % (p=0.001); second BAV: Δp from 73.2 ±33.3 mmHg to 35.0 ± 20.2 mmHg (p&lt;0.001) and LVSF from 26.7 ±9.6 % to 33.3 ±7.4 % (p=0.004). Cardiac surgery during the neonatal period was avoided for all children; median time to valve surgery was 5.75 years. Few complications occurred: mild to moderate aortic regurgitation, one remediable air embolism, and one intimal injury of the ascending aorta. We conclude that BAV is a successful primary treatment for CAVS resulting in left ventricular relief, stabilization of the neonate, and a time gain until cardiac surgery.

In order to examine the impact of geometric configuration of the human vocal tract on the redistribution of acoustic energy in the frequency spectrum, it is necessary to assemble computation models, which facilitate simple modification of the model’s geometric characteristics. This article describes how the human vocal tract model was constructed using so-called super-elements using the improved reduced system method. The mentioned procedure allows using such a model for demanding optimization calculations, for which the model is modified on the super-element level. Furthermore, the article presents an alternative approach, in which an isoparametric element of a higher degree polynomial was derived. Both approaches were compared and used to find the geometric configuration of the human vocal tract amplifying acoustic energy values on the frequency spectrum to around 3kHz.

Abstract Objective：To explore the efficacy of modified Altemeier procedure in the treatment of complete rectal prolapse. Method and Main outcome measures:The enrolled patients with complete rectal prolapse were divided into traditional and modified groups according to the surgical approach.The primary outcomes included complication rates, time and severity of postoperative recurrence, changes in pre- and postoperative Wexner anal incontinence scores and ED-5Q-5L quality-of-life autonomy scores. Secondary outcomes included operative time, intraoperative bleeding, and length of hospital stay. Results:The overall complication rates were 33.33% and 6.67% and recurrence rates were 26.67% and 7% in the traditional and modified groups, respectively (P<0.05)；The duration of surgery in the traditional group was shorter than that in the modified group, but the intraoperative bleeding was significantly higher than that in the modified group, and all differences were statistically significant (P<0.05)；The length of stay and in the two groups, respectively, with no statistically significant difference (P>0.05)；The postoperative Wexner incontinence score and ED-5Q-5L quality of life in the modified group were found to be significantly better than those in the traditional group at 8 months postoperative follow-up, with statistically significant differences (P<0.05). Conclusions:Pathological dilatation of the distal rectum may be another anatomical defect in complete rectal prolapse. The results of the clinical study confirmed the clinical effectiveness and safety of the modified Altmeyer procedure, which has high clinical application value.

The diffusion of electric vehicles (EVs) can be sustained by the presence of integrated solutions offering parking and clean power supply. The recourse to DC systems allows to better integrate EV bidirectional energy exchange, photovoltaic panels and energy storage. In this paper, a methodology for optimal techno-economic sizing of a DC-microgrid for covering EV mobility needs is carried out. It is based on the definition of different scenarios of operation, according to typical EV usage outlooks and environmental conditions. In each scenario, optimal operation is carried out by means of a specific approach for EV commitment on different stations. The sizing procedure is able to handle the modular structure of microgrid devices. The proposed approach is applied to a case study of envisaged EV service fleet for Bari port authority.

Background: Absolute uterine factor infertility (AUFI) is a kind of infertility that is completely attributable to uterine absence (surgical or congenital for women with Mayer-Rokitansky-Küster-Hauser syndrome: MRKH) or anatomic or functional abnormality that prevents embryo implantation or completion of pregnancy to term. Until recently, the only viable option to parenthood for couples with AUFI were adoption or surrogacy. Since a first attempt of uterus transplant (UTx) in 2000, nine babies were born from women with a transplanted uterus from 2014, eight of which in Sweden, and one in the United States. These promising results are raising immense hopes for the women with AUFI and there is optimism about the possibility for UTx to become part of clinical care even though, besides encouraging results, the procedure has also resulted in increased risks and harms for both the donors and recipients and increased risks of premature birth for the fetus. At present UTx is still considered as experimental and requiring more research and safety assessment before becoming a therapeutic option for AUFI. The transition from experimental procedure to therapeutic care would result in less strict ethical scrutiny for UTx and in the possibility for patients to get reimbursement for the procedure by the relevant healthcare insurance or public healthcare providers. In turn, an increase in the number of UTx performed yearly by specialized surgical teams would result in a general improvement of the “field strength”. However, at present it is difficult to establish the amount of evidence that we need in order to consider UTx as no longer experimental but routine clinical practice. The literature on UTx provides recommendations on the different outcomes that should be monitored in this experimental phase but no study is anticipating the number of subjects that should be followed and for how long. Conclusion: As for other transplants that have become routine practice, like renal transplant and heart transplant, it is likely that the decision on “routine practice readiness” will result from available cumulated evidences, from expert capacity to find a consensus on best practices and on political considerations as well, including pressures form patients and patient groups.

Proteases catalyse irreversible posttranslational modifications that often alter a biological function of the substrate. The protease dipeptidyl peptidase 4 (DPP4) is a pharmacological target in type 2 diabetes therapy primarily because it inactivates glucagon-like protein-1. DPP4 also has roles in steatosis, insulin resistance, cancers and inflammatory and fibrotic diseases. In addition, DPP4 binds to the spike protein of MERS virus, causing it to be the human cell surface receptor for that virus. DPP4 has been identified as a potential binding target of SARS-CoV-2 spike protein, so this question requires experimental investigation. Understanding protein structure and function requires reliable protocols for production and purification. We developed such strategies for baculovirus generated soluble recombinant human DPP4 (residues 29-766) produced in insect cells. Purification used differential ammonium sulphate precipitation, hydrophobic interaction chromatography, dye affinity chromatography in series with immobilised metal affinity chromatography, and ion exchange chromatography. The binding affinities of DPP4 to the SARS-CoV-2 full-length spike protein and its receptor binding domain (RBD) were measured using surface plasmon resonance and ELISA. This optimised DPP4 purification procedure yielded 1 to 1.8 mg of pure fully active soluble DPP4 protein per litre of insect cell culture with specific activity >30 U/mg, indicative of high purity. No specific binding between DPP4 and CoV-2 spike protein was detected by surface plasmon resonance or ELISA. In summary, a procedure for high purity high yield soluble human DPP4 was achieved and used to show that, unlike MERS-CoV, SARS-CoV-2 does not bind human DPP4.

The special affine Fourier transform (SAFT) is a useful and powerful analyzing tool in signal processing, optics and communications. In this paper, we mainly discuss the randomized nonuniform sampling and reconstruction for random signals bandlimited in the SAFT domain. First, we show that the nonuniform sampling is identical to the uniform sampling after a pre-filter in the sense of second order statistic characters. Then, we propose an approximate reconstruction based on sinc interpolation for the nonuniform sampling of random signals bandlimited in the SAFT domain. Finally, we give the mean square error estimate for the proposed approximate recovery approach.

Recently, new semantic segmentation and object detection methods have been proposed for direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information and its robustness to dynamic changes in the environment. In addition, the cost of LiDAR sensors has decreased in recent years, which is an important factor for many application scenarios. The challenge with 3D LiDAR sensors like the Velodyne HDL-64E S2 is that they can output large 3D data at up to 1,300,000 points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real-time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed, but although it is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, we propose a new approach to semantic segmentation in forestry that uses a systematic sampling method in which the local neighbors of each point are retained to preserve geometric details. Our approach has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation in large datasets such as Semantic3D. We also present a preliminary study on the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

We show how some concentration inequalities for sampling without replacement can be used for bounding future samples. This process can be extended to bound the sum of future samples from multiple populations, and we analyse an illustrative sample allocation problem.

To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics are important to be characterized but its atomistic details are hard to obtain both experimentally and computationally. We review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: One is the Onsager-Machlup action method, and the other is the weighted ensemble method. Some applications of above methods to biomolecular systems are also discussed and illustrated.

Climate change and net zero are expected to have a significant impact on eco-nomic activity in developing countries, and thus, on the financial cash flow of organizations. In this paper, we propose a procedure for assessing the financial impact of climate change on organizational cash flow in developing countries. Our study contributes to the research on scenario-based climate change impact analysis. The applied research design is exploratory and mainly qualitative, constructing financial impact scenarios combined with narratives to determine the impact of climate change on individual cash flow line items. The narra-tives rationalize cash flow variations and enrich the estimates by adding expert knowledge, in the form of financial micro-stories and descriptions to the pro-posed procedure. We present a hypothetical citrus farming company in a de-veloping country to demonstrate the application of the proposed measurement procedure. The results obtained suggest that (1) the procedure is practical be-cause financial cash flows are known and widely used in financial analysis and because they can be implemented in a spreadsheet; that (2) the procedure pro-vides flexibility because it can be adapted to suit the specific needs of different companies, depending on a selected climate change scenario, the company’s exposure to climate change, its resilience and vulnerability; (3) that the prac-tice of thinking in terms of financial worst-case, base-case and best-case sce-narios, which reflect different levels of uncertainty, helps to model the finan-cial impacts of climate change and provides a forward-looking perspective on how much a company’s cash flow could change.