Pairs of potential and flow variables have long been established for interfacing object-oriented component models of physical systems. Recently, however, the use of triplets has been discovered for the purpose of robust modeling. New and powerful Modelica libraries have been developed such as the DLR ThermoFluid Stream library or the introduction of the Dialectic Mechanics library. Their use of triplets is entangled with a special modeling approach that uses Linear Implicit Equilibrium Dynamics. In this paper, we study the basic motivation of this approach, its benefits and drawbacks before we finally demonstrate how it beneficially impacts the generation of corresponding simulation code.

This paper aims to explore how climate change affects electricity generation (fossil-fueled power plants, hydroelectric stations, wind systems and photovoltaics) and energy demand in residential buildings in Greece for the period up to 2050. In both cases RCP scenarios results are considered in the appropriate spatial and temporal dimension. Energy supply technologies were examined through statistical regression models and/or mathematical equations correlating climatic parameters with energy productivity. With respect to energy demand, bottom-up models were developed that integrate behavioral and policy aspects. The analysis showed that climate change is expected to mainly affect electricity generation from hydro and thermal power plants, while the impacts on solar and wind energy are significantly lower. The range of impacts on hydro potential and electricity generation depends on the geographical location of the power plants and shows significant uncertainty due to the corresponding uncertainty in climate models estimates regarding precipitation and runoff changes while showing significant geographical variations. With respect to energy demand, climate change is expected to affect energy consumption, but the expected range of effects depends on both the implementation and the characteristics (deep vs. shallow renovation) of measures for upgrading the thermal performance of the building stock and the intensity of climate change.

This paper aims to study and analyze a new Peltier cell layout using a PCM in biphasic mode, with the liquid phase encapsulating the p-n junctions and the solid one playing the role of heat sink. The selected PCM is an organic compound that operates within a temperature range corresponding to the melting process. The phase change zone encompasses the working temperature of the Peltier cell, avoiding thermal gradient variation across the cell section and maintaining the operating conditions and the cell efficiency. PCM encapsulates the Peltier cell thermocouples, creating a layer that increases the power generation as the thickness reduces. We simulated the performance of the new configuration for different temperature gaps between both sides of the cell, obtaining an improvement in power generation from 1.65 to 7.93. This power increase ratio depends on the temperature gap and transient time to reach the steady working temperature. The control of the layer thickness, transient time, and working temperatures resides in the external heating power source, which varies the operating conditions regulating the heat power and the airflow. Maximum output power from the cell prototype is 36.5 W, while conventional Peltier cells operating in similar conditions produce 7.3 W, which represents a power gain of 500%.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

The sandwich generation caregivers provide simultaneous care for multiple generations. Caregiving is a dynamic process, and each generation has its unique set of needs. Moreover, studies have indicated a growing population within this group. However, despite extensive research efforts, the caregiving practices employed by the sandwich generation remain inadequately explained. Therefore, the objective of this study was to shed light on the caregiving practices offered by the sandwich generation. The grounded theory approach was selected as the conceptual framework for investigating the research objective. Purposive and snowball sampling methods were utilized to recruit 18 participants from the sandwich generation. The data collected through interviews and observations were analyzed using the 2015 version of Corbin and Strauss methodology. The findings revealed that sandwich generation caregivers are influenced by prominent personality traits, issues related to the elderly, and dependencies and interdependencies within the caregiving process. Sandwich caregivers employ a range of strategies influenced by intervening and background factors. Throughout this process, caregivers encounter both positive and negative consequences. Consequently, policymakers and planners should prioritize the establishment and expansion of support systems for caregiving.

It is currently unknown whether thrombin generation is associated with venous thromboembolism (VTE) recurrence, major bleeding, and mortality in the elderly. Therefore, our aim was to prospectively study the association between thrombin generation and VTE recurrence, major bleeding and mortality in elderly patients with acute VTE. Consecutive patients aged ≥65years with acute VTE were followed for 2 years starting from 1 year after the index VTE. Primary outcomes were VTE recurrence, major bleeding and mortality. Thrombin generation was assessed in 565 patients 1 year after the index VTE. At this time, 59% of patients were still anticoagulated. Thrombin generation was discriminatory for VTE recurrence, but not for major bleeding and mortality in non-anticoagulated patients. Moreover, peak ratio (adjusted subhazard ratio 4.09, 95% CI, 1.12-14.92) and normalized peak ratio (adjusted subhazard ratio 2.18, 95% CI, 1.28-3.73) in presence/absence of thrombomodulin were associated with VTE recurrence, but not with major bleeding and mortality after adjustment for potential confounding factors. In elderly patients, thrombin generation was associated with VTE recurrence, but not with major bleeding and/or mortality. Therefore, our study suggests the potential usefulness of thrombin generation measurement after anticoagulation completion for VTE to help identifying among elderly patients those at higher risk of VTE recurrence.

The paper investigates experimentally the influence of the Fused Filament Fabrication (FFF) pre-processing strategy on the tribological properties of parts made of PEI (polyetherimide), investigating the difference in the weight loss of the samples caused by abrasive wear before and after the test. The samples were tested for abrasive wear resistance on a tribometer constructed according to ASTM G65/16 (dry sand). Two types of specimen printing orientation along X-axis (lengthwise) and along Z-axis (heightwise) were tested in the study. Three types of path generation strategies were used for manufacturing "X" and "Z" specimens and totally 18 samples were made and tested. Path deposition strategy was carried out with path deposition width of 0.5mm and parallel paths in all slicing layers. In order to reduce the voids in volume, deposition path axes in subsequent layers were shifted by 0.25mm offset. Garnet abrasive Fe3Al2(SiO4)3 was used for this test. The analysis of the experimental data shows the relationship between the samples building direction (X, Z), applied deposition strategies (1X - 3X and 1Z - 3Z) on the resulting resistance to abrasive wear.Keywords: PEI, FFF, path generation strategy, abrasive wear

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.

Character education is essential for the current millennial generation and must be applied in everyday life in the surrounding community. The character of the millennial generation is different from the previous generation, which requires us to pay more attention to character education which is starting to erode along with the times. The Millennial generation is a generation that in everyday life can never be separated from science and technology. In this digital era, the millennial generation has a social function of strength in technology. In this study, there are research questions that will be discussed in this article, namely the first regarding what problems occur in the millennial generation who occupy the digital era?. Second, what is the important role and influence of the digital era on character education for the millennial generation in Indonesia?. Writing this article uses a qualitative research method, namely a literature review study, where researchers use or look for references from journals or books. Samples of respondents were taken randomly from each region, and there were ten respondents. From this research, it is found that the millennial generation in the digital era chooses to use social media to access the latest news and information rather than reading print media that contains education. This article aims to find out the importance of character education for the millennial generation, which is starting to erode with the times in the digital era. Many people underestimate character education, especially for the current millennial generation. Therefore, we must implement educational programs at any level, especially in Indonesia, because the character shows the national identity. This article will discuss the vital role and influence of character education for millennials who live in the digital era to be aware of the importance of character education in citizenship.

The increasingly used approach of combining different simulation software in testing of automated driving systems (ADS) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSP) provide the possibility to cover the high demand on testing kilometers for ADS by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand of testing kilometers is not enough to choose a suitable CSP. The complexity level of the used vehicle, object, sensors and environment models is essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, the environments, objects, sensors in VSS as well as traffic parameters in TFSS can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper we present a software design for CSP based on the Model-View-Controller (MVC) design pattern and implementation of a complex CSP for virtual testing of ADS. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive called CarMaker and the TFSS from PTV Group called Vissim. The results have shown that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADS.

Ruminal microorganisms play a pivotal role in cattle nutrition. The discovery of the main microbes responsible for enhancing the gain of weight in beef cattle might be used in therapeutic approaches to increase animal performance and cause less environmental damages. Here, we examined differences in bacterial and fungal composition of rumen samples of Braford heifers raised in a natural grassland from Pampa Biome in Brazil. We aimed to detect microbial patterns in the rumen that could be correlated with the gain of weight. 16S and ITS1 genes were amplified from ruminal samples and sequenced to identify the closest microbial relatives within the microbial communities. A predictive model based on microbes responsible for the gain of weight was build and further tested using the entire dataset. The model detected a set of microorganisms associated with animals in the high gain of weight group, including the bacterial taxa RFN20, Prevotella, Anaeroplasma and RF16 and the fungal taxa Aureobasidium, Cryptococcus, Sarocladium, Pleosporales and Tremellales. Most of these organisms have been correlated to the production of substances that improve the ruminal digestion process. These findings provide new insights about cattle nutrition and suggest the use of these microbes to improve beef cattle breeding.

In this study we announce the draft genome sequence of a newly identified Acinetobacter species cross-reacting with E. coli serotype 0157:H7. The advent of Next-Generation technology has paved to way to discover new species which could otherwise be misidentified using conventional cultural and serotyping methods. The whole genome sequence of this isolate will help to identify potential marker/s of intervention and further genomic analysis might also shed light onto the virulence properties of this newly identified Acinetobacter species which has been provided the new name of Acinetobacter maqsudiensis.

After human genome sequencing and rapid changes in genome sequencing methods, we have entered in the era of rapidly accumulating genome-sequencing data. This has poses development of several types of methods for representing results of genome sequencing data. Circular genome visualizations tools are also critical in this area as they provide rapid interpretation and simple visualization of overall data. In the last 15 years, we have seen rapid changes in circular visualization tools after the development of the circos tool with 1–2 tools published per year. Herein we have summarized and revisited all these tools until the third quarter of 2018.

An understanding of the cultural conditions that determine the factors affecting nurses’ intention to leave is important for countries suffering from nurse shortage. Aim: to examine factors influencing intention to leave among female hospital nurses in a large Japanese sample, classified into four generations by age considering economic conditions. Methods: a cross-sectional survey with convenience sampling was conducted. Anonymous self-administered questionnaires were distributed to all nurses in 30 hospitals. To assess intention to leave, basic attributes, life conditions, work characteristics, and factors of psychosocial work environment were addressed. After classifying data into four generations based on age cohorts, we conducted multivariate logistic regression analysis using the completed data (N = 5,074, mean age = 36.24). Results: regardless of generational characteristics influenced by economic conditions, effort and monetary reward were generation-common factors. Over-commitment, social support, and the presence of a role model were generation-common factors in three generations. While having children increased intention to leave in the generation born 1965–1979, having family members in need of caregiving other than children decreased the risk in the generation born in the 1980s. Conclusion: generational countermeasures considering factors of psychosocial work environment and life conditions are needed to avert female nurse turnover.

This study aimed to evaluate the impact of different closures used in second fermentation on the aromatic fraction of sparkling wine. Six types of closures (cork stoppers and screw cap) and 94 months of aging in bottle were investigated. Head-space-solid phase microextraction (HS-SPME) and thermal desorption (TD) procedures coupled to gas chromatography-mass spectrometry (GCMSMS) analysis was applied. The vectors containing the relative abundance of the volatile compounds are compositional vectors. The statistical analysis of compositional data requires specific techniques that differ from standard techniques.101 volatile compounds were identified. HS-SPME extracted highest percentage of ester, ketones and other compounds while TD was a useful tool for the obtention of alcohol, acid, ether and alkanes compounds. Esters was the most abundant family of compounds. Compositional data analysis applied to study the impact of different closures used in bottle-aging after second fermentation on the volatile composition of sparkling wine concluded that there are differences in the relative abundance of certain volatile compounds between cork stoppers and screw cap closures group. Overall, the most abundant part in screw cap closures was Ethyl hexanoate and, Ethyl octanoate in cork stoppers. Also, the proportion amount of Dimethylamine was higher in screw cap closures than cork stoppers group relative to the entire of the sample.

The growing demand for high-power DC Fast Charging (DCFC) stations for Electric Vehicles (EVs) is expected to lead to increased peak power demand and reduction of grid power quality. To maximize the economic benefits and station utilization under practical constraints set by regulatory authorities and DCFC station operators, this study explores and provides methods for connecting DCFC stations to the grid employing low power interconnection rules and Distributed Energy Resources (DERs). The system uses automotive Second Life Batteries (SLBs) and Photovoltaic (PV) systems as energy buffers and local energy resource to support EV charging and improve the station techno-economic feasibility through load shifting and charge sustaining. The optimal sizing of the DERs and the selection of the grid interconnection topology is achieved by means of a Design Space Exploration (DSE) by means of exhaustive search approach to maximize the economic benefits of the charging station and to mitigate high-power demand to the grid. Without loosing of generality, this study considers a range of DER sizes, grid interconnection specifications, and related electricity tariffs of American Electric Power (AEP) Ohio and the Public Utility Commission of Ohio (PUCO). Various scenarios and strategies have been defined to account for the interconnection requirements of the grid to the DCFC with DERs. The system’s techno-economic performance of different scenarios has been analyzed and compared using a multitude of metrics.

This study investigates effective strategies for second language education in primary schools, analyzing the efficacy of immersive and interactive methods, digital literacy tools, and mixed-method approaches. The findings reveal significant improvements in language proficiency and student engagement through these methodologies, with immersive and interactive strategies showing substantial benefits in oral proficiency and engagement, digital tools enhancing accessibility and engagement, and mixed-method approaches yielding the highest overall effectiveness. The study underscores the importance of a balanced, flexible approach to language education, integrating traditional and modern methods to cater to diverse learner needs. These strategies not only advance linguistic skills but also contribute to the broader cognitive and emotional development of young learners, emphasizing the critical role of effective language education in preparing students for a globalized world.

As participating molecules in nucleic acids, nucleotides also have signaling functions and act as second messengers in both prokaryotic and eukaryotic cells, the most common example being cyclic AMP (cAMP). In bacteria, a great interest has arisen in studying nucleotide signaling. It has been determined that cyclic di-AMP (c-di-AMP), cAMP, and cyclic di-GMP (c-di-GMP) participate in biological events such as bacterial growth, biofilm formation, sporulation, cell differentiation, motility, virulence, and other. Moreover, these cyclic-di-nucleotides (c-di-nucleotides) produced in pathogenic intracellular bacteria can affect eukaryotic host cells for infection. On the other hand, the nucleotide molecules pppGpp, and ppGpp are alarmones involved in regulating bacterial nutritional stress; they are also considered second messengers. These second messengers can be used as therapeutic agents because of their immunological functions on eukaryotic cells. In this review, different topics on the role of c-di-nucleotides as second messengers in different bacterial processes are addressed.

Hund's rule of maximum multiplicity is a powerful empirical tool for determining the electron population of electronic shells. It was recently discovered that it is also the rule of minimum Shannon entropy. This study extends these findings to the Aufbau rule and discusses them from the perspective of emergent dimensionality, providing a monotonically increasing entropy function. We observed that some elements that violate the Aufbau rule have the same entropies in the actual and Aufbau electron configurations. On the other hand, lower entropies of all actual elements' configurations are associated with higher or equal numbers of unpaired electrons in these configurations compared to the Aufbau configurations. The only exception to this rule is palladium.

In this study, we discuss the properties of absolute vacuum space and how these properties can play a vital role in creating a mechanism in which the very first particle gets created simultaneously everywhere and we find the limit in which when the absolute vacuum volume reaches will lead to the collapse that leads to the creation of the first particle. This discussion is made following to the elementary dimensions theory study that was peer-reviewed at the end of 2020, everything in the universe is made from four elementary dimensions, these dimensions are the three spatial dimensions (X, Y, and Z) and the Vacuum resistant as the factor of change among the four, time itself wasn’t considered as the fourth dimension, rather time corresponds to a factor of change, and during the research it was found out that the Vacuum resistant is the factor of change in the Absolute Vacuum space, where time is a hypothetical concept, that represents changes during certain events compared to a constant change rate event.Therefore, time does exist, but as a factor of change, and as the Vacuum resistant in the absolute vacuum space, Time= factor of change= Vacuum resistant. In the study, the internal and external vacuum resistant volume equivalent is found, External Vacuum resistant=3.2857602*10^15 *mass. This equation is used to identify the amount of Free external vacuum resistant created during nuclear fission and fusion: Initial mass of the excited nucleuses mass of the created new nucleuses+ 3.2857602*10^15 * the lost Mass. In elementary dimensions, the energy created during nuclear reactions is equivalent to the free External vacuum resistant created through nuclear reactions, and mass is equivalent to the internal Vacuum resistant.

In this work, generalized geometry of second-order tangent groups and affine configuration complexes is proposed. Initially, geometry for higher weights n=4 and weights n=5 is presented through some interesting and suitable homomorphisms, finally, this geometry is extended and generalized for any weight n.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

This paper examines the quantitative and qualitative situation of the current fixed and mobile Internet and its expected future. First, the number of users and penetration rate of the Internet, various types of services in different countries, the ranking of countries in terms of the mean and median download and upload Internet data speeds, Internet data volume, and number and location of data centers in the world are presented. The second task introduces and details twelve performance evaluation metrics for broadband Internet access. Third, different wired and wireless Internet technologies are introduced and compared based on data rate, coverage, type of infrastructure, and their advantages and disadvantages. Based on the technical and functional criteria, in the fourth work, two popular wired and wireless Internet platforms, one based on optical fiber and the other based on the 5G cellular network, are compared in the world in general and Australia in particular. Moreover, this paper has a look at Starlink as the latest satellite Internet candidate, especially for rural and remote areas. The fifth task outlines the latest technologies and emerging broadband Internet-based services and applications in the spotlight. Sixthly, it focuses on three problems in the future Internet in the world, namely the digital divide due to the different quality of the Internet and new Internet-based services and applications of emerging technologies, the impact of the Internet on social interactions, and hacking and insecurity on the Internet. Finally, some solutions to these problems are proposed.

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis on the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

A genome represents the complete collection of genetic material in an organism or cell. This genetic information is encoded in nucleic acids, which can be either single or double-stranded and arranged in linear or circular forms. DNA, the most common type of genetic material, consists of just four nucleotides: adenine, guanine, thymine, and cytosine. These nucleotides are organized in a double helix structure, a discovery made by Watson and Crick. The sequence of these nucleotides forms the basic blueprint of a gene or genome. The process of determining this sequence is known as genome sequencing. The field of genome sequencing has evolved dramatically over time, advancing from sequencing short DNA fragments to analyzing millions of base pairs. Initially, efforts were focused on determining the sequence of individual genes, but now whole genome sequencing is both rapid and widely accessible. Recent improvements in sequencing technology emphasize faster, more accurate results, lower costs, and better data analysis. Sanger sequencing, which became the gold standard for DNA sequencing for about thirty years, was instrumental in completing various genome sequences, including the human genome. It laid the groundwork for newer sequencing methods. Today, these newer methods, known collectively as “Next-Generation Sequencing” (NGS), include several generations of technology. Second-generation methods, such as Illumina, Pyrosequencing, ABI/SOLiD, and Ion Torrent Sequencing, and third-generation methods like PacBio and Helicos Sequencing, have advanced the field further. Fourth-generation technology, such as Nanopore sequencing, is also emerging. Most modern research now relies on NGS for analyzing biological sequences, and understanding these technologies offers insight into current methods and future developments.

The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a retriever for retrieving chunks based on semantic similarity with the query. In this study, we advocate for and provide experimental evidence supporting integrating and maintaining questions and answers (QA) formatted databases to improve retrieved-context representations and response quality. Our experiments evaluate our approach on benchmark RAG datasets using standard evaluation metrics and provide comparative analyses against state-of-the-art retrieval methods, showing the potential of our approach.

Recent advancements in cognitive neuroscience, particularly in Electroencephalogram (EEG) signal processing, image generation, and brain-computer interfaces (BCI), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of AI to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected is then processed through an LSTM-based network employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain-computer interfaces.

The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

Background: The microbiome of the ocular surface has been characterised, but only limited in-formation is available on a possible silent intraocular microbial colonisation in normal eyes. Therefore, we performed next generation sequencing (NGS) of 16S rDNA genes in aqueous hu-mor. Methods: Aqueous humor was sampled from three patients during cataract surgery. Air swabs, patient’s and healthy donor conjunctival swab served as controls. Following DNA extraction, the V3 and V4 hypervariable regions of the 16S rDNA gene were amplified and sequenced fol-lowed by denoising. The resulting Amplicon Sequence Variants were matched to a subset of the Ribosomal Database Project 16S database. The deduced bacterial community was then statis-tically analysed. Results: DNA content in all samples was low (0-1.49 ng/µl) but sufficient for analysis. Main phyla in all samples were Acinetobacteria (48%), Proteobacteria (26%), Firmicutes (14%), Acidobac-teria (8%) and Bacteroidetes (2%). Patients conjunctival control samples and anterior chamber fluid showed similar patterns of bacterial species containing many waterborne species. Non-disinfected samples showed a different bacterial spectrum than the air swab sample. Conclusions: The data confirm the existence of an ocular surface microbiome. A distinct intraoc-ular microbiome was not discernable from the background, suggesting the absence of an intra-ocular microbiome in normal eyes.

In this work various sample of primary aluminum dross were investigated for composition and evaluation of hydrogen generation via hydrolysis in low concentration alkali solution. Composi-tion revealed low to moderate aluminium content and hydrolysis reactions showed low hydrogen generation volumes per sample mass as well as purity, where composition plays substantial role in the hydrogen evolution, yet showing that there is possible place for dross use in hydrogen generation. It was revealed that some dross samples in addition to hydrogen also release methane via hydrolysis. Rection rate was obtained by the shrinking core model, written for spherical solid particles. Sample composition on hydrogen generation volume is compared to known literature.

Osteosarcoma (OS) are the most prevalent malignant bone tumors in adolescents and young adults.. OS cells grow in a permissive local microenvironment which modulates their behavior and facilitates all steps in tumor development (e.g., proliferation/quiescence, invasion/migration, drug resistance) and contributes to their intrinsic heterogeneity. The lung parenchyma is the most common metastatic site in OS, and metastatic foci are frequently associated with a poor clinical outcome. Although multiple factors may be responsible for the disease, including genetic mutations (e.g., Rb, p53), the molecular mechanism of development of OS remains unclear and the conventional treatment for OS is still based on a sequential approach that combines chemotherapy and surgery. Also, despite the increase in clinical trials, the survival rates for OS have not improved. Non-specific targeting therapies thus show poor therapeutic effects with side effects at high doses. For these reasons, many efforts have been done to characterize the complex genome of osteosarcoma thanks to the whole exome analysis with the aim to identify predictive biomarkers to give to these patients a better therapeutic option. This review aims to summarize and discuss the main recent advances in OS molecular research for precision medicine.

Physical layer secret key (SK) generation is known to be an efficient means to achieve high secrecy rate, on the condition that dynamic channel state information (CSI) is provided. For this reason, the secrecy performance is highly degraded in a static environment. The intelligent reflecting surface (IRS) is a promising solution to create dynamic randomness, and thus lead to enhanced secrecy performance regardless of the user environments. This paper proposes an IRS-assisted physical layer SK generation scheme, by efficiently combing phase information of the direct and reflected channel information in a hybrid way. In particular, the initial SKs are obtained by adopting efficient phase quantization method with symmetric bit allocation to complex numbered channel estimates. Simulation results show that the proposed hybrid phase quantization (PQ) can improve the SK generation rate and the key disagreement probability in a static environment.

Parvovirus B19 infection is associated with a wide range of clinical manifestations, from asymp-tomatic to severe neurological disorders. Its major clinical symptoms, fever and rash, are com-mon to multiple viruses, and laboratory tests to detect B19 are frequently not available. Thus, the impact of B19 on public health remains unclear. We report the case of a 38-day old girl ad-mitted to São Paulo Clinical Hospital, Brazil, with an initial diagnosis of bacterial meningitis, seizures, and acute hydrocephalus. Antibiotic therapy was maintained for one week after admis-sion and discontinued after negative laboratory results were obtained. Nine days after symp-toms onset, a cerebral spinal fluid (CSF) sample revealed persistent pleocytosis. The complete B19 complete genome was subsequently identified in her CSF by a metagenomic next-generation sequencing approach. This report highlights the possible involvement of B19 in the occurrence of acute neurological manifestations, and emphasizes that its possible involve-ment might be better revealed by the use of metagenomic technology to detect viral agents in clinical situations of unknown or uncertain etiology.

Molecular diagnostics in healthcare relies increasingly on genomic and transcriptomic method-ologies and requires appropriate tissue specimens from which nucleic acids (NA) of sufficiently high quality can be obtained. Besides suration of ischemia and fixation type, NA quality depends on a variety of other pre-analytical parameters, like storage conditions and duration. It has been discussed that improper dehydration of tissue during processing influences quality of NAs and shelf-life of fixed tissue. Here we report on establishing a method to determine the amount of residual water in fixed, paraffin-embedded tissue (fixed by neutral buffered formalin, or a non-crosslinking fixative) and its correlation to the performance of NAs in qRT-PCR analyses. The amount of residual water depended primarily on the fixative type and the dehydration pro-tocol and, to a lesser extent on storage conditions and time. Moreover, we found that these pa-rameters were associated with the qRT-PCR performance of extracted NAs. Besides cross-linking of NAs and modification of nucleobases by formalin, hydrolysis of NAs by residual water was found to contribute to reduced qRT-PCR performance. The negative effects of residual water on NA stability are not only important for the design and interpretation of research, but must also be taken into account in clinical diagnostics where reanalysis of archived tissue from a primary tumour may be required, e.g., after disease recurrence. We conclude that improving shelf-life of fixed tissue requires meticulous dehydration and dry storage to minimize the degradative in-fluence of residual water on NAs.

Enhanced supercontinuum generation (SCG) is experimentally demonstrated in integrated silicon nitride (Si₃N₄) waveguides incorporating highly nonlinear graphene oxide (GO) in the form of two-dimensional (2D) films. On-chip integration of the 2D GO films with precise control of their thickness is realized by using a transfer-free and layer-by-layer coating method. The control of the film length and coating position is achieved via window opening in the upper silica cladding of the photonic integrated chips. Detailed SCG measurements are performed using the fabricated devices with different waveguide geometries and GO film thicknesses, and the results are compared with devices without GO. Significantly improved spectral broadening of ultrashort optical pulses with ultrahigh peaks powers exceeding 1000 W is observed for the hybrid devices, achieving up to 2.4 times improvement in the spectral bandwidth relative to devices without GO. Theoretical analyses for the influence of GO film thickness, coating length, coating position, and waveguide geometry are also provided by fitting the experimental results with theory, showing that there is still significant room for further improvement. This work opens up a promising new avenue towards improving the SCG performance of photonic integrated devices by incorporating functional 2D materials.

Nowadays the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. Using the NIST statistical test suite for random and pseudorandom number generators (PRNG), it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of ? − ?????. A high level of the presented algorithm’s parallelization allows for implementation it effectively on calculators with parallel structure. CPU-based architecture, FPGA-based architecture, CUDA- based architecture of PRNG and different PRNG implementations are presented to confirm high performance of the proposed solution.

Sweet cucumber (Solanum muricatum) sect. Basarthrum, is a neglected horticultural crop native of the Andean region. It is naturally distributed very close to potatoes (Solanum sect. Petota) and tomatoes (Solanum sect. Lycopersicon), two groups of high economic importance. To date, molecular tools for this crop are still undetermined. We here obtained the first complete chloroplast (cp) genome of sweet cucumber and compared with seven Solanaceae species. Pair-end clean reads were obtained by PE 150 library and the Illumina HiSeq 2500 platform. The complete cp genome of S. muricatum had a 155,681 bp with typical quadripartite structure, containing a large single copy (LSC) region (86,182 bp) and a small single-copy (SSC) region (18,360 bp), separated by two inverted repeat (IR) regions (25,568 bp). The annotation of chloroplast genome predicted 88 protein-coding genes (CDS), 8 ribosomal RNA (rRNA) genes, 37 transfer RNA (tRNA) genes, and one pseudogene. A total of 48 perfect microsatellites were identified, divided in mononucleotide repeats (32), followed by tetranucleotide (6) and dinucleotides (5). SSRs with trinucleotides repeats (3), pentanucleotide (1) and hexanucleotide (1) repeats motifs in these genomes were identified in lower quantity. Most of these repeats were distributed in the noncoding regions. Whole chloroplast genome comparison with the other seven Solanaceae species revealed that the small and large single copy regions showed more divergence than inverted regions. Finally, phylogenetic analysis resolved that S. muricatum is a sister species to members of sections Petota + Lycopersicum + Etuberosum. This study reports for the first time the genome organization, gene content, and structural features of the cp genome of S. muricatum. Also, this study may provide the basis for evaluating genetic diversity within Solanum, and will be useful to examine the evolutionary processes in sweet cucumber landraces.

Interface solar steam generation (ISSG) are charming for its applications in desalination and wastewater treatment. Biomass is an attractive substrate for utilizing solar vapor evaporators because of its natural pore structure and water transportability. Polymers like polydopamine (PDA) and polypyrrole (PPy) with broadband spectrum absorption are fascinating in photothermal materials (PTMs). Herein, PDA coated daikon-based (PDA-DK) and PPy coated daikon-based (PPy-DK) PTMs have been exploited for solar steam generation. When polyethylene foam (PEF) was used as an insulating layer to limit heat loss from the PTMs to bulk water, the evaporation rate of PDA-DK and PPy-DK was raised from 0.82 kg m–2 h–1 and 0.96 kg m–2 h–1 to 1.50 kg m–2 h–1 and 1.60 kg m–2 h–1, respectively. Meanwhile, the corresponding photothermal conversion efficiency was increased to 89.01% and 98.97%, which was increased by nearly 40% under 1-sun irradiation. In addition, PDA-DK and PPy-DK exhibited remarkable stability for the solar steam generation without significant change through 15 cycles. Furthermore, PDA-DK and PPy-DK could effectively desalt seawater and purify dyeing wastewater. All the results indicate that PDA-DK and PPy-DK have great potential in real-world applications for solar steam generation.

Background: Currently, Indonesia is entering an era that is all digital and uses technology in all fields of activity. The internet has a lot to do with their behavior or character. Millennials are the nation's next generation. The emergence of the digital era caused by globalization has made the millennial generation less appreciative and practicing the values ​​of Pancasila. In this case, character education is something that must be applied to the millennial generation. Purpose: This study aims to analyze the characteristics of the millennial generation, the role of character education in realizing the values ​​of Pancasila in the daily life of the millennial generation, how to cultivate an attitude of patriotism and nationalism towards the nation by practicing Pancasila, and provide motivation for the millennial generation to realize these values. Pancasila values. Methods: This study uses a literature review method. The search for journals was carried out using the google scholar database with the keywords “Character Education”, “millenial generation”, and “Pancasila”. The journals selected are journals from 2019 to 2020. Results: By applying character education to the millennial generation, it will make it easier for them to practice the values ​​of Pancasila. Pancasila should be the way of life of the millennial generation. Recommendation and limitation: As millennials, we must protect the country from values ​​that are contrary to Pancasila. Character education must be applied following the nature of the millennial generation itself. Of course, the applied character education must prioritize and be based on the values ​​of Pancasila as well.

Introduction: The imbalance between technological development and ideological strengthening has resulted in the fading of the Pancasila ideological values of millennials. Technology that accompanies everyday life seems to be the main character in human life. Yet if humans themselves do not use technology wisely, technology can be a threat to both individuals and the life of the nation and state. If technology is increasingly out of control, threats to the Pancasila ideology are very likely to occur, such as in the G 30S PKI incident. Therefore, it is necessary to take action both from the government and the community as individuals to safeguard the development of technology to be used wisely. That way, we can prevent the threat to the Pancasila ideology together. Writing this article aims to analyze how the role and influence of the Pancasila ideology on the millennial generation in the 4.0 industrial revolution and to find solutions so that the values of Pancasila remain attached to the next generation of the Indonesian nation. Method: Writing This article uses a qualitative method by using literature reviews from the results of related research journals that have been published online through websites and other online media. Results: Thirteen journals and one book have met the criteria for the inclusion of a predetermined review. Research is based on the stigma of society regarding technological developments in the Industrial revolution 4.0. Therefore, the government is expected to be more severe in implementing human capital management. Conclusion: By procuring human capital management, it will be possible that Human Resources (HR) in Indonesia will be able to carry out the ideals of a golden Indonesian generation with Pancasila values inherent in the hearts of the people and the nation's future generations.

Plasma cell-free DNA (cfDNA) is frequently analyzed using liquid biopsy to investigate cancer markers. Accordingly, we hypothesized this concept could be applied to the field of exercise physiology. Here, we aimed to identify specific cfDNA (spcfDNA) sequences in the plasma of non-treated human participants using next generation sequencing (NGS) and to clearly define the dynamics regarding the amounts of spcfDNA-fragments upon extreme exercise, such as running a full marathon. NGS analysis was performed using cfDNA of pooled plasma collected from non-treated participants. We confirmed the TaqMan-qPCR assay had a high sensitivity and found the spcfDNA sequence abundance was 16,600-fold higher than a normal genomic region. We then used the TaqMan-qPCR assay to investigate the dynamics of the levels of spcfDNA-fragments upon running a full marathon. Quantities of the spcfDNA fragments were significantly increased post marathon. Furthermore, the amounts of spcfDNA fragments strongly correlated with the numbers of white blood cells and plasma myoglobin concentrations. These results suggest the spcfDNA fragments identified in this study were highly sensitive response markers to extreme physical stress. The findings of this study may provide new insights into exercise physiology and genome biology on the human.

The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change become more severe. Small hydropower (SHP) is evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. As the results, SHP potential for the near future (2021 to 2040) shows a tendency to be increased and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP in order to expand the use of renewable energy. Also, a methodology presented in this study could be used for the future prospect of the small hydropower potential.

MCPH1, otherwise known as the microcephalin gene (*607117) and protein, is a basic regulator of chromosome condensation (BCRT-BRCA1 C-terminus). The Microcephalin protein is made up of three BCRT domains and conserved tandem repeats of interacting phospho-peptide. There is a strong connection between mutations of the MCPH1 and reduced brain growth. Specifically, individuals with such mutations have underdeveloped brains which means smaller size, varying levels of mental retardation, delayed speech and poor language skills, individuals with mild microcephaly and normal intelligence notwithstanding. In this case, a fetus with novel homozygous mutation of the MCPH1 gene ((c.348del)), whose parents were recessive heterozygous for (c.348del), displayed severe microcephaly at 22 weeks of gestation. Due to the effect on splice sites in introns, this mutation causes forming of dysfunctional proteins which lack crucial domains of the C-terminus. Our findings portray an association between the new MCPH1 mutation ((c.348del)) and the clinical features of autosomal recessive primary microcephaly (MCPH) contributing to a broader spectrum related to these pathologies.

We show broadband sum-frequency generation (SFG) in the Green-Yellow-Orange-Red spectral range using bismuth triborate, BiB3O6 crystal (BIBO) as nonlinear material. We perform a noncollinear phase-matching configuration within BIBO crystal using the remaining light behind the second harmonic generation stage and the infrared idler of an optical parametric amplifier (OPA). The obtained mixing radiation of ultrafast light sources to generate femtosecond pulses across 520.5 to 742.5 nm region is observed. SFG spectrum from single-pass cross-correlation intensity over such visible range is showed. The SFG wavelengths as a function of tunable wavelength idler OPA agrees with the expectations of parametric conversion condition and open the door to practical multi-beam or multi-color sum-frequency generators.

SARS-CoV-2 is a novel betacoronavirus and the aetiological agent of the current COVID-19 outbreak that originated in Hubei Province, China. While polymerase chain reaction is the front-line tool for SARS-CoV-2 surveillance, application of amplification-free and culture-free methods for isolation of SARS-CoV-2 RNA, partnered with next-generation sequencing, would provide a useful tool for both surveillance and research of SARS-CoV-2. We here release into the public domain a set of bait capture hybridization probe sequences for enrichment of SARS-CoV-2 RNA from complex biological samples. These probe sequences have been designed using rigorous bioinformatics methods to provide sensitivity, accuracy, and minimal off-target hybridization. Probe design was based on existing, validated approaches for detecting antimicrobial resistance genes in complex samples and it is our hope that this SARS-CoV-2 bait capture platform, once validated by those with samples in hand, will be of aid in combating the current outbreak.

The lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. In response to the insufficient training data of defect images with complex backgrounds such as rust and surface oil leakage in substation equipment, leading to poor performance of the detection model, this paper proposed a novel adversarial deep learning model for substation defect generation: ADD-GAN. In comparison to existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model utilizes a joint discriminator for overall image and defect image to address the issue of low attention to local defect areas, thereby improving the loss of image features. This enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves an mAP of 81.5% on the test dataset, representing an improvement of 9.6% over the original dataset, 5.7% over traditional augmentation methods, and 7% over typical adversarial deep learning methods. This confirms that the ADD-GAN method can generate a high-fidelity dataset of substation equipment defects.

Pregnancy is associated with significant physiological changes, including alterations in lipid metabolism, which can have implications for both maternal and fetal health. This study aimed to evaluate serum lipid profile variations among healthy Sudanese women during the second and third trimesters of pregnancy. A total of 60 pregnant women, divided equally between the second and third trimesters, were compared with 30 non-pregnant, age-matched controls. Serum levels of total cholesterol, triglycerides, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were measured using a BTS-Biosystem analyzer. The results revealed a statistically significant increase in total cholesterol, triglycerides, LDL, and HDL levels among pregnant women compared to the control group (p < 0.01). Additionally, lipid levels were significantly higher in the third trimester than in the second trimester. A weak positive correlation was found between parity and serum levels of total cholesterol, triglycerides, and LDL, while no significant correlation was observed for HDL. These findings suggest that pregnancy, particularly in the third trimester, is associated with substantial increases in serum lipid levels, highlighting the need for monitoring lipid profiles during pregnancy to manage potential risks of hyperlipidemia and its associated complications.

In the periodic table of the elements, ruthenium is ideally placed, right in the middle, just above iron. And like iron, it possesses several oxidation states, with +2 and +3 being the most common. Accordingly, ruthenium chemistry is extremely rich and well developed, ruthenium complexes show excellent catalytic aptitude, tremendous redox capacity, and intriguing biological activity. However, in the design of sensors, the use of ruthenium complexes remains scarce, despite valuable electro- and photochemical properties. Therefore, there is an opportunity here, and ruthenium-based complexes might become one day, key players in sensing technology. Starting ourselves a new research project with ruthenium-based sensors, writing this review was essential to see the current state of research in the field, to identify opportunities, and to have an overview of state-of-the-art examples.

Physical processes are usually described using four-dimensional vector quantities - coordinate vector, momentum vector, current vector. But at the fundamental level they are characterized by spinors - coordinate spinors, momentum spinors, spinor wave functions. The propagation of fields and their interaction takes place at the spinor level, and since each spinor uniquely corresponds to a certain vector, the results of physical processes appear before us in vector form. For example, the relativistic Schrödinger equation and the Dirac equation are formulated by means of coordinate vectors, momentum vectors and quantum operators corresponding to them. In the Schrödinger equation the wave function is represented by a single complex quantity, in the Dirac equation a step forward is taken and the wave function is a spinor with complex components, but still coordinates and momentum are vectors. For a closed description of nature using only spinor quantities, it is necessary to have an equation similar to the Dirac equation in which momentum, coordinates and operators are spinors. It is such an equation that is presented in this paper. Using the example of the interaction between an electron and an electromagnetic field, we can see that the spinor equation contains more detailed information about the interaction than the vector equations. This is not new for quantum mechanics, since it describes interactions using complex wave functions, which cannot be observed directly, and only when measured goes to probabilities in the form of squares of the moduli of the wave functions. In the same way spinor quantities are not observable, but they completely determine observable vectors.In Section 2 of the paper, we analyze the quadratic form for an arbitrary four-component complex vector based on Pauli matrices. The form is invariant with respect to Lorentz transformations including any rotations and boosts. The invariance of the form allows us to construct on its basis an equation for a free particle combining the properties of the relativistic wave equation and the Dirac equation. For an electron in the presence of an electromagnetic potential it is shown that taking into account the commutation relations between the momentum and coordinate components allows us to obtain from this equation the known results describing the interactions of the electron spin with the electric and magnetic field. In section 3 of the paper this quadratic form is expressed through momentum spinors, which makes it possible to obtain an equation for the spinor wave function in spinor coordinate space by replacing the momentum spinor components by partial derivative operators on the corresponding coordinate spinor component. Section 4 presents a modification of the theory of the path integral, which consists in considering the path integral in the spinor coordinate space. The Lagrangian densities for the scalar field and for the electron field, along with their corresponding propagators, are presented. An equation of motion for the electron is proposed that is relativistically invariant, in contrast to the Dirac equation, which lacks this invariance. This novel equation permitted the construction of a relativistically invariant procedure for the second quantization of the fermion field in spinor coordinate space.

Exercising surds to represent figures is a common practice in scientific and Engineering fields, especially in scripts where calculators are banned or unapproachable. Peter Chew Theorem make result becomes simple when dealing with converting Quadratic Surds. The substance of the Peter Chew Theorem lies in enabling the forthcoming generation to simple break problems related to Quadratic Surds more effectively, easing a direct comparison with contemporary results. By employing the Peter Chew Theorem, one can streamline the tutoring and literacy of math, particularly concerning second- order direct equations with constant portions. This theorem's objective aligns with Albert Einstein's famed quotation Everything should be made as simple as possible, but not simpler.

While entropy was introduced in the second half of the 19th century in the international vocabulary as a scientific term, in the 20th century it became common in colloquial use. Popular imagination has loaded “entropy” with almost every negative quality in the universe, in life and in society, with a dominant meaning of disorder and disorganization. Exploring the history of the term and many different approaches on it, we show that entropy has a universal stochastic definition which is not disorder. The accompanying principle of maximum entropy, which lies behind the Second Law, gives explanatory and inferential power to the concept and promotes entropy as the mother of creativity and evolution. As social sciences are often contaminated by subjectivity and ideological influences, we try to explore whether the maximum entropy, applied to the distribution of wealth quantified by annual income, can give an objective description. Using publicly available income data, we show that the income distribution is consistent with the principle of the maximum entropy. The increase of entropy is associated to increase of society’s wealth yet a standardized form of entropy can be used to quantify inequality. Historically, technology has played a major role in development and increase of the entropy of income. Such findings are contrary to the theories of ecological economics and other theories which use the term entropy in a Malthusian perspective.

The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in China is achieving under control in China. We develop a mathematical model based on epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and close contacts. We calculate the basic reproduction numbers 2.5 in China (excluding Hubei province) and 2.9 in Hubei province with the initial time on January 30 which show the severe infectivity of COVID-19, and verify that the current isolation method effectively contains the transmission of COVID-19. Under the isolation of healthy people, confirmed cases and close contacts, we find a noteworthy phenomenon that is the potential second epidemic of COVID-19, and estimate the peak time and value and the cumulative number of cases. Simulations show that the isolation of close contacts tracked measure can efficiently contain the transmission of the potential second epidemic of COVID-19. With isolation of all susceptible people or all infected people or both, there is no potential second epidemic of COVID-19. Furthermore, resumption of work and study can increase the transmission risk of the potential second epidemic of COVID-19.

In Taiwan, colorectal cancer is ranked second and third in terms of mortality and cancer incidence, respectively. In addition, medical expenditures related to colorectal cancer are considered to be the third highest. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five data mining classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. In total, 4,287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.

This paper is concerned with representing sums of finite products of Chebyshev polynomials of the second kind and of Fibonacci polynomials in terms of several classical orthogonal polynomials. Indeed, by explicit computations each of them is expressed as linear combinations of Hermite, generalized Laguerre, Legendre, Gegenbauer and Jacobi polynomials which involve the hypergeometric functions ${}_1 F_1$ and ${}_2 F_1$.

It is argued that if the non-unitary measurement transition, as codified by Von Neumann, is a real physical process, then the ‘probability assumption’ needed to derive the Second Law of Thermodynamics naturally enters at that point. The existence of a real, indeterministic physical process underlying the measurement transition would therefore provide an ontological basis for Boltzmann’s Stosszahlansatz and thereby explain the unidirectional increase of entropy against a backdrop of otherwise time-reversible laws. It is noted that the Transactional Interpretation (TI) of quantum mechanics provides such a physical account of the non-unitary measurement transition, and TI is brought to bear in finding a physically complete, non-ad hoc grounding for the Second Law.

Next-generation sequencing(NGS) for HIV drug resistance(DR) testing has an increasing number of applications for detection of Low-abundance drug resistant variants(LA-DRVs) as its features of quasispecies. However there is less information on detection performance in DR detection with NGS.To determine the feasibility of NGS technology in LA-DRVs detection for HIV-1 pre-treated patients.In this study,80 pre-treated patients’ samples accepted both NGS and Sanger sequencing(SS) in HIV-1 drug resistance testing. The results show that NGS presents higher sensitivity in mutation sites discovery at 5% threshold. NGS shows a satisfactory consistency comparing with SS in both protease inhibitors(PIs) and Integrase inhibitor(INSTIs), more than 90%. But worse consistency in Nucleotide reverse transcriptase inhibitors(NRTIs), only ranging from 61.25%-87.50%. The consistency of Non-nucleotide reverse transcriptase inhibitors(NNRTIs) between NGS and SS was around 85%. NGS shows the highest sensitivity of 91.3% at 5% threshold. The application of NGS technology in HIV-1 genotype resistance detection in different populations infected with HIV needs more documents and to be confirm further.

This study examines a case involving a 7-year-old child who developed dengue symptoms following Qdenga vaccination. Despite initial negative diagnostic results, molecular analysis confirmed an infection with DENV4. Next-generation sequencing detected viral RNA from both DENV2 and DENV4 serotypes, which were identified as vaccine-derived strains using specific primers. Phylogenetic analysis further confirmed that these sequences belonged to the Qdenga vaccine rather than circulating wild-type viruses. This case underscores the critical need for precise diagnostic interpretation in vaccinated individuals to avoid misdiagnosis and to strengthen public health surveillance. A comprehensive understanding of vaccine-induced viremia is essential for refining dengue surveillance, improving diagnostic accuracy, and informing public health strategies in endemic regions.

Purpose: This study investigates the influence of human brands on the formation of Generation Z's digital identity. Given the increasing importance of the digital space in this generation's life, understanding how their identity is shaped by human brands in this environment is crucial. Method: Using a netnographic approach, qualitative data was collected and analyzed from 500 posts and interactions of Gen Z users on Instagram and TikTok platforms. Thematic content analysis was conducted using NVivo 12 software, with inter-coder reliability confirmed (κ = 0.85). Findings: Results indicate that human brands influence Gen Z's digital identity through three main mechanisms: 1) lifestyle emulation (β = 0.42, p < 0.001), 2) value alignment (β = 0.38, p < 0.001), and 3) self-presentation modeling (β = 0.35, p < 0.01). Multiple regression analysis showed these three factors explain 67\% of the variance in digital identity formation (R² = 0.67, F(3,496) = 335.42, p < 0.001). Conclusion: This study demonstrates that human brands play a significant role in shaping Generation Z's digital identity. Understanding this influence can help marketers and policymakers design more effective strategies for engaging with this generation.

Digital twin technology, a cutting-edge approach that creates dynamic digital replicas of physical systems, is increasingly integral to various industrial applications. However, the effective deployment of digital twins often faces challenges associated with data availability, quality, and interoperability, which can impede the development and operational efficacy of these virtual counterparts. This paper presents a systematic review of the application of artificial intelligence (AI) in enhancing and managing digital twin technologies across multiple domains such as industry, healthcare, urban planning, business, education, technology, and others. It specifically examines how machine learning models, particularly neural networks and deep generative models, are being employed to optimize the creation, maintenance, and functionality of digital twins. The review explores the role of artificial intelligence in overcoming data-related limitations by providing robust, scalable data solutions for digital twins. Additionally, it addresses critical considerations of real-time data processing and system interoperability within these applications. This survey not only identifies the prevailing challenges and opportunities within this emerging field but also highlights potential future research directions that could further the integration of AI with digital twin technology. Through a detailed exploration of the intersection between AI and digital twins, this paper aims to contribute significantly to the knowledge base and encourage further innovations in this interdisciplinary area.

Coupling faults that occur simultaneously widely exist in the operation of mechanical equipment, and coupling fault features are formed by nonlinear coupling of multiple base fault features. In this paper, hypergraph neural network is used to obtain the characteristics of coupling faults, and two coupling fault diagnosis frameworks based on hypergraph are provided. 1. Coupling fault diagnosis framework based on feature generation: The hypergraph neural network is used as a generator to provide negative samples for discriminator training. The generator outputs hyperedge vectors as fake samples, and each hyperedge connects a base fault containing a fault type. 2. Coupling fault diagnosis framework based on feature extraction: Each node is regarded as a fault type, and the fault characteristics of each type of fault are extracted through the aggregation operation of hypergraph. The attention mechanism is introduced to realize the dynamic adjustment of the hyperedge, and the dynamic vertex is used to classify the unknown faults. The results show that the diagnostic accuracy of coupling faults under the two frameworks reaches 88.6% and 86.76%, respectively.

Research in renewable energy sources and micro-grid systems is critical for the evolving power industry. Micro-grids can supply diverse loads while reducing dependency on the utility grid. This paper models both pico-grids and nano-grids in the Singapore Institute of Technology Punggol campus, incorporating solar PV, energy storage systems (ESS), power electronics converters, and both DC and AC loads, along with utility grid connections. The pico-grid includes a battery storage system, a single-phase inverter linked to a single-phase grid, and DC and AC loads. The nano-grid comprises solar PV panels, a boost converter, a battery storage system, a three-phase inverter connected to a three-phase grid, and AC loads. Both the pico-grid and nano-grid are configurable in standalone or grid-connected modes. This configuration flexibility allows for detailed operational analysis under various conditions. The study conducts subsystem-level modeling before integrating all components into the simulation environment. MATLAB/Simulink is utilized to model, simulate, and analyze the power flow in both the pico-grid and nano-grid under different operating conditions.

This paper explores the development of a sophisticated system for automatic test generation tailored specifically for Java web applications utilizing REST interfaces. Writing tests ensures the quality of the software solution event after the introduction of subsequent changes, because automated regression testing can detect potential bugs in the code event before the application is resealed into production. By utilizing and comparing various automatic test generators, the system described provides users with a visual representation of code structure and coverage, enhancing insights through call graph visualization. The paper describes the concepts of theoretical automatic test generators, gives an overview of generators, and details the design and implementation of the implemented solution, which integrates the system test generator and the unit test generator based on open source to achieve the maximum code coverage level.

In recent years, bioengineered and biotechnological products have gained tremendous attention to identify unique biologic features associated with oncology. To overwhelm the complexity of tumor personalized medicines (PM) or vaccines could be a promising way-out to be considered. Cancer is a heterogeneous disease. This characteristic variability of cancer provides itself to the emergent era of PM i.e., precision oncology. The prime objective of PM is merely to deliver the right treatment to the right patient at the right time. For instance, imatinib was effectively targeted chronic myeloid leukemia. To date, attainment in using PM approaches to cancer treatment have been mixed. The next-generation sequencing (NGS), and the use of several biomarkers (e.g., HER2, BRAF, ALK, etc.), to drive precise cancer treatment through a timely and cost-effective manner. In current review, author summarizes the advancement in the field of PM such as CRISPR-Cas9, NGS, pharmacogenomics, and the benefits of machine learning techniques. The precise communication between healthcare professionals, regulatory bodies, and patients is essential to translate advances in PM into immense benefits for patients with cancer globally.

Securing funding is a critical yet complex task for organizations and individuals. This study presents an innovative chatbot designed to streamline the process using advanced Natural Language Processing (NLP) techniques and, specifically, a Retrieval-Augmented Generation (RAG) pipeline optimized for real-world applications. Our chatbot assists users in identifying suitable public tenders for financial support through natural language queries and a comprehensive public data database. The chatbot operates in a two-stage interaction model, initially providing summarized tender information for exploratory brainstorming, followed by detailed data upon user selection. A custom filtering mechanism ensures that the user interface elements responsible for swapping the interaction stages are consistent with the responses generated by the conversational agent. Human-evaluation tests demonstrated an average accuracy of 90.4% in document retrieval, with an average of 2.11 interactions required to find a specific tender. User satisfaction, rated on a scale of 1 to 5, averaged 3.14 (±1.73), indicating generally positive user experience with room for improvement. This approach addresses challenges of relevance, accuracy, and conversational flow, resulting in a reliable chatbot that simplifies the process of finding funding opportunities.

The gastrointestinal (GI) tract harbors trillions of microorganisms known to influence human health and disease, and next-generation sequencing (NGS) now enables the in-depth analysis of their diversity and functions. Although a significant amount of research has been conducted on the GI microbiome, comprehensive metagenomic datasets covering the entire tract are scarce due to cost and technical challenges. Despite the widespread use of fecal samples, integrated datasets encompassing the entire digestive process, beginning at the mouth and ending with feces, are lacking. With this study, we aimed to fill this gap by analyzing the complete metagenome of the GI tract, providing insights into the dynamics of the microbiota and potential therapeutic avenues. In this study, we delved into the complex world of the GI microbiota, which we examined in five healthy Japanese subjects. While samples from the whole GI flora and fecal samples provided sufficient bacteria, samples obtained from the stomach and duodenum posed a challenge. Using a principal coordinate analysis (PCoA), clear clustering patterns were identified; these revealed significant diversity in the duodenum. Although this study was limited by its small sample size, the flora in the overall GI tract showed unwavering consistency, while the duodenum exhibited unprecedented phylogenetic diversity. A visual heat map illustrates the discrepancy in abundance, with Fusobacteria and Bacilli dominating the upper GI tract and Clostridia and Bacterioidia dominating the fecal samples. Negativicutes and Actinobacteria were found throughout the digestive tract. This study demonstrates that it is possible to continuously collect microbiome samples throughout the human digestive tract. These findings not only shed light on the complexity of GI microbiota but also provide a basis for future research.

Congenital heart disease (CHD) is the most commonly detected congenital anomaly and affects up to 1 % of all live-born neonates. Current guidelines support the use of Chromosomal Microarray Analysis (CMA) and Next Generation Sequencing (NGS) as diagnostic approaches to identify genetic causes. The aim of our study was to evaluate the diagnostic yield of CMA and NGS in a cohort of neonates with both isolated and syndromic CHD. The present study included 188 infants under 28 days of age with abnormal echocardiography findings hospitalized at the Department of Neonatology, UMC Ljubljana, between January 2014 and December 2023. Phenotypic data were obtained for each infant by retrospective medical chart review. We established the genetic diagnosis of 22 distinct syndromes in 17% of neonates. The most common genetic diagnoses were 22q11.2 microdeletion (22.7%) and CHARGE syndromes (22.7%), Noonan syndrome (9.1%), and Williams syndrome (9.1%). In addition, we detected variants of uncertain significance in 4.8% of neonates. Timely genetic diagnosis is important for the detection of syndrome-related comorbidities, prognosis, reproductive genetic risks and, when appropriate, genetic testing of other family members.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

Remote distributed generation control and demand response is nowadays a basic need, especially for the Distributed Network Operators (DNOs). The integration of Renewable Energy Sources (RES) with stochastic production characteristics into the grid in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the ef-forts of the DNOs to keep the grid’s operation within safe limits and prevent cascading black-outs while staying in compliance with the SAIDI and SAIFI indices during maintenance and re-pair operations. Taking also under consideration the aging of the existing grid infrastructure, making it year by year more prone to failure, it is yet of great significance for the DNOs to have access to real-time feedback from grid’s infrastructure, fast, with low-cost upgrade interventions, easily deployed on the field and with fast response potential, in order to be able to perform real-time grid management (RTGM). In this article, we describe the architecture, development and deployment of a hybrid real-time control system of distributed generation (DG) units, which has the potential to be applied easily to DNO’s substations, RES plants and consumers (i.e. electric vehicles’ charging stations), that combines a low-cost minimum intervention into the electricity grid and its components and eliminates all communication interoperability problems between the different vendors’ installed electrical equipment.

This paper investigates the polarization and heat generation characteristics of batteries under different ambient temperatures and discharge rates by means of using a coupled electric-thermal model. The study found that the largest percentage of polarization is ohmic polarization, followed by concentration polarization and electrochemical polarization. The values of the three types of polarization are generally small and stable under normal-temperature environments and low-discharge rates. However, they increase significantly in low-temperature environments and high-discharge rates and continue to rise during the discharge process. Additionally, ohmic heat generation and polarization generation also increase significantly under these conditions. Reversible entropy heat is less sensitive to ambient temperature but increases significantly with the increase of the discharge rate. Ohmic heat generation and polarization heat generation contribute to the total heat generation of the battery at any ambient temperature, while reversible entropy heat only contributes to the total heat generation of the battery at the end of discharge.

This study explores the potential of data science software solutions like Customer Relationship Management Software (CRM) for increasing the revenue generation of businesses. We focused on those businesses in Accommodation and Food Service sector across the European Union (EU). The investigation is contextualized within the rising trend of data-driven decision making, examining the potential correlation between data science application and business revenue. Employing a comprehensive evaluation of Eurostat datasets from 2014 to 2021, we used both univariate and multivariate analyses, we assessed e-commerce sales data across countries, focusing on the usage of big data and CRM tools. Big data utilization showed a clear, positive relationship with enhanced e-commerce sales. However, CRM tools exhibited a dualistic impact: while their use in marketing showed no significant effect on sales, their application in non-marketing functions had a negative correlation. These findings underscore the potential role of CRM and data science solutions in enhancing business performance in the EU's Accommodation and Food Service industry.

Continuous glucose monitoring devices allow diabetes condition management. However, when limited data is available, one option is to increase their size by generating synthetic samples. From a homemade wearable prototype was created a real dataset with 18 instances and 53 attributes that capture characteristics of capillary and venous blood glucose, oxygen concentration, pulse rate, skin temperature, and 24 modules and 24 phases related to bio-impedance. The objective of this article is to generate synthetic datasets, and also it investigates the ideal features subset and optimal model for non-invasive diabetes prediction. Gaussian-Copulas (GC), conditional generative adversarial networks (CG), variational autoencoders, and Copula-GAN techniques' were used to generate five synthetic datasets. Experiments show that GC1 and GC2 datasets follow min/max boundaries and are not copies of the original data. Multilayer perceptron regressor outperformed (train and test) with 2.17, 2.51 in MAE; 9.29, 13.59 in MSE; 3.05, 3.69 in RMSE, and 0.95, 0.92 in R2 in GC1, and 2.64, 3.02 in MAE; 11.43, 15.11 in MSE; 3.38, 3.89 in RMSE, and 0.94, 0.92 in R2 in GC2 with eight features. Future work is necessary to explore autoencoder and generative architectures, datasets with diverse characteristics, and the effect of the number of features.

Living beings are composite thermodynamic systems in non-equilibrium conditions. Within this context, there are a number of thermodynamic potential differences (forces) between them and the surroundings, as well as internally. These forces lead to flows, which, ultimately, are essential to life itself. Living beings are under the pressures of natural selection, thus are biological flows as well. At the same time, the maintenance of homeostatic conditions, the tenet of physiology, demands regulation of these flows by control of variables. However, due to the very nature of these systems, regulation of flows and control of variables become entangled in closed loops. Therefore, the search for adaptation in flows takes a different path than the search for adaptation in morphological traits. Being at the roots of transfer processes, thermodynamic criteria turn out to be as natural physical candidates. Likewise, being at the roots of physiology, control turns out to be as a natural biological candidate in that path. Here we show how to combine entropy generation, with respect to a generalized process, and control of parameters (in such a generalized process) in order to create a criterium of optimal ways to regulate changes in generalized flows.

This study addresses the motivations behind Generation Z's attendance at conferences and aims to contribute to a better understanding of their conference participation behavior. The study identified six latent factors that influence conference attendance motivation: Green and Digital Conference Experience, Educational and professional opportunities, Conference costs, Destination and conference stimuli, Stimulating factors, and Conference accessibility. These factors align with Generation Z's unique characteristics and preferences, such as their environmental consciousness, reliance on digital technologies, desire for continuous learning, career advancement, and experiences. The study also found that destination characteristics and educational and professional opportunities are significant motivators for Generation Z's conference attendance across different cultural contexts. Additionally, Generation Z's strong interest in sustainability and financial challenges highlights the need for eco-friendly practices and affordable registration fees to make conferences more appealing and accessible. The study further explores the impact of socio-demographic characteristics, revealing the gender and past conference attendance differences in motivation. Finally, the study discusses the implications for the conference industry and suggests areas for future research to enhance the understanding of conference attendance motivation among Generation Z.

Huge-scale video surveillance systems have become essential in crime prevention and situation recording. Traditional surveillance systems relied on human monitoring of video streams, which often led to errors and difficulties in understanding events. Furthermore, locating specific scenes within recorded videos required extensive human investigation. To overcome these challenges of inefficiency, inconvenience, and potential risks, we propose an intelligent analysis scheme that utilizes abnormal behavior recognition and metadata retrieval algorithms to replace human monitoring. The proposed method consists of three stages: i) metadata generation through object detection and tracking, ii) abnormal behavior recognition, and iii) SQL-based metadata retrieval. By incorporating specific information such as object color and aspect ratio, our technique enhances the usability of retrieval. Moreover, our abnormal behavior recognition module demonstrates robust classification capabilities for activities such as pushing, violence, falling, and crossing barriers. The proposed method can be seamlessly deployed on both edge cameras and analysis servers, making it adaptable to various surveillance setups. This approach revolutionizes the traditional surveillance paradigm, enabling more efficient, reliable, and secure video monitoring and analysis.

This research aimed to determine salient factors affecting the decision to become a beginning organic farmer. New and beginning organic farmers have unique characteristics, showcasing their dedication to environmental justice and social justice at the expense of their own businesses. This research employed a multiple-case case study design. We conducted semi-structured interviews with 40 first-generation farmers who operate organic farms in Arkansas, Florida, or Georgia. We analyzed interview transcripts using the qualitative analysis approach of coding. Our results revealed the reasons that people with little practical knowledge start farms. They are inspired by those around them who succeed and encouraged by influential characters in the field who assure them they can do something they love and be profitable. This research showed that first-generation farmers find inspiration and develop values rooted in food justice. Our findings have implications for developing and implementing current and future programmatic activities that aim to enhance beginning farmer training and workforce development. Identifying sources of inspiration will help researchers and service providers target newer and beginning farmers to support a vibrant food system, including a burgeoning market opportunity, developing strong communities around food, and building grassroots solutions.

Wearable thermoelectric generators (w-TEGs) convert thermal energy into electrical energy to realize self-powering of intelligent electronic devices, thus reducing the burden of battery replacement and charging, and improving the usage time and efficiency of electronic devices. Through finite element simulation, this study successfully designed high-performance thermoelectric generator and made it into wearable thermoelectric module by adopting “rigid device - flexible connection” method. It was found that higher convective heat transfer coefficient on cold-end leads to larger effective temperature difference and better power generation performance of device in typical wearable scenario. Meanwhile, at same convective heat transfer coefficient on the cold-end, longer TE leg length leads to larger temperature difference established at both ends of device, larger device output power and open-circuit voltage. However, when the convective heat transfer coefficient increases to a certain level, optimization effect of increasing TE leg length on device power generation performance will gradually diminish. For devices with fixed temperature difference between two ends, longer TE leg length leads to higher resistance of TEG, resulting in lower device output power but slight increase in open-circuit voltage. Finally, sixteen 16×4×2 mm2 TEGs (L=1.38 mm, W=0.6 mm) and two modules were fabricated and tested. At hot end temperature Th=33 ℃ and cold end temperature Tc=30 ℃, the actual maximum output power Pout of TEG is about 0.2 mW, and the actual maximum output power Pout of TEG module is about 1.602 mW, which is highly consistent with the simulated value. This work brings great convenience to research and development of wearable thermoelectric modules and provides new, environmentally friendly and efficient power solution for wearable devices.

We attempted to understand the cervical cancer patient samples through Whole Exome Sequencing. We derived the variants from raw reads via our in-house benchmarked pipeline and validated the variants by IGV. This is the first cervical cancer exome data from the Indian cohort. Background: Cervical cancer (CC) is caused mainly by persistent infections of high-risk HPV, reduced parity, and factors like a decrease in average socioeconomic levels. We keep this because no cervical cancer exome data is available from the Indian cohort. Methods: The CC patient clinical samples were initially subjected to preparation using Qiagen DNA extraction, quantified, and the library preparation using the Agilent target enrichment system. Further, exome capture by Illumina platform (100X), quality check, alignment, and variant calling followed by the downstream analysis and finally visualized by IGV. Results: We observed a large number of SNVs or mutations from an Indian perspective, such as KMT2C, OR4M1, PDPR2P, EPHB1, FAS, OPCML, MGST1, C1QTNF9, HS6ST3, OR4K2, PRPSAP2, KCNJ12, FIGNL1, SFXN1, BAGE2, ARVCF, NAMPT variants of significance, unknown significance and possibly significant are reported. Conclusion: For the first time, KMT2C is observed as a novel potent mutation and pathogenic, showing the variant position at (7:152265091, T>A, SNV 62478356) from the Indian context in CC. Further, we visualized and validated the mutations using the Integrative Genomic Viewer (IGV) browser. Finally, we discuss the inherent challenges through KMT2C mutations.

The biological heterogeneity of glioblastoma (GBM), the most aggressive type of brain cancer, is a critical hallmark, caused by changes in the genomic mutational asset and influencing the clinical progression over the time. The understanding and monitoring of the mutational profile is important not only to reveal novel therapeutic targets in this set of patients, but also to ameliorate the clinical stratification of subjects and the prognostic significance. As neurosurgery represents the primary technique to manage GBM, it is of outmost importance to optimize alternative and less invasive methods to monitor the dynamic mutation profile of these patients. Extracellular vesicles (EV) are included in the liquid biopsy analysis and have emerged as the biological mirror of escaping and surviving mechanisms by many tumors as well as glioblastoma. Very few studies have investigated the technical feasibility to detect and analyze the genomic profile by Next Generation Sequencing (NGS) in circulating EV of patients with grade IV glioblastoma. Here, we attempted to characterize and to compare with the corresponding matched tissue samples, potential variants with pathogenic significance of the DNA contained in peripheral blood derived EV. The NGS analysis has revealed that patients with grade IV glioblastoma, exhibited lesser DNA content in EV than controls and that both in EV and matched cancer tissues, the NF1 gene was consistently mutated in all patients with the c.2568C&gt;G as the most common pathogenic variant expressed. This study supports the clinical utility of the circulating EV in glioblastoma and as eligible tool for personalized medicine.

Four-wire low voltage microgrids supply one-phase consumers with continuously changing electricity demand. For addressing climate change concerns, governments implemented incentive schemes for residential consumers, encouraging the installation of home PV panels for covering self-consumption needs. In the absence of sufficient storage capacities, the surplus is sold back by these entities, called prosumers, to the grid operator or in local markets, to other consumers. While these initiatives encourage the proliferation of green energy resources, and ample research is dedicated to local market designs for prosumer-consumer trading, the main concern of distribution network operators is the influence of power flows generated by prosumer surplus injection on the operating states of microgrids. The change in power flow amount and direction can greatly influence the economic and technical operating conditions of radial grids. This paper proposes a metaheuristic algorithm for prosumer surplus management that optimizes the power surplus injections using the automated control of three-phase inverters, with the aim of improving the active power losses and balancing the phase voltage profiles. A case study is performed on two real distribution networks with distinct layouts and load profiles and the algorithm shows its efficiency in both scenarios.

Forest health is dependent on the variability of microorganisms interacting with the host tree/holobiont. Symbiotic microbiota and pathogens engage in a permanent interplay, which influences the host. Thanks to the development of NGS technologies, a vast amount of genetic information on the virosphere of temperate forests has been gained the last seven years. To estimate the qualitative/quantitative impact of NGS in forest virology, we have summarized viruses affecting major tree/shrub species and their fungal associates, including fungal plant pathogens, mutualists and saprotrophs. The contribution of NGS methods is extremely significant for forest virology. Reviewed data about viral presence in holobionts, allowed us to address the role of the virome in the holobionts. Genetic variation is a crucial aspect in hologenome, significantly reinforced by horizontal gene transfer among all interacting actors. Through virus-virus interplays synergistic or antagonistic relations may evolve, which may drastically affect the health of the holobiont. Novel insights of these interplays may allow practical applications for forest plant protection based on endophytes and mycovirus biocontrol agents. The current analysis is conceived in light of the prospect that novel viruses may initiate an emergent infectious disease and that measures for avoidance of future outbreaks in forests should be considered.

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.

Left ventricular noncompaction (LVNC) and hypertrophic cardiomyopathy (HCM) commonly occur as separate disorders with distinct clinical and pathoanatomical features. However, these cardiomyopathies may have a similar genetic origin with mutations encoding sarcomeric proteins. The described case demonstrates an example, in which phenotypic expression of both diseases occurs in the same patient.

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 review recent work on narrowband orthogonally polarized optical RF single sideband generators as well as dual-channel equalization, both based on high-Q integrated ring resonators. The devices operate in the optical telecommunications C-band and enable RF operation over a range of either fixed or thermally tuneable frequencies. They operate via TE/TM mode birefringence in the resonator. We achieve a very large dynamic tuning range of over 55 dB for both the optical carrier-to-sideband ratio and the dual-channel RF equalization for both the fixed and tunable devices.

Targeted next-generation sequencing (NGS) technology detects specific mutations that can provide treatment opportunities for colorectal cancer (CRC) patients. We included 145 CRC patients who underwent surgery. We analyzed the mutation frequencies of common actionable genes and their association with clinicopathological characteristics and oncologic outcomes using targeted NGS. Approximately 97.9% (142) of patients showed somatic mutations. Frequent mutations were observed in TP53 (70%), KRAS (49%), and APC (47%). TP53 mutations were significantly linked to higher overall stage (p=0.038) and lower disease-free survival (DFS) (p=0.039). ATM mutation was significantly associated with higher tumor stage (p=0.012) and shorter overall survival (OS) (p=0.041). Stage 3 and 4 patients with ATM mutations (p=0.023) had shorter OS, and FBXW7 mutation was significantly associated with shorter DFS (p=0.002). In multivariate Cox regression analysis, ATM mutation was an independent biomarker for poor prognosis of OS (p=0.022). TP53 and FBXW7 mutations are independent biomarkers for poor prognosis of DFS (p=0.042 and 0.030, respectively). A comprehensive analysis of the molecular markers for CRC can provide insights into the mechanisms underlying disease progression and help optimize a personalized therapy.

We present a method to improve the reconstruction and generation performance of variational autoencoder (VAE) by injecting an adversarial learning. On the other hand, instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The training process of the VAE is then divided into two steps, training the encoder and then training the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The adversarial constraints allow the decoder to generate data with better authenticity and more realistic than the conventional VAE. We present experimental results to show that our method gives better performance than the original VAE.

In these times electricity is big trouble of problem facing by the world. Therefore, there is necessitating for the property basis of energy that is employed for as bio-electricity. Many of the scientists and researchers are trying to find out the sustainable energy generates with the help of plant microbial fuel cell. Plant microbial fuel cell (P-MFCs) could be feasibility technology approach of bio-electricity generation which is mutualism interaction of the plants along with their rhizospheric bacteria. In the present study, Plant-microbial fuel cell was observed in grass e-table evaluated in term of bioelectricity generation from Cynodon dactylon (Dooba Ghas). This e-table was connected with electrode and different condition (physical and chemical) to detect the change in bioelectric potential. It was found that maximum voltage generated among all the conditions was 4.24 ±2V at 15 days by using Cynodon dactylon through P-MFCs. The potential difference generated through P-MFCs was measured using a multimeter. The generation of bioelectricity was observed under different conditions like exposure to light and shade condition measured for voltage was found to be significantly different parameters. The maximum recorded under light and shade conditions were 3.82 ±2 V and 4.25 ±2 V respectively at 15 days of incubation.

Computational Fluid Dynamics (CFD) simulations, based on Reynolds Averaged Navier Stokes (RANS) models, are a useful tool for a wide range of coastal and offshore applications, providing a high fidelity representation of the underlying hydrodynamic processes. Generating input waves in the CFD simulation is performed by a numerical wavemaker (NWM), with a variety of different NWM methods existing for this task. While NWMs, based on impulse source methods, have been widely applied for wave generation in depth averaged, shallow water models, they have not seen the same level of adoption in the more general RANS based CFD simulations, due to difficulties in relating the required impulse source function to the resulting free surface elevation for non-shallow water cases. This paper presents an implementation of an impulse source wavemaker, which is able to self-calibrate the impulse source function to produce a desired wave series in deep or shallow water at a specific point in time and space. Example applications are presented, for a numerical wave tank (NWT), based on the opensource CFD software OpenFOAM, for wave packets in deep and shallow water, highlighting the correct calibration of phase and amplitude. Also, the suitability for cases requiring very low reflection from NWT boundaries is demonstrated. Possible issues in the use of the method are discussed and guidance for good application is given.

Computational Fluid Dynamics (CFD) is utilized to study entropy generation for the rarefied steady state laminar 2-D flow of air-Al2O3 nanofluid in square cavity equipped with two solid fins at the hot wall. Such flows are of great importance in industrial applications, such cooling of the electronic equipment’s and nuclear reactors. In the current study, effects of Knudsen number (Kn), Rayleigh number (Ra) and the nano solid particles volume fraction (ϕ) on the entropy generation are investigated. The values of parameters considered in this work are as follows: 0≤Kn≤0.1, 〖10〗^3≤Ra≤〖10〗^6,0≤ϕ≤0.2. Length of the fins (LF) is considered to be fixed and equals to 0.5 m, whereas the location of the fins with respect to the lower wall (HF) is set to 0.25 and 0.75 m. Simulations demonstrate that there is an inverse direct effect of Kn on the entropy generation. Moreover, it is found that when Ra is less than 104, the entropy generation, due to the flow, increases as ϕ increases. In addition, the entropy generation due to the flow will decrease at Ra greater than 104 as ϕ increases. Moreover, the entropy generation due to heat will increase as both the ϕ and Ra increase. In addition, a correlation model of the total entropy generation as a function of all of the investigated parameters in this study is proposed. Finally, an optimization technique is adapted to find out the conditions at which the total entropy generation is minimized

The primary purpose of this article is to study the asymptotic and numerical estimates in detail for higher degree polynomials in $\pi(x)$ having a general expression of the form, \begin{align*} P(\pi(x)) - \frac{e x}{\log x} Q(\pi(x/e)) + R(x) \end{align*} $P$, $Q$ and $R$ are arbitrarily chosen polynomials and $\pi(x)$ denotes the \textit{Prime Counting Function}. The proofs require specific order estimates involving $\pi(x)$ and the \textit{Second Chebyshev Function} $\psi(x)$, as well as the famous \textit{Prime Number Theorem} in addition to certain meromorphic properties of the \textit{Riemann Zeta Function} $\zeta(s)$ and results regarding its non-trivial zeros. A few generalizations of these concepts have also been discussed in detail towards the later stages of the paper, along with citing some important applications.

Existing research concerning the effects of the sitting birth position during the second stage of labour on maternal and neonatal outcomes remains controversial, and there is a lack of studies to explore its effect on the childbirth experience. The objective of this study is to explore whether the sitting birth position would influence maternal and neonatal outcomes, as well as the childbirth experience reported by Chinese women using the Childbirth Experience Questionnaire (CEQ). A total of 296 women (including primiparous women and multiparous women) were enrolled in our study, and they were divided into the sitting position cohort (n=106) or the lithotomy position cohort(n=116). The study found that primiparous women in the sitting position cohort had a shorter duration of the second stage of labour, higher spontaneous vaginal birth rates, lower episiotomy rates and better childbirth experience (p<0.01). There was no significant difference for perineal lacerations, 2h-postpartum haemorrhage (P>0.05) between the two cohorts of primiparous women. After adjusting for confounding factors through multiple linear and logistic regression analyses, the results remained consistent with those reported above. No neonate in each cohort had Apgar scores at 1min and 5 min postpartum less than 7 or the Cord artery pH less than 7.00, regardless of parity. In addition, among multiparous women, there was no significant difference in any maternal and neonatal outcomes and women’s CEQ scores (p>0.05) between two cohorts. Based on the findings, we recommend that women could take the sitting birth position into account when giving birth for a positive childbirth experience, especially for primiparous women. The study could also serve as a reference for healthcare providers in the management of childbirth positions and the development of high-quality maternal care.

Direct femtosecond laser writing or inscription is a useful technique, and it has been 1 employed to engineer various materials in many applications including nonlinear photonic crystals, 2 which are of periodically patterned second-order nonlinearity to get and control the coherent light at 3 new frequencies. By manipulation of second-order nonlinearity, either erased or poled, quasi-phase 4 matching has been achieved in several crystals, especially three-dimensional nonlinear photonic 5 crystals have been originally proposed and proved to be truly three-dimensional. Here we shortly 6 review on the recent advances in the research field of nonlinear photonic crystals inscribed by 7 femtosecond laser. We also discuss some phenomena that not understood yet and the future possible research topics in this field.

Hallyu or the Korean wave has sweeped through nations especially the Philippines. This exploratory study offers a dip into the effects of the Hallyu wave on a Filipino speaker through ambient exposure, consequently on the development of a Korean proto-lexicon through indirect vocabulary acquisition. Finally, a focus group discussion and a preliminary assessment tested out the waters of the effects of Hallyu on casual Filipino speakers. A thorough statistically comprehensive qualitative study on the acquisition framework is recommended to provide substantial evidence to support the framework.

We introduce and investigate a simple and explicitly mechanical model of Maxwell's demon -- a device that interacts with a memory register (a stream of bits), a thermal reservoir (an ideal gas) and a work reservoir (a mass that can be lifted or lowered). Our device is similar to one that we have briefly described elsewhere [1], but it has the additional feature that it can be programmed to recognize a chosen reference sequence, for instance, the binary representation of $\pi$. If the bits in the memory register match those of the reference sequence, then the device extracts heat from the thermal reservoir and converts it into work to lift a small mass. Conversely, the device can operate as a generalized Landauer's eraser (or copier), harnessing the energy of a dropping mass to write the chosen reference sequence onto the memory register, replacing whatever information may previously have been stored there. Our model can be interpreted either as a machine that autonomously performs a conversion between information and energy, or else as a feedback-controlled device that is operated by an external agent. We derive generalized second laws of thermodynamics for both pictures. We illustrate our model with numerical simulations, as well as analytical calculations in a particular, exactly solvable limit.

The article addresses the function of (post)colonial nostalgia in a context of multidirectional memory (Rothberg 2009) in contemporary Europe. How can different cultural memories of the Second Word War be put into respectful dialogue with each other? The text is based on a contrapuntal reading (Said 1994) of British and Egyptian popular narratives, using a qualitative content analysis of 10 British tv documentary films about the North Africa Campaign, and data from qualitative interviews collected during ethnographic fieldwork in Alexandria and Cairo, Egypt, during visits 2013--2015. The study highlights considerable differences between the British and Egyptian narratives, but also significant similarities regarding the use and function of nostalgia. In addition, the Egyptian narrative expresses a profound cosmopolitan nostalgia and a longing for what is regarded as Egypt’s lost, modern Golden Age, identified as the decades before the nation’s fundamental change from western-oriented monarchy to Nasser’s Arab nationalist military state. The common elements between the two national narratives indicate a possibly fruitful way to open up for a shared popular memory culture about the war years, including postcolonial aspects.

This work introduces the mathematical framework of the novel “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-FASAM-NODE). The 1st-FASAM-NODE methodology produces and computes most efficiently the exact expressions of all of the first-order sensitivities of NODE-decoder responses with respect to the parameters underlying the NODE’s decoder, hidden layers, and encoder, after having optimized the NODE-net to represent the physical system under consideration. Building on the 1st-FASAM-NODE, this work subsequently introduces the mathematical framework of the novel “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE).” The 2nd-FASAM-NODE methodology yields and computes most efficiently the exact expressions of the second-order sensitivities of NODE decoder-responses with respect to the NODE parameters. Since the physical system modeled by the NODE-net necessarily comprises imprecisely known parameters that stem from measurements and/or computations subject to uncertainties, the availability of the first-and second-order sensitivities of decoder responses to the parameters underlying the NODE-net are essential for performing sensitivity analysis and quantifying the uncertainties induced in the NODE-decoder responses by uncertainties in the underlying uncertain NODE-parameters.

This research investigates the impact of Al and Ti elements on the formation of self-generated ordered phase in high-entropy alloys (HEA), which have been receiving widespread concentration in present times thanks to their remarkable multifunctional properties and wide range of component selection. By adjusting the dual elements of Al and Ti, the study explores the regulation of quantity and distribution of the in-situ self-generated ordered phases in the alloy. The results of the annealing heat treatment process demonstrated that, through accession Al and Ti elements, the Cr-Fe-Ni medium-entropy alloy transfer from BCC phase to BCC+FCC phase and the precipitation of new phases such as B2, L21, and σ. This led to a synergistic impact of second phase strengthening, and also of solid solution strengthening, resulting in improved compressive properties of HEA.

Despite being stored at 113 K and atmospheric pressure, LNG cold potential is not exploited to reduce green ships’ energy needs. An innovative system based on three Organic Ranking Cycles integrated into the regasification equipment is proposed to produce additional power and recover cooling energy from condensers. A first-law analysis identified ethylene and ethane as suitable working fluids for the first and the second ORC making available freshwater and ice. Propane, ammonia and propylene could be arbitrarily employed in the third ORC for air-conditioning. An environmental analysis that combines exergy efficiency, ecological indices and hazard aspects for marine environment and ship’s passengers, indicated propylene as safer and more environmentally friendly. The exergy analysis confirms more than 20% of the LNG potential can be recovered from every cycle to produce a net clean power of 76 kW, whereas 270 kW can be saved by recovering condensers' cooling power to satisfy some ship needs. Assuming the sailing mode, a limitation of 162 kg in LNG consumptions was determined, avoiding the emission of 1584 kg of CO2 per day. Marine thermal pollution is reduced by 3.5 times by recovering the working fluids' condensation heat for the LNG pre-heating.

In this chapter we study the unstudy effect on MHD preamble porous medium through with thermal absorption and heat generation in the oscillatory flow of visco-elastic fluid between two plates in the presence of Chemical reaction. The expressions for the velocity, temperature and concentration distributions are arthemitiaclly solved by perturbation method.