The quality assurance of malaria microscopy unit was established by National Malaria Control Program, Ministry of Health (MoH) in 2009 to respond to the internal malaria microscopy validation process, in microscopy training for malaria diagnosis in the country, monitoring and supervision, and other relevant activities regarding laboratory diagnosis of malaria in Timor-Leste is described. Trained laboratory technicians who are working in the relevant unit. There are two ways of malaria microscopy validation process such as onsite validation and monthly validation after received malaria blood smear from senior analyst at the municipality level. The aim of this work is to describe the quality assurance of malaria microscopy system in Timor-Leste. The management of quality assurance service support, the importance of quality assurance of malaria microscopy, the role of quality assurance of malaria microscopy, challenge and opportunities encountered by quality assurance microscopy unit, National Health Laboratory are highlighted. Key words: Quality assurance of malaria microscopy, system, Timor-Leste.

Telomeres are specialized structures at the ends of linear chromosomes that protect them from degradation and fusion. Telomere replication is a complex process that involves both DNA polymerases and a specialized enzyme called telomerase. Telomerase is a ribonucleoprotein complex that synthesizes telomeric DNA by using an internal RNA template. However, telomerase alone cannot fully replicate the telomeric DNA, and requires the cooperation of other factors, such as shelterin, CST, and DNA repair proteins. Moreover, telomere replication is tightly regulated by various mechanisms, such as cell cycle checkpoints, telomere length homeostasis, and telomere position effect. Dysregulation of telomere replication can lead to genomic instability, cellular senescence, and cancer. Therefore, understanding the molecular details of telomere replication is crucial for elucidating the role of telomeres in aging and disease.

The reduction of Ce cations in CeO2 can be enhanced by its partial substitution by Fe cations. The enhanced reduction of Ce cations results in a considerable increase in the reaction rates for thermal water splitting reaction when compared to CeO2 alone. This mixed oxide has a smaller crystallite size when compared to CeO2 in addition to a smaller lattice size. In this work, two Fe-substituted Ce oxides are studied (Ce0.95Fe0.05O2 and Ce0.75Fe0.25O2) by core and valence level spectroscopy in their as-prepared and Ar-ions sputtered states. Ar-ions sputtering substantially increases Ce4f lines at about 1.5 eV below the Fermi level. In addition, it is found that the XPS Ce5p/O2s ratio is sensitive to the degree of reduction, most likely due to a higher charge transfer from the oxygen to Ce ions upon reduction. Quantitatively it is also found that XPS Ce3d of the fraction of Ce3+ (uo, u’ and vo, v’) formed upon Ar-ions sputtering and the ratio Ce5p/O2s lines are higher for reduced Ce0.95Fe0.05O2 than for reduced Ce0.75Fe0.25O2. XPS Fe2p showed however no preferential increase for Fe3+ reduction to Fe0 with increasing time for both oxides. Since water splitting was higher on Ce0.95Fe0.05O2 when compared to Ce0.75Fe0.25O2, it is inferred that the reaction centers for the thermal water splitting to hydrogen are the reduced Ce cations and not the reduced Fe cations. These reduced Ce cations can be tracked by their XPS Ce5p/O2s ratio in addition to the common XPS Ce3d lines.

The collapse of the Rana Plaza factory profoundly affected the fashion industry and society, serving as a reminder of the importance of social issues in academics and practice. Some scholars have pointed out that academia lacks fashion studies on social issues that are more prevalent in developing countries. Therefore, this study aims to determine a meta-analysis of sustainable fashion research since the Rana Plaza collapse and how it aligns with the United Nations' sustainable development goals (SDGs). The applied method is automated content analysis, and seven academic agendas were extracted by analyzing sustainable fashion literature. The findings showed that academic scholarship has covered various topics, ranging from design to social issues, whereas the results have not clarified the relationship between social issues and developing countries. The findings also show that SDG 12 stood out, which is responsible consumption and production. Aside from the production community, consumers need to be aware of socio-environmental problems to make harmonious lifestyle choices with nature. Furthermore, scholars need to align themselves with an easier-to-understand concept of sustainable fashion to make a public appeal for it as a societal importance.

Background: In Malaysia, the mortality from melioidosis infection was reported to be higher than in other infectious diseases. The research on melioidosis is still limited in Malaysia but slightly increasing. Objective: To give an overview of the study designs, statistical methods and comparison of research in identifying the predictors of melioidosis mortality in Malaysia between January 2010 to December 2021. This review articles were divided into two sections; Section 1: Review of literature and Section 2: Findings on predictors of melioidosis mortality in Malaysia. Data sources: Pubmed/Medline. Study Eligibility Criteria: Original English-language articles were abstracted. The articles that identified the predictors of melioidosis from mortality in Malaysia only included. Letters to the editor, editorials, reviews, systematic reviews, meta-analysis, case reports and any other ineligible articles were excluded. Results: A total of 7 studies were identified related to predictors of melioidosis mortality in Malaysia. From the selected articles, all of the data were retrospectively collected. Six out of 7 articles (85.7%) used the logistic regression in identifying the predictors of melioidosis mortality. Only 1 (14.3%) used advanced survival analysis methods of Cox regression analysis. Conclusion: Logistic regression methods remain the most common methods of analysis in publications on predictors of melioidosis mortality in Malaysia while retrospective research designs are favoured. There is a limitation of research in predictors of melioidosis mortality and the use of advanced statistical techniques reported using the melioidosis data in Malaysia. More published research on melioidosis will provide input to the clinicians on a more detailed understanding of how to improve the diagnosis of melioidosis and the prognosis factors of this disease.

Klebsiella pneumoniae complex is one of the most commonly isolated bacteria within human infections. These microorganisms are opportunistic pathogens that pose a serious threat to public health due to possibility of transmission in the human population. Resistance to carbapenems is one of the most important antimicrobials resistance mechanisms amongst them. In this situation therapeutic options are limited. Therefore, the aim of this study was to evaluate fosfomycin, colistin, ceftazidime-avibactam and meropenem-vaborbactam in vitro activity against multidrug-resistant K. pneumoniae complex strains. The study involved 160 strains of Gram-negative rods: K. pneumoniae – 138 and K. variicola - 22. Minimal inhibitory concentration of fosfomycin was estimated by agar dilution method and for colistin by microdilution method. The susceptibility to ceftazidime-avibactam and meropenem-vaborbactam was determined by gradient strip method. All of the analysed K. pneumoniae complex isolates produced extended-spectrum beta-lactamases and 60.0% carbapenemases. The most of the analysed strains were susceptible to fosfomycin and colistin (62.5%). Among pandrug-resistant K. pneumoniae complex isolates the most were susceptible to colistin (43.9%). Fosfomycin has good activity against ESBL- and VIM-positive isolates from this complex. In turn, colistin has satisfactory in vitro activity against KPC- and VIM-positive isolates from K. pneumoniae complex. Ceftazidime-avibactam has good activity against K. pneumoniae complex strains producing ESBL, KPC and OXA enzymes. In turn, meropenem-vaborbactam has satisfactory in vitro activity against ESBL- and KPC-positive isolates from this complex.

A stable match, an intercuspated maxilla-mandible relation is the universally accepted goal for dental restorative interventions, yet the underlying sensorimotor mechanisms to achieve this goal in the natural jaw closure remains to be elucidated. Dentists have intuitively regarded peripheral sensory innervation from tooth contacts as a significant control element for jaw muscle actions, both in the state of health and in cases of pathological dysfunction. However, therapeutical interventions for reshaping the dental arch anatomy and jaw relations have not always been successful. Repetitive masticatory chewing cycles, swallowing, talking, as well as volitional jaw-opening and -closing involve stereotyped neural commands from the central nervous system that are modulated in reaction to the sensory afferent feedback from the peripheral neurons and their sensor organs. The aim of this article is to describe, on a fraction of a second time-scale, the minuscule tilts and kinematic events of mandible during the making of a stable match with maxilla. Evidence of the importance of tooth contacts as the sensorimotor motivation of each of the successive kinematic steps of the “mandible-meets-maxilla-narrative” are discussed.

The Republic of Venice was one of the longest-lived states in modern history, and its stability and survival has been studied through many different angles, and one of them is to try and find pivotal moments in its history that explain it, or its eventual demise. In this paper, through the rigorous statistical analysis of the dataset of marriages by nobles in the Republic, we will try to define a methodology for the detection of these events through mono and multivariate change point analysis, and validate that methodology through cross-validation of different procedures, as well as matching the results to historical events. Our analysis shows that these change points occur with statistical significance and that they match political and historical events. These results can be built upon for a better understanding of the historical causes of the success and failure of the Republic of Venice and, by extension, other states.

The identified Urban Heat Island (UHI) phenomenon, coupled with diminished vegetation and anthropogenic heat release, poses a significant environmental challenge for major urban centers. Particularly worrisome is the UHI effect in hot and temperate climates such as the Mediterranean region during the summer season. This exacerbates discomfort and heightens the risks of diseases linked to high temperatures. Additionally, it contributes to environmental consequences, including elevated electricity consumption due to increased demand for cooling [1]. The utilization of Remotely Sensed imagery for estimating Land Surface Temperature has become more prevalent in various applications associated with evaluating urban micro-climates and the Urban Heat Island (UHI) phenomenon. This study presents an algorithm for the automatic mapping of Land Surface Temperature (LST) from Landsat-8 data using a vegetation index-based technique in the Google Earth Engine (GEE) platform. The primary aim is to discern patterns and trends in LST over a 5-year period, from 2017 to 2021. The application was tested in the urban area of Thessaloniki, Northern Greece, which is particularly representative of the Mediterranean urban environment. Computation of Normalized Difference Indices (NDVI, NDBI, NDWI) for the study area was also performed within the GEE platform and correlation analysis was implemented to evaluate the impact of the urban landscape on the distribution of LST.

Cognitive linguistics is founded on the assumption that language is a reflection of universal cognitive principles, implying that all cognitive processes share a common origin. This assumption is known as the 'Cognitive Commitment.' The present article examines the sensory schema hypothesis, which traces all forms of expression back to sensations. This theoretical framework integrates the physicality of the body with sensations that underlie human experiences and their linguistic and non-linguistic expressions. By challenging the traditional dichotomy between mind and body, it provides a more integrated understanding.

We prove a quasi-pseudometric version of the strong Ekeland principle proved by P. Georgiev and T. Suzuki.

Atrial Fibrillation (AF) is known as one of the dominant cardiac disease that is associated with severe consequences such as risk of strokes. This cardiac arrhythmia can be efficiently diagnosed using EEG signals. However, AF episodes in EEG records are not always permanent for all subjects. In addition, lengthy subject preparation and ECG recording sessions makes this practice time consuming and not attractive for efficient diagnosis. One attractive solution for this problem will be the use of wearable Internet of Medical Things IoMT devices for EEG recording and AF detection. From another side the use machine learning for efficient diagnosis could provide high accurate diagnosis. However, the deployment of these wireless wearable devices faces the problems of limited processing capabilities and of energy scarcity. In this context, the design of energy efficient accurate on-sensor AF detection scheme using machine learning algorithms still requires a focused research effort. This paper proposes a wearable solution for ECG-based processing scheme for embedded AF detection. The proposed method achieved 100% sensitivity and 94.5% of specificity and accuracy of around 97.4 % for wide range of subjects while showing low energy-consumption. The obtained results show the efficiency of using Logistic Regression algorithm for accurate AF detection and attest about practical feasibility of the proposed approach in wearable IoMT devices.

We will demonstrate how the CMB temperature simply can be predicted from what we will call geometric mean Planck gravitational potential energy in the universe. This falls nicely in line with the recent discovery that the CMB temperature also is a geometric mean of the minimum and maxium temerature in the Hubble sphere, and also that the CMB tem- perature can be derived from the Stefan-Boltzman law. Since the Coulomb force for Planck charges are identical to the Newton force for two Planck mass particles this also seems to give a potential connection between electrostatic force and the CMB temperature.

Abstract The number of non-melanoma skin cancers (NMSC) removed from Australians is increasing every year. The number of deaths from NMSC is increasing but so is the population. However, the population has greatly changed with many people at low risk for skin cancer migrating to Australia. The death rate from NMSC for the entire population and susceptible populations since 1971 is examined in this article. Materials and methods Data on the Australian population was obtained from the Australian Bureau of Statistics (ABS). Every five years a census is held in Australia and detailed information of the population is provided. The ABS also provided yearly data on the causes of death in Australia. Results The total population increased from 12,755,638 in 1971 to 25,738,140 in 2021. However, the susceptible population increased by far less, from 12,493,780 to 19,773,783. The number of deaths from NMSC increased from 143 to 765. The crude death rate for the susceptible population increased for 1.1 per 100,000 to 3.9 per 100,000. The crude death rate in the susceptible population aged 65 or more increased from 9.4 to 18.2 per 100,000. Conclusion Deaths from NMSC are increasing despite public health campaigns to prevent skin cancer. On current trends, NMSC will cause more deaths than melanoma in Australia.

The phytoplankton community in 66 reservoirs ubicated at the hydrographic basin of the Ebro River (Spain) was analyzed. Samples were collected in the summer season during the period 2010-2015, with the main purpose of evaluate the Trophic State and Ecological Potential according to the Water Framework Directive of the European Union. In this work we presented a representative overview of several consecutive years of the state of the reservoirs and their associated trophic status; methodology was to obtain statistically significant relationships between the phytoplankton community and main physicochemical water quality variables (water transparency, pH, chlorophyll a, total and soluble phosphorus, anoxic zone depth, nitrite, nitrate and ammonium concentrations). We provide data for retrospective comparative of the state of the reservoirs with previous and current studies in order to improve the management of these reservoirs, and to serve as data reference for future works in a context of global change (climate and pollution).

Active front-end (AFE) rectifiers have been becoming widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines named the detailed model and the time average model. The former approach deals with the switching element model (SEM) leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has contained problems during the starting stage (lack of control signals) and over the dead-time periods which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion.

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. A simple model in [] of Electric Submersible Pump [ESP] lifted production was previously streamlined into a dynamic model suitable for illustration of the dynamics of oil transport, as well for control studies, with some comparison of two popular modeling languages: Modelica, and ModelingToolkit for Julia, []. Here, the discussion on dimensionless equipment models goes into more detail, and the comparison between Modelica and ModelingToolkit is significantly expanded upon with code comparison, numerical performance, and more experiments. Some added possibilities with ModelingToolkit and Julia wrt. sensitivity analysis and control design is included.

This comprehensive statement explores the concept of educational excellence, emphasizing the limitations of traditional metrics and proposing a holistic model for excellent educational systems. The discussion delves into the ambiguity surrounding excellence, challenges in measurement, and the influence of social, cultural, and economic factors on education. The objectives of the study include shedding light on the nature of excellence, its global significance, and introducing a model for improving school performance. The theoretical framework integrates constructivism, Maslow's Hierarchy of Needs, and Ecological Systems Theory to emphasize student-centered learning, holistic development, and environmental factors. The proposed model envisions an educational ecosystem prioritizing personalized, inclusive, and relevant education, with key components such as student-centered learning, inspirational educators, curriculum innovation, and community involvement. The abstract concludes by highlighting the model's potential implications in transforming education, challenging the status quo, and fostering well-rounded individuals equipped for a rapidly changing world.

Here, we describe the bicomplex (p,q)- Fibonacci numbers and the bicomplex (p,q)- Fibonacci quaternions that are based on these numbers and give some of their equations, including the Binet formula, generating function, Catalan, Cassini, d’Ocagne’s identities, and some summation formulas for both of them. Finally, we create a matrix for bicomplex (p,q)- Fibonacci quaternions, and we obtain a determinant of a special matrix that gives the terms of that quaternion.

Contact inhibition (CI) represents a crucial tumor-suppressive mechanism responsible for controlling the unbridled growth of cells, thus preventing the formation of cancerous tissues. CI can be further categorized into two distinct yet interrelated components: CI of locomotion (CIL) and CI of proliferation (CIP). These two components of CI have historically been viewed as separate processes, but emerging research indicates that they share both distinctive and common pathways. Moreover, they exhibit intriguing intersections with mechanotransduction, a process that involves cells sensing and responding to mechanical forces. This review article describes the intricate interplay between mechanical forces and CI, shedding light on how these forces regulate CIL and CIP. Emphasis is placed on filamin A (FLNA)-mediated mechanotransduction, elucidating how FLNA senses mechanical forces and translates them into crucial biochemical signals that regulate cell locomotion and proliferation. In addition to FLNA, trans-acting factors (TAFs) emerge as sensitive players in both CI and the mechanotransduction process. This article presents methods for identifying these TAFs and profiling the associated changes in chromatin structure, offering valuable insights into CI and mechanotransduction regulation. Finally, it addresses unanswered research questions in these fields and delineates their possible future directions.

Paddy fields are classified as wetland environments, and they comprise a freshwater ecosystem. They are ecologically important habitats and breeding grounds for many aquatic insects, amphibians, and reptiles. However, the paddy field ecosystem is constantly threatened by climate change and indiscriminate use of pesticides. The meta-barcode analysis of eDNA (environmental DNA) method is highly effective in accumulating information on many organisms that inhabit the paddy field ecosystem because it can explore organisms that are difficult to identify with traditional sampling methods. The eDNA method is very effective for accumulating information on many organisms in the rice field ecosystem. It can indirectly identify the existence of taxa no longer found in the target ecosystem due to behavioral characteristics like amphibia. In the metabarcoding results of this study, genes of more than five species of frogs were found, but it was impossible to confirm all frog taxa, frog's morphological pictures, and croak sounds. On the other hand, some frog taxa had been found only in the metabarcoding analysis. The eDNA of frogs found only in metabarcoding is estimated to have been introduced from nearby areas inhabited by frogs rather than the target region. Due to the powerful analytical resolution of eDNA metabarcoding, this eDNA-based paddy field search is expected to help investigate the biodiversity in agricultural ecosystems.

This article focuses on the description of the other view of Valencia in the fifteenth century, that of social marginalization, which could be manifested by the fact of exercising various trades considered immoral. In the following pages, a description is offered of two female collectives that are particularly treated as vile and despicable: that of healers, sorceresses and witches, whose strength was considered to stem from a demonic origin, and that of prostitutes, dedicated to covering a "lesser evil" in society but seen as unworthy by society because of the practical exercise of their trade.

Runaway electrons (RAEs) generation in high-pressure gases is an important physical phenomenon that influences significantly discharge shapes and properties of initiated plasma. The diffuse discharges formed due to RAEs in air and other gases at atmospheric pressure find wide application. In the present review, theoretical and experimental results that explain the reason for the RAEs occurrence at high pressures are analyzed and recommendations are given on the implementation of conditions under which the runaway electron beam (RAEB) with the highest current can be obtained at atmospheric pressure. Experimental results were obtained using subnanosecond, nanosecond, and submicrosecond generators, including those specially developed for runaway electron generation. The RAEB were recorded by oscilloscopes and collectors with picosecond time resolution. To describe theoretically the phenomenon of continuous electron acceleration, the method of physical kinetics has been used based on the Boltzmann kinetic equation that takes into account the minimum but sufficient number of elementary processes, including shock gas ionization and elastic electron scattering. Results of modeling allowed the main factors to be established that control the RAE appearance, the most important of which is electron scattering on neutral atoms and/or molecules. Theoretical modeling has allowed the influence of various parameters (including the voltage, pressure, gas type, and geometrical characteristics of the discharge gap) to be taken into account. The results of research presented here allow the RAEs accelerators with desirable parameters to be developed and the possibility of obtaining diffuse discharges to be accessed under various conditions. The review consists of the Introduction, 5 Sections, the Conclusion, and the References.

This cross-sectional study investigates the impact of Virtual Private Networks (VPNs) on Nigerians during the 2021 Twitter ban, prompted by the Nigerian government's suspension of Twitter operations following the deletion of a tweet by the President. The study aims to analyze the frequency and awareness of VPN and Twitter usage among Nigerians during the ban, addressing specific objectives. Employing a survey research method, data was collected from 400 respondents through online and offline questionnaires. Descriptive statistical tools were used for data analysis, revealing significant findings. Results indicate a substantial decrease in Twitter usage during the ban, with IPVanish emerging as the preferred VPN brand. The study also highlights an increase in VPN awareness post-ban, emphasizing the need for educational initiatives. The demographic analysis reveals a dominant age group of 21 to 25 among social media users. Overall, the research underscores the complex interplay between social media dynamics, VPN adoption, and demographic factors, emphasizing the importance of nuanced regulatory approaches and ongoing user education in Nigeria's online landscape.

Extending from the current Elaboration likelihood model scholarship, this article proposes a framework for applying ELM to cricket bat purchase. Professional cricketers or fans require comprehensive and adequate pieces of information about cricket bat through the Internet. Buyers simultaneously encourage internet reviews for product impressions, and they experience meaningful associations. ELM and the Internet exemplify the cricket bat buying process by engaging buyers with product specification and create a meaningful relationship between buyers and sellers. At the same time, buyers feel empowered to choose a product of their choice. Describing the Elaboration likelihood model, we have facilitated an approach for cricket bats buying. This article has explored how ELM through the Internet can encourage buyers to be informative about cricket bats while silencing retailers' voices and visions into a unique buyer-seller relationship.

In order to search possible residual minor phases in Y-Ba-Cu-O family, powdered mixtures of Y₂O₃ + BaCO₃ + CuO and, independently, superconducting compound YBa₂Cu₃O_7-x have been treated in evacuated cells and elevated temperatures.YBa₂Cu₃O_7-x was reduced to YBa₂Cu₃O₅ by use of the special home designed Taconis-Knudsen vacuum device. A subsequent doping by oxygen converts produced insulator YBa₂Cu₃O₅ to semiconductor or metal YBa₂Cu₃O_5+x (0<x<0.3). In addition to YBa₂Cu₃O₅, 0.05 volume percent of the minor delafossite phase Y₂Cu₂O₄ was being spotted in the powder mixture 1/2 Y₂O₃ + 2BaCO₃+6Cu₂O, heated up to 818 °C in an inert gas atmosphere. An attempt to prepare the insulating bulk delafossite samples was successful, and subsequent doping by oxygen produces novel metallic phases.

The aim of the research presented here is to assess the impact of depressive disorders on the potential of the labour markets in the countries of the European Union, with particular reference to the disparities between the 'old' and 'new' EU countries. The initial hypothesis of persistent inequalities in the system under study was verified by analysing the process of convergence in the burden of depressive disorders among people aged 20-54 that occurred in the EU-27 group of countries between 1990 and 2019. The study used data from the Global Burden of Disease Study 2019 (IHME, 2020), which examined the number of years lived with disability (YLD) due to depression in the working age population. The analysis showed that the high burden of the consequences of depressive disorders is particularly true for the highly developed economies of the EU-14. The results of the convergence assessment did not confirm the real effect of catching up over time with economies in a more favourable situation, and the inequalities observed at the beginning of the analysis period deepened..

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Dysprosody is a commonly described feature of speech deterioration due to Parkinson’s disease. Descriptions of the tonal movements underlying dysprosody have been attempted but has not afforded automation. The current study assessed a fully automated acoustic analysis pipeline in terms of its ability to predict human raters’ perception of dysprosody in patients with Parkinson’s disease. Read speech samples of 68 speakers with PD (45 male and 23 female) aged 65.0±9.8 years were assessed by four human clinical experts in terms of dysprosody severity. The recordings were also submitted to a speech processing pipeline in which portions with speech were identified and provided with a parametrization of intonation. Five models were trained and tuned based on 75% of utterances and their perceptual evaluations in a 10-fold cross-validation procedure and evaluated in terms of their ability to predict the perceptual assessments of recordings not included in the training. The performances of models were compared to human raters’ agreement with the majority vote on a speech sample. The results showed that human raters’ assessments of dysprosody can be approximated by the automated procedure. Variability in pitch does not adequately describe the level of dysprosody due to Parkinson’s disease.

Breast cancer is a global health concern, necessitating continuous advancements in diagnostic medical imaging technologies for early detection and improvement of treatment. This paper presents an overview of the past, present and future of breast cancer imaging using light and some results from the current studies. The paper specifically presents the role of Diffuse Optical Tomography (DOT) in breast cancer imaging. Conventional methods, such as X-ray mammography, face limitations. Diffuse Optical Tomography, a non-invasive light-based imaging modality can provide quantitative descriptive and architectural information about breast tissue. The paper discusses the key challenges and recent developments in breast cancer imaging using DOT to overcome some key challenges. This paper aims to contribute to the understanding of the evolving technological landscape in breast cancer imaging using light for improving early detection and patient outcomes.

Modification the biodegradable matrix was carried by graft-copolymerization polylactide with methacrylic acid of various concentrations. Ultraviolet irradiation with different exposure times to the samples was chosen as the initiator of copolymerization. In the experiment the effect of ultraviolet light on polylactide and graft copolymerization was investigated: the resulting samples were studied by viscosimetry, gravimetry, FTIR, static cation- exchange capacity analysis . The conditions for obtaining grafted copolymers and the dependence of the characteristics on the reaction conditions are established.

The paper deals with the Model Predictive Control (MPC) algorithm as applied to the sensorless control of a Permanent Magnet Synchronous Motor (PMSM). The proposed estimation strategy, based on the unscented Kalman filter (UKF), uses only the measurement of the motor current for the online estimation of speed, rotor position and load torque. Information about the system state feeds the MPC. Results verify the effectiveness and applicability of the proposed sensorless control technique. An implementation in low speed direct drive astronomy telescope mount systems is investigated.

This academic article examines the effectiveness of international humanitarian intervention in conflict zones. Humanitarian intervention refers to the use of military, diplomatic, or economic measures by external actors to protect civilians, promote human rights, and alleviate suffering in areas affected by armed conflict. The research employs a comprehensive approach, incorporating content analysis, case study, and historical analysis in its methodology. It explores the key challenges faced by international actors in implementing humanitarian interventions and evaluates their impact on conflict resolution and post-conflict reconstruction. Through this thorough analysis of case studies and scholarly literature, this article contributes to the ongoing debate surrounding the efficacy of international humanitarian intervention.

The study provide insights into the technological characteristics of green practices adoption and the potential benefits they offer in terms of socio-economic development, environmental and public health. The study adopted a cross-sectional research design. A structured questionnaire was developed to collect quantitative data from the participants. The questionnaire include closed-ended questions, Likert scale items, and demographic questions. Both stratified and simple random sampling techniques were employed to select the study participants (government officials, employees, managers, and community members). A total of 321 copies of questionnaires were duly filled and returned. The collected data were analyze using descriptive statistics, such as frequencies, percentages, mean, and standard deviation, were used to summarize the data and provide an overview of the key variables related to green practices adoption. The findings indicate that the manufacturing company in Ogun State, Nigeria, has shown moderate commitment to adopting green practices. While there is room for improvement in certain areas, such as waste-to-energy technologies and green building practices. Additionally, green practices have positively influenced employee well-being and community health, while aligning with the company's commitment to safeguarding the environment and public health.

This article conducts a bibliometric analysis focusing on the influence of globalization and technology in the academic context. Previous studies have focused on analyzing the relationship between the number of research citations and international collaboration, but no study has explored it in the scope of consumer behavior and retail. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in academic output related to the topic of technology, reflecting a growing interest in understanding the interaction between technological factors and consumer behavior. The study also explores trends in international collaboration in publications as well as its impact on citation counts.

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is derived both by tracing out the environment from a bosonic eigenstate of the union of environment and system of interest (the empirical approach) and by tracing out the environment from the mixed state of the union of environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common ''infomechanical'' framework.

Changing the symmetry of spin from SU(2) to the quaternion group, $Q_8$, has a number of ramifications. In this paper we first summarize the properties of Q-spin and then discuss some of those resulting changes. Many are counter to well-established ideas which change our view of the microscopic.

The ocean of the planet Earth, which is as thin as a cicada's wings, play a role of catalyst to the evolving of the ancient plates. The ocean and the ancient plates of the planet Earth formed a magic Whirlpool of the Cross “十” shape with double whirl arms. The central core of the Cross “十” shape Ocean-Plate Whirlpool is the Hint Black Hole (HBH) located at the Northwestern Pacific Ocean floor which is decorated with invisible geological age distribution of clam shell. The double whirl arms of ancient plates are distinguished to be left and right at Bering Strait and just formed the ring of fire of the Pacific Ocean.The Cross “十” shape Ocean-Plate Whirlpool of the planet Earth was born at the equatorial area about 66 Ma ago and is rotating anticlockwise and drifting northward by a mini footprint of spiral at s speed of as slow as a snail moves. Obviously, it would come to an end at the North Pole about 100 Ma in the future. So, the lifespan of the Cross “十” shape Ocean-Plate Whirlpool would be about 166 Ma, from birth to death on the planet Earth.What is hidden behind of the evolving of the oceans and the ancient plates is the golden law of geometrical progression of ladder pagoda of attributes evolving of them, which is “1➨2➨4➨2➨1”, including upgrading and downgrading process. It seems to hint that what is happening on the planet Earth, including the top-level species of human race, is actually unique in the Milky Way galaxy in essence.The property roles of expanding and shrinking are existing in the Cross “十” shape Ocean-Plate Whirlpool by face to face and the exchange of property roles took place tens of millions of years ago. And that situation would happen again tens of millions of years in the future. It seems a giant circle of evolving of the exchange of property roles would be drawn by the Cross “十” shape Ocean-Plate Whirlpool in her lifetime.

This short technical note proves some observed novel corollaries of the Collatz Conjecture. For this, a unique ‘Collatz Representation’ of Hailstone numbers has been employed. After that, an extension of this corollary is explained using the fact that only certain differences in the last digit of the ‘Collatz Representation’ gives a whole number difference between numbers. A more general result is obtained. The final proof is for a corollary that some numbers the same number of steps to reach 1 if the Collatz Conjecture is true and all variables are whole numbers.

For a number of reasons, a curative treatment for most chronic diseases is still unrealized [2–4] and the incidence of many chronic diseases is rapidly increasing [5–9]. A new model is offered as a solution; that being the individualized diagnosis of homeostasis, leading to the correction of epimutations. Homeostasis diagnosis and epimutation correction, HDEC. Individuals with chronic diseases are increasingly being defined by their epimutation signatures [10,11]. Modern innovations in toxicogenomic studies are also becoming more accurate in their ability to identify the unique epigenetic signature of each toxin [12–14]. Hormesis studies (over 5,000) [15] suggest a universal principle, that a small dose is always a stimulant [16–18].These three facts present an opportunity; a method to diagnose and reverse chronic disease tendencies. This new model requires a paradigm shift in our understanding of disease origins: that dysfunctions in homeostasis [19,20] are the primary and deepest level of chronic disease causation. This is the level where the diagnosis needs to be made and where the corrections need to take place [21].

Cross-industry clusters are important for the economic prosperity of a region. However, studies are lacking to address competitive clusters among the aviation and tourism industries. Thus, this paper considers the case of both industries in terms of clusters and synergies formed. This research aim is to provide an understanding of both industries' protagonists' involvement in contributing to knowledge insights on the establishment of synergies or clusters among the two industries. Cyprus is highly dependent on tourism and aviation because they facilitate connectivity. The research is filled with semi-structured interviews with the main players and organizations of the airline and tourism industries (government bodies, airports, airlines, tour operators, and hotels). The results are indicative and suggest that, in a small geographical context, the notion of clusters involves a synergetic relationship among tourism stakeholders. Airlines’ role is important because it has an effect on all stakeholders involved in tourism.

Origin, mechanics and properties of the Solar System are analysed in the framework of the Complete Relativity theory (by the same author). According to Complete Relativity, everything is relative. Any apparent absolutism (notably invariance to scale of dimensional constants, absolute elementariness, invariance to time) is an illusion stemming from limits imposed by [or on] polarized observers that will inevitably lead to misinterpretation of phenomena (another illusion) occurring on non-directly observable scales or even on observable but distant scales in space or time. If everything is relative, reference frames will exist where particles are planets and where planets are living beings. Earth is, therefore, analysed here in more detail, both as a particle and, as a living evolving being (of, hypothesized, extremely introverted intelligence). The analysis confirms the postulates and hypotheses of the theory (ie. existence of discrete vertical energy levels) with a significant degree of confidence. During the analysis, some new hypotheses have emerged. These are discussed and confirmed with various degrees of confidence. To increase confidence or refute some hypotheses, experimental verification is necessary. Main conclusions that stem from my research and are further confirmed in this paper are: universes are, indeed, completely relative; Solar System is a scaled (inflated, in some interpretations) Carbon isotope with a nucleus in a condensed (bosonic) state and components in various vertically excited states; life is common everywhere, albeit extroverted complex forms are present on planetary surfaces only during planetary neurogenesis; anthropogenic climate change is only a part (trigger from one perspective) of bigger global changes; major extinction events on a surface of a planet are relative extinctions, a regular part of transformation and transfer of life in the process of planetary neurogenesis.

Liquid argon technology is widely used by many previous and current neutrino experiments, and it is also promising for future large-scale neutrino experiments. When detecting neutrinos using liquid argon, many hadrons are involved, which can also interact with argon nuclei. In order to gain a better understanding of detection processes, and to simulate neutrino events, knowledge of hadron-argon cross sections is needed. This paper describes a new procedure, which improves upon the previous work with a multi-dimensional unfolding, to measure hadron-argon cross sections in a liquid argon time projection chamber. Through a simplified version of simulation, we demonstrate the validity of this procedure.

From the perspective of human cognitive evolution to analyze the origin and sustained development of science, there is a puzzle, because the driving force behind cognitive evolution is natural selection, which favors biological traits improving individual adaptation rather than seeking the truth. Some scholars argue that cultural evolution provides an explanation for the development (or progress) of science, wherein scientific beliefs can be transmitted intergenerationally within the scientific community, thus continually converging toward truth. However, this explanation has ecological validity problem. Research on cultural learning strategies suggests that when choosing beliefs for imitation learning, people tend to consider contextual and content factors rather than the truth value of beliefs. On the other hand, due to the absence of universally accepted criterion for theory selection recognized by all scientists, the best theory choice always fails. I will develop a minimum model for the best theory choice to defend the scientific progress's cultural evolution approach. The criterion for the best theory choice is the mark of a scientific school's establishment and the demarcation line with each other. As a scientific school's member, a scientist could select the best theory as his/her learning model according to the school's criterion. Hence, it is plausible for the approach to explain scientific progress. Finally, my model will show a scientific school is a driver to improve the local scientific progress, and the competition between schools promotes global scientific progress. The school's size and the scientific reasoning diversity decide a result of the competition.

Liquid and gas are distinct in their extent of dynamic atomic correlations; in gas atoms are almost uncorrelated whereas they are strongly correlated in liquid. This distinction applies also to electronic systems. Fermi liquids are actually gas-like, whereas strongly correlated electrons are liquid-like. Doped Mott insulators share characteristics with supercooled liquids. Such distinctions have important implications on superconductivity. We discuss the evolution of dynamic atomic correlations in liquid with temperature, and a possible effect of dynamic correlations on the high-temperature superconductivity in the cuprates.

The Topographic Ruggedness Index (TRI) is one of the essential measurement in topographic analysis, as well as in explaining biodiversity and geo-diversity. This study demonstrate how to utilize Geographic Information Systems (GIS) to generate TRI for Nyanga District using Digital Elevation Model (DEM). Though the TRI strongly depends on a local scale slope derived from an average adjacent neighbor slope algorithm, and selection of different lag distances in the computation of spatial variability, the measurement demonstrated that areas of higher altitude have higher ruggedness index. The TRI model was generated by computing focal statistics to determine focal sum of DEM and of DEM ². The final TRI was computed using Math Algebra (Raster Calculator) to generate layers of different ruggedness suitable for topographic and biodiversity analysis. The algorithm presented could also be used for smaller areas with high quality data and corrected DEMs. Despite widespread adoption, ruggedness metrics require thorough testing using both artificial landscapes and real world applications.

The official recommendations to manage COVID-19 in the outpatient setting focus on high-cost drugs (remdesivir, nirmatrelvir-ritonavir, molnupiravir), suffering from many problems. Therefore, one may consider also other treatments meeting these criteria: • promising effectiveness, based on favorable randomized controlled trials (RCTs) of sufficient validity. Even without definitive evidence, physicians and informed patients can consider them if they are also: • safe, to the state of knowledge • with a very favorable opportunity cost • biologically plausible/not implausible • accessible • without major commercial sponsors in the trials promoting them, and with researchers without major conflicts of interests. The main focus is on a limited group of functional foods and some their active ingredients, with further mention of other substances of different origin, yet fully meeting all the aforementioned criteria. The functional foods and substances described and discussed certainly do not cover all the known effective therapies for COVID-19 (in one case also for Long-COVID), but the only therapies proposed in this paper currently satisfy all the above criteria. The supporting scientific evidence will favor meta-analyses of RCTs that include the most definitive outcome: all-cause mortality, i.e. the outcome that most fully informed people tend to prioritize. Furthermore, the reported evidence favors clinical studies of the most valid design, i.e. RCTs.

Ceramic composites have good strength, fracture toughness and hardness characteristics and are widely used in industry, medicine. The properties of ceramic composites can be controlled at the production stage by setting the internal structure. This is made possible by the development of additive manufacturing. However, not all properties are well understood, especially dynamic fracture resistance and fracture toughness. In order to predict the mechanical behavior of ceramic composites in the manufacturing phase with a controlled structure under dynamic loads, it is convenient to use methods of numerical analysis. The aim of this work was to investigate the influence of loading speed and internal structure on dynamic resistance and fracture toughness of ATZ nanocomposites. Within the framework of the study physical and mathematical model that is used in computational mechanics of materials is developed. In the paper is shown the influence of the loading rate on the strain rate in the region of the shock transition for materials based on ZrO2 - Al2O3 system. The research shows nonlinear effects under intense dynamic loads in the shown composite materials are bound up with either the processes of self-organisation of deformation modes at the mesoscopic level or the occurrence of martensitic phase transformations in matrix volumes adjacent to the strengthening particles.

Computational Fluid Dynamics (CFD) is revolutionizing the oil and gas industry by offering powerful insights and innovative solutions. This paper explores the cutting-edge applications of CFD in enhancing and innovating within the sector. CFD simulations provide engineers with invaluable insights into fluid behavior, enabling optimized designs and informed decision-making. The software facilitates foresight by predicting system performance, optimizing drilling techniques, and maximizing reservoir management strategies. Moreover, CFD optimizes equipment design, increases operational efficiency, and reduces costs through virtual testing, eliminating the need for costly physical prototypes. The application of CFD aligns with the United Nations Sustainable Development Goals (SDGs), promoting energy efficiency, climate action, responsible production, and sustainable consumption. It addresses flow assurance challenges, enhances safety protocols, and mitigates environmental impacts. CFD is a powerful tool driving innovation, resilience, and sustainability within the oil and gas industry, ensuring a more efficient, safe, and environmentally conscious future. As advancements in CFD methodologies continue, the potential for further enhancements and breakthroughs within the sector becomes even more promising, driving the industry towards a sustainable and prosperous future.

The present study investigates the role of a simple inerter in supplemental devices for possible implementation in the matured seismic base isolation technique. Firstly, the response of the base-isolated structure with an optimally tuned mass damper-inerter (TMDI) is investigated to see the tuning effects. The time required to tune the TMDI was found to be significantly longer than the duration of a strong-motion earthquake. There was still a reduction in the response of the isolated structure, which is primarily due to the added damping and stiffness (ADAS) of TMDI and not because of the tuning effects. Hence, it is proposed that the corresponding ADAS of the TMDI be directly added to the isolation device. Secondly, the response of the base-isolated structures with the fluid inerter damper (FID) is studied. It was observed that the inerter of the FID does not influence the displacement variance of an isolated structure under broadband earthquake excitation. It implies that the response of the isolated structure with FID is primarily controlled by its counterpart fluid damper (FD). The performance of optimal TMDI, ADAS, FID, and FD to mitigate the seismic response of the flexible multi-story base-isolated structure under real earthquake excitations is also investigated. In terms of suppressing the displacement and acceleration responses of the isolated structure, it has been found that TMDI and ADAS perform similarly. Comparing the response of the isolated structure with FID and FD demonstrated that the inerter in the FID has detrimental effects on the isolated structures, in which the top floor's acceleration and base shear are substantially increased.

Abstract: The search for alternative protein sources to soy-bean meal (SBM) in animal feeding is a strategic objective to reduce production costs and contribute to a sustainable animal produc-tion. Spirulina, due to the high protein content, has emerged as a potential cost-effective, sus-tainable, viable and high nutritional value food resource for many animal species. Insect larvae (Tenebrio molitor and Hermetia illucens) are also considered potential alternatives to SBM, given their high edible percentage of almost 100%, as well as a protein value higher than that of vegetable proteins. Rapeseed meal and grain legumes, such as faba beans, peas, lupins and chickpea can also be used as locally producible protein ingredients. This study reviews the nutritional value of these potential alternatives to SBM in pig diets, effects on animal perfor-mance, digestion, immune system, physicochemical and sensorial characteristics of meat, in-cluding processed meat products of pork. Limits on their use in pig feeding are also reviewed to indicate gaps to be filled in future research on the supplementation level of these potential al-ternative protein sources into pig diets.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

In contrast to its traditional form, in which large, powerful companies were solely and exclusively responsible for supplies, manufacturing, and distribution, the supply chain has undergone substantial change over the past half-century. Today, nearly all supply chain processes are outsourced, thanks to the efforts of innovative, modern companies. The degree of risk associated with contemporary supply chains is greater than the majority of supply chain managers acknowledge, according to an accumulation of studies, analysts, and news outlets. Vulnerabilities within the supply chain remain a significant challenge for numerous organizations. Inadequate management of these risks—which include, but are not limited to, natural disasters, acts of terrorism, cyber assaults, the credit crisis, pandemic scenarios, and war—could lead to significant declines in profitability, productivity, revenue, competitive advantage, and other metrics. Concerns regarding the risk assessment of the supply chain remain unresolved. The objective of this article is to present a risk evaluation framework that can be effectively implemented in the assessment of supply chain risks. This study makes a fundamental contribution to the body of knowledge by providing supply chain managers with a practical instrument for assessing their processes, irrespective of the mathematical underpinnings or the diverse variables employed in risk assessment. A case study from an automotive EMS production is used to compare the outcomes of multiple aggregation methods; the conclusions are then validated by risk and FMEA specialists from the same factory.

The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

Climate change is a global phenomenon that exerts significant impacts on ecosystems. Estuaries, as critical aquatic habitats, are susceptible to these changes; however, our comprehension of their specific responses to climate change remains limited. Beyond temperature rise and alterations in precipitation patterns, climate change induces indirect effects, influencing a multitude of biotic and abiotic variables within aquatic ecosystems. In this study, we assess seagrass health as an indicator of estuary well-being in three estuaries located in northern to central California, USA. Our investigation focuses on the marine angiosperm Zostera marina (commonly known as eelgrass) and its associated parasite, Labyrinthula zosterae. Z. marina, a temperate marine plant, faces the threat of eelgrass wasting disease (EWD) caused by the protist L. zosterae. Notably, this pathogen demonstrates sensitivity to warming ocean temperatures, underscoring the imperative to comprehend its impacts on host health amidst climate change. Our study assessed EWD prevalence with relation to spatial and temporal patterns, the relationship, if any, between disease prevalence and eelgrass percent cover, the spatial patterns, if any, of eelgrass percent cover, and what climatic variables were most strongly correlated with EWD and eelgrass percent cover.

Academic integrity faces persistent challenges in Anambra State's tertiary institutions due to fraudulent behaviors among undergraduate students. This study addresses gaps in the literature by investigating lecturers' attitudes toward these fraudsters, recognizing the need for localized insights and informed interventions. Employing a survey research design, the study focuses on 20 lecturers. A validated questionnaire, utilizing a 4-point Likert scale, captures lecturers' perspectives. The study examines attitudes, motivations, and potential variations based on academic discipline, teaching experience, and institutional context. Preliminary findings indicate a nuanced spectrum of attitudes among lecturers, with male lecturers exhibiting slightly more positive attitudes than their female counterparts. Variability in attitudes is influenced by factors such as academic discipline and teaching experience. The study identifies inadequacies in existing interventions and highlights the role of lecturers in preventive strategies

This article develops duality principles and related convex dual formulations suitable for the local optimization of non-convex primal formulations for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a Ginzburg-Landau type equation.

Background Scientific literature in psychology considers personality as an organization of ways of being, knowing, and acting that ensures unity, coherence and continuity, stability and planning of the individual's relations with the world (Caprara & Gennaro, 1994). Personality is the outcome of two distinct constructs: Temperament and Character. So, personality is the result of the interaction between biological aspects (temperament) and the experiences that anybody experiences in his own life, interacting with the environment (Cloninger et al., 1993). Scientific literature now largely agrees that adversity in the early years of life has broad long-term consequences on the neuroendocrine, immune, and metabolic systems (Kim et al., 2019), as well as on neuroplasticity and neuronal morphology that alter the overall evolution of (van Bodegom, et al., 2017; Edwards et al., 2010). Here we analyze the hypothesis that maternal environmental factors (air and chemical pollutants, maternal health, eating behaviors, maternal and fetal stresses, caregivers' behaviors, etc.) can cause epigenetic alterations in the offspring in the prenatal and perinatal period that shape the trait's personality of the offspring themselves and that orient part of their character in adult life. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to develop this systematic review. PubMed, Scopus, PsycINFO, and Biosis databases were searched for primary research articles published between 2010 and 2023 looking for English language articles containing the term "prenatal" and any of the following terms: "anxiety"; "brain development"; "depression"; "gestation"; "maternal health"; "maternal stress"; "mental health"; "newborn health"; "perinatal programming"; "pregnancy"; "prenatal stress"; "resilience to stress"; "transgenerational epigenetic inheritance"; "personality"; "personality trait"; "environmental epigenetics". Retrieved articles were screened and a subset of relevant abstracts was then selected for a more detailed evaluation. The final studies selected consisted of those resources that directly evaluated the relationship between maternal environmental factors and the personality traits of the offspring. Results and Conclusions One hundred and eleven studies were identified but only three met the primary question. There is evidence to support the hypothesis of correlations between environmental exposure during the intrauterine fetal period and personality traits of the newborn. Limits: multiple limitations were found based on the lack of research on this specific topic, although such a field of research can be important for developing effective strategies for primary prevention.

The modernization of food and food production techniques resulted in a major decline in the nutritional profile of food products. Fortification and enrichment are the modern solutions to consider. But to what extent will it help? Traditional food products and food production methods flourish with vital nutrients. The current study restructures the conventional approach to biscuit making, intended to prepare and evaluate the whole nutritious food made from Navathaniyam, a blend of nine grains. From the organoleptic evaluation, the combination of flours employed in trial 2 was more preferred. Incorporating the blend of nine diverse varieties of cereals and pulses will be a novel food product.

This article critically examines Warren Bennis's understanding of leadership, with a particular focus on the overlooked aspect of followship. While Bennis's theories have greatly influenced the leadership discourse, their limited consideration of follower perspectives raises crucial concerns. By exploring the intersection of leadership and followship, this article uncovers the gaps in Bennis's approach and emphasizes the need for a more integrative understanding of leadership dynamics. Through a comprehensive analysis of existing literature, this article aims to contribute to a more nuanced and holistic viewpoint on leadership, highlighting the importance of follower perspectives in effectively sustaining leadership excellence.

Alternaria alternata is a saprophyte and is also known to cause many opportunistic infections in humans. Alternaria infections are important factors of morbidity and mortality in im-mune-compromised and solid organ transplant (SOT). Sensitivity to the airborne fungus Alter-naria alternata is also believed to be a common cause of allergic asthma. Therapies are limited by the lack of knowledge about the role of individual fungal gene products and symptoms in im-mune-compromised and transplant patients. This review summarizes the published case studies on Alternaria alternata infections in transplant patients.

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

In this paper, we present a general version of polygonal fitting problem called Unconstrained Polygonal Fitting (UPF). Our goal is to represent a given 2D shape S with an N-vertex polygonal curve P with a known number of vertices, so that the Intersection over Union (IoU) metric between S and P is maximized without any assumption or prior knowledge of the object structure and the location of the N-vertices of P that can be placed anywhere in the 2D space. The search space of the UPF problem is a superset of the classical polygonal approximation (PA) problem, where the vertices are constrained to belong in the boundary of the given 2D shape. Therefore, the resulting solutions of the UPF may better approximate the given curve than the solutions of the PA problem. For a given number of vertices N, a Particle Swarm Optimization (PSO) method is used to maximize the IoU metric, which yields almost optimal solutions. Furthermore, the proposed method has also been implemented under the equal area principle so that the total area covered by P is equal to the area of the original 2D shape to measure how this constraint affects IoU metric. The quantitative results obtained on more than 2,800 2D shapes included in two standard datasets quantify the performance of the proposed methods and illustrate that their solutions outperform baselines from the literature.

Level index was introduced in 2017 for rooted trees which is a component of Gini index. In the origin, Gini index is a tool for economical investigations but Balaji and Mahmoud defined the graph theoretical applications of this index for statistical analysis of graphs. Level index is an important component of Gini index. In this paper we define a new graph polynomial which is called level polynomial and calculate the level polynomial of some classes of trees. We obtain some interesting relations between the level polynomials and some integer sequences.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α&gt;1 is more likely than α&lt;1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α&lt;1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

The pressing process and density profile of particleboards produced with addition of waste wood containing phenol-formaldehyde (PF) resin was presented. The particles were melamine-urea-phenol-formaldehyde (MUPF) resinated. Recovered particles were obtained from post-industrial waste plywood. The press closing time recorded during the pressing of mats containing only recovered particles in the core layer (100%), at a unit pressure of 3 MPa, in which the face layer rate was 50%, resin load for face and core layer was 12% and 10% was shorter by 29% than for the industrial particle mats. Regardless of the level of variability of independent factors the overheating time of mats to a temperature of 100 °C, containing recovered particles was 10% - 20% shorter than the time of overheating mats with industrial particles. The greatest impact (52%) on maximum density of the face layer of particleboards was demonstrated by the content of the recovered particles and the resin load (32%). The maximum density area of the face layer was located closer to the surface in particleboards produced with a higher (80%, 100%) content of the recovered particles, a higher (i.e. 12% and 10%, respectively, for face and core layer) resin load, a lower (35%) face layer rate and a higher (3 MPa ) unit pressure.

Penrose’s singularity from 1965 predicted that in the center of the black hole space-time curvature is infinite and consequently gravity force there is infinite. In 2014 NASA measured universal space has Euclidean shape. This means that stellar objects cannot curve universal space and that the space-time singularity model has no physical existence. In the center of black holes energy density of superfluid space is so low that the electromagnetic properties of space are changed. This causes electromagnetic forces between the nucleus and orbiting electrons to become unstable. In the center of a black hole, atoms are falling apart into elementary particles. Black holes are rejuvenating systems of the universe, they transform old matter into fresh energy in the form of elementary particles. Astrophysical jets are the outcome of this process.

Through the refinement of approaches to applying visual augmentation to virtual environments, the possibilities of utilising this process in robotics continues to be explored. However these endeavours have only yielded a collaborative relationship between the two technologies, where the augmented reality (AR) component is separate from the robot functionality. Regarding telepresence robots, research has shown that their perceived usefulness wanes in comparison to that of video conferencing tools as they do not offer a level of immersion and modified visual experience to justify their use. Therefore this paper contributes a novel virtually augmented robot (VAR) model that embeds AR functionality into telepresence, through the use of augmentation and facial recognition sub-models and algorithms. Experimental results show that using pulse width modulation (PWM), duty cycles, digital signals and accelerometer angles provides an approach for stabilising robot motion and the transference of human to robot head movement. The results also show the benefit of having a robot model with intrinsic AR functionality, as it offers a greater level of immersion and visual control of an environment.

A symbolic analysis of Archimedes’ periodical number system is developed, from which a natural link emerges with the modern positional number systems with zero. After the publication of Fibonacci’s Liber Abaci, the decimal Indo-Arabic positional system was the basis of the algorithmic and algebraic trend of modern mathematics, but even if zero plays a crucial role in algebra and mathematical analysis, zeroless positional systems show the same capability of producing efficient arithmetical algorithms based on operation tables over digits. The crucial role of digits is assessed, by considering a representation of numbers based on strings in lexicographic order. A new algorithm for the determination of decimal periods is presented,, by remarking the cruciality of this topic in number theory. Periods of ordinal numbers, and enumerations of recursive enumerability are shortly recalled. Concluding remarks are formulated about the deep relationship among numbers and information, which shed new light on a red line passing through the whole history of mathematics

This study investigates the socio-economic determinants correlated with Post-Traumatic Stress Disorder (PTSD) symptoms amid the 'Guardian of the Walls' operation, in the Israel-Hamas conflict. Our investigation primarily focused on assessing the effects of missile strikes on Israeli civilians. By conducting an in-depth statistical analysis of individual experiences, we sought to unravel the correlation between socioeconomic status and PTSD susceptibility.Our findings reveal a stark disparity in PTSD risks across different socio-economic groups. Individuals from lower socio-economic backgrounds exhibited a notably higher propensity for PTSD symptoms, suggesting that socio-economic adversity intensifies the psychological toll of conflict exposure. Conversely, higher education and stable family environments appeared to mitigate PTSD risks, underscoring the protective role of certain socio-economic factors.The study's implications extend far beyond the immediate context of the Israel-Hamas conflict. By elucidating the heightened vulnerability of lower socio-economic groups to PTSD, it calls for a nuanced approach to mental health intervention in conflict zones. This approach should not only address the direct psychological impact of conflict but also consider the broader socio-economic fabric that shapes individual experiences of trauma.This research contributes to a growing body of knowledge on PTSD in conflict settings, offering valuable insights for policymakers, mental health professionals, and humanitarian organizations. By highlighting the socio-economic dimensions of PTSD, it advocates for more comprehensive, context-sensitive mental health strategies. Such strategies are crucial for addressing the complex array of factors influencing mental health in conflict zones, ultimately paving the way for more resilient and supportive communities.

The temporo-spatial theory of consciousness postulates that the brain implements its own inner time and space for conscious processing of outside world. In line with this hypothesis, I present a mechanistic model of mutually connected processes that encode the inner space and time. The model is used to elaborate the binding mechanism between two sets of processes representing the inner space and time, respectively. Further, a stochastic version of the model is developed to investigate the interplay between the binding strength and noise. The spectral entropy is used to characterize the noise effects on the systems of processes when the binding strength is varied. The stochastic modeling results reveal that the spectral entropy values for strongly bound systems have similar entropy values for weakly bound or even decoupled systems. Thus, the analysis performed in this study allows to conclude that the binding mechanism is noise-resilient.

We develop a renormalizable model of Quantum General Relativity (QGR). We do this by building on earlier work with quantum electrodynamics (QED). Working with the Feynman path integral (FPI) approach to QED, we generalize the paths from varying in space to varying in space and time. This gives the standard results plus dispersion in time of order attoseconds or left. Over longer times, the dispersion in time averages out, but at attosecond times it should be detectable with current technology. As an unexpected but welcome side-effect, the approach caused the usual loop integrals to be finite. Since the usual UV divergences are significantly harder to renormalize in QGR than in QED, and since using paths in time plus space is definitely more in keeping with the general spirit of GR, we here apply the same approach to QGR. We show that with an appropriate choice of gauge (trace-reversed de Donder gauge) we can extend the earlier results to include QGR. Sub-attosecond measurements of individual gravitons are not within our reach, but at least the ultraviolet divergences in QGR are contained in the same way. We have therefore a renormalizable model of QGR

Topologically non-trivial magnetization configurations in ferromagnetic materials on the nanoscale, such as hopfions, skyrmions, vortices, etc., attract considerable attention of researchers during the last years. In this article applying the theory of micromagnetism, I demonstrate that the toroidal hopfion magnetization configuration is a metastable state of a thick cylindrical ferromagnetic nanodot or a nanowire of a finite radius. Existence of this state is a result of the competition of the exchange, magnetostatic, and magnetic anisotropy energies. The Dzyaloshinskyi-Moriya exchange interaction and surface magnetic anisotropy are of the second importance for the hopfion stabilization. The toroidal hopfion metastable magnetization configuration may be reached in the process of the sample re-magnetizing by applying an external magnetic field along the cylindrical axis.

Recent theory of tachyon neutrinos, providing an explanation for Dark Matter, leads to two sets of experiments for testing that theory. One predicts an enhanced signal in detection of the Cosmic Neutrino Background. The other compares models of Dark Matter used to fit data on galaxy rotation curves and also gravitational lensing. In addition, an apparent contradiction within the new theory, related to Dark Energy, is identified and addressed.

The Langmuir isotherm is a widely used model for analyzing adsorption equilibrium data. This study evaluated the efficiency and accuracy of all four linear forms of the Langmuir isotherm and its non-linear form using 67 experimental data sets selected from the literature. The results showed that only if all four linear forms simultaneously show high accuracy, then the non-linear form also shows high accuracy, and therefore it can be said that the process probably follows the Langmuir isotherm. On the contrary, when at least one of the four linear forms of the Langmuir isotherm has low accuracy, it means that the non-linear form also has low accuracy, and it can be concluded that this process does not follow the Langmuir isotherm. This research suggests that all four linear forms of the Langmuir isotherm should be evaluated simultaneously to conclude whether the studied system follows the Langmuir isotherm or not. In other words, relying on only one of the four linear forms of the Langmuir isotherm to model adsorption and calculate the Langmuir constant and maximum adsorption capacity is an incomplete approach, contrary to the conventional approach.

Nitrogen (N) is the most limiting nutrient for the production of vegetable crops, but anthropogenic sources pose risks due to its transformation into several reactive forms and movement throughout the environment. To mitigate the impact of environmental losses, current research efforts aim to improve management practices to obtain a greater Nutrient Use Efficiency (NUE). From a chemical, biological, and ecophysiological perspective, at a reductionist level we have acquired a considerable mechanistic understanding about the N cycle, the varied N chemical forms, microbial interactions and physiology of crop uptake. However, at a holistic level, we are far from obtaining an adequate understanding of the myriad of multi-level cropping system, genetic, socioeconomic, environmental, and ecological interactions that have an effect on the N cycle and NUE. Here, based on a selected review of the literature, we provide a perspective on the potentials to increase the NUE of vegetable crops, by adopting a range of best management practices and by highlighting some of the N x system interactions that may be considered to better understand the complex dynamics of the N cycle, to optimize the NUE of vegetable crops, and to explore alternatives to the reliance on the use of synthetic N sources.

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)), and deploys it to locate optimal conditions that maximize C2 yield for a catalyst. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions, while the predictions of methane conversion are driven by the reaction conditions. The machine learning (ML) regressor is used as a surrogate to develop performance curves for each of the catalysts via a multi-objective optimization routine that seeks to maximize the KPIs in the decision space of reaction conditions, is seen to locate optimal conditions at which the maximum C2 yields for catalysts are predicted to be 15%, higher on average. Analyzing the catalysts in the space of their performance curves with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, reveals distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts.

Greenhouse gases (GHGs) trap heat in the atmosphere, causing the Earth's surface temperature to rise. The main GHGs are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), and sulfur hexafluoride (SF6). Human activities are increasing GHG concentrations rapidly, which is causing global climate change. Global climate change is increasing environmental and public health problems. To reduce greenhouse gas (GHG) emissions, identify where the emissions are coming from, develop a plan to reduce them, and then implement and monitor the plan to ensure that emissions are actually reduced. Anthropogenic global climate change has large and increasingly adverse economic effects. Cities emit the most GHGs due to fossil fuel burning and power usage. The four major GHG emitters are energy, transportation, waste management, and urban land use sectors. Organizations should prepare action plans to lower their GHG emissions and stop the worst consequences of climate change. These action plans require companies and local authorities to submit their GHG emissions reports on a yearly basis. A GHG emissions management system includes several processes and tools created by organizations to understand, measure, monitor, report, and validate their GHG emissions. Two of the most widely adapted frameworks for GHG inventory reporting are ISO 14064 and the GHG Protocol. This review paper aims to identify some of the key points of GHG inventory preparation and mitigation strategies.

This paper investigates the constructability of angles multiples of 3 within the framework of Euclidean geometry. It makes a significant contribution by presenting the first geometric construction for all such angles, offering a rigorous solution to a longstanding geometric problem. The paper reaffirms the efficacy of Euclidean geometry in providing precise constructions and robust proofs for these angles, demonstrating the enduring strength of Euclidean principles from classical to modern times. The presented workflow goes beyond Euclidean geometry to examine non-Euclidean methods, particularly analytical approaches, revealing misconceptions that compromise the genetic and geometric rigor of Euclidean principles. The paper exposes incongruities when algebraic proofs related to angle constructability are applied to the Euclidean system, emphasizing the misalignment of fundamental geometric concepts. A notable result in the paper is the construction of a angle, introducing the “ angle chord” as a novel geometric property. This property challenges assumptions made by non-Euclidean methods and highlights the nuanced geometric properties crucial for rigorous constructions. The paper refutes the fallacy of relying solely on algebra for solutions to angles multiples of , emphasizing the necessity of embracing Euclidean geometry for geometric discoveries. The paper underscores the merits and resilience of Euclidean geometry, showcasing its independence and depth across historical and modern perspectives. The newly presented geometric construction not only resolves a longstanding question but also emphasizes the intrinsic strength and uniqueness of Euclidean principles in contrast to alternative methodologies.

In this study, we consider a Lambert series whose coefficients are the sum of divisors function. Utilizing the Lambert series in the sequel we introduce a normalized linear operator JR_(α,β) (z) by applying the convolution with Rabotnov function. We then, acquire sufficient conditions for JR_(α,β) (z) to be Univalent, Starlike and Convex respectively. In each component of this study, we expand the derived results by applying two Robin's inequalities, one of which is equivalent to the Riemann hypothesis.

The purpose of this study is to ​​explore the use of a custom Transformer model in brain-computer interfaces (BCIs) that translate the neural activity present when an individual with limited verbal and fine-motor skills attempts to handwrite. As found in previous studies, Transformers have performed better than recurrent neural networks (RNNs) in translation tasks which are similar to its usage here in decoding neural signals into intended handwritten text. Due to the known benefits of a Transformer, the hypothesis was that the Transformer would show promise in the context of a BCI through the recorded metrics. The neural signals of a tetraplegic individual, when they attempted to handwrite, were provided by existing research. Four trials were conducted using data with or without augmentation which the model used when training to separately minimize training loss and validation loss. When comparing the results of a BCI with the implementation of a Transformer model with the original RNN BCI (the original data source), the Transformer model performed less favorably across all four trials. Although the results do not indicate that the Transformer model currently outperforms an RNN BCI, it is important to note that further testing of the model's capabilities (such as training it with a larger and more preferable dataset and/or for longer, comparing training times between the RNN and Transformer, and/or seeing how the Transformer is improved with an offline autocorrect feature) is necessary before determining whether Transformers can enhance communication in this manner.

Negative thermal quenching (NTQ) denotes that the integral emission spectral intensity of a given phosphor increases continuously with increasing temperature up to a certain elevated temperature. NTQ has been a subject of intensive investigation in recent years and a large number of phosphors were reportedly to have exhibited NTQ. In this paper, a collection of results in the archival literature about NTQ of specific phosphors are discussed from a macroscopic viewpoint in the following three aspects:1) Could NTQ of a given phosphor be reproducible?2) Could the associated data for a given phosphor exhibited NTQ be in line with the law of conservation of energy?3) Could NTQ of a given phosphor be demonstrated in a prototype WLED device? By analyzing typical cases based on common sense, it is hoped to increase the awareness for the issues with the papers reporting NTQ of the specific phosphors based on spectral intensity along with for the importance of keeping stability and consistency of the measurement conditions in temperature-dependent spectral intensity measurement which is a prerequisite for the validity of the measurement results.

The continental shale layers are more complex in matrix pore-microfracture interactions and connectivity characteristics under the influence of tuff devitrification, which limits the explicit understanding of the coupling relationship between wettability and pore structure, as well as hydrocarbon flow behavior. Therefore, the influence of tuffaceous components on the reservoir formation mechanism and oil-rich effect of the continental shale formation system is a critical issue to be solved. The Upper Triassic Yanchang Formation tuffaceous shale in Ordos Basin and the Middle Permian Jingjingzigou Formation tuffaceous shale in the southern margin of Junggar Basin is taken as the research objects, using whole-rock mineral composition analysis, FE-SEM observation and digital image processing and extraction, as well as wettability angle determination and spontaneous imbibition experiments to illustrate the influence of the difference of devitrification extent on reservoir pore connectivity and interfacial wettability in two different environments of freshwater and saline lacustrine basins, and summarize the reservoir formation differential pattern under the influence of devitrification. The results show that the saline lacustrine is significantly modified by devitrification. The pore plane porosity of the devitrified tuffaceous shales in the saline lacustrine is higher than that of the tuffaceous shales in the freshwater; the oleophilic of the saline lacustrine tuffaceous shales is better than that of the tuffaceous shales in the freshwater, moreover, the pore connectivity of the saline lacustrine tuffaceous shales is better than that of the tuffaceous shales in the freshwater. Wettability and connectivity control the storage performance of shale oil in the tuffaceous shales formation system. The more oleophilic the reservoir wettability is, the better the pore connectivity is, and the better the reservoir of tuffaceous shale formation system is. According to the different extents of devitrification, two different reservoir formation models of freshwater and saline lacustrine are established, which are of great theoretical and practical significance to enrich the theory of shale oil enrichment and reservoir formation in the continental shale formation system and to guide the exploration of continental shale oil benefits.

Fungi belong to the species with the highest biodiversity and high potential for use in biotechnology and industry. Therefore, their long-term quality conservation is very important and it is necessary to continuously develop new methods and improve existing methods. Currently, cryopreservation appears to be the best available technique for the preservation of fungi. This study summarises the latest trends and advances in fungal cryopreservation over the last 7 years, i.e. findings from scientific publications from 2017 to 2023 dealing with cryopreservation of both basidiomycetes and non-basidiomycete fungi. The work analyzed the studied organisms and the cryopreservation method used, focused on the use of new procedures and modifications of existing methods; summarized the conditions for successful cryopreservation, such as cryopreservation time and temperature, as well as the substrate and cryoprotectant used.

Benveniste’s experiments – known in the lay press as the “water memory” phenomenon – are generally considered to be a closed case. However, the amount of data generated by twenty years of well-conducted experiments prevents closing the file so simply. An issue, which has been little highlighted so far, merits to be emphasized. Indeed, if Benveniste failed to persuade his peers of the value of his experiments, it was mainly because of a stumbling block, namely the difficulty of convincingly proving the causal relationship between the supposed cause (“informed water”) and the experimental outcomes in different biological models. To progress in the understanding of this phenomenon, we abandon the idea of any role of water in these experiments (“water memory” and its avatars). In other words, we assume that “control” and “test” conditions to be evaluated were physically identical and differed only by their respective designations. We show in this article how simple probabilistic considerations allow to build a model that accounts for all aspects of Benveniste’s experiments. In this model based on probability amplitudes, constructive and destructive interferences emerge – or not – according to the experimental context. The recording of a statistical regularity shapes the intertwined whole constituted by the probability amplitudes of the states of the experimenter’s cognitive structures and of the experimental system. This model provides an alternative explanation to Benveniste’s experiments where water plays no role and where the place of the experimenter is central.

Surface processes such as coatings, corrosion, photocatalysis, and tribology are greatly diversified by acid-base interaction at the surface overlayer. This study focuses on the action of metallic copper surface as an electron donor/acceptor related to inactivation of viruses; it was found that regarding Cu2O or Cu materials electrostatic interaction plays a major role in virus inactivation. We applied TPPE method to clarify the mechanism of electron transfer (ET) occurring at light irradiated copper surfaces. The TPPE characteristics was strongly influenced by the environments, which correspond to the temperature and environment dependence of the total count of emitted electrons in the incident light wavelength scan (PE total count, NT), the photothreshold, and further the activation energy (E) analyzed from Arrhenius plot of NT values obtained in the temperature-increase and subsequent temperature-decrease processes. In this study we reexamined the dependence of the TPPE data from two types of Cu metal surfaces: Sample A, which was mechanically abraded in alcohols, water, and air, and Sample C, which was only ultrasonically cleaned in these liquids. The NT for both Samples slowly increased with increasing temperature, reached a maximum (NTmax) at 250C (maximum temperature, Tmax), and after that decreased. For Sample A the NTmax value decreased in the order H2O > CH3OH > C2H5OH > (CH3)2CHOH > C3H7OH, although the last alcohol gave Tmax = 100C, while with Sample C the NTmax value decreased in the order: C3H7OH > (CH3)2CHOH > C2H5OH > CH3OH > H2O. Interestingly, both orders of the liquids were completely opposite: this means that Cu surface can possess two-way character. The NT intensity was found to be strongly associated with the change of the hydroxyl group (Cu-OH) to the oxide oxygen (O2) in the O1s spectra in the XPS measurement. The difference between the above orders was explained by the acid-base interaction mode of the Cu-OH group with the adsorbed molecule on the surfaces. The H2O adsorbed on Sample A produces the electric dipole Cu-OH+---:OH2 (--- hydrogen bond), while the C3H7OH and (CH3)2CHOH adsorbed on Sample C produce ROH+---:O(H)-Cu (R = alkyl groups). Gutmann’s acceptor number (AN) representing the basicity of the liquid molecules was found to be related to the TPPE characteristics: (CH3)2CHOH (33.5), C2H5OH (37.1), CH3OH (41.3), H2O (54.8) (the AN of C3H7OH could not be confirmed). With Sample A the values of NTmaxa and EaUp1 both increased with increasing AN (Up1 means the 1st temperature-increase process). On the other hand, with Sample C the values of NTmaxc and EcUp1 both decreased with increasing AN. These findings suggest that Sample A acts as an acid, while Sample C functions as a base. However, in the case of both types of Samples A and C the NTmax values were found to increase with increasing EUp1. It was explained that the EUp1 values depending on the liquids originate from the difference in the energy level of the hydroxyl group radical at the surface denoted. This is able to attract electrons in the neighborhood of the Fermi level of the base metal by tunnelling. After that due to Auger emission electrons are released, leading to the ET in the overlayer. These electrons are considered to have a strong ability of reducibility.

This research delves into the integration of green roofs with Building Information Modeling (BIM), a pivotal advancement in sustainable urban construction. Aligning with the United Nations Sustainable Development Goals, particularly Goal 11, the study explores how this integration can address global challenges like climate change and resource depletion. Using the Dynamo Visual Programming Language within Autodesk Revit, the research develops a computational modeling approach for green roofs, focusing on their thermal and structural characteristics under varied environmental conditions. Key findings demonstrate the significant influence of substrate and drainage material combinations on green roofs’ thermal performance, highlighting the need for tailored designs based on climatic conditions. The study also emphasizes the importance of considering structural performance in both dry and saturated conditions for overall building integrity. The research identifies gaps in current practices, such as limited focus on specific green roof materials and reliance on certain software tools, suggesting the need for broader material selection and software adaptability. Future research directions include expanding material selections, exploring diverse environmental conditions, and integrating green roofs with various BIM software platforms. The study's implications extend to stakeholders in the construction industry, offering a framework for architects, urban planners, and policymakers to design and implement green roofs aligned with environmental goals. This work contributes to the field by proposing a novel approach to sustainable construction, integrating ecological design with digital technology, and setting a new benchmark in the integration of green roofs with BIM. By addressing these aspects, the research paves the way for future developments in sustainable urban construction, enhancing the efficiency, sustainability, and resilience of urban communities in line with global sustainability objectives.

A Berry geometrical phase is identified in a strongly metastable system containing dynamically responsive clathrate hydrate structures within a crystal-fluid material. High energy degeneracy in the associated chemistry produces local stability and false vacuum conditions that lead to non-extensive and non-additive contributions in the fundamental thermodynamic relation. Application of Ginzburg-Landau theory and the scaling laws reveals a coherence length and a penetration depth for a macro-scale dual superconductor. The coherence length determines non-extensive volume changes whilst its inverse gives the Higgs mass. The penetration depth determines the extent of QCD vacuum suppression whilst its inverse gives the vector boson mass together with indirect manifestation as non-additive hyperbolic curvature. Simultaneous emergence of the Ginzburg-Landau superconducting phase transition is consistent with gauge-invariant coupling of the scalar field to the Yang-Mills action in QCD. The discovery of an energy gap in the gradient energy term of the system Lagrangian is associated with a critical correlation length revealed in the transition from a gapped to a gapless superconducting state. Together with the emergence and absorption of the Higgs-like scalar field, a mechanism for describing a renormalized QCD mass gap arises.

This paper presents a concise exploration of the synthetic biology currently to artificially 1 create life, juxtaposed against natural life’s emergence over Earth’s 4.5-billion-year history. It explores 2 the complexities of life’s origins, sustaining factors, and the consequences of synthetic life creation, 3 addressing key scientific milestones such as the Urey-Miller experiment and Jacques Monod’s pro- 4 posals. The narrative navigates through the roles of energy, Maxwell’s demons, bifurcations, and 5 genetic information in the emergence and evolution of complex biological systems. Despite significant 6 strides in genetic engineering, the challenge of creating life from scratch persists, necessitating a deep 7 understanding of diverse scientific disciplines. While synthetic biology holds promise for medicine 8 and biotechnology, the creation of truly autonomous synthetic life forms raises ethical concerns. The 9 paper also stresses the imperative for careful ethical evaluation of the societal and environmental 10 implications arising from these advancements.

In the present work, a methodology for wind field reconstruction based on the Meteo Particle Model (MPM) from Numerical Weather Prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP Limited Area Models (LAMs) are run at spatial resolution of about 1 km, this level of information is not sufficient for many applications, for example to support drone operation in urban contexts. The coupling of MPM with the NWP Limited Area Model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values in the generical point considered for the investigation. The numerical results obtained reveal a good behavior of the method in reproducing the general trend of wind speed, as confirmed also by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of spatial and temporal resolution even if the MPM inherits the bias that inevitably affects the COSMO output.

Researchers and organizations are pursing 100 Day Vaccine Development to reduce the impact of any new pandemic-causing virus. They propose monitoring viral emergence and developing vaccines for discovered viruses which would undergo safety testing in humans. If one of these viruses emerges, phase III trials would then take place. There are two problems with this approach - first, phase III trials are too long for the 100-day goal and second, the virus that emerged might not have been discovered in their surveillance. An alternative approach would be to first develop vaccines for viral families similar to the pan coronavirus vaccines being proposed for next generation COVID vaccines. Phase III trials for them would be run even if a virus in the family wasn’t having an outbreak. If a virus in a family for which a vaccine has been developed has an outbreak and the phase III trialed vaccine is not effective against it, develop a booster based on the phase III trialed vaccine. Since booster development and approval takes less than 100 days, the 100 Day Vaccine Development goal would be met.

Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

How crucial is big data in contemporary molecular design? In this publication we elucidate fundamental concepts and terminology in this field, critically addressing overlooked issues. We thoroughly examine the size, accessibility, quality, and structural aspects of big data alongside the primary methodologies employed for their analysis. Within chemical compounds, properties and descriptors represent two distinct data types, forming the basis for categorizing molecular big data. The primary objective of chemistry is property production, which means we are searching for novel drugs or materials rather than chemical compounds, and big data is the central issue of this philosophy. The increasing availability of data in computer-aided technology propels advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL). Accordingly, a broad chemical audience must comprehend these methods to understand data-centered chemistry. Thus, we aim to systemize big data issues through a simple illustrative framework with fundamental descriptor categories: coding, computer-generated descriptors, and property correlates. Although we employ computer-generated descriptors as big data for predictions, the measured data are irreplaceable for achieving high-quality and reliable outcomes and controlling molecular effects. The scarcity of property data remains a significant hurdle limiting comprehensive studies on the structure-property relationships within big data. Accordingly, guided by pragmatics not-so-big data is an option for drug design. We presented also a brief review of the recent big data literature.

Polymer-based (nano)composite foams, especially those having a microcellular structure, containing suitable conductive (nano)fillers, have been shown to act as good shielding materials, hence coming as promising to act as shields in electronic devices, limiting/avoiding electromagnetic interference (EMI) pollution. Nevertheless, due to their high (micro)structural complexity, related to their multiphase nature, there is still a lot of unawareness behind the shielding mechanisms acting in these materials, coming as a necessity to study their microstructure-cellular/porous structure-properties relations, especially in terms of their electrical conductivity and EMI shielding efficiency (EMI SE). To target and control the electrical conductivity through a controlled distribution/dispersion of conductive nanofillers, single or combined (nanohybrids), as there is a direct relation between electrical conductivity and EMI SE, as well as the main EMI shielding mechanisms working on the developed microcellular structure of the foams, which are expected to be absorption-dominated or absorption/multiple reflection-based, are two of the main objectives when combining polymer foaming with the addition of conductive nanofillers. The present review work considers the recent developments and trends on polymer-based foams containing conductive nanofillers, especially carbon-based such as carbon nanotubes or graphene-based materials, as well as similar porous structures created using trending technologies such as 3D printing, for EMI shielding applications. It is divided in different sections, starting with the strategy of microcellular foaming to develop polymer-based foams with enhanced EMI shielding characteristics, especially focusing on technologies and methods using supercritical CO2 (sCO2); continuing with the use of polymer foams as templates to synthesize carbon foams with improved EMI shielding performance for high use temperature applications; the recent strategy of combining different functional (nano)fillers to create hybrid nanofillers/nanohybrids to be used at lower amounts as conductive fillers in polymer foams; the control and selective distribution of the nanofillers in terms of favoring the formation of a more effective conductive network and hence promote the enhancement of the EMI SE; the very recent use of computational approaches to tailor the EMI shielding properties of composite foams; and the very trending possibility of creating cellular components with varied porous structures from these materials using 3D printing; to finish, some future perspectives are discussed.

This paper introduces a Unified Field Theory through the generalization of General Relativity, following the methodology outlined by Einstein for incorporating complex metrics in the pursuit of a comprehensive Unified Field Theory.

Feedback control plays a crucial role in improving system accuracy and stability for a variety of scientific and engineering applications. Here, we theoretically and experimentally investigate the implementation of feedback control in microwave photonic (MWP) transversal filter systems based on optical microcomb sources, which offer advantages in achieving highly reconfigurable processing functions without requiring changes to hardware. We propose four different feedback control methods including (1) one-stage spectral power reshaping, (2) one-stage impulse response reshaping, (3) two-stage spectral power reshaping, and (4) two-stage synergic spectral power reshaping and impulse response reshaping. We experimentally implement these feedback control methods and compare their performance. The results show that the feedback control can significantly improve not only the accuracy of comb line shaping as well as temporal signal processing and spectral filtering, but also the system’s long-term stability. Finally, we discuss the current limitations and future prospects for optimizing feedback control in microcomb-based MWP transversal filter systems implemented by both discrete components and integrated chips. Our results provide a comprehensive guide for the implementation of feedback control in microcomb-based MWP filter systems in order to improve their performance for practical applications.

Seventy-three male specimens of ”Platorchestia platensis”from Japan were inspected on 13 morphological characters. Most characters revealed high variation. The coxa and propodus of gnathopod 2 and the carpus of pereopod 7 indicated that the specimens comprised two species: Platorchestia pacifica Miyamoto and Morino, 2004 and Demaorchestia joi (Stock and Biernbaum, 1994) sensu lato. Both species were rediagnosed.　Demaorchestia hatakejima Lowry and Myers, 2022 was synonymized to P. pacifica. A key to allied species in Platorchestia and Demaorchestia from Japan and the surrounding countries was given.

A proposed interpretation of the characteristics and causes of the variation in science dynamics for technological evolution is described. The theoretical approach focuses on variability in research topics that guide scientific and technological evolution. Statistical method verifies the proposed approach by applying entropy, measures of dispersion and variance decomposition. The statistical evidence, using data of four main research fields in quantum technologies, suggests the following empirical properties: a) the growth of heterogeneity in research topics is inversely proportional to the age of research fields: recent research fields have a higher variability between topics with uncertainty in evolutionary dynamics, whereas older research fields have a lower variability and more concentration in specific research topics with stable evolution towards clear directions; b) the nature of research fields has a systematic effects in variability guiding the evolution of technologies. The theoretical approach and findings clarify main characteristics and causes of the evolution of science and technologies considering the underlying homogeneity or heterogeneity in research topics, providing main implications to improve the scientific and technological forecasting that may support appropriate R&amp;D investments towards promising research fields and technologies

This paper evaluates the global sustainable competitiveness of three blocks of power (US, China and EU) based on OGT (Organic Growth Theory) structure. In addition, a sensitivity analysis of COVID-19 on three measures of global sustainable competitiveness, economy, environment and social is also presented. OGT is a well-thought-through and standard framework for evaluation of any type of organiza-tion in their pathway to experience truly sustainable growth. GDP (Gross Domestic Product) is used as an economic measure; total carbon emissions equivalent is used as an environmental measure and HMI (Human Development Index) as a social measure. It is concluded that the pandemic has paused the evolution of countries to be truly world centric countries. However, the recovery plans seem to be highly aligned with the OGT scores and it is expected that the scores to improve dramatically.

In recent years, the Canadian dairy sector has faced escalating challenges due to its significant contribution to greenhouse gas emissions, particularly methane. This paper critically examines a spectrum of innovative techniques aimed at mitigating methane emissions within this sector, scrutinizing their cost-effectiveness, efficiency, compatibility with animal welfare standards, and adherence to both existing and prospective Canadian environmental legislations. The discourse commences with an exhaustive overview of contemporary methane reduction methodologies pertinent to dairy farming, followed by a rigorous analysis of their economic feasibility. This includes a detailed cost-benefit analysis, juxtaposed with the efficiency and technological advancements these techniques embody. A pivotal aspect of this examination is the alignment of animal welfare with emission reduction objectives, ensuring that strategies employed do not compromise the health and well-being of dairy cattle. Furthermore, the paper delves into the legislative landscape of Canada, evaluating the congruence of these techniques with current environmental laws and anticipating future regulatory shifts. Performance indicators for emission reduction are critically assessed, establishing benchmarks tailored to the Canadian context. This is complemented by an exploration of the market potential of these innovations, including factors influencing their adoption and scalability in the market. The analysis culminates with a synthesis of case studies and best practices within Canada, offering insights into successful implementations and drawing lessons for future endeavors. This comprehensive approach serves not only to address the immediate environmental and health impacts associated with dairy farming emissions but also contributes significantly to the overarching goal of sustainable development in the agricultural sector.