In the last ten years, social networks have appeared as main opinion-sharing and discussionenabling resources. At the same time, the development of machine learning (ML) and natural language processing (NLP) technologies has allowed for new approaches to analyzing the huge quantities of data created by users. This research uses data loading, class imbalance handling, text preprocessing and tokenization, sentiment analysis, and model assessment techniques to analyze the sentiment of the tweets. Using metrics like accuracy, precision, recall, and F1 score the study reveals that SVM and Logistic Regression are the most suitable machine-learning models for this purpose. SVM attained an accuracy of 90% for training and 77% for testing while Logistic Regression showed 83% for training and 78% for testing.

The purpose of this paper is to explore how New York City is perceived as a tourist destination. The quantitative research method offered a structured approach to collecting data, allowing for the analysis and quantification of participant responses. The convenience sample consisted of 108 respondents. The aim of this study is to analyze the factors influencing both the attraction to and deterrence from visiting New York City as a tourist destination, as well as to gauge overall perceptions of the city among potential visitors. Through a comprehensive survey, key motivators such as iconic monuments, a vibrant cultural scene, and accessibility were identified as primary draws for tourists. Respondents expressed strong support for experiencing attractions like the Statue of Liberty and Broadway shows, highlighting the significance of cultural richness and unique architectural features. Conversely, the study also revealed significant barriers to visitation. The high cost of accommodation emerged as the most prominent deterrent, underscoring economic accessibility as a critical factor in tourism decision-making. Other concerns included congestion, security issues, and limited public transport options, which may dissuade potential visitors from traveling to the city. In terms of overall impressions, the results showed that a vast majority of respondents view New York City as an iconic and glamorous location with a diverse cultural landscape. Most participants expressed a willingness to recommend the city to friends and family, although a notable percentage exhibited reservations. Furthermore, the analysis emphasized the importance of affordable options for activities and attractions, suggesting that enhancing economic accessibility could positively influence visitation rates. Overall, the findings provide valuable insights for tourism agencies and local authorities. By addressing key concerns and highlighting the city's unique offerings, strategies can be developed to enhance New York City's appeal and accessibility, ultimately boosting its status as a leading tourist destination.

African swine fever virus (ASFV) is the etiological agent of African swine fever, a highly contagious hemorrhagic disease affecting both wild boars and domestic pigs with lethality rates up to 100%. Until now, the most effective measure to prevent an outbreak of ASFV was early detection. In this situation, whole genome sequencing (WGS) allows the gathering of detailed information about the identity and epidemiology of the virus. However, due to the large genome size and complex genome ends, WGS is challenging. Current WGS workflows require either elaborate enrichment methods or are based on tiled PCR approaches, which are susceptible to genetic differences between ASFV strains. To overcome this, we developed a novel approach for WGS of ASFV, using the Phi29 DNA polymerase-based multiple displacement amplification in combination with only seven primers. Furthermore, we applied an alkaline-based DNA denaturation step to significantly increase the number of viral reads, which resolves the near-full genome of ASFV. This novel isothermal WGS approach can be used in authorized laboratories for the genomic epidemiological analysis of ASFV outbreaks caused by different genotypes.

Rapid reaction-thermal diffusion during frontal polymerization (FP) with variations in initial and boundary conditions destabilizes the planar mode of front propagation, leading to spatially varying complex hierarchical patterns in polymeric materials. Although modern reaction-diffusion models can predict the patterns resulting from unstable FP, the inverse design of patterns, which aims to retrieve process conditions that produce a desired pattern, remains an open challenge due to the nonunique and nonintuitive mapping between process conditions and patterns. In this work, we propose a novel probabilistic generative model named univariate conditional variational autoencoder (UcVAE) for the inverse design of hierarchical patterns in FP-based manufacturing. Unlike the cVAE, which encodes both the design space and the design target, the UcVAE encodes only the design space. In the encoder of the UcVAE, the number of training parameters is significantly reduced compared to the cVAE, resulting in a shorter training time while maintaining comparable performance. Given desired pattern images, the trained UcVAE can generate multiple process condition solutions that produce high-fidelity hierarchical patterns.

This paper investigates the dynamics of non-autonomous cooperative systems of difference equations with asymptotically constant coefficients. We are mainly interested in global attractivity results for such systems and the application of such results to evolutionary population cooperation models. We use two methods to extend the global attractivity results for autonomous cooperative systems to related non-autonomous cooperative systems which appear in recent problems in evolutionary dynamics.

Heat dissipation and thermal management is a rising concern for nanoelectronic devices and threatens to curtail their adoption in integrated circuits, sensors, and energy converters. Joule heating due to dissipation in the channel region of nanoelectronic devices causes increased temperature and may lead to mobility degradation and long-term reliability issues. Here we study thermal transport and cross-plane thermal boundary conductance in a variety of “beyond graphene” 2D materials and few-layer stacks on several amorphous and crystalline substrates using a combination of first principles methods and Boltzmann transport of phonons. We employ machine learning to accelerate the discovery of 2D-substrate pairings with enhanced thermal conductance. Beyond that, we couple electronic and thermal transport to study dissipation in field effect MOS transistors and show that heat dissipation is non-uniform and that self-heating reduces mobility. We find that judicious selection of the number of layers and substrate can significantly reduce the deleterious effects of Joule heating.

Despite their potential for miniaturization, electronic devices made of 2D materials face thermal management challenges due to their reduced dimensionality, which can limit their efficiency and lifespan. Low thermal boundary conductance (TBC) is one major limiting factor in realizing efficient heat transfer to the substrate. Due to the roughness at the interface, the adhesion of 2D materials to their substrates tend to be weak, resulting in low TBC. Therefore, to improve heat flow from the 2D material, we need to discover novel ways of increasing TBC. In this study, we have used a numerical model combined with first-principles DFPT simulations to investigate a possible method to increase TBC using an electrostatic field due to gate voltage. Our study shows that electrostatic pressure can be used to effectively enhance TBC for an interface formed by a 2D material and a rough substrate. We find that electrostatic pressure can improve TBC by more than 300 % when an electric field of 3 V/nm is applied. This is due to an improvement in the vdW spring coupling constant, which shows a more than two-fold increase when a substrate roughness of 1.6 nm and correlation length of 10.8 nm, 2D-material's bending stiffness of 1.5 eV, and adhesion energy of 0.1 $J/m^{2}$ were used. We show that TBC is enhanced more when the substrate has a large roughness and small correlation length, and the $2D$ material has a large bending stiffness. This is because a stiff 2D sheet resist bending when voltage/pressure is applied, thus causing it to press more on the roughness peaks, resulting in a tremendous increase in the coupling constants at the peaks in the atomically rough surface of the substrate. However, a flexible 2D material can easily bend to conform to the topography of the rough substrate when voltage/pressure is applied, which makes the coupling constants across the interface more uniform. Here we show that TBC is enhanced more when adhesion is weak because a weak vdW bond is easily compressed by external pressure. Therefore, our study provides valuable information that can be applied in designing electronic devices with efficient heat management by using gate voltage, substrate roughness combined with the mechanical properties.

Raman thermometry has gained immense popularity for probing the thermal properties of nanostructured materials due to its excellent spatial resolution and lack of contact error; however, it has a key weakness in its temperature resolution. In this work, we aim to improve the temperature resolution of Raman thermometry through training neural networks to track the locations, widths, and relative heights of multiple peaks at once. We find that in training a multilayer perceptron on 13 pixel values representing the Raman peak of silicon, the variance and standard deviation in thermal conductivity predictions can be reduced as compared to those resulting from the predominant method of tracking the peak location as it shifts with temperature. We expect that this work may contribute to greater accuracy of thermal measurements from non-contact Raman-based techniques and thereby improve the consensus on the thermal properties of 2D materials.

Plant pathogenic fungi have evolved different strategies to interfere with plant defense mechanisms. The well described fungal plant pathogen Fusarium graminearum is not only able to produce trichothecene toxins like deoxynivalenol, but also the plant hormone auxin. Highly elevated levels of auxin and auxin derivatives such as IAA-glucoside or IAA amino-acid conjugates were observed in wheat cultivar Apogee infected with F. graminearum. We report that F. graminearum is able to cleave tryptamine-derived hydroxycinnamic acid amides, e.g. the defense compound coumaroyl-tryptamine. In this study we investigated copper amine-oxidases, candidate genes for auxin biosynthesis converting tryptamine into the IAA precursor indole-3-acetyldehyde. After consecutive knock outs of all seven copper amine oxidases the resulting septuple knock out strain had strongly reduced ability to produce auxin. Virulence of the septuple mutant was significantly impaired while DON production in planta was comparable to the wild type. We conclude that F. graminearum, often presumed to be a simple nectrotroph, has a biotrophic phase and is able to employ plant defense compounds by converting them into defense suppressing auxin.

Uloga ustavnog sudstva u svakoj savremenoj demokratski uređenoj državi od nemjerljivog je značaja za očuvanje njenog ustavnopravnog poretka i osnovnih principa pravne države i vladavine prava, ustavnosti i zakonitosti. Pozicija Ustavnog suda Bosne i Hercegovine u sistemu državnih institucija zauzima posebno mjesto, zbog specifičnosti njenog državnog uređenja, stepena demokratskog razvoja i opredijeljenosti političkih aktera ka poštivanju uvodno navedenih principa. Da bi Ustavni sud Bosne i Hercegovine odgovorio svojoj zahtjevnoj ulozi “čuvara” Ustava Bosne i Hercegovine i njenog ustavnopravnog poretka, posebno je važno osigurati da u sastav Ustavnog suda Bosne i Hercegovine budu izabrani samo i isključivo nezavisni, nepristrasni, stručni i profesionalni pravnici visokih moralnih kvaliteta, bez obzira na njihovu etničku pripadnost, oslobođeni mogućnosti bilo kakvih političkih ili drugih uticaja na njihove stavove, mišljenja i odluke, osigurati objektivne i nezavisne procedure donošenja odluka bez bilo kakvih institucionalnih ili vaninstitucionalnih uticaja i pritisaka, posebno kada su u pitanju procedure donošenja odluka koje se ne mogu i ne smiju vezati za etničku ili entitetsku pripadnost pojedinog sudije Ustavnog suda Bosne i Hercegovine. Upravo kroz mehanizme etnicizacije i politizacije pitanja sastava, izbora sudija i načina donošenja odluka Ustavnog suda Bosne i Hercegovine, a posmatrajući ulogu Ustavnog suda Bosne i Hercegovine u očuvanju državnosti i državnopravnog kontinuiteta, svjedoci smo očiglednom podrivanju ustavnopravnog poretka države Bosne i Hercegovine.