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.

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.

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.

Conjugated polymers (CP) are frequently doped to modulate their transport and optical properties. Doping alters the intrinsic Gaussian density of states (DOS) by adding Coulomb energy and inducing an exponential tail. Changes in transport or optical properties are mainly tracked back to changes in DOS and carrier hopping rates. Conductivity shows a power-law like increase and the Seebeck coefficient a decrease with carrier concentration. This results in a trade-off between transport properties with doping. However, their modification with doping is still not well understood. Here we show that capture transport and optical properties of doped CPs, by developing a tight-binding Hamiltonian that includes dopant-induced energetic disorder (DID) via Coulomb interactions. We utilize perturbation theory to calculate transition rates between wavefunctions from the calculated eigenenergies and eigenfunctions. With the obtained transition rates, we solve Pauli master equation for occupational probabilities to compute transport properties of doped CPs. Additionally, we capture optical absorption features by simply simulating the joint DOS and IR absorption features via simulated AC conductivity. We anticipate our work to significantly contribute to understanding of underlying transport and optical physics of doped CPs.

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.

Bosna i Hercegovina je u različitim historijskim fazama svog društvenopolitičkog razvoja kao zemlja i kao država egzistirala, svoj zaseban identitet i svoju državnost razvijala u okvirima širih imperija i državnih zajednica. Nakon međunarodnog priznanja i sticanja statusa nezavisne i suverene države 1992. godine i uspostavljanja mira 1995. godine Bosna i Hercegovina se opredjeljuje da postane punopravna članica EU. EU je sa svojom policentričnom strukturom jedinstven primjer ugovorne integracije i saradnje evropskih država. Politički sistem EU zasniva se na kompleksnoj uravnoteženoj strukturi nacionalnog i nadnacionalnog suvereniteta. Opredjeljenje Bosne i Hercegovine da postane punopravna članica EU podrazumijeva prilagođavanje cjeline našeg političko-pravnog i ekonomskog sistema standardima i pravilima šireg sistema EU. Cilj ovog rada jeste pokušaj da se identificiraju najvažnije strukturalne promjene koje prema kriterijima članstva očekuju Bosnu i Hercegovinu na putu ka punopravnom članstvu u EU, a koje će utjecati na promjenu karaktera državnosti Bosne i Hercegovine koja je uspostavljena Općim okvirnim sporazumom za mir u Bosni i Hercegovini 1995. godine.

Asymmetries in sports are common and can lead to various issues; however, different training programs can facilitate change. This study aimed to assess the effects of opposing plyometric programs on tensiomyography lateral symmetry (TMG LS)/inter-limb asymmetry in female athletes’ lower-body muscles, alongside kinematic and body composition parameters. Twenty female subjects from basketball, volleyball, and track and field (sprinting disciplines) were divided into two experimental groups (n = 10 each). Two six-week plyometric programs (two sessions/week) were implemented: the first program (E1) focused on eccentric exercises, depth landings, while the second (E2) emphasized concentric exercises, squat jumps. TMG assessed LS in six muscles: vastus lateralis, vastus medialis, biceps femoris, semitendinosus, gastrocnemius lateralis, and gastrocnemius medialis. A kinematic analysis of the countermovement jump (CMJ) and body composition was conducted using “Kinovea; Version 0.9.4” software and InBody 770, respectively. The results showed significant increases in LS percentages (E1—VL 9.9%, BF 18.0%, GM 10.6% and E2—BF 22.5%, p < 0.05), and a significant large effect in E1 for VL, and in E2 for BF, p < 0.01). They also showed that E1 had a significant effect on VL, and that E2 had a significant large effect on BF (p < 0.01). E1 also led to increased lean muscle mass in both legs (left: 1.88%, right: 2.74%) and decreased BMIs (−0.4, p < 0.05). Both programs improved LS, with E1 enhancing muscle mass and lower-body positioning in CMJ. We recommend future studies use varied jump tests, incorporate 3D kinematic analysis, include male subjects, and examine more muscles to enhance TMG LS analysis.

Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, is a chronic and relapsing condition characterized by persistent inflammation of the gastrointestinal tract. The complex pathogenesis of IBD involves a combination of genetic, environmental, and immune factors, which complicates the achievement of long-term remission. Lower abdominal pain, stomach cramps, blood in stool, chronic diarrhea, fatigue, and unexpected weight loss are common presenting symptoms. Despite the range of therapies and medications, including anti-inflammatory and anti-diarrheal drugs, immunosuppressants, antibiotics, and analgesics aimed at managing symptoms and controlling inflammation, a definitive cure for IBD remains elusive. Current therapy targets inflammation, mainly cytokines, inflammatory receptors, and immune cells, however, there is a need for novel targets to improve clinical outcomes. To identify novel targets and interactions among various factors, we performed a network analysis using various cytokines, TLRs, and NLRP3 inflammasome as inputs. This analysis revealed orosomucoid-like protein 3/ORMDL sphingolipid biosynthesis regulator 3 (ORMDL3) as a central hub gene interacting with multiple factors. While the role of ORMDL3 in IBD pathogenesis is not well-established, our findings and existing literature suggest that ORMDL3 plays a role in inflammation, impaired mitochondrial function, and disrupted autophagy, all contributing to the disease progression. Given its central role in these pathogenic processes, targeting ORMDL3 presents a promising therapeutic target. Modulating ORMDL3 activity could alleviate inflammation, restore mitochondrial function, and enhance autophagy, potentially leading to more effective treatments and improved outcomes for IBD patients.

This research delves into the dynamic shifts in human resource management strategies prompted by the COVID-19 pandemic. It investigates the mediating influence of information technology (IT) challenges on the connection between pandemic-induced international human resource management (IHRM) practices and the competitiveness of multinational corporations (MNCs) in Malaysia with focusing on sustainable development. Through the analysis of data collected from 172 respondents via self-administered questionnaires in Malaysian MNCs across various sectors including education, general services, ICT, property, construction, and healthcare, the study employs partial least squares structural equation modeling (PLS-SEM) to validate the proposed hypotheses. The findings highlight the substantial impact of compensation and staffing practices on technology transfer challenges within MNCs. Furthermore, the study reveals that the implementation of remote work, particularly during and post-lockdowns, is associated with elevated compensation and enhances overall company competitiveness. These outcomes offer theoretical and practical insights, furnishing human resource managers, especially in multinational corporations, with valuable guidance for maintaining competitiveness amidst the disruptions of a pandemic and promoting sustainability in HR practices. By highlighting the potential benefits of working from home in terms of both IHRM outcomes and competitiveness, the study contributes to ongoing discussions about the future of work and the role of technology-enabled practices in driving organizational success and sustainable development.

The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pruning. A new frontier in this area is given by \emph{dynamic, non-uniform} compression methods, which adjust the compression levels (e.g., sparsity) per-block or even per-layer in order to minimize accuracy loss, while guaranteeing a global compression threshold. Yet, current methods rely on heuristics for identifying the"importance"of a given layer towards the loss, based on assumptions such as \emph{error monotonicity}, i.e. that the end-to-end model compression error is proportional to the sum of layer-wise errors. In this paper, we revisit this area, and propose a new and general approach for dynamic compression that is provably optimal in a given input range. We begin from the motivating observation that, in general, \emph{error monotonicity does not hold for LLMs}: compressed models with lower sum of per-layer errors can perform \emph{worse} than models with higher error sums. To address this, we propose a new general evolutionary framework for dynamic LLM compression called EvoPress, which has provable convergence, and low sample and evaluation complexity. We show that these theoretical guarantees lead to highly competitive practical performance for dynamic compression of Llama, Mistral and Phi models. Via EvoPress, we set new state-of-the-art results across all compression approaches: structural pruning (block/layer dropping), unstructured sparsity, as well as quantization with dynamic bitwidths. Our code is available at https://github.com/IST-DASLab/EvoPress.

Zagovornici nekritičkog korištenja informatičke tehnologije u obrazovanju tvrde da je budućnost obrazovanja u kibernetskome prostoru i digitalizaciji. No, digitalna tehnologija nije alternativa za poželjnu interakciju. Zato se danas u suvremenoj pedagogiji i govori o socijalnome biću škole kao polazištu humane škole. Kritičkomu mišljenju treba posvetiti više pozornosti u odgoju i obrazovanju, a pretpostavka za razvoj kritičkoga mišljenja jest uvođenje toga kolegija na svim društveno-humanističkim fakultetima, a onda i kao izborni predmet u osnovnim školama. U tome kontekst predlažemo znatno šire od sadašnjih aspekte sadržaja medijske kulture u čitankama za hrvatski jezik i književnost, osobito u osnovnim školama. Ključne riječi: mediji; manipulacija; indoktrinacija; odgoj za kritičko mišljenje; medijska kultura.

Abstract The extent and rate of release of active substances from topical products must be sufficient to ensure their effectiveness, which depends on selecting the most appropriate formulation. This study examined allantoin emulsions and gel formulations. In water-in-oil (W/O) and oil-in-water (O/W) emulsions, the main emulsifier was varied, while the same gelling agent was used in all formulations to test the effects of oil phase presence and emulsifier type on allantoin release, as well as the formulations’ rheological and textural characteristics. O/W emulsions exhibited similar release rates and the overall amount released over six hours (11–14.8%), while the highest amount of allantoin (20.9%) was released from the gel formulation. Conversely, the amount of allantoin released from the W/O emulsion (0.77%) was insufficient. Experimental data generally fit best with the Higuchi model kinetics. The formulations demonstrated shear-thinning thixotropic behavior. The greatest deviation from the Newtonian type of flow, with the smallest value of constant n (0.106-0.13) and the largest thixotropic loop area (6602.67-8140 Pas-1) were shown by O/W emulsions. The W/O emulsion exhibited the highest constant n (0.70) and smaller hysteresis area (991.23 Pas-1). Firmness and consistency values increased in the order: gel < W/O emulsion  < O/W emulsions. The O/W emulsions showed similarity in microstructure and textural characteristics, likely explaining their similar release behavior. Graphical Abstract

The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pruning. A new frontier in this area is given by dynamic, non-uniform compression methods, which adjust the compression levels (e.g., sparsity) per-block or even per-layer in order to minimize accuracy loss, while guaranteeing a global compression threshold. Yet, current methods rely on estimating the importance of a given layer, implicitly assuming that layers contribute independently to the overall compression error. We begin from the motivating observation that this independence assumption does not generally hold for LLM compression: pruning a model further may even significantly recover performance. To address this, we propose EvoPress, a novel evolutionary framework for dynamic LLM compression. By formulating dynamic compression as a general optimization problem, EvoPress identifies optimal compression profiles in a highly efficient manner, and generalizes across diverse models and compression techniques. Via EvoPress, we achieve state-of-the-art performance for dynamic compression of Llama, Mistral, and Phi models, setting new benchmarks for structural pruning (block/layer dropping), unstructured sparsity, and quantization with dynamic bitwidths. Our code is available at https://github.com/IST-DASLab/EvoPress}.

Effective real-time monitoring technique is crucial for detecting material degradation and maintaining the structural integrity of nuclear systems to ensure both safety and operational efficiency. Traditional physical sensor systems face limitations such as installation challenges, high costs, and difficulties in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a promising solution by enhancing physical sensor capabilities to monitor critical degradation indicators like pressure, velocity, and turbulence. However, conventional machine learning models struggle with real-time monitoring due to the high-dimensional nature of reactor data and the need for frequent retraining. This paper explores the use of Deep Operator Networks (DeepONet) within a digital twin (DT) framework to predict key thermal-hydraulic parameters in the hot leg of an AP-1000 Pressurized Water Reactor (PWR). In this study, DeepONet is trained with different operational conditions, which relaxes the requirement of continuous retraining, making it suitable for online and real-time prediction components for DT. Our results show that DeepONet achieves accurate predictions with low mean squared error and relative L2 error and can make predictions on unknown data 160,000 times faster than traditional finite element (FE) simulations. This speed and accuracy make DeepONet a powerful tool for tracking conditions that contribute to material degradation in real-time, enhancing reactor safety and longevity.