Response Fluctuations Research Articles

Probability‐Based Finite‐Time Security Control for Switched Stochastic Systems via a Novel Event‐Triggered Mechanism and Its Application

ABSTRACTThis article discusses the finite‐time boundedness (FTB) issue and ‐gain analysis for switched stochastic systems, particularly under the dual influence of DoS attacks and false data injection attacks, by employing a novel event‐triggered mechanism. Different from the moment calculation method, a probability‐based FTB is studied which offers more pertinence. To obtain a less conservative condition of FTB, a switched Lyapunov function is constructed. Additionally, a memory‐based dynamic event‐triggered mechanism is designed to reduce the amounts of triggering and mitigate the state response fluctuations. Based on the incomplete information above, state feedback controllers are devised to satisfy stochastic FTB, ensuring the successful attainment of finite‐time ‐gain. Sufficient conditions are cast into a convex optimization problem by LMIs which can be solved easily. Finally, a compared numerical example and an RLC series circuit are adopted to demonstrate the availability of the theoretical results.

Winding Back the Clock on Advanced Therapies: It's Time to Get Smart.

Our language affects patients' perceptions of therapies. In Parkinson's disease, emergent response fluctuations and dyskinesias typically trigger conversations around commencing an "Advanced Therapy" which carries notions of Advanced Disease. The patient, resolute in their commitment to fighting the disease, is misled. Chasing reassurance that their disease has not yet progressed considerably; they may therefore resist a potentially life-changing therapy. Instead, we should offer a "Smart Therapy". This term more accurately and positively describes therapies on offer that stabilize response fluctuations and improve quality of life, without a focus on the negative connotations of progression to more advanced disease.

On identifying dynamic length scales in crystal plasticity

Materials are often heterogeneous at various length scales, with variations in grain structure, defects, and composition which has a strong influence on the emergent macroscopic plastic behavior. In particular, heterogeneities lead to fluctuations in the plastic response in the form of jerky flow and ubiquitous strain bursts. One of the crucial aspects of plasticity modeling is scale bridging: In order to deliver physically correct crystal plasticity models, one needs to determine relevant microstructural length scales. In this paper we advance the idea that continuum descriptions of dislocation mediated plasticity cannot neglect dynamic correlations related to the avalanche behavior. We present an extensive weakest link analysis of crystal plasticity by means of three-dimensional discrete dislocation dynamics simulations with and without spherical precipitates. We investigate strain bursts and related length scales and conclude that while sufficiently strong obstacles to dislocation motion tend to confine the dislocation avalanches within well-defined sub-volumes, in pure dislocation systems the avalanches may span the system, implying that the dynamic length scale is, in fact, the size of the entire sample. Consequences of this finding on continuum modeling are thoroughly discussed.

Influence of d-d transition electrons and thickness variations on the excited-state optical properties: Nonlinear optical characterization of Cr2O3 thin film

The structural, morphological, linear, and nonlinear optical properties of Cr2O3 thin films with different thicknesses deposited by the thermal deposition, were measured. The structural and morphological parameters of films, determined by XRD, FESEM, and AFM images, are compared to the linear and nonlinear optical characteristics of these media. The bandgap of prepared thin films was obtained from DRS spectra. The electron effective mass (me∗/m0), linear refractive index (n0), optical static, and high frequency dielectric constant (ε0 , ε∞) values were calculated by using the band gap energy values. Increasing the thicknesses of thin films causes to decrease the band gap, an increase RMS, increase the size of nanoparticles and the nonlinear responses of thin films. The high magnitude of n2, and β belonged to the Cr2O3 (300 nm-thickness) in the order of 10−5 cm2/W, and 10−1 cm/W respectively. The fluctuations in nonlinear responses observed at different thicknesses are attributed to d-d transitions and intraband scattering of equilibrium electrons influenced by laser radiation, as indicated by the nonlinearity data. The considerably elevated refractive non-linearity values in the analyzed film materials suggest their potential for practical applications in optoelectronic devices.

Effects of website-based risk communication of radio-frequency electromagnetic fields on general public.

Radio-frequency electromagnetic fields (RF-EMFs) are utilized in communications and appliances and are indispensable in daily life. However, some people have concerns about the adverse health effects of RF-EMFs; therefore, effective risk communication (RC) is needed in this field. In this study, we investigate public attitudes towards RF-EMFs and examine the impact of RC via a website on these attitudes and objective knowledge. Three web surveys were conducted over 10 weeks with the same participants. The questionnaires were conducted at three different time points with 5-week intervals: baseline survey (T1), RC evaluation survey (T2), and follow-up survey (T3). Participants of T2 were randomly recruited from among those of T1, and participants of T3 were randomly selected from among the T2 respondents. Approximately half of the respondents in each of T2 and T3 were assigned to the control group. Twelve items regarding attitudes toward RF-EMFs and objective knowledge were evaluated in all surveys (T1-T3). After removing low-engagement data, the number of valid answers was 782 in T3. Differences between T1 and T2 (Sub T1-T2) and T1 and T3 (Sub T1-T3) were analyzed. Participant selection was randomized and the authors were blind to this selection until analysis. Four clusters were identified: Cluster 1 (Non-anxious, 25.0%), Cluster 2 (Anxious, 16.0%), Cluster 3 (Low-interest, 40.5%), and Cluster 4 (High-interest, 18.5%). A decrease in subjective RF-EMF exposure levels was noted in Cluster 2 immediately after website viewing. Temporary increases and decreases in health concerns about RF-EMF usage activities were observed in Clusters 1 and 2, respectively, immediately after viewing. Clusters 1 and 3 showed a temporal decrease in needs for RF-EMF usage activities at T2 but it returned to the baseline level 5 weeks later. Cluster 4 was less responsive to the risk communication. Subanalysis stratified by gender and age showed fluctuations in responses, especially in Clusters 1 and 2. We demonstrate the effectiveness of RF-EMF risk communication via websites, particularly for Cluster 2. The results of this study showed that offering objective and comprehensible information through a website can significantly reduce concerns and perceived risks related to RF-EMFs.

Neuronal firing rate diversity lowers the dimension of population covariability.

Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the activity of neurons. These two features define the quality of neuronal representation. We link these two aspects of population response using a recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Using our relation we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.

Intense intrusion of low-oxygen waters into mid-Cambrian surface ocean carbonate factories

The Phanerozoic surface ocean is characterized by its high dissolved oxygen content owing to mixing with the atmosphere. However, atmospheric oxygen levels varied in the early Paleozoic and it remains unclear whether the surface ocean was susceptible to significant redox fluctuations in response to extreme environmental events. In this study, we probed the redox structures of shallow middle Cambrian marine depositional environments across the North China Platform, ranging from open tidal flats to relatively deep subtidal environments. We utilized a combination of least diagenetically altered carbonate materials (such as ooid cortices, calcimicrobes, and their fringing cements), as well as in situ element measurement and imaging techniques. By analyzing a set of redox-related elements (e.g., Ce anomaly, Zn/Fe molar ratio, Mn and Cr) and mineralogical proxies (hydrogenetic Fe oxides), we revealed a stratified redox structure in the Drumian surface oceans. Compared to earlier Drumian conditions, late Drumian surface oceans experienced significant intrusions of ferruginous waters, probably reaching into shallow subtidal environments with water depths less than 10 m. Furthermore, we identified shallow subtidal microbial O2-producing factories, characterized by dendritic Epiphyton thalli. These calcimicrobes exhibited more oxygenated signatures (negative Ce anomalies and enrichment of hydrogenetic Fe oxides) relative to contemporaneous less oxic shallower and deeper environments. This finding indicates that they produced oxygen oases or refuges during periods of both normal and poor dissolved O2 conditions. This study has the potential to broaden our understanding of redox conditions and microbial oxygen-producing mechanisms in the surface ocean, particularly during intervals characterized by low atmospheric oxygen levels or episodic anoxic events.

Involuntary motor responses are elicited both by rare sounds and rare pitch changes

Unpredictable deviations from an otherwise regular auditory sequence, as well as rare sounds following a period of silence, are detected automatically. Recent evidence suggests that the latter also elicit quick involuntary modulations of ongoing motor activity emerging as early as 100 ms following sound onset, which was attributed to supramodal processing. We explored such force modulations for both rare and deviant sounds. Participants (N = 29) pinched a force sensitive device and maintained a force of 1–2 N for periods of 1 min. Task-irrelevant tones were presented under two conditions. In the Rare condition, 4000 Hz tones were presented every 8-to-16 s. In the Roving condition, 4000 Hz and 2996 Hz tones were presented at rate of 1 s, with infrequent (p = 1/12) frequency changes. In the Rare condition, transient force modulations were observed with a significant increase at ~ 234 ms, and a decrease at ~ 350 ms. In the Roving condition with low frequency deviant tones, an increase in force was observed at ~ 277 ms followed by a decrease at ~ 413 ms. No significant modulations were observed during perception of high frequency deviants. These results suggest that both rare silence-breaking sounds and low-pitched deviants evoke automatic fluctuations of motor responses, which opens up the possibility that these force modulations are triggered by stimulus-specific change-detection processes.

Climate Change and Vegetation Greening Jointly Promote the Increase in Evapotranspiration in the Jing River Basin

Within the Earth’s terrestrial environment, evapotranspiration significantly contributes to the hydrological cycle, accounting for around 80% of the precipitation on landmasses to be reintroduced into the atmosphere. This mechanism profoundly affects the distribution and availability of surface water resources throughout the ecosystem. Gaining insight into the factors influencing local evapotranspiration fluctuations in response to varying climatic and vegetative scenarios is crucial for effective water management strategies and rehabilitating ecosystem resilience. To this end, our study focuses on the Jing River Basin in the Loess Plateau, utilizing multi-source remote sensing data and climatic information to investigate the spatiotemporal dynamics of evapotranspiration from 1984 to 2018 through the application of the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model. Our research results indicate a general ascending tendency in evapotranspiration across the investigated region, demonstrating a notably discernible escalation at a pace of approximately 3.11 mm/year (p < 0.01), with an annual vegetation ET volume reaching 533.88 mm. Across different vegetation types in the Jing River Basin between 1984 and 2018, the mean yearly ET was observed to be highest in forests (572.88 mm), followed by croplands (564.74 mm), shrublands (536.43 mm), and grasslands (503.42 mm). The leaf area index (LAI) demonstrated the strongest partial correlation with ET (r = 0.35) and contributed the most significantly to the variation in ET within the Jing River Basin (0.41 mm/year). Additionally, LAI indirectly influences ET through its impact on vapor pressure deficit (VPD), precipitation (Pre), and temperature (Temp). Radiation is found to govern most ET changes across the region, while radiation and precipitation notably affected ET by modulating air temperature. In summary, these radiant energy changes directly affect the evaporation rate and total evapotranspiration of surface water. It provides important support for understanding how evapotranspiration in the Jing River Basin is adjusting to climate change and increased vegetation cover. These findings serve as a theoretical foundation for devising sustainable vegetation restoration strategies to optimize water resource utilization within the region.

Network state dynamics underpin basal craving in a transdiagnostic population.

Emerging fMRI methods quantifying brain dynamics present an opportunity to capture how fluctuations in brain responses give rise to individual variations in affective and motivation states. Although the experience and regulation of affective states affect psychopathology, their underlying time-varying brain responses remain unclear. Here, we present a novel framework to identify network states matched to an affective experience and examine how the dynamic engagement of these network states contributes to this experience. We apply this framework to investigate network state dynamics underlying basal craving, an affective experience with important clinical implications. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (total N = 252), we utilized connectome-based predictive modeling (CPM) to identify brain networks predictive of basal craving. An edge-centric timeseries approach was leveraged to quantify the moment-to-moment engagement of the craving-positive and craving-negative subnetworks during independent scan runs. We found that dynamic markers of network engagement, namely more persistence in a craving-positive network state and less dwelling in a craving-negative network state, characterized individuals with higher craving. We replicated the latter results in a separate dataset, incorporating distinct participants (N = 173) and experimental stimuli. The associations between basal craving and network state dynamics were consistently observed even when craving-predictive networks were defined in the replication dataset. These robust findings suggest that network state dynamics underpin individual differences in basal craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our affective experiences.

‘I don’t want to be seen as period prone’: An exploration of psychological strategies used across the menstrual cycle

Despite increased attention on the menstrual cycle in sport, research has focused largely on physiological changes, with relatively little consideration for fluctuations in psychological responses and coping strategies used in training and competition. This study's primary focus was to explore athlete's coping strategies throughout the menstrual cycle. Participants ( n = 12) took part in a focus group or interview, facilitated by reflections from a diary and mapping exercise. Reflexive thematic analysis was used to analyse the data, generating three themes: Impact of symptoms, coping strategies, and contextual constraints. Participants had increased awareness of physiological over psychological fluctuations, and associated symptoms were considered to impact practicalities of sport and performance. Participants used avoidance, acceptance, and planning to cope with the impact of menstrual symptoms. Coping options were influenced by contextual constraints, including the level of competition and type of sport. The findings showed that some participants lacked menstrual health literacy, especially in relation to psychological fluctuations. Continued efforts to raise awareness around the psychological symptoms of the menstrual cycle and its impact on performance and well-being are needed, not only for the athlete but importantly for the coaching population too. Specifically, consideration of the sporting culture and the space in which athletes feel they can communicate their coping strategies could be pivotal in moving forwards to help develop ‘period pro’ athletes.

Hindcasting long‐term data unveils the influence of a changing climate on small mammal communities

AbstractAimShifting climates are reshaping ecosystems globally and are projected to intensify over the coming century. Understanding how biodiversity will respond to these shifts is crucial for developing effective climate adaptation measures. We generate predictive models built from long‐term data to hindcast historic fluctuations in small mammal abundances as they have responded to shifting rainfall and fire conditions. This data set serves as the basis for predicting historical variations (hindcasting) in small mammal abundances, allowing us to examine their responses to decadal changes in fire and rainfall conditions within our study landscape.LocationAustralia (Victoria).TaxaSmall mammals (Mammalia).Time Period1970–2022.MethodsSmall mammal abundance was surveyed at 36 long‐term trapping sites and modelled against coinciding fire history, vegetation productivity and rainfall using generalized additive mixed models. Six species were then used in predictive modelling against these variables for the decades preceding our monitoring programme (1970–2007).ResultsAll species abundances increased with higher rainfall. Time since fire was also an important variable in all but one species model, with species displaying varying responses to time since fire. Hindcasting predictions for small mammal abundances varied with some species showing marked declines over time. Clear trends emerged, indicating more volatile population fluctuations in response to intensified fire and rainfall extremes in the 21st century. This suggests that periods of higher rainfall and less frequent fire events in the decades preceding our monitoring period supported higher and more stable small mammal abundances.ConclusionsNative species show distinct sensitivity to the combined effects of drought and fire, which has occurred in recent times. Intensification of these drivers has caused increased volatility in small mammal abundances with low abundance extremes occurring more frequently.

A novel methodology for gamma-ray spectra dataset procurement over varying standoff distances and source activities

The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including expected pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ratio (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) model are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing a problem space, ranging from 5 cm to 100 cm standoff distances and 5 μCi–100 μCi of 137Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.

Exploring the effect of thin film thicknesses on linear and nonlinear optical properties of MoO3 thin film

The structural, morphological, linear, and nonlinear optical properties of MoO3 thin films deposited by thermal deposition were measured. The effect of various thicknesses of films was studied. The structural and morphological parameters of films, determined by x-ray diffraction, field emission scanning electron microscopy, and AFM images, are compared to the linear and nonlinear optical characteristics of these media. The bandgap of the prepared thin films was obtained from the diffuse reflectance spectroscopy spectra. The electron’s effective mass (me*/m0), linear refractive index (n0), and optical static and high frequency dielectric constant (ɛ0, ɛ∞) values were calculated by using the bandgap energy values. Increasing the thicknesses of thin films decreased the bandgap, increased the root mean square, and increased the size of nanoparticles and the nonlinear response of thin films. The high magnitude of n2 and β was because of MoO3 (300 nm-thickness), which were of the order of 10−5 cm2/W and 10−1 cm/W, respectively. The fluctuations in nonlinear responses observed at different thicknesses are attributed to d–d transitions and intraband scattering of equilibrium electrons influenced by laser radiation, as indicated by the nonlinearity data. The considerably elevated refractive nonlinearity values in the analyzed film materials suggest their potential for practical application in optoelectronic devices.

Physiological Response to the COVID-19 Vaccine: Insights From a Prospective, Randomized, Single-Blinded, Crossover Trial.

Rapid development and implementation of vaccines constituted a crucial step in containing the COVID-19 pandemic. A comprehensive understanding of physiological responses to these vaccines is important to build trust in medicine. This study aims to investigate temporal dynamics before and after COVID-19 vaccination in 4 physiological parameters as well as the duration of menstrual cycle phases. In a prospective trial, 17,825 adults in the Netherlands wore a medical device on their wrist for up to 9 months. The device recorded their physiological signals and synchronized with a complementary smartphone app. By means of multilevel quadratic regression, we examined changes in wearable-recorded breathing rate, wrist skin temperature, heart rate, heart rate variability, and objectively assessed the duration of menstrual cycle phases in menstruating participants to assess the effects of COVID-19 vaccination. The recorded physiological signals demonstrated short-term increases in breathing rate and heart rate after COVID-19 vaccination followed by a prompt rebound to baseline levels likely reflecting biological mechanisms accompanying the immune response to vaccination. No sex differences were evident in the measured physiological responses. In menstruating participants, we found a 0.8% decrease in the duration of the menstrual phase following vaccination. The observed short-term changes suggest that COVID-19 vaccines are not associated with long-term biophysical issues. Taken together, our work provides valuable insights into continuous fluctuations of physiological responses to vaccination and highlights the importance of digital solutions in health care. RR2-10.1186/s13063-021-05241-5.

Stability of Long-Term Therapeutic Responses to Tralokinumab in Adults with Moderate-to-Severe Atopic Dermatitis

Introduction: To ensure minimal residual disease and to prevent relapses, recently published consensus reports have defined optimal long-term treatment targets for atopic dermatitis (AD).1,2 Tralokinumab, a monoclonal antibody specifically neutralizing interleukin-13, is approved for the treatment of moderate-to-severe AD. ECZTEND (NCT03587805) is an ongoing open-label, 5-year extension trial investigating the long-term safety and efficacy of tralokinumab 300 mg every other week (Q2W) plus optional topical corticosteroids (TCS). Objectives of this analysis were to determine the proportion of patients treated for up to 4 years with tralokinumab in AD clinical trials who: 1) exhibit stable improvement, with no or minimal fluctuations, in lesion extent and severity long-term (ie, response in ≥80% of attended visits), and 2) exhibit a stable long-term composite response (ie, up to 4 years of tralokinumab treatment and response in ≥80% of attended trial visits) in signs and symptoms of AD, and quality of life based on recent treat-to-target recommendations (EASI ≤7 and either DLQI ≤5 or Itch NRS ≤4). Methods: This post hoc analysis included 347 patients who were continuously treated with tralokinumab for 52 weeks in the identically designed phase 3 monotherapy trials ECZTRA 1&2 and subsequently for up to 152 weeks in ECZTEND as of the April 30, 2022 data cutoff. Stability of long-term response, with no or minimal fluctuations, was defined as meeting the target endpoints at ≥80% of attended visits between Weeks 16-152 in ECZTEND. Endpoints analyzed were EASI ≤7, EASI ≤2, and a composite long-term treatment target: EASI ≤7 and either DLQI ≤5 or worst weekly pruritus NRS ≤4. Results: A stable EASI ≤7 response (at ≥80% of attended visits) was observed in 70.2% (233/332) of tralokinumab-treated patients over Weeks 16-152 of ECZTEND. A stable EASI ≤2 response was observed in 34.0% (113/332) of patients, and a long-term optimal composite target, EASI ≤7 and either DLQI ≤5 or Itch NRS ≤4, was observed in 60.5% (201/332) of patients. Conclusions: High proportions of clinical trial patients maintained stable responses, with no or minimal fluctuations in efficacy, with continued tralokinumab 300 mg Q2W plus optional TCS for up to 4 years of treatment.

Metabolic Aspects of Lentil-Fusarium Interactions.

Fusarium oxysporum f. sp. lentis (Fol) is considered the most destructive disease for lentil (Lens culinaris Medik.) worldwide. Despite the extensive studies elucidating plants' metabolic response to fungal agents, there is a knowledge gap in the biochemical mechanisms governing Fol-resistance in lentil. Τhis study aimed at comparatively evaluating the metabolic response of two lentil genotypes, with contrasting phenotypes for Fol-resistance, to Fol-inoculation. Apart from gaining insights into the metabolic reprogramming in response to Fol-inoculation, the study focused on discovering novel biomarkers to improve early selection for Fol-resistance. GC-MS-mediated metabolic profiling of leaves and roots was employed to monitor changes across genotypes and treatments as well as their interaction. In total, the analysis yielded 178 quantifiable compounds, of which the vast majority belonged to the groups of carbohydrates, amino acids, polyols and organic acids. Despite the magnitude of metabolic fluctuations in response to Fol-inoculation in both genotypes under study, significant alterations were noted in the content of 18 compounds, of which 10 and 8 compounds referred to roots and shoots, respectively. Overall data underline the crucial contribution of palatinitol and L-proline in the metabolic response of roots and shoots, respectively, thus offering possibilities for their exploitation as metabolic biomarkers for Fol-resistance in lentil. To the best of our knowledge, this is the first metabolomics-based approach to unraveling the effects of Fol-inoculation on lentil's metabolome, thus providing crucial information related to key aspects of lentil-Fol interaction. Future investigations in metabolic aspects of lentil-Fol interactions will undoubtedly revolutionize the search for metabolites underlying Fol-resistance, thus paving the way towards upgrading breeding efforts to combat fusarium wilt in lentil.

Hysteresis-loop phenomena in disordered ferromagnets with demagnetizing field and finite temperature.

Using numerical simulations, we investigate the impact of the demagnetization field and finite temperature on the hysteresis phenomena in disordered ferromagnetics systems. We model the behavior of thin systems employing the thermal nonequilibrium random field Ising model driven by a finite-driving rate protocol to study the shape of the hysteresis loop and demagnetization line and the magnetization fluctuations for varied parameters. Our results reveal a significant interplay of the disorder, the demagnetizing fields, and thermal fluctuations. In particular, at a fixed disorder and temperature, the increasing demagnetization coefficient gradually prologues the magnetization reversal process, altering the multifractal nature of the magnetization fluctuations. The process accompanies the appearance of extended linear segments in the hysteresis loop and changes in the demagnetization line while practically preserving the value of the coercive field and slightly changing the remanent magnetization. On the other hand, increasing temperature expands the system's response fluctuations and narrows the loop, affecting the coercive field and remanent magnetization. The interplay of thermal fluctuations and demagnetizing fields fully manifests in limiting the large-scale magnetization fluctuations, revealed by the multifractal spectra and the scaling functions of the avalanches. Our research offers new modeling perspectives with a more realistic scenario and provides insight into new hysteresis loop phenomena relevant to theoretical analysis and applications.

The role of Nuclear Magnetic Resonance (NMR) spectroscopy in cattle metabolism

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool that has significantly advanced the understanding of cattle metabolism. Nuclear Magnetic Resonance (NMR) spectroscopy plays a pivotal role in the study of cattle metabolism, offering distinct advantages over other spectrometric methods. NMR spectroscopy is a powerful analytical tool that provides detailed molecular insights by exploiting the magnetic properties of atomic nuclei. Unlike mass spectrometry and infrared spectroscopy, NMR does not require extensive sample preparation or destruction, preserving the integrity of biological samples. This non-invasive nature is particularly beneficial for longitudinal studies in cattle, where metabolic changes over time are of interest. One of the key strengths of NMR spectroscopy is its ability to simultaneously detect and quantify a broad range of metabolites in complex biological matrices, such as blood, urine, and tissue extracts. This comprehensive metabolic profiling is crucial for understanding the biochemical pathways and physiological states in cattle. NMR's high reproducibility and quantitative accuracy further enhance its suitability for metabolic studies, enabling precise monitoring of metabolic fluctuations in response to dietary changes, environmental stressors, or disease conditions. NMR spectroscopy also offers unique advantages in elucidating structural information about metabolites. Through multidimensional NMR techniques, researchers can determine the molecular structure and conformation of metabolites, providing deeper insights into metabolic functions and interactions. This structural elucidation is often challenging with other spectrometric methods, which may lack the resolution or require derivatization of samples. Moreover, NMR spectroscopy's non-destructive nature allows for the analysis of living tissues and in vivo studies, facilitating real-time monitoring of metabolic processes. This capability is instrumental in studying dynamic metabolic responses and adaptations in cattle under different physiological states. Additionally, the development of advanced NMR techniques, such as high-resolution magic angle spinning (HR-MAS) and hyperpolarization, has further expanded the scope of NMR applications in metabolic research. NMR spectroscopy stands out as a superior method for studying cattle metabolism due to its non-destructive approach, comprehensive metabolic profiling, structural elucidation capabilities, and potential for in vivo analysis. These advantages make NMR an indispensable tool in advancing our understanding of cattle metabolism and improving livestock health and productivity. Keywords: Nuclear Magnetic Resonance (NMR), Cattles, Metabolomics.

Multiphoton-initiated laser writing of semiconductors using nanosecond mid-infrared pulses

The advent of more powerful mid-infrared nanosecond laser sources allows using them as attractive tools in material laser processing. For semiconductor bulk processing, laser intensities must remain modest to avoid detrimental nonlinear propagation and pre-focal plasma screening effects. Accordingly, nanosecond pulses are usually appropriate to locally initiate energy deposition by multiphoton absorption and subsequent modification, hardly achievable with ultrashort pulses. Employing a 2.8-μm 2.5-ns laser source, we explore the possibility to modify silicon and other semiconductors. With interactions initiated by three-photon absorption, we modify and determine the surface and bulk modification thresholds of Si, GaAs, and InP. For Si, compared to previous studies at telecom wavelengths, we report on a reduction of the energy threshold for volume modification with processing depth, and repeatable rear-surface modifications. Compared to Si, we find for GaAs and InP important fluctuations in the modification responses, which we attribute to an absorption mechanism initially triggered by a greater presence of defects. We also study germanium, as this narrower bandgap semiconductor very interesting for mid-infrared electro-optical applications is transparent at the newly introduced wavelength. Despite the ability to modify the surface, the bulk of Ge remains inaccessible in this two-photon regime, mainly attributed to beam aberrations and nonlinearities. Nonetheless, we demonstrate the ability to process Si chips integrating Ge layers. All these results showcase the potential of mid-infrared wavelengths to offer new degrees of flexibility for 3D laser processing technologies turned to silicon photonics and microelectronics packaging.