Decay Factor Research Articles

Particle swarm optimization with historical return decay enhances cooperation in public goods games with investment risks

In the realm of spatial public goods games, heterogeneous investment has emerged as a potent mechanism to enhance cooperative behavior. Savvy investors dynamically adjust their investment levels to optimize returns based on environmental conditions and personal circumstances. This study delves into the evolution of cooperation within a spatial public goods game framework, characterized by heterogeneous investment and associated risks, conducted on a square lattice. We introduce the investors (I) with heterogeneous investment, who updates his investment amount according to the current environment of the game and his own historical experience. We introduce a particle swarm optimization algorithm with decaying historical returns to fine-tune the investment levels of investors. Furthermore, a risk mechanism inspired by the ultimatum game is integrated into the model. This paper investigates the impact of the risk threshold θ on cooperative behavior and examines the influence of the risk decay factor α on cooperation. Additionally, it analyzes the investment behavior of investors in scenarios where two types of investors coexist. Finally, this study explores the effects of the historical best payoff decay factor β and the self-learning rate c1 on cooperative behavior. This research contributes to a nuanced understanding of heterogeneous investment behaviors in public goods games under specified risk mechanisms, providing novel insights into the intricate dynamics of cooperation in complex systems.

UPI-LT: Enhancing Information Propagation Predictions in Social Networks Through User Influence and Temporal Dynamics

The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model’s technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model’s enhanced predictive capabilities for information spread.

Drivers of growth and decay of Sargassum in the Tropical Atlantic: A Lagrangian approach

The proliferation of Sargassum in the Tropical Atlantic has occurred almost every year since 2011, but a strong variability of biomass is observed among years. Essential knowledge about the drivers of Sargassum growth and decay is still lacking to explain this interannual variability. Benefiting from accurate basin scale Sargassum detections provided by remote sensing, and from physical and biogeochemical ocean simulations, we developed a Lagrangian drift-growth model to simulate Sargassum distribution over the period 2016–2020. The resulting trajectories and biomass time series of Sargassum aggregates were analyzed to highlight the main limiting factors of growth and decay. The nitrogen and phosphorous concentrations are found to be weakly restrictive compared to physical limiting factors, especially the temperature. In particular, the warm waters found off northern Brazil appear to be instrumental in triggering the end of seasonal growth of Sargassum. The timing of the seasonal warming of this region strongly impacts the quantities of Sargassum simulated each year. This suggests that this region should be monitored to anticipate the development of Sargassum and resulting strandings.

The RNA Binding Protein Tristetraprolin Contributes to CFTR mRNA Stability in Cystic Fibrosis.

Cystic Fibrosis (CF) is the most common inherited disorder and is characterized by an inflammatory phenotype. Here, we found that in bronchial epithelium reconstituted form lung tissue biopsies from patients with CF, the RNA-binding protein tristetraprolin (TTP), a key regulator of inflammation, is dysregulated in cells that strongly express cytokines and interleukins. TTP activity is regulated by extensive post-translational modifications, particularly phosphorylation. We found that in addition to mRNA downregulation, phosphorylated TTP (which cannot bind to mRNA) accumulated in CF cultures, suggesting that the imbalance in TTP phosphorylation status could contribute to the inflammatory phenotype in CF. We confirmed TTP destabilizing role on IL8 mRNA through its 3'UTR sequence in CF cells. We next demonstrated that TTP phosphorylation is mainly regulated by MK2 through activation of ERK, which also was hyperphosphorylated. TTP is considered a mRNA decay factor with some exception, and we present a new positive role of TTP in CF cultures. We determined that TTP binds to specific ARE motifs on the 3'UTR of mRNA sequences and also, for the first time, to the 3'UTR of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) where TTP binding stabilizes the mRNA level. This study identified new partners that can be targeted in CF and proposes a new way to control CFTR gene expression.

Widespread regulation of the maternal transcriptome by Nanos in Drosophila.

The translational repressor Nanos (Nos) regulates a single target, maternal hunchback (hb) mRNA, to govern abdominal segmentation in the early Drosophila embryo. Nos is recruited to sites in the 3' UTR of hb mRNA in collaboration with the sequence-specific RNA-binding protein Pumilio (Pum); on its own, Nos has no binding specificity. Nos is expressed at other stages of development, but very few mRNA targets that might mediate its action at these stages have been described. Nor has it been clear whether Nos is targeted to other mRNAs in concert with Pum or via other mechanisms. In this report, we identify mRNAs targeted by Nos via 2 approaches. First, we identify mRNAs depleted upon expression of a chimera bearing Nos fused to the nonsense mediated decay (NMD) factor Upf1. We find that, in addition to hb, Upf1-Nos depletes approximately 2,600 mRNAs from the maternal transcriptome in early embryos. Virtually all of these appear to be targeted in a canonical, hb-like manner in concert with Pum. In a second, more conventional approach, we identify mRNAs that are stabilized during the maternal zygotic transition (MZT) in embryos from nos- females. Most (86%) of the 1,185 mRNAs regulated by Nos are also targeted by Upf1-Nos, validating use of the chimera. Previous work has shown that 60% of the maternal transcriptome is degraded in early embryos. We find that maternal mRNAs targeted by Upf1-Nos are hypoadenylated and inefficiently translated at the ovary-embryo transition; they are subsequently degraded in the early embryo, accounting for 59% of all destabilized maternal mRNAs. We suggest that the late ovarian burst of Nos represses a large fraction of the maternal transcriptome, priming it for later degradation by other factors in the embryo.

Modeling window smoothing effect hidden in fringe projection profilometry

The reconstruction using MEMS micro-vibration mirrors is the most promising. However, the influence mechanism of the line laser, which serves as the sole light source of MEMS-based systems, on reconstruction is unclear. To fill this theoretical gap, we propose the first interpretable laser influence model called the window smoothing model. This model reveals the window smoothing effect of laser beam scanning and quantifies its impact on modulation intensity through a decay factor. Given the absence of lens defocusing issues, the window smoothing effect becomes the primary factor affecting the contrast of fringe patterns. Therefore, a novel method using laser parameters to evaluate system accuracy is introduced. Leveraging these insights, we further propose an optimal fringe number calibration method, also based on laser parameters, to maximize the reconstruction accuracy of any given system. Finally, experimental results validate the effectiveness of the proposed method. As the first model tailored for MEMS systems, the window smoothing model is interpretable, highly accurate, and universal. Therefore, this model can provide new insights and a theoretical foundation for achieving high-performance MEMS-based reconstruction.

Numerical study on smoke temperature distribution in naturally ventilated inclined tunnels: Effects of tunnel width and tunnel slope

Inclined tunnel fires exhibit unique characteristics compared to horizontal tunnel fires as a result of the stack effect. Nevertheless, there is still a lack of consensus among previous scholars regarding the smoke temperature distribution in inclined tunnel fires. This work conducts a numerical investigation on the smoke back-layering length upstream of the fire source and the temperature decay downstream of the fire source in naturally ventilated inclined tunnels with varying widths and slopes. Results indicate that as the tunnel slope increases from 0° to 8°, the smoke back-layering length first decreases and then continues at a constant value of 1.47H4/3/W1/3, while the effect of tunnel width is negligible. The decay factor in the one-dimensional flow stage (x/H≥4) exhibits two distinct patterns related to tunnel width: it initially remains constant and then drops when the tunnel is wide, whereas it monotonically drops when the tunnel is narrow. Two empirical models are developed to identify the segmented characteristics and verified through the relevant data from existing literature with similar fire scenarios. This work offers a method for estimating the smoke temperature distribution in inclined tunnels, providing crucial insights for fire prevention, smoke detection, and safety strategies in tunnel engineering.

Relationship between Maternal Prenatal Vitamin D Status and Early Childhood Caries in Their Children: A Cross-sectional Survey.

Vitamin D deficiency in expectant mothers is very common in India. As a consequence, the possibility of hypoplasia of teeth and resultant propensity for faster progression of early childhood caries (ECC) is expected to be higher in Indian children. This study aimed to determine the relationship between prenatal vitamin D intake of mothers and dental caries experience in their preschool children, and whether vitamin D deficiency in mothers could be a risk factor for tooth decay in their children. This cross-sectional study included 120 mothers of children aged up to 71 months with dental caries attending the Department of Pediatric Dentistry in India. Mothers were surveyed about their prenatal vitamin D intake and their practices regarding vitamin D and sunlight exposure. Children were clinically examined, and their caries status was recorded using the decayed, extracted, filled teeth (deft) index. Data were analyzed descriptively and correlated using an independent t-test. Binary logistic regression was employed to predict the effects of the duration of sun exposure and vitamin D deficiency on dental decay. The correlation of mothers' prenatal vitamin D intake was significantly associated weekly with children's caries experience. Their sun exposure (p = 0.002) and practices adopted (p = 0.0001) regarding vitamin D levels were statistically significant for children's caries status. Improper brushing frequency was also significantly associated with higher deft scores. The association between mothers' prenatal vitamin D intake and health practices related to vitamin D with dental caries was not confirmed. Subjects with vitamin D deficiency and their children had significantly higher odds of developing dental decay. However, our findings suggest that 25-hydroxyvitamin D insufficiency may be a risk factor for developing dental caries in children. Kalra G, Kumar Y, Langpoklakpam C, et al. Relationship between Maternal Prenatal Vitamin D Status and Early Childhood Caries in Their Children: A Cross-sectional Survey. Int J Clin Pediatr Dent 2024;17(8):860-863.

Five-body D→V semileptonic decays

Our main objective is to derive the decay rate for the semileptonic decays D→Vℓ+νℓ(ℓ=e,μ), where V represents a vector particle. In these decays, the vector particle V decays into three pseudoscalar particles. To accomplish this, we evaluate the phase-space factor for the five-body decay with a set of eight independent variables which uniquely define a point in the phase space. We further conduct a detailed investigation of the D→ωℓ+νℓ, where ω subsequently decays into π+π−π0, within the Standard Model and in a general effective field theory description of the weak interactions at low energies. The outcomes of this study have potential applications in the measurement of D→ω form factors. These measurements can be performed using data obtained from BESIII. Published by the American Physical Society 2024

The DNA damage response and RNA Polymerase II regulator Def1 has posttranscriptional functions in the cytoplasm.

Yeast Def1 mediates RNA polymerase II degradation and transcription elongation during stress. Def1 is predominantly cytoplasmic, and DNA damage signals cause its proteolytic processing, liberating its N-terminus to enter the nucleus. Cytoplasmic functions for this abundant protein have not been identified. Proximity-labeling (BioID) experiments indicate that Def1 binds to an array of proteins involved in posttranscriptional control and translation of mRNAs. Deleting DEF1 reduces both mRNA synthesis and decay rates, indicating transcript buffering in the mutant. Directly tethering Def1 to a reporter mRNA suppressed expression, suggesting that Def1 directly regulates mRNAs. Surprisingly, we found that Def1 interacts with polyribosomes, which requires its ubiquitin-binding domain located in its N-terminus. The binding of Def1 to ribosomes requires the ubiquitylation of eS7a (Rsp7A) in the small subunit by the Not4 protein in the Ccr4-Not complex. Not4 ubiquitylation of the ribosome regulates translation quality control and co-translational mRNA decay. The polyglutamine-rich unstructured C-terminus of Def1 is required for its interaction with decay and translation factors, suggesting that Def1 acts as a ubiquitin-dependent scaffold to link translation status to mRNA decay. Thus, we have identified a novel function for this transcription and DNA damage response factor in posttranscriptional regulation in the cytoplasm and establish Def1 as a master regulator of gene expression, functioning during transcription, mRNA decay, and translation.

Turbulence coherence in complex wind field: Focusing on its on-site characteristics based on multi-point clustering

Turbulence coherence in mountainous areas necessitates meticulous evaluation due to the complex terrain blocking. This study focuses on the turbulence coherence of the non-uniform wind field in a canyon via the anemometers positioned along a bridge span. Based on a proposed multi-point clustering, two categories of strong winds impacted by a nearby ridge are identified. The turbulence coherence of the two strong winds is then investigated with an extra focus on the phase spectrum. Several complex coherence functions are extended from commonly employed real forms, including the simplified Krenk's formula proposed. The fitting results of them all suggest that parameters such as the decay factor of the two strong winds show a negative linear correlation with separation but their relationship with wind speed is not significant. For the phase spectrum, in addition to skew winds, the contribution of horizontal wind shear is first emphasized in the two strong winds with piecewise fittings. This paper also discusses single-point coherence between the along-wind and vertical turbulence components. It is found to be unusually positive in sign due to the influence of the local topography and a formula with a constant term is suggested that substantially improves the fitting compared to Solari's formula.

Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

Energy generation from friction-induced vibration of a piezoelectric beam

The primary challenge in harnessing vibration energy with piezoelectric materials is the discrepancy in frequency between the energy source and the energy generator, which lowers the efficiency of energy harvesting. To address the challenge, a piezoelectric beam under friction-induced vibration (FIV) is designed, modeled, and studied for the first time to realize the pronounced FIV contributing to energy generation by adapting the vibrations of the continuum structure to align close to its resonant frequencies. The Stribeck friction model is applied to characterize the variation of friction based on the relative sliding velocity vr between contacting objects. The analytical solution is derived to solve the dynamic responses, and transient charging simulation validated by the experiment is utilized to assess energy output. Furthermore, parameter studies are conducted based on the validated model with regard to the material properties of the beam and piezoelectric material, electrode connections of piezoelectric patches, and friction model parameters to investigate their influences on energy output. Considering the same dimensional properties, materials with low Young's modulus E and density ρ are desired for the host structure to facilitate large dynamic strain in piezoelectric materials. With an exponential decay factor C = 8, representing optimal material contact interface, pronounced higher FIV mode can be induced leading to higher output power. A root mean square charging power PeRMS of 42.4 mW and a peak instant charging power Pe − peak of 263 mW can be achieved. In the current study, the model is implemented on a beam coupled with one piezoelectric patch, which has potential applicability to non-uniform beams with various layouts of piezoelectric patches. The presented model enables efficient optimization of continuum structural design for higher piezoelectric energy generation under friction.

Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement.

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

Integration of simulated annealing into pigeon inspired optimizer algorithm for feature selection in network intrusion detection systems.

In the context of the 5G network, the proliferation of access devices results in heightened network traffic and shifts in traffic patterns, and network intrusion detection faces greater challenges. A feature selection algorithm is proposed for network intrusion detection systems that uses an improved binary pigeon-inspired optimizer (SABPIO) algorithm to tackle the challenges posed by the high dimensionality and complexity of network traffic, resulting in complex models, reduced accuracy, and longer detection times. First, the raw dataset is pre-processed by uniquely one-hot encoded and standardized. Next, feature selection is performed using SABPIO, which employs simulated annealing and the population decay factor to identify the most relevant subset of features for subsequent review and evaluation. Finally, the selected subset of features is fed into decision trees and random forest classifiers to evaluate the effectiveness of SABPIO. The proposed algorithm has been validated through experimentation on three publicly available datasets: UNSW-NB15, NLS-KDD, and CIC-IDS-2017. The experimental findings demonstrate that SABPIO identifies the most indicative subset of features through rational computation. This method significantly abbreviates the system's training duration, enhances detection rates, and compared to the use of all features, minimally reduces the training and testing times by factors of 3.2 and 0.3, respectively. Furthermore, it enhances the F1-score of the feature subset selected by CPIO and Boost algorithms when compared to CPIO and XGBoost, resulting in improvements ranging from 1.21% to 2.19%, and 1.79% to 4.52%.

Substrate diversity of NSUN enzymes and links of 5-methylcytosine to mRNA translation and turnover.

Maps of the RNA modification 5-methylcytosine (m5C) often diverge markedly not only because of differences in detection methods, data depand analysis pipelines but also biological factors. We re-analysed bisulfite RNA sequencing datasets from five human cell lines and seven tissues using a coherent m5C site calling pipeline. With the resulting union list of 6,393 m5C sites, we studied site distribution, enzymology, interaction with RNA-binding proteins and molecular function. We confirmed tRNA:m5C methyltransferases NSUN2 and NSUN6 as the main mRNA m5C "writers," but further showed that the rRNA:m5C methyltransferase NSUN5 can also modify mRNA. Each enzyme recognises mRNA features that strongly resemble their canonical substrates. By analysing proximity between mRNA m5C sites and footprints of RNA-binding proteins, we identified new candidates for functional interactions, including the RNA helicases DDX3X, involved in mRNA translation, and UPF1, an mRNA decay factor. We found that lack of NSUN2 in HeLa cells affected both steady-state levels of, and UPF1-binding to, target mRNAs. Our studies emphasise the emerging diversity of m5C writers and readers and their effect on mRNA function.

Summer and winter performance evaluation of double layered PTFE dome with aerogel-glass wool thermal insulation- field experiments and numerical simulation

Achieving optimal thermal performance in large membrane-enclosed buildings presents a significant challenge. Aerogel mats offer potential for enhanced insulation, but their effectiveness in varying seasonal conditions is yet to be fully determined. The current study investigates the thermal performance of an enclosed dome with double-layered PTFE membrane roof integrated with aerogel-glass wool insulation mats. This research utilizes field experiments to assess indoor thermal comfort and numerical model to evaluate the effectiveness of the dome’s insulation capabilities during summer and winter conditions. The Aerogel integrated areas show a difference of 10.26/3.72 °C for summer/winter while that of 9.36/3.22 °C for glass wool-integrated areas. An online thermal comfort tool utilized maximum indoor temperature values during both seasons obtained from numerical results indicating that winter comfort falls within ASHRAE comfort range with PMV value −0.48 but summer conditions require enhancements. Additionally, the study assessed the insulation’s effectiveness using Maximum Reduction in Temperature (MRT), Decay Factor (DF), and Time Delay (TD) indicators. Results showed significant thermal lag, with peak temperature delays of up to 3.1 h in summer and 2.2 h in winter. Notable temperature differences between the dome’s inner and outer surfaces, alongside 28 % and 27 % DF reductions during summer and winter, respectively, demonstrated the roof insulation’s effectiveness. These findings support the use of both experimental and numerical methods to develop thermal management practices that improve sustainable building design, emphasizing occupant comfort and energy efficiency.

Constructing the Discount Function with Parsimonious Extended Nelson-Siegel Function: Evidence from Nigerian Eurobond

Nigeria currently carrying out economic reforms is under the surveillance of institutional investors. To construct the market discount function through its yield curve, we applied the extended functional Nelson-Siegel parsimonious function with time-decay parameters to the Eurobond data to employ flexibility of the model and estimate the time varying parameters. In order to present a deep investigation of the Euro-bond market by reason of time to maturity, our contribution is anchored on following objectives: (i) construct the discount function (ii) investigate the limiting behavior of the yield curves and (iii) predict the in-sample yield. The data presented involves the daily closing of the Nigerian Eurobond yield covering January to December 2021 and 2022. The data was fitted to the observed Nigerian Eurobond yield curve to model the discount function. The ordinary least square method is used for the analysis and the estimated parameters were used to compute the in-sample yield. Test of goodness of fit was conducted showing that the model fits in well to the observed data demonstrated by the model’s R-square adjusted through the predicted yields after obtaining the two decay factors. This paper has implications on life insurance products associated with minimum guaranteed benefit schemes for the insured. Based on the terms and conditions of the contract, the insured pays regular premium invested in debt instruments and receives benefit at death or at maturity of the policy depending on the market performance of the fund. There is a guaranteed benefit which the insured earns irrespective of the performance of the life fund. The insurer would then pay the guaranteed amount even if the benefit eventually drops below the guaranteed amount. Given the discount 𝛿(𝜏)and benefit∅, the present value of future death benefits is modeled as 𝑃𝑉𝐹𝐵 = ∅1𝛿(1) + ∅2𝛿(2) + ⋯ + ∅𝑇−1𝛿(𝑇 − 1) + ∅𝑇𝛿(𝑇).

HF/HR VSP acquisition, processing, and interpretation in the deviated section of the high angle geothermal borehole of Grigny GGR5, targeting intra Dogger thin, porous beds.

Defining 5m thin, or even thinner, porous reservoir beds was the feasibility objective assigned to the High Frequency/High Resolution (HF/HR) look ahead VSP survey to be recorded in the deviated section of a geothermal production well drilled down to the top of the carbonate Dogger aquifer in the Paris basin, around 1500m in vertical depth, with a landing angle of 65°. The idea was to predict the depth of the first porous bed(s) to be drilled at high angle, and to determine the minimal DLS (Dog Leg Severity) to be applied by the driller after setting a production casing at the top Dogger level and changing the drilling fluid composition, so as to avoid directional drilling with sharp angles. A 50-100m vertical depth range was targeted for the acoustic impedance to be predicted below the top Dogger carbonate aquifer. Besides, a Pilot Hole (PH) was drilled prior to the high angle leg to assess with full reliability the depth of productive thin beds to be subsequently drilled at high angle. These actions were undertaken within a global R&D project named “SISMOSUB” aiming reservoir characterisation, conducted by the reservoir and drilling engineers of GEOFLUID/GPCIP, in a partnership with IFPEN, and partially financed by ADEME (French Agency for Ecological Transition). High Frequency (HF) VSP still represents a frontier seismic domain to investigate, for which technical difficulties are piling up: First, the surface fix source needs to be extremely repeatable and preferably deliver an increased amount of HF energy to compensate somehow for the higher loss of seismic amplitudes with high frequency. Second, downhole receiver tool or toolstring may not present constant vector fidelity of 3 component reception over the whole recording frequency range, mainly in high frequency, due to mechanical coupling characteristics of the VSP tool hardware and the open borehole ruggedness; in cased hole, the mechanical coupling of the VSP tool to the formation is insured by a good cementing quality between casing and borehole wall. Third, the physics of the seismic attenuation is not fully understood and depends on several factors, mainly viscoelasticity and heterogeneity, potentially variable in depth and laterally to the wellbore. Additionally, the geometrical spreading represents the main factor of amplitude decay, independent from frequency, and can be estimated at processing stage. Surprisingly, the VSP image produced underneath the deviated hole section evidenced a faulted structure. A remarkable abrupt amplitude loss of high frequencies above 120Hz occurs where the direct P-wave ray crosses one of the confirmed minor faults before reaching Total Depth (TD); secondary downgoing arrivals appear locally in depth, mainly in the high frequencies, which are not strictly parallel to the first P-wave arrival...

Advances in strawberry postharvest preservation and packaging: A comprehensive review.

Strawberries spoil rapidly after harvest due to factors such as the ripening process, weight loss, and, most importantly, microbial contamination. Traditionally, several methods are used to preserve strawberries after harvest and extend their shelf life, including thermal, plasma, radiation, chemical, and biological treatments. Although these methods are effective, they are a concern from the perspective of safety and consumer acceptance of the treated food. To address these issues, more advanced environment-friendly technologies have been developed over the past decades, including modified and controlled atmosphere packaging, active biopolymer-based packaging, or edible coating formulations. This method can not only significantly extend the shelf life of fruit but also solve safety concerns. Some studies have shown that combining two or more of these technologies can significantly extend the shelf life of strawberries, which could significantly contribute to expanding the global supply chain for delicious fruit. Despite the large number of studies underway in this field of research, no systematic review has been published discussing these advances. This review aims to cover important information about postharvest physiology, decay factors, and preservation methods of strawberry fruits. It is a pioneering work that integrates, relates, and discusses all information on the postharvest fate and handling of strawberries in one place. Additionally, commercially used techniques were discussed to provide insight into current developments in strawberry preservation and suggest future research directions in this field of study. This review aims to enrich the knowledge of academic and industrial researchers, scientists, and students on trends and developments in postharvest preservation and packaging of strawberry fruits.