Previous studies have shown the effectiveness of ICT-based writing instructional practices, but they do not show an in-depth analysis of their instructional elements, which could be key to explaining such effectiveness. This study aims to analyze the instructional design of effective ICT-based writing practices according to content and instructional dimensions. An empirical review was performed following the PRISMA statement guidelines with a sample of 22 studies. For the content dimension, learning objectives were coded in terms of target focus, subject-specific knowledge, and learning outcomes. For the instructional dimension, types of learning and instructional activities were coded, identifying their specific elements. Results showed that learning objectives were mostly focused on practice/internalization of high-level processes. Regarding learning activities, ICT was mainly used in practicing/applying, structuring, and observing/noting activities. Regarding instructional activities, application and demonstration principles appeared most frequently in the interventions, with application activities being most frequently performed by ICTs. Product-focused and corrective types of feedback, as well as static and procedural scaffolding types, were the most frequently performed using ICT. In conclusion, this study provides useful information on what writing activities can be performed using ICT and provide the basis for future studies analyzing to what extent these activities contribute to program effectiveness. Full article

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot’s speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration. Full article

Categorical data analysis of 2 × 2 contingency tables is extremely common, not least because they provide risk difference, risk ratio, odds ratio, and log odds statistics in medical research. A ${\mathsf{\chi }}^2$ test analysis is most often used, although some researchers use likelihood ratio test (LRT) analysis. Does it matter which test is used? A review of the literature, examination of the theoretical foundations, and analyses of simulations and empirical data are used by this paper to argue that only the LRT should be used when we are interested in testing whether the binomial proportions are equal. This so-called test of independence is by far the most popular, meaning the ${\mathsf{\chi }}^2$ test is widely misused. By contrast, the ${\mathsf{\chi }}^2$ test should be reserved for where the data appear to match too closely a particular hypothesis (e.g., the null hypothesis), where the variance is of interest, and is less than expected. Low variance can be of interest in various scenarios, particularly in investigations of data integrity. Finally, it is argued that the evidential approach provides a consistent and coherent method that avoids the difficulties posed by significance testing. The approach facilitates the calculation of appropriate log likelihood ratios to suit our research aims, whether this is to test the proportions or to test the variance. The conclusions from this paper apply to larger contingency tables, including multi-way tables. Full article

In silicon carbide processing, the surface and subsurface damage caused by fixed abrasive grinding significantly affects the allowance of the next polishing process. A novel grinding wheel with a soft and hard composite structure was fabricated for the ultra-precision processing of SiC substrates, and the grinding performance of the grinding wheel was assessed in this study. Different types of gels, heating temperatures, and composition ratios were used to fabricate the grinding wheel. The grinding performance of the grinding wheel was investigated based on the surface integrity and subsurface damage of SiC substrates. The results showed that the grinding wheel with a soft and hard composite structure was successfully fabricated using freeze-dried gel with a heating temperature of 110 °C, and the component ratio of resin to gel was 4:6. A smooth SiC substrate surface with almost no cracks was obtained after processing with the grinding wheel. The abrasive exposure height was controlled by manipulating the type and ratio of the gel. Furthermore, the cutting depth in nanoscale could be achieved by controlling the abrasive exposure height. Therefore, the fabrication and application of the grinding wheels with soft and hard composite structures is important for the ultra-precision processing of large-size SiC substrates. Full article

This study aims to investigate the effects of parent rock and minerals on lateritic weathering. The study presents X-ray diffraction (XRD), whole-rock geochemistry, and Nd-Sr isotopic data for examining two profiles, 10 and 12 m thick, respectively, that illustrate the regional tropical weathering status in the Midwest of Brazil. The profiles, developed from metasedimentary and sedimentary rocks, are constituted by saprolite, mottled horizon, lateritic duricrust, and oxisol. Across the profiles, the minerals controlling the weathering geochemistry are muscovite, microcline, quartz, kaolinite, hematite, goethite, and gibbsite. Red and yellow zones in the saprolite and mottled horizon as well as the lateritic duricrust with breccia/fragmental, pisolitic, and oolitic textures make profile 1 more complex. In contrast, profile 2 has an oxisol that mantles the homogeneous vermiform lateritic duricrust. Fe₂O₃, accumulated during surface weathering, is a potent element in the geochemical profile control since it forms the harder goethite to hematite lateritic duricrust, bearing most of the trace elements (As, Cu, Cs, Pb, Sc, Sr, Th, U, V, and Zn) with similar ionic radii and electrovalence. The LREE have affinity for the elements of the Fe₂O₃ group of the lateritic duricrust. On the other hand, the K₂O group together with Zr and TiO₂ e in the phyllite, saprolite, and mottled horizon of profile 1, are associated with the HREE. Additionally, in profile 2, the HREE are mostly associated with the Al₂O₃ group and the residual minerals in the oxisol. The indication that REE is associated with phosphates, zircon, rutile/anatase, cereanite, and muscovite/illite, which have variable weathering behavior, caused the REE fractionation to occur across and between the profiles. Despite the REE fractionation, the Ɛ_Nd(0) values along the profiles consistently maintain the signature of the parent rock. Muscovite and microcline weathering, in profiles 1 and 2, respectively, control the decrease in ⁸⁷Sr/⁸⁶Sr signatures of both profiles and the distinct radiogenic ratios. The development of lateritic duricrust in both profiles indicates a similar weathering intensity, although the gibbsite–kaolinite predominance in the oxisol of profile 2 highlights a geochemical reorganization under humid conditions, as well as near-intense soluble silica leaching. Full article

Bemisia tabaci is distributed globally and incurs considerable economic and ecological costs as an agricultural pest and viral vector. The entomopathogenic fungus Metarhizium anisopliae has been known for its insecticidal activity, but its impacts on whiteflies are understudied. We investigated how infection with the semi-persistently transmitted Cucurbit chlorotic yellows virus (CCYV) affects whitefly susceptibility to M. anisopliae exposure. We discovered that viruliferous whiteflies exhibited increased mortality when fungus infection was present compared to non-viruliferous insects. High throughput 16S rRNA sequencing also revealed significant alterations of the whitefly bacterial microbiome diversity and structure due to both CCYV and fungal presence. Specifically, the obligate symbiont Portiera decreased in relative abundance in viruliferous whiteflies exposed to M. anisopliae. Facultative Hamiltonella and Rickettsia symbionts exhibited variability across groups but dominated in fungus-treated non-viruliferous whiteflies. Our results illuminate triangular interplay between pest insects, their pathogens, and symbionts—dynamics which can inform integrated management strategies leveraging biopesticides This work underscores the promise of M. anisopliae for sustainable whitefly control while laying the groundwork for elucidating mechanisms behind microbe-mediated shifts in vector competence. Full article

Background: Acute cardiac injury (ACI) after COVID-19 has been linked with unfavorable clinical outcomes, but data on the clinical impact of elevated cardiac troponin on discharge during follow-up are scarce. Our objective is to elucidate the clinical outcome of patients with elevated troponin on discharge after surviving a COVID-19 hospitalization. Methods: We conducted an analysis in the prospective registry HOPE-2 (NCT04778020). Only patients discharged alive were selected for analysis, and all-cause death on follow-up was considered as the primary endpoint. As a secondary endpoint, we established any long-term COVID-19 symptoms. HOPE-2 stopped enrolling patients on 31 December 2021, with 9299 patients hospitalized with COVID-19, of which 1805 were deceased during the acute phase. Finally, 2382 patients alive on discharge underwent propensity score matching by relevant baseline variables in a 1:3 fashion, from 56 centers in 8 countries. Results: Patients with elevated troponin experienced significantly higher all-cause death during follow-up (log-rank = 27.23, p < 0.001), and had a higher chance of experiencing long-term COVID-19 cardiovascular symptoms. Specifically, fatigue and dyspnea (57.7% and 62.8%, with p-values of 0.009 and <0.001, respectively) are among the most common. Conclusions: After surviving the acute phase, patients with elevated troponin on discharge present increased mortality and long-term COVID-19 symptoms over time, which is clinically relevant in follow-up visits. Full article

Leaf mass per area (LMA) is a key structural parameter that reflects the functional traits of leaves and plays a vital role in simulating the material and energy cycles of plant ecosystems. In this study, vertical whorl-by-whorl sampling of LMA was conducted in a young Larix principis-rupprechtii plantation during the growing season at the Saihanba Forest Farm. The vertical and seasonal variations in LMA were analysed. Subsequently, a predictive model of LMA was constructed. The results revealed that the LMA varied significantly between different crown whorls and growing periods. In the vertical direction of the crown, the LMA decreased with increasing crown depth, but the range of LMA values from the tree top to the bottom was, on average, 30.4 g/m², which was approximately 2.5 times greater in the fully expanded phase than in the early leaf-expanding phase. During different growing periods, the LMA exhibited an allometric growth trend that increased during the leaf-expanding phase and then tended to stabilize. However, the range of LMA values throughout the growing period was, on average, 40.4 g/m². Among the univariate models, the leaf dry matter content (LDMC) performed well (adjusted determination coefficient (R_a²) = 0.45, root mean square error (RMSE) = 13.48 g/m²) in estimating the LMA. The correlation between LMA and LDMC significantly differed at different growth stages and at different vertical crown whorls. The dynamic predictive model of LMA constructed with the relative depth in the crown (RDINC) and date of the year (DOY) as independent variables was reliable in both the assessments (R_a² = 0.68, RMSE = 10.25 g/m²) and the validation (absolute mean error (MAE) = 8.05 g/m², fit index (FI) = 0.682). Dynamic simulations of crown LMA provide a basis for elucidating the mechanism of crown development and laying the foundation for the construction of an ecological process model. Full article

Recently, increasing attention of researchers in the field of membrane technology has been paid to the development of membranes based on biopolymers. One of the well-proven polymers for the development of porous membranes is cellulose acetate (CA). This paper is devoted to the study of the influence of different parameters on ultrafiltration CA membrane formation and their transport properties, such as the variation in coagulation bath temperature, membrane shrinkage (post-treatment at 80 °C), introduction to casting CA solution of polymers (polyethylene glycol (PEG), polysulfone (PS), and Pluronic F127 (PL)) and carbon nanoparticles (SWCNTs, MWCNTs, GO, and C₆₀). The structural and physicochemical properties of developed membranes were studied by scanning electron and atomic force microscopies, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, and contact angle measurements. The transport properties of developed CA-based membranes were evaluated in ultrafiltration of bovine serum albumin (BSA), dextran 110 and PVP K-90. All developed membranes rejected 90% compounds with a molecular weight from ~270,000 g/mol. It was shown that the combination of modifications (addition of PEG, PS, PL, PS-PL, and 0.5 wt% C₆₀) led to an increase in the fluxes and BSA rejection coefficients with slight decrease in the flux recovery ratio. These changes were due to an increased macrovoid number, formation of a more open porous structure and/or thinner top selective, and decreased surface roughness and hydrophobization during C₆₀ modification of blend membranes. Optimal transport properties were found for CA-PEG+С₆₀ (the highest water—394 L/(m²h) and BSA—212 L/(m²h) fluxes) and CA-PS+С₆₀ (maximal rejection coefficient of BSA—59%) membranes. Full article

Using the N400 component of event-related brain potentials, a neurophysiological marker associated with processing incongruity, we examined brain responses to sentences spoken by a robot that had no arms or legs. Statements concerning physically impossible actions (e.g., knitting) elicit significant N400 responses, reflecting that participants perceived these statements as incongruent with the robot’s physical condition. However, this effect was attenuated for participants who indicated that the robot could have hidden limbs, indicating that expectations modify the way an agent’s utterances are interpreted. When it came to statements relating to emotional capabilities a distinct pattern was found. Although participants acknowledged that the robot could have emotions, there were significant N400 responses to statements about the robot’s emotional experiences (e.g., feeling happy). This effect was not modified by participants’ beliefs, suggesting a cognitive challenge of accepting robots as capable of experiencing emotions. Our findings thus point to a boundary in human acceptance of artificial social agents: while physical attributes may be negotiable based on expectations, emotional expressions are more difficult to establish as credible. By elucidating the cognitive mechanisms at play, our study informs the design of social robots that are capable of more effective communication to better support social connectivity and human well-being. Full article

Orthogonal polynomials have been widely employed by renowned authors within the context of geometric function theory. This study is driven by prior research and aims to address the —Fekete-Szegö problem. Additionally, we provide bound estimates for the coefficients and an upper bound estimate for the second Hankel determinant for functions belonging to the category of analytical and bi-univalent functions. This investigation incorporates the utilization of Euler polynomials. Full article

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VI_opt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VI_opt) and the corresponding VI_DRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VI_DRI indexes were more beneficial than the VI_opt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VI_DRI indexes (NDVI_DRI, NDVIre1_DRI, NDVIre2_DRI, and S2REP_DRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2_DRI and S2REP_DRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters. Full article

Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress. Full article

Background: The SARS-CoV-2 pandemic has significantly affected the pediatric population. Long-term sequelae (Long COVID-19) may particularly involve the central nervous system, with possible effects on psychological well-being and quality of life (QoL), aspects that were already influenced by the restrictive measures and general social impact of the pandemic. Methods: We conducted a cross-sectional survey that aims at investigating the neuropsychological effects and the QoL impairment of SARS-CoV-2 on a cohort of children and adolescents in the Abruzzo region (Italy). A questionnaire was submitted to caregivers with the help of the PEDIATOTEM platform. A control group of healthy subjects was also included to distinguish between the effects of infection from the general influence of the pandemic. Results: A total of 569 subjects responded: 396 COVID-19 patients (99 of whom had Long COVID-19) and 111 controls. After the pandemic, when compared with the COVID-19 group, the controls reported significantly increased appetite, sleeping habits, and time spent remotely with friends and a reduction in physical activity and time spent in person with friends. A significant higher rate of controls asked for psychological/medical support for emotional problems. On the other hand, the Long COVID-19 group showed more fatigue and emotional instability with respect to non-Long-COVID-19 subjects. No differences in QoL results (EuroQOL) were found between the COVID-19 patients and controls, while the Long-COVID-19 subgroup showed significantly higher rates of pain/discomfort and mood instability, as confirmed by the analysis of variation of responses from the pre-COVID-19 to the post-COVID-19 period. Conclusions: Among COVID-19 patients, neuropsychological and QoL impairment was more evident in the Long COVID-19 subgroup, although emotional and relational issues were also reported by uninfected patients, with a growing request for specialist support as a possible consequence of social restriction. Full article

B-cell non-Hodgkin’s lymphoma (NHL) refers to a heterogenous group of diseases, all of which have a wide range of treatment strategies and patient outcomes. There have been multiple novel, immune-based therapies approved in NHL in the last decade, including bispecific antibodies (BsAbs) and chimeric antigen receptor therapy (CAR-T). With a host of new therapies, an important next step will be determining how these therapies should be sequenced in contemporary management strategies. This review seeks to offer a framework for the ways in which BsABs can be incorporated into the current management paradigm for NHL, with special attention paid to diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Full article

The use of warm-mix recycling technology can reduce the mixing temperature and the secondary aging of binders in reclaimed asphalt pavement (RAP), which is one of the effective ways to recycle high-content RAP. In this study, the penetration, softening point, ductility, and viscosity were used to characterize the conventional physical properties of aged asphalt after regenerating, while a dynamic shear rheometer (DSR), force ductility tester (FDT), and atomic force microscope (AFM) were used to evaluate the rheological performance and micro-morphology of aged asphalt incorporating a new bio-based warm-mix rejuvenator (BWR) and a commercial warm-mix rejuvenator (ZJ-WR). The regeneration mechanism of warm-mix rejuvenators on aged asphalt was analyzed by Fourier transform infrared spectroscopy (FTIR). The results show that the new bio-based warm-mix rejuvenator can restore the conventional physical properties, low-temperature performance, and micro-morphology of aged asphalt with an appropriate dosage, but it has a negative effect on high-temperature performance. In comparison with 2D area parameters, 3D roughness parameters were more accurate in evaluating the variation in micro-morphology of aged asphalt after regeneration. The FTIR analysis results indicate that both the new bio-based warm-mix rejuvenator and the commercial warm-mix rejuvenator regenerate aged asphalt by physical action, and A_S=O and A_C-H values are more reasonable than the A_C=O value for the restoration evaluation of aged asphalt. And the new bio-based warm-mix rejuvenator has a better regeneration effect on the performance and micro-morphology of aged asphalt than the commercial warm-mix rejuvenator. Full article

Background and Objectives: The impact of positive peritoneal cytology has been a matter of controversy in early-stage endometrial cancer for several years. The latest staging systems do not take into consideration its presence; however, emerging evidence about its potential harmful effect on patient survival outcomes suggests otherwise. In the present systematic review and meta-analysis, we sought to accumulate current evidence. Materials and Methods: Medline, Scopus, the Cochrane Central Register of Controlled Trials CENTRAL, Google Scholar and Clinicaltrials.gov databases were searched for relevant articles. Effect sizes were calculated in Rstudio using the meta function. A sensitivity analysis was carried out to evaluate the possibility of small-study effects and p-hacking. Trial sequential analysis was used to evaluate the adequacy of the sample size. The methodological quality of the included studies was assessed using the Newcastle–Ottawa scale. Results: Fifteen articles were finally included in the present systematic review that involved 19,255 women with early-stage endometrial cancer. The Newcastle–Ottawa scale indicated that the majority of included studies had a moderate risk of bias in their selection of participants, a moderate risk of bias in terms of the comparability of groups (positive peritoneal cytology vs. negative peritoneal cytology) and a low risk of bias concerning the assessment of the outcome. The results of the meta-analysis indicated that women with early-stage endometrial cancer and positive peritoneal cytology had significantly lower 5-year recurrence-free survival (RFS) (hazards ratio (HR) 0.26, 95% CI 0.09, 0.71). As a result of the decreased recurrence-free survival, patients with positive peritoneal cytology also exhibited reduced 5-year overall survival outcomes (HR 0.50, 95% CI 0.27, 0.92). The overall survival of the included patients was considerably higher among those that did not have positive peritoneal cytology (HR 12.76, 95% CI 2.78, 58.51). Conclusions: Positive peritoneal cytology seems to be a negative prognostic indicator of survival outcomes of patients with endometrial cancer. Considering the absence of data related to the molecular profile of patients, further research is needed to evaluate if this factor should be reinstituted in future staging systems. Full article

Oligomeric alpha-synuclein (α-syn) in saliva and phosphorylated α-syn deposits in the skin have emerged as promising diagnostic biomarkers for Parkinson’s disease (PD). This study aimed to assess and compare the diagnostic value of these biomarkers in discriminating between 38 PD patients and 24 healthy subjects (HSs) using easily accessible biological samples. Additionally, the study sought to determine the diagnostic potential of combining these biomarkers and to explore their correlations with clinical features. Salivary oligomeric α-syn levels were quantified using competitive ELISA, while skin biopsies were analyzed through immunofluorescence to detect phosphorylated α-syn at Ser129 (p-S129). Both biomarkers individually were accurate in discriminating PD patients from HSs, with a modest agreement between them. The combined positivity of salivary α-syn oligomers and skin p-S129 aggregates differentiated PD patients from HSs with an excellent discriminative ability with an AUC of 0.9095. The modest agreement observed between salivary and skin biomarkers individually suggests that they may reflect different aspects of PD pathology, thus providing complementary information when combined. This study’s results highlight the potential of utilizing a multimodal biomarker approach to enhance diagnostic accuracy in PD. Full article

Energy consumption is one of the important indicators of green development. The pressure drop and the particle kinetic energy loss in the pipe bend result in high energy consumption of wheat pneumatic conveying. In this paper, the CFD-DEM method is used to study the characteristics of flow field in horizontal pipe bend. The results show that the particles converge together under the force of the curved pipe wall to form a particle rope. With increasing pipe bend ratio R/D, the aggregation of particle bundles becomes stronger and the particle spiral phenomenon decreases. The particles impact the pipe wall at an angular position of θ = 30–60° around the bend, and their velocity decreases slowly under the friction resistance of the pipe wall. The velocity loss caused by particles impacting on the pipe wall increases with increasing initial velocity. When the particle mass flow rate is 1.26 kg/s and the gas velocity is 10 m/s, the pressure drop in the bend decreases and then increases with increasing R/D. The pressure drop of the bend is smallest for R/D = 2 and increases gradually with increasing gas-phase velocity. With increasing of R/D, the wall shear force between the particles and the bending pipe decreases and then increases, and the position of the maximum force moves towards the bottom of the bending pipe. The area over which the wall shear force acts continues to decrease because of the aggregation of particle bundles. The research results provide a theory for optimal design and application of pneumatic conveying equipment for wheat particles. Full article

Rice is a staple food for more than half of the world’s population. However, the pervasive problem of salinity is severely undermining rice production, especially in coastal and low-lying areas where soil salinization is widespread. This stress, exacerbated by climate change, necessitates the development of salt-tolerant rice varieties to ensure food security. In this study, an F_2:3 population (n = 454) from a cross of KDML105 and its chromosome segment substitution line (CSSL) was used to identify genomic regions associated with salt tolerance at the seedling stage. Using the QTL-seq approach, a QTL significantly associated with salt tolerance was identified on chromosome 1. Annotation of candidate genes in this region revealed the potential regulators of salt tolerance, including MIKC-type MADS domain proteins, calmodulin-binding transcription factors, and NB-ARC domain-containing proteins. These and other identified genes provide insights into the genetic basis of salt tolerance. This study underscores the importance of using advanced genomics tools and CSSL populations in the study of complex traits such as salt tolerance in rice. Several candidate genes identified in this study could be used in further studies on molecular or physiological mechanisms related to the salt response and tolerance mechanism in rice. Additionally, these genes could also be utilized in plant breeding programs for salt tolerance. Full article

Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies. Full article

Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work examined four types of GBM models in terms of the presence of memory and the kind of volatility estimations. These models include the classical GBM model with memoryless and constant volatility assumptions, the SVGBM model with memoryless and stochastic volatility assumptions, the GFBM model with memory and constant volatility assumptions, and the SVGFBM model with memory and stochastic volatility assumptions. In this study, these models were utilized in an empirical study to forecast the future index price of the energy sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, the mean square error (MSE) and mean absolute percentage error (MAPE). The results show that the SVGFBM model demonstrates the highest accuracy, resulting in the lowest MSE and MAPE, while the GBM model was the least accurate of all the models under study. These results affirm the benefits of combining memory and stochastic volatility assumptions into the GBM model, which is also supported by the findings of numerous earlier studies. Furthermore, the findings of this study show that GFBM models are more accurate than GBM models, regardless of the type of volatility. Furthermore, under the same type of memory, the models with a stochastic volatility assumption are more accurate than the corresponding models with a constant volatility assumption. In general, all models considered in this work showed a high accuracy, with MAPE ≤ 10%. This indicates that these models can be applied in real financial environments. Based on the results of this empirical study, the future of the energy sector in Saudi Arabia is forecast to be predictable and stable, and we urge financial investors and stockholders to trade and invest in this sector. Full article