Additional Variables Research Articles

Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B-spline functions.

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c2 ) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2 ) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

A unified higher-order unsplit CFS-PML technique for solving second-order seismic equations using discontinuous Galerkin method

The absorbing boundary conditions play a key role in numerical simulations. The complex frequency-shifted perfectly matched layer (CFS-PML), one of the absorbing boundary conditions, has attracted widespread attention because it can efficiently attenuate near-grazing incident waves and evanescent waves. In practical application, the distinct boundary reflections still exist using the CFS-PML in a thin PML region. To further enhance the absorbing performance, we have developed a novel unified high-order unsplit CFS-PML technique. The high-order CFS-PML combined with the discontinuous Galerkin (DG) method is applied to solve the second-order seismic wave equation on an unstructured mesh. In this work, we establish a unified framework for high-order unsplit CFS-PML formulations. The classical CFS-PML is the first-order form of our proposed unsplit high-order approach. By using auxiliary variables and auxiliary ordinary differential equations (AODEs), a higher-order CFS-PML formulation can be reduced to a first-order CFS-PML formula. For an Nth-order AODE CFS-PML, it will lead to (2N-1) additional variables and (2N-1) auxiliary differential equations. Taking second- and third-order as examples, we have derived the high-order unsplit AODE CFS-PML expressions in detail. The DG method can effectively deal with complex geological structures, such as caves, cracks, faults, etc., which yields more accurate numerical solutions. We incorporate the high-order unsplit AODE CFS-PML technique into the DG method to approximate seismic wave equations. Their strengths are combined to calculate the propagating wavefield in more complex finite models and obtain better results. In addition, the stability conditions of the second- and third-order CFS-PMLs are provided. We perform the high-order unsplit CFS-PML implementation for seismic wave modeling in several models. The isotropic and anisotropic examples prove that the high-order unsplit CFS-PML enjoys better absorbing performance than the classical CFS-PML. The numerical result for the graben model certifies the feasibility and flexibility of our proposed high-order CFS-PML combined with the DG method.

Predictive Performance of Machine Learning–Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study

Background Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined. Objective This study aims to examine how much data-driven models with machine learning improve predictive performance for poststroke outcomes compared with conventional stroke prognostic scores and to elucidate how explanatory variables in machine learning–based models differ from the items of the stroke prognostic scores. Methods We used data from 10,513 patients who were registered in a multicenter prospective stroke registry in Japan between 2007 and 2017. The outcomes were poor functional outcome (modified Rankin Scale score >2) and death at 3 months after stroke. Machine learning–based models were developed using all variables with regularization methods, random forests, or boosted trees. We selected 3 stroke prognostic scores, namely, ASTRAL (Acute Stroke Registry and Analysis of Lausanne), PLAN (preadmission comorbidities, level of consciousness, age, neurologic deficit), and iScore (Ischemic Stroke Predictive Risk Score) for comparison. Item-based regression models were developed using the items of these 3 scores. The model performance was assessed in terms of discrimination and calibration. To compare the predictive performance of the data-driven model with that of the item-based model, we performed internal validation after random splits of identical populations into 80% of patients as a training set and 20% of patients as a test set; the models were developed in the training set and were validated in the test set. We evaluated the contribution of each variable to the models and compared the predictors used in the machine learning–based models with the items of the stroke prognostic scores. Results The mean age of the study patients was 73.0 (SD 12.5) years, and 59.1% (6209/10,513) of them were men. The area under the receiver operating characteristic curves and the area under the precision-recall curves for predicting poststroke outcomes were higher for machine learning–based models than for item-based models in identical populations after random splits. Machine learning–based models also performed better than item-based models in terms of the Brier score. Machine learning–based models used different explanatory variables, such as laboratory data, from the items of the conventional stroke prognostic scores. Including these data in the machine learning–based models as explanatory variables improved performance in predicting outcomes after stroke, especially poststroke death. Conclusions Machine learning–based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores, although they required additional variables, such as laboratory data, to attain improved performance. Further studies are warranted to validate the usefulness of machine learning in clinical settings.

Dampak Reputasi, Responsifitas, Pemberian Hadiah, dan Rekomendasi terhadap Nasabah yang Berpindah dari BSI ke Bank Konvensional

This research aims to investigate the impact of reputation, responsiveness, gift-giving, and recommendations on Brand Switching, especially the movement of customers from Conventional Banks to Bank Syariah Indonesia (BSI). The research method uses multiple linear regression with a Likert Scale to collect data from 100 respondents who are customers from various cities in Indonesia, including Yogyakarta, Gorontalo, and Surakarta. The validity test results show that all variables have a valid correlation with Brand Switching. Multiple linear regression revealed that gifts had the greatest impact, while recommendations had a lower impact. The F test and t-test confirm that reputation, responsiveness, gift-giving, and recommendations simultaneously and partially significantly influence Brand Switching. Even though the Multiple R shows a relationship of 61.7%, the Adjusted R Square of 35.5% indicates that the model does not fully explain variations in Brand Switching. The conclusions of this research provide insight for bank management to improve reputation, responsiveness, and gift-giving strategies to reduce the level of Brand Switching. Future research is recommended to consider additional variables and a qualitative approach to explore the factors that influence customer decisions.

Sex Differences in the Relationship between Personal, Psychological and Biochemical Factors with Blood Pressure in a Healthy Adult Mexican Population: A Cross-Sectional Study.

Hypertension is one of the main risk factors related to cardiovascular mortality, being the levels of blood pressure (BP) related to a variety of personal, anthropometric, biochemical and psychological variables; however, the study evaluating the association of all these factors in systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a sample of relatively healthy subjects has not been performed. The aim of the study was to determine the main variables associated with SBP and DPB in a sample of relatively healthy subjects. A total of 171 participants were included, in which personal, anthropometric, positive and negative psychological variables and biochemical variables were measured. We observed that men showed higher levels of SBP and DBP than women, with more differences for SBP. Among the biochemical factors and SBP, we found that albumin and monocytes were positively correlated with it, while potassium, phosphorus and eosinophils were negatively correlated with it. Additionally, schooling was a constant variable negatively correlated with SBP in all samples (global, men and women). Among psychological variables, we observed that emotional perception was negatively correlated with SBP in men's and women's samples, while autonomy was positively correlated with SBP in the men's sample; however, their association was less when compared with the personal and biochemical variables included in the multivariate model. With regard to DBP, we observed that the biochemical variables, hemoglobin, sodium, uric acid and glucose, were positively correlated with DBP in the global sample, while chloride and BUN were negatively correlated with it. In addition, many personal and behavioral variables, including BMI, age and smoking consumption frequency, also correlated with DBP in the global sample. In conclusion, BP is affected by different factors, and these affect each sex differently.

Approximate Kernel Learning Uncertainty Set for Robust Combinatorial Optimization

Support vector clustering (SVC) has been proposed in the literature as a data-driven approach to build uncertainty sets in robust optimization. Unfortunately, the resulting SVC-based uncertainty sets induces a large number of additional variables and constraints in the robust counterpart of mathematical formulations. We propose a two-phase method to approximate the resulting uncertainty sets and overcome these tractability issues. This method is controlled by a parameter defining a trade-off between the quality of the approximation and the complexity of the robust models formulated. We evaluate the approximation method on three distinct, well-known optimization problems. Experimental results show that the approximated uncertainty set leads to solutions that are comparable to those obtained with the classic SVC-based uncertainty set with a significant reduction of the computation time. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by the German-French Academy for the Industry of the Future [Data-driven collaboration in Industrial Supply Chains project].

The Anti-Müllerian Hormone as Endocrine and Molecular Marker Associated with Reproductive Performance in Holstein Dairy Cows Exposed to Heat Stress.

Anti-Müllerian hormone (AMH) is proposed as a biomarker for fertility in cattle, yet this associative relationship appears to be influenced by heat stress (HS). The objective was to test serum AMH and AMH-related single nucleotide polymorphisms (SNPs) as markers potentially predictive of reproductive traits in dairy cows experiencing HS. The study included 300 Holstein cows that were genotyped using BovineSNP50 (54,000 SNP). A genome-wide association study was then executed. Nine intragenic SNPs within the pathways that influence the AMH gene were found important with multiple comparisons adjustment tests (p < 1.09 × 10-6). A further validation study was performed in an independent Holstein cattle population, which was divided into moderate (MH; n = 152) and severe heat-stressed (SH; n = 128) groups and then subjected to a summer reproductive management program. Serum AMH was confirmed as a predictor of fertility measures (p < 0.05) in MH but not in the SH group. Cows were genotyped, which revealed four SNPs as predictive markers for serum AMH (p < 0.01), reproductive traits (p < 0.01), and additional physiological variables (p < 0.05). These SNPs were in the genes AMH, IGFBP1, LGR5, and TLR4. In conclusion, serum AMH concentrations and AMH polymorphisms are proposed as predictive markers that can be used in conjunction with genomic breeding value approaches to improve reproductive performance in Holstein cows exposed to summer HS conditions.

Expanding the Interpretive Potential of Eye Tracking for the Rorschach: Replication and Extension of the Findings of Ales et al. (2020)

The present study was undertaken to determine if the findings of Ales et al. (2020) could be replicated and extended, especially in light of the replication crisis in psychology and the social sciences. Ales et al. (2020) found that measures from the engagement and cognitive processing domain of the Rorschach performance assessment system (R-PAS) were associated with Eye Tracking variables that reflect cognitive engagement and effort. Notably, Complexity was associated with the number of fixations participants made while scanning the blots and Vg% was inversely associated with a participant’s average fixation durations. The present study utilized a non-clinical sample of 60 adult participants. The basic findings of Ales et al. (2020) were replicated. In addition, we found that Complexity and Vg% are associated with additional Eye Tracking variables not utilized in the original study. The current findings bolster and extend the interpretation of Ales et al. (2020), indicating that higher levels of Complexity are also associated with scanning more regions of the blot overall, albeit at a slower rate. Similar effect size values were observed in the two different cultural contexts. Higher levels of Vg% are associated with measures indicating shallower and more superficial search strategies, consistent with the interpretation of Vg% as indicative of a vague, impressionistic, and unsophisticated cognitive style.

Cultural determinants of capital structure in Indonesian banking

Capital structure decisions play a crucial role in shaping the financial landscape of companies, especially in developing countries. This study examines the cultural determinants of capital structure in Indonesian banking within the macroeconomic context from 2013 to 2022. The capital structure represents the mix of various company funding methods, impacting its value and influencing stakeholders. The debate on how the interplay of debt and assets affects a company's value continues, with the determining factors of capital structure still being explored in financial studies. Using STATA V.17, this research analyzes 30 banking companies listed on the Indonesia Stock Exchange, with leverage as the dependent variable. Financial ratios, including liquidity ratio, profit volatility, growth opportunities, bank size, and external macroeconomic variables such as inflation and Gross Domestic Product (GDP), are examined as potential influencers on the capital structure. The results reveal high leverage in Indonesian banks, with variations in financial approaches. Return on assets, growth opportunities, and inflation exhibit a positive and significant relationship with leverage, while profit volatility, tangibility, bank size, and GDP show a negative and significant relationship. The research suggests limitations in scope, recommending including all Indonesian banking companies and additional external macroeconomic variables like exchange and interest rates.

Enviromic prediction enables the characterization and mapping of Eucalyptus globulus Labill breeding zones

Genotype-environment interaction is pervasive in forest genetics. Delineation of spatial breeding zones (BZs) is fundamental for accommodating genotype-environment interaction. Here we developed a BZ classification pipeline for the forest tree Eucalyptus globulus in 2 Australian regions based on phenotypic, genomic, and pedigree data, as well on a detailed environmental characterization (“envirotyping”) and spatial mapping of BZs. First, the factor analytic method was used to model additive genetic variance and site–site genetic correlations (rB) in stem volume across 48 trials of 126,467 full-sib progeny from 2 separate breeding programs. Thirty-three trials were envirotyped using 145 environmental variables (EVs), involving soil and landscape (71), climate (73), and management (1) EVs. Next, sparse partial least squares-discriminant analysis was used to identify EVs that were required to predict classification of sites into 5 non-exclusive BZ classes based on rB. Finally, these BZs were spatially mapped across the West Australian and “Green Triangle” commercial estates by enviromic prediction using EVs for 80 locations and 15 sets of observed climate data to represent temporal variation. The factor analytic model explained 85.9% of estimated additive variance. Our environmental classification system produced within-zone mean rB between 0.76 and 0.84, which improves upon the existing values of 0.62 for Western Australia and 0.67 for Green Triangle as regional BZs. The delineation of 5 BZ classes provides a powerful framework for increasing genetic gain by matching genotypes to current and predicted future environments.

Predictors of batting and pitching performance in the USA baseball prospect development pipeline

ABSTRACT This study examined the relationship between psychomotor abilities and baseball performance by analysing data from 379 athletes who participated in the USA Baseball, Prospect Development Pipeline (PDP). Hit and pitch metrics were generated during practice sessions using the RapsodoTM System. Data were compared through exploratory factor analysis and hierarchical regression. Factor analysis grouped batter’s PDP evaluations into four latent variables accounting for 63% of variance. Pitcher performance grouped into three factors accounting for 51% of variance. Regression on batter data revealed a significant demographic/anthropometric base model with height, weight, and age that accounted for 58% of the batted ball speed (R2 = 0.581). Player position explained 2% of the variance (R2 = 0.604), and PDP evaluation scores contributed an additional 3% (R2 = 0.631). Regression of pitcher data showed a significant base demographic/anthropometric model accounting for 36% of fastball pitch speeds (R2 = 0.363), with the PDP evaluation scores adding 6% additional variance (R2 = 0.424). Uniformly, assessments of lower body strength added the greatest predictive information. Hand grip strength did not correlate with pitch metrics. While demographics/anthropometrics are major contributors to batted and pitched ball speed, position and psychomotor variables add statistically significant contributions and may be of practical value for player selection.

Kettle-hole peatlands as carbon hot spots: Unveiling controls of carbon accumulation rates during the last two millennia

Understanding carbon sequestration patterns in time and space is crucial for models and future projections of carbon uptake. However, their past provides an insight to this understanding, and peatlands play an essential role in the carbon cycle. Hence, peat carbon accumulation rates (PCAR) were reconstructed for the last ±1500 years in three Polish kettle-hole peatlands (Jaczno, Głęboczek and Pawski Ług bogs). Absolute chronologies, retrieved from the Bayesian age-depth models based on high-resolution 14C AMS dating, provided good time control, whereas the selected time interval spanned a broad spectrum of environmental changes and human activity. An additional variable was connected to climate, as the chosen sites are situated along the west-east gradient of northern Poland.The aim was to find factors responsible for changes in carbon accumulation, so the results of PCAR were combined with pollen, testate amoebae, plant macrofossil, and charcoal data. The research showed that the investigated peatlands varied regarding local environmental conditions (e.g. water level), peatland development, vegetation changes, and fire activity. Generally, the PCAR values were higher in the Sphagnum-dominated sections of the profiles with high shares of mixotrophic testate amoebae unless fire hampered the carbon sequestration. However, our research showed that the changes in carbon uptake resulted from various overlapping factors rather than just one. Nevertheless, high values of PCAR in Jaczno and Pawski Ług (mean 74.6 and 79.64 g C/m2/yr, respectively) point to kettle-hole peatlands being exceptionally efficient in carbon sequestration.

How Does a Company Being Acquired Affect Rivals Innovation in Technology Industry?

Previous research on mergers and acquisitions has predominantly focused on the companies involved in the acquisition process. This paper aims to extend the scope of study by including the response of rivals, specifically those of the companies being acquired. This paper studies how the rivals of companies being acquired change their innovation in technology industy. This study compares R&amp;D expenses of rival firms before and after the acquisitions to analyze these changes. However, our analysis does not reveal a strong correlation between a company's level of innovation and the occurrence of its rivals being acquired. Other factors such as company size, industry competitiveness, and leverage held by the company appear to influence a company's innovation.To gain a more comprehensive understanding of the impact of rivals being acquired on a company's innovation, further research is required. Future studies should explore additional variables and factors that may mediate or moderate the relationship between M&amp;A activities and innovation outcomes.In conclusion, while our study does not provide clear evidence of the effect of rivals being acquired on a company's innovation, it emphasizes the crucial role of considering multiple dimensions when examining the relationship between M&amp;A and innovation. This study contributes to the ongoing discussion on understanding the complex dynamics of M&amp;A and their influence on firms' innovation strategies.

CEO power and stock price crash risk in India: the moderating effect of insider trades

PurposeThis study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.Design/methodology/approachA study of 236 companies from the S&amp;P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.FindingsStakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.Originality/valueThis study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

Distinguishing Immunologic and Behavioral Effects of Vaccination.

The interpretation of vaccine efficacy estimands is subtle, even in randomized trials designed to quantify the immunologic effects of vaccination. In this article, we introduce terminology to distinguish between different vaccine efficacy estimands and clarify their interpretations. This allows us to explicitly consider the immunologic and behavioral effects of vaccination, and establish that policy-relevant estimands can differ substantially from those commonly reported in vaccine trials. We further show that a conventional vaccine trial allows the identification and estimation of different vaccine estimands under plausible conditions if one additional post-treatment variable is measured. Specifically, we utilize a "belief variable" that indicates the treatment an individual believed they had received. The belief variable is similar to "blinding assessment" variables that are occasionally collected in placebo-controlled trials in other fields. We illustrate the relations between the different estimands, and their practical relevance, in numerical examples based on an influenza vaccine trial.

Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns

Excitable systems give rise to important phenomena such as heat waves, epidemics and cardiac arrhythmias. Understanding, forecasting and controlling such systems requires reliable mathematical representations. For cardiac tissue, computational models are commonly generated in a reaction–diffusion framework based on detailed measurements of ionic currents in dedicated single-cell experiments.Here, we show that recorded movies at the tissue-level of stochastic pacing in a single variable are sufficient to generate a mathematical model. Via exponentially weighed moving averages, we create additional state variables, and use simple polynomial regression in the augmented state space to quantify excitation wave dynamics. A spatial gradient-sensing term replaces the classical diffusion as it is more robust to noise. Our pipeline for model creation is demonstrated for an in-silico model and optical voltage mapping recordings of cultured human atrial myocytes and only takes a few minutes.Our findings have the potential for widespread generation, use and on-the-fly refinement of personalised computer models for non-linear phenomena in biology and medicine, such as predictive cardiac digital twins.

Causes for 30-day readmissions and accuracy of the LACE index in regional Victoria, Australia.

Clinicians and funders continue to search for ways to reduce costs without sacrificing quality of care. Ongoing research should focus on innovative care models that identify patients at high-risk for hospitalisation and thereby reduce healthcare costs. This study examined readmission rates, comorbidity profiles and the performance of the LACEi (Length of stay, Acuity of admission, Charlson Comorbidity Index, ED admissions in the previous 6 months index) to predict the risk of 30-day readmissions in a regional population. Furthermore, we tested a novel clinician-orientated classification for the causes of 30-day readmissions. Using a nested case-control design, data were extracted from administrative health records using 30-day readmission status as the outcome. We defined cases as discharges within 30 days before readmission and controls without a discharge within 30 days before admission between 1 July 2020 and 30 June 2022. The study was conducted at South West Healthcare in Victoria, Australia. All adult medical patients were discharged alive from the facility. We excluded planned readmissions, surgical and obstetric admissions, dialysis, transfers to alternative facilities and discharges against medical advice. Thirty-day readmission rate, comorbidity profile for all admissions, LACEi for all admissions, the performance of the LACEi in our setting and the causes leading to readmission using a clinician-orientated classification tool. Comorbidity burden, male sex and age > 65 years were associated with increased readmission risk but not length of stay. The LACEi demonstrated modest predictive ability to identify high-risk patients for readmissions (area under the receiver operating characteristic curve = 0.59). Additional variables were needed to increase accuracy. The novel classification identified 42% of readmissions as potentially avoidable. Our study identified comorbidity burden, male sex and age ≥ 65 years as critical indicators for readmission risk. Although the LACEi showed moderate predictive ability, additional variables were needed for increased accuracy. Over 40% of readmissions were potentially avoidable, and nearly two thirds occurred within 14 days of discharge from the hospital.

Energy-efficient trajectory design for secure SWIPT systems assisted by UAV-IRS

Due to its controllable maneuverability, wide coverage, and low cost, unmanned aerial vehicle (UAV) has great potential in post-disaster rescue, cargo transport and emergency communication. Considering its limited onboard energy, energy-efficient UAV communication is a challenge. This research examines the security of simultaneous wireless information and power transfer (SWIPT) systems assisted by intelligent reflecting surfaces (IRS) and UAVs while considering the flight energy of rotary-wing UAVs. Specifically, an IRS is mounted on a UAV to enhance the quality of legitimate transmission, and artificial noise (AN) is introduced into the base station (BS) to reduce eavesdropping quality. The power splitting (PS) technology is adopted at ground devices (GDs) to simultaneously decode information and harvest energy. First, we jointly design the BS transmit beamforming, UAV-IRS phase shifts and trajectory/velocity as well as GDs PS ratio with the aim of maximizing the sum secrecy rate of all GDs. Then, an iterative algorithm is developed to address the formulated problem. In particular, additional variables are introduced to handle this complicated objective function, and the original problem is decoupled into multiple sub-problems, which can be solved alternately by invoking the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Finally, numerical results demonstrate that the proposed scheme exhibits a substantial performance in the security rate of SWIPT systems assisted by UAV-IRS, and its performance is improved by at least 12% compared to benchmark schemes at the flight energy budget ethr=5KJ and the number of reflecting elements Nr=25.

Exploring the impact of social media on tourist behavior in rural mountain tourism during the COVID-19 pandemic: The role of perceived risk and community participation

PurposeThis research article aims to assess the behavior of tourists in sustainable rural mountain tourism during the COVID-19 pandemic, focusing on the role of social media usage. It investigates the key social media features that influence tourist behavior and introduces the concept of perceived risk as a novel variable within the Technology Acceptance Model (TAM) to deepen our understanding of the relationship between social media use and tourist behavior. Design/methodology/approachThe study conducts an extensive literature review and utilizes survey analysis. Three parameters—perception of risk, perception of value, and trust in social media—are employed to measure the impact of influential social media features. Statistical tests are applied to validate the new variable of perceived risk within the TAM. FindingsThe study unveils that tourists' perception of risk associated with social media has the most significant influence on their behavior in rural mountain tourism during the pandemic. Additionally, it identifies that active engagement of tourists in online discussions positively affects both tourist behavior and social media usage, emphasizing the importance of community participation. Research limitations/implicationsThe research acknowledges limitations, including the need for further validation of the perceived risk variable and the consideration of contextual influences. Future studies should explore additional variables and encompass diverse tourist populations to expand the understanding of social media usage and tourist behavior in rural mountain regions. Contribution of researchThis research significantly advances the understanding of tourist behavior in sustainable rural mountain tourism during the COVID-19 pandemic by introducing the novel variable of “perceived risk” within the Technology Acceptance Model. The study identifies influential social media features and underscores the positive effects of participatory habits, providing valuable insights for promoting sustainable tourism through targeted social media interventions. Practical implicationsThis research holds significant practical implications for practitioners and policymakers seeking to promote sustainable tourism in rural mountain regions, particularly during the COVID-19 pandemic. The identification of influential social media features and the emphasis on participatory habits among tourists offer actionable insights. Practitioners can leverage these findings to design targeted social media interventions that foster community engagement and enhance tourists' perception of value while mitigating perceived risks. Policymakers can use this information to shape strategies that encourage interactive online platforms, creating a sense of community participation to boost sustainable tourism.

Transforming Agriculture through Nanotechnology: A Comprehensive Overview

Nanotechnology has emerged as a transformative force in agriculture, offering innovative solutions to address critical challenges in food production, sustainability, and resource management. This comprehensive overview explores the multifaceted impact of nanotechnology on agriculture, providing insights into its potential to revolutionize the sector and by elucidating the fundamental principles of nanotechnology and its applicability in agriculture, emphasizing nanomaterials’ unique properties that enable precise manipulation at the molecular and nanoscale levels. It explores diverse applications, including nanoparticle-based nutrient delivery systems, nanoencapsulation for controlled release of agrochemicals, and the development of nanosensors for real-time monitoring of crop health and environmental parameters. Furthermore, this delves into the emerging field of precision farming enabled by nanotechnology, where data-driven decision-making and targeted interventions optimize resource utilization, minimize environmental impacts, and maximize crop yields. It examines the potential of nano pesticides in integrated pest management, highlighting their eco-friendly nature and reduced environmental contamination. Nanotechnology’s role in soil management and remediation is also discussed, showcasing its capacity to improve soil health, remediate contaminated lands, and enhance nutrient availability. The review sheds light on the application of nanofertilizers, which not only enhance nutrient use efficiency but also mitigate nutrient runoff, contributing to sustainable agriculture. While nanotechnology holds immense promise, it is imperative to consider safety and environmental implications. This examines the challenges and regulatory considerations surrounding the use of nanomaterials in agriculture, emphasizing the need for responsible development and rigorous risk assessment.