bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of digital technologies on manufacturing firm resilience during COVID-19 pandemic: a PLS-SEM and artificial neural network analysis
Authors: Basit Abdul , Wang Laijun , Javed Asma , Shoaib Muhammad , Aslam Muhammad Umer 
Published: November 2024

PurposeThe emergence of the COVID-19 epidemic has considerably increased the intricacy of information, exacerbating the difficulties firms encounter in efficiently processing and understanding accurate data and knowledge. Consequently, the COVID-19 epidemic has profoundly exacerbated production ambiguity for firms, thereby disrupting their regular business operations and supply chain activities. Digital technologies (DTs) are essential tools for firms to process and interpret information and knowledge, thereby improving their resilience against supply chain interruptions.Design/methodology/approachThis research investigates the effect of digital technologies on firm resilience throughout COVID-19, utilizing PLS-SEM and artificial neural networks (ANN) derived from a comprehensive survey of Pakistani manufacturing firms.FindingsOur research assesses the mediating role of supply chain integration, memory, and absorptive capacity, as well as the moderating influence of information complexity. The outcomes demonstrate that supply chain integration (SCI), memory (SCM), and absorptive capacity (SCAC) mediate digital technologies’ influence on firm resilience. Moreover, in situations where information is highly complex, DTs have a greater effect on a firm’s resilience.Originality/valueThe results enhance our comprehension and awareness of the resilience-related effects of DTs and offer significant management insights for strengthening firm resilience in the setting of the COVID-19 pandemic.
Distinguishing altruism from warm glow: psychological motivations for money donations to Thailand’s Buddhist society
Authors: Amonhaemanon Dalina , Khongkaew Panita , Akartwipart Ausawatap , Thamthanakoon Nithicha 
Published: November 2024

PurposeThis study investigates the factors influencing monetary donations in Thailand, a developing Buddhist nation. We explore the interplay of demographic, socioeconomic, psychological and social factors on individual donation behavior, focusing on frequency, amount, planning and motivation. Our primary aim is to distinguish pure altruism from the “warm glow” effect and identify the optimal model explaining how donation behavior impacts donor life satisfaction.Design/methodology/approachA structured, self-administered questionnaire surveyed 450 Thai charitable donors (convenience sample) using logistic regression analysis in SPSSFindingsThe study revealed females and those with higher education exhibited stronger altruistic motives, while a warm glow motivated five distinct groups: 1) older, 2) unmarried, 3) those with high external locus of control, 4) reputation-conscious donors and 5) those aware of public health benefits like medical care access. Additionally, pre-planned donations were associated with five factors: 1) debt-free, 2) Buddhist faith, 3) belief in societal improvement through donations, 4) witnessing family members donate and 5) awareness of public health benefits including receiving royal decorations.Practical implicationsThis research provides valuable insights for policymakers and charities. By understanding donor motivations, targeted incentives (e.g. emphasizing the “warm glow” effect) can be designed to increase donations and address social issues.Originality/valueThe study suggests tax deduction campaigns may be less effective than expected, as awareness of deductions correlated with lower donation frequency and amount. To incentivize giving, the government should focus on factors most linked to donor satisfaction. This research indicates warm glow is the strongest driver, followed by donation frequency, planning behavior and amount donated.
RSM applied to lattice patterns for stiffness optimization
Authors: Donnici Giampiero , Freddi Marco , Liverani Alfredo 
Published: November 2024

Purpose
In this study, response surface methodology (RSM) is applied to a three-point bending stiffness analysis of low-cost material (PLA) specimens printed using FDM technology to analyze the performance of different internal lattice structures (Octet and IsoTruss principally). The purpose of this study is to extend the definition from a discrete (lattice) model to an analytical one for its use in subsequent design phases, capable of optimizing the type of cell to be used and its defining parameters to find the best stiffness-to-weight ratio.

Design/methodology/approach
The representative function of their mechanical behavior is extrapolated through a two-variable polynomial model based on the cell size and the thickness of the beam elements characterizing it. The polynomial is obtained thanks to several tests performed according to the scheme of RSM. An analysis on the estimation errors due to discontinuities in the physical specimens is also conducted. Physical tests applied to the specimens showed some divergences from the virtual (ideal) behavior of the specimens.

Findings
The study allowed to validate the RSM models proposed to predict the behavior of the system as the size, thickness and type of cells vary. Changes in stiffness and weight of specimens follow linear and quadratic models, respectively. This generally allows to find optimal design points where the stiffness-to-weight ratio is at its highest.

Originality/value
Although the literature provides numerous references to studies characterizing and parameterizing lattice structures, the industrial/practical applications concerning lattice structures are often still detached from theoretical research and limited to achieving functioning models rather than optimal ones. The approach here described is also aimed at overcoming this limitation. The software used for the design is nTop. Subsequent three-point bending tests have validated the reliability of the model derived from the method’s application.
Deep learning‐based inversion with discrete cosine transform discretization for two‐dimensional basement relief imaging of sedimentary basins from observed gravity anomalies
Authors: Roy Arka , Ekinci Yunus Levent , Balkaya Çağlayan , Ai Hanbing 
Published: November 2024

AbstractSedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two‐dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one‐dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non‐Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization‐based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration.
Soil moisture mediates the effect of plant below‐ground carbon allocation on the decomposition of root litter in a subtropical forest
Authors: Xi Meijie , Zeng Xiaoyue , Yang Yin , Liang Shuang , Cai Liangyuan , Pan Zichen , Liu Yu , Fernandez Christopher W. , Koide Roger T. , Chen Weile 
Published: November 2024

Abstract

Trees allocate carbon below‐ground to fuel the functioning of roots and mycorrhizal fungi, which affect litter decomposition, but the direction and magnitude of this effect are variable. While tree mycorrhizal type is often suggested to mediate this above‐ground–below‐ground linkage, previous studies yield mixed results. In this study, we investigated how absorptive root traits, soil conditions and litter type influence the response of litter decomposition to altered below‐ground carbon allocation, both within and across mycorrhizal types.

We girdled transport roots of seven subtropical tree species to eliminate carbon allocation to distal absorptive roots. We monitored leaf and root litter decomposition surrounding girdled and un‐girdled root branches.

We found that girdling generally slowed leaf litter decomposition. However, the effect of girdling on root litter decomposition depended on soil moisture, stimulating decomposition in dry soil but suppressing it in moist soil. Absorptive root traits did not influence the girdling effect on either leaf or root litter decomposition. These findings suggest that disturbance in carbon allocation can impact litter decomposition, with the outcome largely contingent on litter type and soil moisture.

Synthesis. Our findings highlight the importance of accounting for local soil variability in understanding the relationship between above‐ground and below‐ground carbon dynamics. This study underscores a critical need for comprehensive assessment of below‐ground ecosystem responses to above‐ground disturbances, as it is essential for accurately predicting future forest carbon and nutrient cycles.
Promoting sustainable agri‐food production to achieve food and nutrition security: The role of soil conservation practices
Authors: Li Junpeng , Vatsa Puneet , Ma Wanglin , Luu Phong Quoc 
Published: November 2024

AbstractWe examine the role of soil conservation practices (SCPs) in promoting sustainable agri‐food production to achieve food and nutrition security. To this end, we use the endogenous treatment regression model, as it allows us to address the selection bias associated with SCP adoption. Data sourced from the 2020 China Land Economic Survey, collected by Nanjing Agricultural University, China, are examined. First, we analyse the association between the adoption of SCPs and rice yield; we focus on rice as it is the most common staple food globally. Then, we examine the links between SCP adoption and dietary diversity and the consumption of seven specific foods (rice, potato, pork, poultry, egg, fruits and vegetable oil). This allows us to glean insights into how SCPs may affect the dietary patterns in rural communities. The findings show that adopting SCPs is associated with an 8.5% increase in rice yield. Rice yields increase with the intensification of SCP adoption. Disaggregated analysis suggests that low‐income farmers receive the most rice yield promotion by adopting SCPs compared with wealthier ones. Meanwhile, among the three SCPs (i.e. soil remediation, commercial organic fertiliser application and formulated fertilisation) considered in our study, only formulated fertilisation increases rice yields. Furthermore, adopting SCPs is associated with higher consumption of protein‐rich foods and lower dietary diversity.
A Political–Economic Model of Community and Societal Health Resources: A 92‐Country Global Analysis
Authors: Omidvar Tehrani Shadi , Perkins Douglas D. , Mihaylov Nikolay L. 
Published: November 2024

ABSTRACTThe quality and access to healthcare systems depend on community health resources, infrastructure, and funding; however, a significant disparity in these resources persists globally. The effectiveness of national health systems depends on a balanced approach to health spending, access to facilities and a skilled local health workforce. What accounts for country‐level differences in those critical community and societal health resources? We proposed and tested a model that leverages political and socioeconomic factors to predict various health resources and services in countries. Data, including community health training, research, and support, universal health coverage, healthcare infrastructure, and per capita health expenditure, were collected and analysed by statistical methods, like bivariate correlations and hierarchical multiple linear regressions from 105 countries. Countries with more grassroots activism, fiscal decentralisation, freedom, and globalisation and less perceived corruption and inequality had more community and societal health resources. In multivariate analyses, stronger community health training and research is associated with the globalisation index, freedom score, government fiscal decentralisation, and income inequality. The strongest predictor of health insurance coverage and hospital beds was the country's population education index, and of nurses and midwives‐per‐capita and health expenditures‐per‐capita was GDP‐per‐capita. These insights could guide policymaking to reduce global health inequalities.
Evaluation of silver nanoformulated plant extracts against larvae of the fall armyworm, Spodoptera frugiperda (J. E. Smith), under laboratory and field conditions
Authors: Abbas Umair , Majeed Muhammad Zeeshan , Alkherb Wafa Abdullah Hamad , Alshehri Mohammed Ali , Alkeridis Lamya Ahmed , Sayed Samy , Majeed Muhammad Irfan , Riaz Muhammad Asam 
Published: November 2024

AbstractThe fall armyworm, Spodoptera frugiperda (J. E. Smith), is a destructive pest of Zea mays (maize) and other agricultural crops. The synthetic insecticides predominantly used against this pest lead to pest resistance, environmental contamination and health hazards. This study evaluated nanoformulated aqueous extracts of some promising local plant species against third instar larvae of S. frugiperda under laboratory and field conditions. The initial screening bioassay showed highest larval mortality with a 20% extract of Nicotiana tabacum L. (66.67%), followed by Azadirachta indica A. Juss. (53.33%), Withania somnifera L. (46.67%), Melia azedarach L. (40%) and Dodonaea viscosa Jacq. (33.33%). The two most effective plant extracts (A. indica and N. tabacum) were further nanoformulated with silver nitrate (AgNO3) and bioassayed against S. frugiperda larvae using different concentrations. The results showed that these nanoformulated extracts caused significant larval mortality, with LC50 (lethal concentration that kills 50% of the population) and LT50 (lethal time to kill 50% of the population) values of 37.36 and 28.21% at 72 h, and 52.19 and 33.25 h at 80% concentration, respectively. Field experiments on Zea mays L. (maize) plants showed maximum larval reduction by nanoformulated A. indica extract (48%), followed by N. tabacum extract (36%), whereas 80% and 20% larval reduction was noted for the positive (SuperLock®, emamectin benzoate and tebufenozide) and the negative (water) controls, respectively. Furthermore, characterization of both silver nanoparticles‐based plant extract formulations was performed using ultraviolet–visible (UV–vis) spectroscopy, Raman spectroscopy and scanning electron microscopy (SEM) techniques, which confirmed the formation of silver nanoparticles. It is concluded that nanoformulated plant extracts can be an effective alternative to synthetic pesticides in combatting S. frugiperda and other lepidopteran pests.
Analysis and control of hybrid convection‐radiation drying systems toward energy saving strategy
Authors: Qenawy Mohamed , Ali Mona , El‐Mesery Hany S. , Hu Zicheng 
Published: November 2024

AbstractFood drying is a vital technology in agricultural preservation, where hybrid drying provides efficient moisture removal. However, the transient behavior of the moisture ratio () and associated drying rate () could increase energy consumption if not managed with an effective energy‐saving strategy. This study investigates the impacts of hybrid convection‐radiation drying on , , drying time, and energy consumption. Unlike previous studies, which have not fully addressed the connections between operating conditions and drying kinetics, this work provides new insights into these relationships. Additionally, an artificial neural network (ANN) control model is applied to optimize energy consumption, offering a new approach to improving the efficiency of the drying process. During the experiment, the airflow velocity was varied from 0.7 m/s to 1.5 m/s, the airflow temperature was varied from 40°C to 60°C, and the radiation intensity was varied from 1500 W/m2 to 3000 W/m2. The results showed that the transient behavior exhibited four data groups with consistent and through the mean and statistical analysis. Increasing radiation intensity and air temperature has decreased the drying time, while higher airflow has increased the drying time. The energy indices were enhanced by increasing radiation intensity and temperature while reducing airflow velocity. The measured of all groups exhibited similar kinetics behavior, while the associated exhibited similar clustering through the self‐organizing map. Those findings were further controlled using an ANN model with 99% predicting accuracy. With airborne heating at 60°C and airflow at 0.7 m/s, the radiation intensity is transiently controlled, showing a 6.5% drying time reduction and a 36% energy saving. Thus, controlling the radiation intensity and its impact on the slice properties is highly desirable in future work. This work could help designers improve the processing efficiency and energy conservation of garlic slices drying.
Influences of serving temperatures on human perceived spiciness intensity of commercial spicy sauces
Authors: Chon Seo‐yeong , Shin Yejin , Kim Mina K. 
Published: November 2024

AbstractThe objective of this study was to examine the effects of serving temperatures on the perceived spiciness intensities of commercial spicy sauces, which vary in capsaicinoid content. A total of 9 types of commercially available spicy sauces were used and capsaicinoid content was analyzed using liquid chromatography–mass spectrometry. To assess spiciness intensity, a highly trained panel (n = 6) rated the spiciness intensities at 4, 25, and 60°C. The spiciness intensity was assessed on a 15‐point scale using the spectral method. Panelists were instructed to assess the spiciness intensities from the moment the sauce was in the mouth until 50 s after expectoration, hence, allowing evaluations of intensities both in‐mouth and at Tmax. This allowed us to evaluate both the time and intensity of Tmax, the time when the spiciness in the mouth is most intense. The range of total capsaicinoids analyzed in this study spanned from 0.06 to 25.70 µg/mL. Serving temperature was found to have minimal impact on spiciness intensity (p > 0.05), and the liquid type sauces elicited higher spiciness intensities than the paste type sauces. Regardless of serving temperature, spiciness intensities measured at Tmax consistently exceeded those measured in‐mouth. These findings suggest considerations for formulation and presentation temperature when developing sauce products.Practical ApplicationThis study found no significant differences in spiciness intensity at the three tested temperatures (4, 25, and 60°C). However, the peak intensity of spiciness was observed approximately 20 s after expectoration, not immediately upon intake. These findings may contribute significantly to our understanding of the mechanisms of spicy perception and the interactions between spicy taste and the nervous system, gustatory system, and other senses.
Changes in quality characteristics and inactivation of Salmonella in cake, including oleogel used as a fat replacer, baked with two different methods
Authors: Ozdemir‐Orhan Necla , Eroglu Zeynep , Omac Basri 
Published: November 2024

AbstractThis is the first study to assess the impact of substitution of shortening (50%) with sunflower‐beeswax oleogel in cake formulations on the inactivation kinetics of Salmonella spp. and the quality attributes of cakes baked in conventional (CO) and microwave (MWO) ovens. Four distinct cake samples were examined in the study: Cake samples containing oleogel prepared in two different ovens (Oleo‐Cake‐CO and Oleo‐Cake‐MWO) and control cake samples baked in two different ovens (Cont‐Cake‐CO and Cont‐Cake‐MWO). The control‐batter and oleogel‐batter demonstrated shear thinning behavior (pseudoplastic, n < 1), with a good fit to the Power Law model, but viscosity and viscoelastic moduli decreased when the oleogel was used in place of shortening in cake recipes. In addition, the Cont‐Cake‐MWO had the greatest special volume value (2.31 ± 0.04 mL/g), whereas the Oleo‐Cake‐CO had the lowest (1.65 ± 0.02 mL/g) (p < 0.05). Furthermore, compared to the samples baked with CO, lower water activity and moisture values (p < 0.05) were observed in the samples baked in MWO due to their higher cooking loss values (p < 0.05). In all baking techniques, the addition of oleogel to the cake formula, used as a fat substitute, resulted in higher values for cohesiveness, hardness, springiness, and chewiness (p < 0.05). As a result, the inactivation of Salmonella in cakes slightly reduced with using oleogel as a fat substitute (p > 0.05), whereas it affected some quality properties of cakes baked with both heat treatments.
Oxidation protection efficiency of the combination of Minthostachys mollis K. and Origanum vulgare L. essential oils with “chain‐breaking” and “termination‐enhancing” antioxidant mechanisms
Authors: Juncos Nicolle Stefani , Ponso Carolina Florencia Cravero , Grosso Nelson Rubén , Olmedo Rubén Horacio 
Published: November 2024

AbstractThe use of antioxidants is known to reduce lipid oxidation. This study aimed to assess the interaction of two antioxidant mechanisms, namely, “chain‐breaking” and “termination‐enhancing” by combining natural antioxidants derived from “oregano” (Origanum vulgare L.) and “peperina” (Minthostachys mollis K.) essential oils (EOs) in an accelerated oxidation process of sunflower oil at 60°C. Concentrations of 0.05% w/w (1) and 0.02% w/w (2) of the combined oregano and peperina EOs were evaluated. Chemical and volatile oxidation markers were determined in comparison with the pure compounds, an antioxidant‐free control, and a BHT‐treated sample (0.02% w/w). Although the combinations exhibited lower oxidation indicator values (Anisidine ≈ 136) compared to the control (Anisidine ≈ 213), no evidence of a synergistic effect was observed (Anisidine: Oregano 1 ≈ 52; Oregano 2 ≈ 199; Peperina 1 ≈ 155; Peperina 2 ≈ 153), indicating that the combination did not enhance their antioxidant activity beyond that of the pure EOs. Thus, evaluating these combinations in other food matrices is important.Practical ApplicationFood oxidation is one of the main causes of food spoilage, and the search for natural antioxidants has become a necessity for society's increasing food knowledge and consumption preference. The research is focused on demonstrating that not only natural phenolic compounds with a “chain‐breaking” mechanism have efficient antioxidant activity but also non‐phenolic terpenes using the “termination enhancer” mechanism are efficient, but also the combination of them, which provides more alternative antioxidants for industrial applications.