Search results

Spatio-temporal variations in precipitation pattern of district Sargodha is one of the most significant researchable questions because of the massive reliance on rainfall for agricultural practice in the study area. The pattern of current rainfall in the study area is unexpectedly changed. The purpose of the present study is to examine the changing precipitation pattern and to link it with climate change.

The study was conducted by using rainfall data of the past 30 years collected from 8 meteorological stations around the study area. The averages of rainfall on monthly basis were temporally arranged, and the fluctuation trends were studied using GIS and statistics. The temporal data of rainfall were compared and contrasted with the precipitation normals of the study area from 1981to 2010. The rainfall deviation in the present study was calculated. The spatial pattern of rainfall was plotted by interpolating the eight points of Punjab around the study area for the first two decades, whereas the past decade was analysed by incorporating five more points of Tehsils in the existing eight. The spatial and statistical representation of data were examined by compare and contrast with the previous findings.

The rainfall in the study area showed remarkable changes in magnitude and spatiality. The rainfall in the district is on the rise, whereas the spatial pattern of rainfall is becoming more complex and anomalous in character. This paper provides convincing evidence about the impact of climate change on the magnitude and spatial patterns of precipitation in the study area.

It will be helpful for understanding the shifts in the rainfall pattern in future as well as for the preparation of response to the issue of climate change and its impacts.

The current manuscript, for the very first time, provided detailed insights about the precipitation pattern shifting during the last 30 years in district Sargodha, Punjab, Pakistan. Furthermore, agricultural sector would likely get severally affected because of seasonal changes in climatic factors like rainfall and have strong food security implications. The current findings will be useful to manage the climate change-related issues in Pakistan and helpful for the policy makers to design a coping strategy for climate change impacts.

This paper aims to develop a fuzzy logic-based algorithm to predict the intermetallic compound (IMC) size and mechanical properties of soldering material, Sn96.5-Ag3.0-Cu0.5 (SAC305) alloy, at different levels of temperature. The reliability of solder joint in materials selection is critical in terms of temperature, mechanical properties and environmental aspects. Owing to a wide range of soldering materials available, the selection space finds a fuzzy characteristic.

The developed algorithm takes thermal aging temperature for SAC305 alloy as input and converts it into fuzzy domain. These fuzzified values are then subjected to a fuzzy rule base, where a set of rules determines the IMC size and mechanical properties, such as yield strength (YS) and ultimate tensile strength (UTS) of SAC305 alloy. The algorithm is successfully simulated for various input thermal aging temperatures. To analyze and validate the developed algorithm, an SAC305 lead (Pb)-free solder alloy is developed and thermally aged at 40, 60 and 100°C temperature.

The experimental results indicate an average IMCs size of 5.967 (in Pixels), 19.850 N/mm² YS and 22.740 N/mm² UTS for SAC305 alloy when thermally aged at an elevated temperature of 140°C. In comparison, the simulation results predicted 5.895 (in Pixels) average IMCs size, 19.875 N/mm² YS and 22.480 N/mm² UTS for SAC305 alloy at 140°C thermally aged temperature.

From the experimental and simulated results, it is evident that the fuzzy-based developed algorithm can be used effectively to predict the IMCs size and mechanical properties of SAC305 at various aging temperatures, for the first time.

Single-valued neutrosophic sets (SVNSs) are efficient models to address the complexity issues potentially with three components, namely indeterminacy, truthness and falsity. Taking advantage of SVNSs, this paper introduces some new aggregation operators (AOs) for information fusion of single-valued neutrosophic numbers (SVNNs) to meet multi-criteria group decision-making (MCGDM) challenges.

Einstein operators are well-known AOs for smooth approximation, and prioritized operators are suitable to take advantage of prioritized relationships among multiple criteria. Motivated by the features of these operators, new hybrid aggregation operators are proposed named as “single-valued neutrosophic Einstein prioritized weighted average (SVNEPWA) operator” and “single-valued neutrosophic Einstein prioritized weighted geometric (SVNEPWG) operators.” These hybrid aggregation operators are more efficient and reliable for information aggregation.

A robust approach for MCGDM problems is developed to take advantage of newly developed hybrid operators. The effectiveness of the proposed MCGDM method is demonstrated by numerical examples. Moreover, a comparative analysis and authenticity analysis of the suggested MCGDM approach with existing approaches are offered to examine the practicality, validity and superiority of the proposed operators.

The study reveals that by choosing a suitable AO as per the choice of the expert, it will provide a wide range of compromise solutions for the decision-maker.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

In real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

The study highlights our findings, including the confirmation of phase stability through XRD analysis, the characterization of optical properties revealing high absorption and conductivity and the analysis of mechanical stability through elastic constants. Additionally, we present detailed results on the band gap, EELS analysis and the suitability of SrZrO3 perovskite oxides for next-generation optoelectronic devices.

Cubic SrZrO₃ perovskite oxides were designed within the framework of density functional theory (DFT) via the CASTEP code under varying stress conditions (0–100 GPa), aiming to explore the key properties for diverse applications. The phase stability was confirmed by XRD analysis. From 0 to 40 GPa, there is an increase in the band gap from 3.330 to 3.615 eV, while it narrows from 3.493 to 3.155 eV beyond 60 GPa. The optical characteristics revealed high absorption, superior conductivity and a lower loss function. Significantly, the elastic constants (C₁₁, C₁₂ and C₄₄) satisfy the Born-stability criterion, ensuring the mechanical stability of the compound. Additionally, the Poisson’s ratio, Pugh ratio (B/G), Frantsevich ratio, Cauchy pressure (P_C) and anisotropy factor ensured both ductile and anisotropic characteristics. Higher values of Young’s modulus and shear modulus signify a superior ability to withstand longitudinal stresses. In the EELS analysis, distinctive energy-loss peaks resulting from absorption and emission correlated with diverse electronic transitions and energy levels associated with Sr, Zr and O atoms are used to probe the precise exploration of the electronic and optical characteristics of materials with a high degree of accuracy. Based on these findings, the designed SrZrO₃ perovskite oxides are particularly suitable for applications in various optoelectronic devices.

CASTEP codes were utilized to design the cubic SrZrO₃ perovskite under varying stress conditions ranging from 0 to 100 GPa. The phase stability was confirmed through XRD analysis. A distinctive trend in the band gap was observed: an increase from 3.330 eV to 3.615 eV as the stress increased from 0 to 40 GPa and a decrease from 3.493 to 3.155 above 60 GPa. A higher absorption and conductivity and a lower loss function were found for the optical properties. The mechanical stability was ensured by elastic constants (C₁₁, C₁₂, and C₄₄) satisfying the Born-stability criteria. Additionally, the Poisson’s ratio, Pugh’s ratio (B/G), Frantsevich ratio, Cauchy pressure (P_C) and anisotropy factor were used to verify the ductility and anisotropy of the materials. Higher values of Young’s modulus and shear modulus indicate a superior ability to withstand longitudinal stresses. EELS analysis revealed distinctive energy-loss peaks associated with Sr, Zr and O atoms, enabling precise exploration of the electronic and optical characteristics with a high degree of accuracy. As expected, the designed SrZrO₃ perovskite oxides exhibit favorable properties, making them particularly suitable for next-generation optoelectronic devices.

In this study, we utilized DFT within the CASTEP code framework to investigate the properties of cubic SrZrO₃ perovskite oxides under varying stress conditions ranging from 0 to 100 GPa. Our research aimed to explore the key properties of SrZrO₃ for diverse applications, particularly in optoelectronic devices.

Drawing upon social cognitive theory, this study aims to investigate the potential predictors and consequences of social media health-misinformation seeking behavior during the coronavirus (COVID-19) pandemic.

Using a sample of 230 international students studying at Wuhan University and Beijing Language and Cultural University, China, this study employs structural equation modeling to analyze the collected data.

The results indicate that personal factors such as lack of health information literacy, environmental factors, information overload and social media peer influence have a significant effect on behavior, namely social media health-misinformation seeking behavior, which further influences outcomes, namely social media users' anxiety during the COVID-19 pandemic. In addition, both lack of health information literacy and social media peer influence have significant and direct effects on social media users' anxiety. However, the direct effect of information overload on social media users' anxiety is insignificant.

First, this study contributes to the literature on the individuals' social media health-misinformation seeking behavior, its precursors and its consequences, specifically on their mental healthcare during a pandemic situation. Second, this research is one of the pioneer studies that extend social cognitive theory to the context of social media health-misinformation seeking behavior and users' anxiety relationship.

The present study develops a conceptual model that shows how the manipulation attributes of word choice, sentence fluency, convention of meaning, and organization of sentence structure in online hotel reviews are connected to linguistic errors, such as spelling and grammar and argument errors, how such errors intensify the likelihood that messages will be misunderstood, and how these misunderstandings affect customers' responses.

A structured questionnaire was employed to collect data from 591 inbound tourists in Beijing, China. Data analysis was conducted using SPSS 25.0 and Amos Graphics 23.0. Descriptive analysis was performed to explain the sociodemographic characteristic of respondents. Structural equation modeling was performed to examine hypothesized relationships.

Results demonstrate that manipulation attributes increase linguistic errors, and two linguistic errors have profound positive effects on customers' understanding of meaning, which influence their responses in the form of negative online ratings and low purchase intentions.

The study's findings contribute to the literature on hospitality, linguistics, and consumer behavior, and have managerial implications for online review websites, online travel agents, and hotel management. Research limitations lead to suggestions for future research for hospitality scholars.

This study investigates the interaction between organizational strategic factors (Leadership and management support [LMS] and green learning orientation [GLO]) and green innovation performance (GIP), through the lens of resource-based view (RBV) theory. It examines both the direct and indirect impacts of these factors on GIP via green knowledge management (GKM), and explores how green absorptive capacity (GAC) enhances these relationships.

Using Partial least squares structural equation modeling (PLS-SEM) and moderated mediation analysis, we analyzed responses from 419 individuals across 154 manufacturing firms in Pakistan to understand these dynamics.

Results show that LMS and GLO significantly affect GIP, both directly and indirectly, through GKM. Furthermore, GAC intensifies the impact of GLO on GKM and the influence of GKM on GIP, indicating a moderated mediation effect.

Highlighting the importance of LMS, GLO, GKM, and GAC, the study suggests that focusing on these areas can help firms align their strategies with sustainability goals, enhancing their GIP. These insights can guide policymakers in creating supportive strategies for businesses to improve their GAC, facilitating better knowledge adoption and application.

The research contributes to the RBV theory by clarifying the role of strategic organizational factors in enhancing GIP within manufacturing firms, offering a clearer path to achieving sustainability goals.

This study drawing upon opportunity-ability-motivation (OAM) theory focuses on digital entrepreneurship opportunities (DEOs), knowledge generation capabilities (EKGCs) and enterprise market-sensing capabilities (EMSC) affecting digital innovation in terms of exploitative and exploratory DIs.

Employing quantitative methodology on a sample of 352 software SMEs' managers, the study employed a hierarchical regression analysis to investigate whether and how digital entrepreneurship opportunities and knowledge generation capabilities support and nurture both DIs. Additionally, the moderated–mediation effect of market-sensing capabilities on the relationships among digital entrepreneurship opportunities, enterprise knowledge generation capabilities and DIs are considered.

The study findings reveal that digital entrepreneurship opportunities influence exploitative and exploratory DIs. Knowledge generation capabilities partially mediate the relationship between digital entrepreneurship opportunities and exploitative and exploratory DIs, respectively. Moreover, market-sensing capabilities strengthen not only the effect of digital entrepreneurship opportunities on knowledge generation capabilities but also the effects of knowledge generation capabilities on exploratory DI. The moderated–mediation outcomes reveal that the mediating role of knowledge generation capabilities on the relationship between digital entrepreneurship opportunities and both DIs is stronger when EMSC are high.

This research integrates the opportunity-ability-motivation perspective to present a comprehensive framework that reveals the intricate interdependencies among digital entrepreneurship opportunity, knowledge generation and market-sensing capabilities in driving both exploratory and exploitative digital innovation in software SMEs. This approach significantly enhances our understanding of how software SMEs can strategically strengthen their internal skills and resources, ultimately leading to superior digital innovation outcomes.

The research on lead-free solder alloys has increased in past decades due to awareness of the environmental impact of lead contents in soldering alloys. This has led to the introduction and development of different grades of lead-free solder alloys in the global market. Tin-silver-copper is a lead-free alloy which has been acknowledged by different consortia as a good alternative to conventional tin-lead alloy. The purpose of this paper is to provide comprehensive knowledge about the tin-silver-copper series.

The approach of this study reviews the microstructure and some other properties of tin-silver-copper series after the addition of indium, titanium, iron, zinc, zirconium, bismuth, nickel, antimony, gallium, aluminium, cerium, lanthanum, yttrium, erbium, praseodymium, neodymium, ytterbium, nanoparticles of nickel, cobalt, silicon carbide, aluminium oxide, zinc oxide, titanium dioxide, cerium oxide, zirconium oxide and titanium diboride, as well as carbon nanotubes, nickel-coated carbon nanotubes, single-walled carbon nanotubes and graphene-nano-sheets.

The current paper presents a comprehensive review of the tin-silver-copper solder series with possible solutions for improving their microstructure, melting point, mechanical properties and wettability through the addition of different elements/nanoparticles and other materials.

This paper summarises the useful findings of the tin-silver-copper series comprehensively. This information will assist in future work for the design and development of novel lead-free solder alloys.