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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.

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.

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.

The main goal of this study is to analyze how monetary debt effects firm behavior of 167 registered manufacturing companies in G-7 countries.

The sample of the present study is taken from the listed firms in G-7 countries. For the building companies, the yearly financial statements of 2007–2018 have been taken from world stock exchange and Thomson Reuters Data Stream. In this study, regression analysis are directed with panel data over the period of 2007–2018 using ordinary least square summary statistics, correlation matrix and generalized method moments. Data were analyzed by employing E Views and Stata 13 software.

The significant findings of the current study indicated that fixed assets, tangible assets, taxes, net cash and profitability have positive association with debt level.

The current work include only registered manufacturing firms in G-7 countries. Moreover, ownership types are not accounted for in this study.

The current analysis is an empirical investigation of antecedents of debt regarding G-7 countries with up-to-date data. Various regression inquires have been made to design the models using different measures of debt and measure of firm performance indicators. These works will assist G-7 countries firms to know the effects of identified factors on time raising debt level.

The current work has been finalized using genuine data of yearly reports and database. This study incorporated antecedents of debt, which have limited discourse in prior literature. Furthermore, this study explores the connection between debt level and firm performance of G-7 countries.

Strengthening the middle class has become a major policy goal in both developed and developing economies due to its social, economic, cultural and political importance. Keeping in view the importance of middle class, the purpose of this paper is to measure the size of the middle class in the province of Punjab (the biggest province of Pakistan by population).

The study calculates a weighted composite index to measure the size of the middle class in the province of Punjab using the microcosmic data set, and the Pakistan Social and Living Standards Measurement 2010-2011 survey data set. The index is composed of five major socioeconomic dimensions, that is, income, occupation, education, lifestyle and housing. The above-mentioned dimensions are weighted through “principal component analysis”.

The results show that 46 percent of the population of Punjab falls in the middle class. Furthermore, the findings explain that middle class is generally an urban phenomenon in Punjab with relatively high level of education and non-manual occupations.

Only a limited research is available for measuring the middle class in Pakistan. The current research is an attempt to fill this gap by providing some important insight to the research in this area.

Given the decisive role of knowledge-oriented leadership (KOL) in boosting organizational innovation capacities, the research intends to investigate the effect of KOL on ambidextrous innovation with the mediating effect of knowledge management capability (KMC). Furthermore, technological turbulence (TT) is regarded as a moderator in the relationship between KMC and ambidextrous innovation.

The data obtained from 122 Pakistani manufacturing firms were used to evaluate the proposed relationships using the partial least square structural equation modeling approach.

The empirical findings demonstrate that KOL positively affects both aspects of ambidextrous innovation, namely exploitative innovation (EII) and exploratory innovation (ERI), with a higher effect on EII. Additionally, knowledge management process capability (KMPC) partially mediates the association between KOL and both dimensions of ambidextrous innovation (EII and ERI). Similarly, knowledge management infrastructure capability (KMIC) mediates the link between KOL and ERI but does not mediate the relationship between KOL and EII. The impacts of the KMPC and KMIC on EII are also significantly moderated by TT, although the link between the two components of the KMC (KMPC and KMIC) and ERI is unaffected.

The research provides useful knowledge and a novel strategy for policymakers to foster KOL and invest in KMC to improve the capabilities of Pakistani manufacturing firms in terms of innovation.

The research has contributed significantly to the resources-based view and knowledge-based view (KBV) literature by examining the various mediation moderation mechanisms and offering greater insights into the relationship between KOL and firms, KMC, and ambidextrous innovations.