Dividend Policy as a Core Determinant of Earning Management: Evidence from Pakistan

The current study estimated the impact of dividend policy on earnings management for the non-financial firms of Pakistan listed at the Karachi Stock Exchange. The data covering the period from 2005 to 2017 were estimated by using a random effect-generalized least square regression. The study findings report that dividend policy is a significant determinant of earning management and limits the probability of a firm’s earning management practices. This research gives us some empirical evidence regarding the role of key contributing factors in the scope of earnings management. Regulators can implement corporate governance rules and regulations based on empirical tracts in place of motivational debates on politics. This study results offer a compact platform for investors to eradicate ambiguity by recognizing the likelihoods of resourceful goals and improving their policymaking process. The research findings will help to give investors a clear idea about the various factors that play a contributing part for making financial reporting and misreporting of profits.  These contributing factors allow investors to be careful about the ingenious purpose and effectiveness of management to obtain returns for their benefit

Drivers of food waste reduction behaviour in the household context

Studies on the drivers of household consumer engagement in various food waste reduction strategies have been limited. We thus address this gap by developing a research model that utilises two well-known theories, namely, the Theory of Interpersonal Behaviour (TIB) and the Comprehensive Model of Environmental Psychology (CMEP), to explain food waste reduction behaviour in household consumers. The model hypothesises positive associations between emotional, social, and cognitive factors and food waste reduction behaviour, as conceptualised using the 3Rs (reuse, reduce, and recycle). A total of 515 U.S. household consumers participated in the cross-sectional survey. The results suggest that emotional (anticipated guilt), social (sense of community), and cognitive factors (awareness about consequences and environmental knowledge) were positively associated with food waste reduction behaviour. However, the study results did not support the association between a sense of community and reuse intentions. Moreover, anticipated guilt and awareness of consequences were significant drivers of the reuse and reduce food waste behaviours, respectively. The age of the study participants also had a significant controlling influence on the reduce intentions. The study findings have significant implications for governments, policymakers, marketers, and academics that are interested in developing strategies to mitigate the impact of food waste.publishedVersio

Drivers of food waste reduction behaviour in the household context

Studies on the drivers of household consumer engagement in various food waste reduction strategies have been limited. We thus address this gap by developing a research model that utilises two well-known theories, namely, the Theory of Interpersonal Behaviour (TIB) and the Comprehensive Model of Environmental Psychology (CMEP), to explain food waste reduction behaviour in household consumers. The model hypothesises positive associations between emotional, social, and cognitive factors and food waste reduction behaviour, as conceptualised using the 3Rs (reuse, reduce, and recycle). A total of 515 U.S. household consumers participated in the cross-sectional survey. The results suggest that emotional (anticipated guilt), social (sense of community), and cognitive factors (awareness about consequences and environmental knowledge) were positively associated with food waste reduction behaviour. However, the study results did not support the association between a sense of community and reuse intentions. Moreover, anticipated guilt and awareness of consequences were significant drivers of the reuse and reduce food waste behaviours, respectively. The age of the study participants also had a significant controlling influence on the reduce intentions. The study findings have significant implications for governments, policymakers, marketers, and academics that are interested in developing strategies to mitigate the impact of food waste.publishedVersio

Phytohormones as Growth Regulators During Abiotic Stress Tolerance in Plants

Phytohormones (PHs) play crucial role in regulation of various physiological and biochemical processes that govern plant growth and yield under optimal and stress conditions. The interaction of these PHs is crucial for plant survival under stressful environments as they trigger signaling pathways. Hormonal cross regulation initiate a cascade of reactions which finely tune the physiological processes in plant architecture that help plant to grow under suboptimal growth conditions. Recently, various studies have highlighted the role of PHs such as abscisic acid, salicylic acid, ethylene, and jasmonates in the plant responses toward environmental stresses. The involvement of cytokinins, gibberellins, auxin, and relatively novel PHs such as strigolactones and brassinosteroids in plant growth and development has been documented under normal and stress conditions. The recent identification of the first plant melatonin receptor opened the door to this regulatory molecule being considered a new plant hormone. However, polyamines, which are not considered PHs, have been included in this chapter. Various microbes produce and secrete hormones which helped the plants in nutrient uptake such as N, P, and Fe. Exogenous use of such microbes help plants in correcting nutrient deficiency under abiotic stresses. This chapter focused on the recent developments in the knowledge related to PHs and their involvement in abiotic stresses of anticipation, signaling, cross-talk, and activation of response mechanisms. In view of role of hormones and capability of microbes in producing hormones, we propose the use of hormones and microbes as potential strategy for crop stress management.Fil: EL Sabagh, Ayman. Scientific And Technological Research Council Of Turkey; TurquíaFil: Islam, Mohammad Sohidul. Kafrelsheikh University; EgiptoFil: Hossain, Akbar. Hajee Mohammad Danesh And Technology University; BangladeshFil: Iqbal, Muhammad Aamir. University Of Poonch; PakistánFil: Mubeen, Mohammad. Comsats University Islamabad; PakistánFil: Waleed, Mirza. Comsats University Islamabad; PakistánFil: Reginato, Mariana Andrea. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Battaglia, Martin. Cornell University; Estados UnidosFil: Ahmed, Sharif. International Rice Research Institute; FilipinasFil: Rehman, Abdul. The Islamia University Of Bahawalpur; PakistánFil: Arif, Muhammad. The University Of Agriculture; PakistánFil: Athar, Habib-Ur-Rehman. Bahauddin Zakariya University; PakistánFil: Ratnasekera, Disna. University Of Ruhuna; Sri LankaFil: Danish, Subhan. Bahauddin Zakariya University; PakistánFil: Raza, Ali. Sichuan Agricultural University; ChinaFil: Rajendran, Karthika. Vellore Institute Of Technology; IndiaFil: Mushtaq, Muntazir. Icar-national Bureau Of Plant Genetic Resources; IndiaFil: Skalicky, Milan. Czech University Of Life Sciences Prague; República ChecaFil: Brestic, Marian. Czech University Of Life Sciences Prague; República ChecaFil: Soufan, Walid. King Saud University; Arabia SauditaFil: Fahad, Shah. University Of Haripur; PakistánFil: Pandey, Saurabh. Guru Nanak Dev University; IndiaFil: Abdelhamid, Magdi T.. National Research Centre Dokki; Egipt

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Drivers of food waste reduction behaviour in the household context

Studies on the drivers of household consumer engagement in various food waste reduction strategies have been limited. We thus address this gap by developing a research model that utilises two well-known theories, namely, the Theory of Interpersonal Behaviour (TIB) and the Comprehensive Model of Environmental Psychology (CMEP), to explain food waste reduction behaviour in household consumers. The model hypothesises positive associations between emotional, social, and cognitive factors and food waste reduction behaviour, as conceptualised using the 3Rs (reuse, reduce, and recycle). A total of 515 U.S. household consumers participated in the cross-sectional survey. The results suggest that emotional (anticipated guilt), social (sense of community), and cognitive factors (awareness about consequences and environmental knowledge) were positively associated with food waste reduction behaviour. However, the study results did not support the association between a sense of community and reuse intentions. Moreover, anticipated guilt and awareness of consequences were significant drivers of the reuse and reduce food waste behaviours, respectively. The age of the study participants also had a significant controlling influence on the reduce intentions. The study findings have significant implications for governments, policymakers, marketers, and academics that are interested in developing strategies to mitigate the impact of food waste

Design and Performance Evaluation of a Step-Up DC–DC Converter with Dual Loop Controllers for Two Stages Grid Connected PV Inverter

In this work, a non-isolated DC–DC converter is presented that combines a voltage doubler circuit and switch inductor cell with the single ended primary inductor converter to achieve a high voltage gain at a low duty cycle and with reduced component count. The converter utilizes a single switch that makes its control very simple. The voltage stress across the semiconductor components is less than the output voltage, which makes it possible to use the diodes with reduced voltage rating and a switch with low turn-on resistance. In particular, performance principle of the proposed converter along with the steady state analysis such as voltage gain, voltage stress on semiconductor components, and design of inductors and capacitors, etc., are carried out and discussed in detail. Moreover, to regulate a constant voltage at a DC-link capacitor, back propagation algorithm-based adaptive control schemes are designed. These adaptive schemes enhance the system performance by dynamically updating the control law parameters in case of PV intermittency. Furthermore, a proportional resonant controller based on Naslin polynomial method is designed for the current control loop. The method describes a systematic procedure to calculate proportional gain, resonant gain, and all the coefficients for the resonant path. Finally, the proposed system is simulated in MATLAB and Simulink software to validate the analytical and theoretical concepts along with the efficacy of the proposed model

Design and Performance Evaluation of a Step-Up DC–DC Converter with Dual Loop Controllers for Two Stages Grid Connected PV Inverter

In this work, a non-isolated DC–DC converter is presented that combines a voltage doubler circuit and switch inductor cell with the single ended primary inductor converter to achieve a high voltage gain at a low duty cycle and with reduced component count. The converter utilizes a single switch that makes its control very simple. The voltage stress across the semiconductor components is less than the output voltage, which makes it possible to use the diodes with reduced voltage rating and a switch with low turn-on resistance. In particular, performance principle of the proposed converter along with the steady state analysis such as voltage gain, voltage stress on semiconductor components, and design of inductors and capacitors, etc., are carried out and discussed in detail. Moreover, to regulate a constant voltage at a DC-link capacitor, back propagation algorithm-based adaptive control schemes are designed. These adaptive schemes enhance the system performance by dynamically updating the control law parameters in case of PV intermittency. Furthermore, a proportional resonant controller based on Naslin polynomial method is designed for the current control loop. The method describes a systematic procedure to calculate proportional gain, resonant gain, and all the coefficients for the resonant path. Finally, the proposed system is simulated in MATLAB and Simulink software to validate the analytical and theoretical concepts along with the efficacy of the proposed model