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The threat of global warming has increased due to industrialization, urbanization, population expansion, and changes in lifestyle among the Group of Seven(G7) Carbon dioxide emissions (CO2) directly affect how much electricity can be generated from various sources. This research aims to identify environmental hazards associated with various energy sources. Analyzing the impact of various energy sources on CO2 emissions from electricity and heat production using data from the G7. The data is analyzed using quantile regression (QR), generalized method of moments (GMM), random effects (RE), and fixed effects (FE). Our results indicate a substantial positive impact on CO2 emissions regardless of the technology used to generate coal and gas power. Coal-fired power plants have a larger impact on the environment than other sources of emissions. Also, all coal and gas coefficients are significant in FE, RE, GMM, and QR. Oil coefficients have a negative impact on environmental degradation and are significant for FE, RE, and D-GMM regressions. Hydroelectric and renewable energy production can reduce CO2 emissions in all regression models. Nuclear energy has a beneficial impact on the environment, but the coefficients are only significant for S-GMM and the last quantile. However, the most significant result of this study is the identification of a cause-and-effect relationship between CO2 emissions and energy production. Carbon dioxide (CO2) emissions can be lowered by shifting away from fossil fuels and toward renewable and hydroelectric sources. The research also suggests several renewable and alternative electricity production policies for sustainable energy.

Agriculture is crucial for the existence of humankind. Agriculture provides a significant portion of the income for many people all around the world. Additionally, it provides a large number of work possibilities for the general public. Numerous farmers desire for a return to the old-fashioned techniques of farming, which provides little profit in today’s market. Long-term economic growth and prosperity are dependent on the success of agriculture and associated companies in the United States. Agribusiness crop yields may be increased by carefully selecting the right crops and putting in place supportive infrastructure. Weather, soil fertility, water availability, water quality, crop pricing, and other factors are taken into consideration while making agricultural predictions. Machine learning is critical in crop production prediction because it can anticipate crop output based on factors such as location, meteorological conditions, and season. It is advantageous for policymakers and farmers alike to be able to precisely estimate crop yields throughout the growing season since it allows them to anticipate market prices, plan import and export operations, and limit the social cost of crop losses. The use of this tool assists farmers in making informed decisions about which crops to grow on their land. In this study, a machine learning framework for agricultural yield prediction is presented. Crop information is collected in an experiment’s data set. Then, feature selection is performed using the Relief algorithm. Features are extracted using the linear discriminant analysis algorithm. Machine learning predictors, namely, particle swarm optimization-support vector machine (PSO-SVM), K-nearest neighbor, and random forest, are used for classification.

The goal of this research was to look into how artificial intelligence (AI) and machine learning (ML) techniques are being used in food industry and to come up with future research directions based on that. This study investigates the articles available on several scientific platforms that link both AI and supply chain from one side and ML and food industry from the other side, using a systematic literature review methodology. The findings of this research stated that although AI and machine learning technologies are yet in their beginning, the prospective for them to enhance the performance of the food industry (FI) is quite promising. Various investigators created AI and ML-related models that were verified and found to be effective in optimising FI, and so the use of AI and ML in FI networks provides competitive advantages for improvement. Other academics suggest that AI and machine learning are both now adding value, while others believe that they are still underutilised and that their tools and methodologies can harness the overall value of the food business. According to the findings, AI and machine learning have the potential to reduce economic losses, thereby supporting the food industry's efficiency and responsiveness.

Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

We previously reported that normothermic ex vivo kidney  perfusion (NEVKP) is superior in terms of organ protection compared to static cold storage (SCS), which is still the standard method of organ preservation, but the mechanisms are incompletely understood. We used a large animal kidney autotransplant model to evaluate mitochondrial function during organ preservation and after kidney transplantation, utilizing live cells extracted from fresh kidney tissue. Male porcine kidneys stored under normothermic perfusion showed preserved mitochondrial function and higher ATP levels compared to kidneys stored at 4 °C (SCS). Mitochondrial respiration and ATP levels were further enhanced when AP39, a mitochondria-targeted hydrogen sulfide donor, was administered during warm perfusion. Correspondingly, the combination of NEVKP and AP39 was associated with decreased oxidative stress and inflammation, and with improved graft function after transplantation. In conclusion, our findings suggest that the organ-protective effects of normothermic perfusion are mediated by maintenance of mitochondrial function and enhanced by AP39 administration. Activation of mitochondrial function through the combination of AP39 and normothermic perfusion could represent a new therapeutic strategy for long-term renal preservation. The authors previously reported that normothermic ex vivo kidney perfusion is superior to static cold storage in terms of organ protection, but the detailed mechanism was unclear. Here the authors show that the organ-protective effects of normothermic perfusion are mediated by maintenance of mitochondrial function and enhanced by administration of AP39, a mitochondria-targeted hydrogen sulfide donor.

Extant empirical studies have predominantly focused on the nexus between working capital management (WCM) and corporate profitability. While there is a dearth of literature on the nexus between WCM and a firm's risk, the present study examines Pakistani-listed firms coming from 12 diverse industrial segments to observe this association for a time span of ten years (2005-2014). To ensure robustness, we employed a System Generalized Method of Moments (SGMM) regression estimation to investigate the influence of WCM on the operational and market risk for firms. Empirical testing revealed that higher working capital levels were associated with lower volatility in firms' stock price, which shows that shareholders prefer a conservative working capital policy. Moreover, firms with better cash positions were subject to lesser stock market volatility. In contrast, excess working capital and a larger net trade cycle were associated with increased volatility in the operating income. Besides, firms with lower working capital levels relative to their respective industry experienced fewer fluctuations in their operating profits. Our findings assert that short-term financial management has important ramifications for firms' operating and market fundamentals. Practical implications are discussed for corporate managers and relevant stakeholders.

A growing body of literature probes the impact of geopolitical risk (GPR) on CO 2 emissions. However, no study compares the findings in the case of developed and developing countries. Hence, this study aims to probe the impact of GPR on CO 2 emissions for selected developed and developing countries while controlling for energy consumption, foreign direct investment, and economic growth. For this purpose, we make use of a panel dataset covering the period 1990–2020. In the long-run, we report that the Environmental Kuznets Curve hypothesis exists for developing countries. Next, the pollution haven hypothesis is validated for the developed countries in the long-run. Also, GPR escalates emissions for developed and developing countries in the long-run. In the short-run, the Environmental Kuznets Curve and pollution haven hypothesis are found invalid. Moreover, in the short-run, GPR impedes emissions in both developed and developing countries. Further, energy consumption upsurges emissions across all samples (i.e., either developed or developing countries) in either its short- or long-run. The heterogeneous findings across the long- and short-run, for developed and developing countries, propose to formulate unalike policies for countries with different levels of income.

Customer churn reduction remains a critical challenge for the Indian telecommunications industry, where intense competition and low switching costs fuel high churn rates. While customer engagement has been recognized as a key driver of loyalty, limited research has examined whether digital or human engagement is more effective in reducing churn. This study investigates the comparative influence of these two modalities on customer churn reduction, using Structural Equation Modeling (SEM) with data collected from 800 telecom users across India. The findings reveal that both digital and human engagement significantly reduce churn; however, digital engagement exerts a stronger influence on churn reduction outcomes, particularly through personalized and continuous interactions via apps, websites, and social media platforms. Human engagement, while comparatively weaker in preventing churn, remains crucial for building trust and relational satisfaction. This research contributes to engagement theory by clarifying the distinct and complementary roles of digital and human interactions in churn reduction. For practitioners, the results highlight the need to prioritize digital engagement strategies while maintaining human touchpoints to strengthen customer trust and loyalty.  

Ectopic gall bladder under the left lobe of liver is a rare congenital anomaly of the position of gall bladder, which is mostly detected during surgery and causes technical difficulty at the time of operation. We operated a 64-year-old male who presented with gall stone disease and pre-operative ultrasound did not report any abnormality in position. On laparoscopy, it was found to be attached on the left side of falciform ligament under segment III. It was a true ectopic gall bladder without situs inversus. Early division of the falciform ligament and a careful and complete dissection of the gall bladder are advocated before clipping the cystic artery and duct to avoid complications. The present case report discusses about this rare anomaly and the available literature on the subject.

Duodenal leaks (DL) contribute to most graft losses following pancreas transplantation. However, there is a paucity of literature comparing graft preservation approach versus upfront graft pancreatectomy in these patients. We reviewed all pancreas transplants performed in our institution between 2000 and 2020 and identified the recipients developing DL to compare based on their management: percutaneous drainage vs. operative graft preservation vs. upfront pancreatectomy. Of the 595 patients undergoing pancreas transplantation, 74 (12.4%) developed a duodenal leak with a median follow up of 108 months. Forty-five (61%) were managed by graft preservation strategies, with the rest being treated with upfront graft pancreatectomy. DL managed by graft preservation strategies had similar graft survival rates at 1 and 5-year compared to the matched cohort of population without DL (95% and 59% vs. 91% and 62%; p = 0.78). Multivariate analysis identified male recipient (OR: OR: 6.18; CI95%: 1.26–41.09; p = 0.04) to have higher odds of undergoing an upfront graft pancreatectomy. In appropriately selected recipients with DL, graft preservation strategies utilizing either interventional radiology guided percutaneous drainage or laparotomy with/without repair of leak can achieve comparable long-term graft survival rates compared to recipients without DL.