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This work investigates the relational behavior of corporate social responsibility (CSR) and its effect on firms' financial distress (FD). The population of the study consists of all the non-financial firms presently listed in the equity market of Pakistan. The yearly data set of 213 non-financial companies is selected from 2005 to 2017 with total observations of 2769. The analysis of the study based on OLS regression, fixed effect, and random effect models. The study also uses the GMM technique to guard against potential problems of endogeneity and heteroskedasticity that arise from the use of panel data. Results indicate that higher investment in CSR leads to reduced/lower financial distress. It suggests that investment in CSR raises the reputation and creditworthiness of firms. Key findings are robust as confirmed by alternative proxies of financial distress. Overall findings advocate that CSR helps in reducing default risk or financial distress and creates a better corporate environment that ultimately improves organizations' economic outlook.

Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward the large-scale integration of renewable energy resources (RERs). Wind energy is a type of RERs with vast energy potential and no environmental pollution is associated with it. The sustainable development goals: affordable and clean energy, climate action, and industry, innovation and infrastructure, can be achieved by integrating wind energy into the existing power systems. However, the integration of wind energy will bring instability challenges due to its intermittent nature. Mitigating these challenges necessitates the implementation of effective wind power forecasting models. Therefore, we have proposed a novel integrated approach, Boost-LR, for hour-ahead wind power forecasting. The Boost-LR is a multilevel technique consisting of non-parametric models, extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and random forest (RF), and parametric approach, linear regression (LR). The first layer of the Boost-LR uses the boosting algorithms that process the data according to their tree development architectures and pass their intermediary forecast to LR which is deployed in layer two and processes the intermediary forecasts of layer one models to provide the final predicted wind power. To demonstrate the generalizability and robustness of the proposed study, the performance of Boost-LR is compared with the individual models of CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) and gated recurrent unit (GRU), Transformer and Informer models using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and normalized root mean square error (NRMSE). Findings demonstrate the effectiveness of the Boost-LR as its forecasting performance is superior to the compared models. The improvement in MAE of Boost-LR is recorded as to be 31.42%, 32.14%, and 27.55% for the datasets of Bruska, Jelinak, and Inland wind farm, respectively as compared to the MAE of CatBoost which is revealed as the second-best performing model. Moreover, the proposed study also reports a literature comparison that further validates the effectiveness of Boost-LR performance for short-term wind power forecasting.

Plant-to-plant volatile-mediated communication and subsequent induced resistance to insect herbivores is common. Less clear is the adaptive significance of these interactions; what selective mechanisms favour plant communication and what conditions allow individuals to benefit by both emitting and responding to cues? We explored the predictions of two non-exclusive hypotheses to explain why plants might emit cues, the kin selection hypothesis (KSH) and the mutual benefit hypothesis (MBH). We examined 15 populations of sagebrush that experience a range of naturally occurring herbivory along a 300 km latitudinal transect. As predicted by the KSH, we found several uncommon chemotypes with some chemotypes occurring only within a single population. Consistent with the MBH, chemotypic diversity was negatively correlated with herbivore pressure; sites with higher levels of herbivory were associated with a few common cues broadly recognized by most individuals. These cues varied among different populations. Our results are similar to those reported for anti-predator signalling in vertebrates.

Background: In Turkey, the SMART pharmacist program was launched to enable community pharmacists to deliver patient-centric care services. However, how far this programme has achieved success and what are the challenges faced by the programme need to be evaluated. Objective: This study aimed to explore the experiences of community pharmacists in providing person-centric care services in pharmacies and provide recommendations for improved care effectiveness. Methods: A phenomenological approach was adopted in this study. To conduct qualitative interviews, a semi-structured interview guide was devised to gather insights from the pharmacists. The interviews were coded verbatim. Subsequently, various themes and sub-themes were developed based on the aim and objectives of the study. A total of 14 pharmacists engaged in semi-structured interviews conducted between May and August 2023. Results: Two main themes emerged from the data: challenges in person-centric care and recommendations to improve person-centric care. Barriers were subdivided into patient-related challenges and personal challenges. Recommendations consisted of pharmacists' professional tasks and recommendations. Conclusion: The findings of this study, suggest that the concerned healthcare authorities should re-evaluate the 'SMART pharmacist' program in Turkey and address professional and personal challenges faced by community pharmacists in delivering effective person-centric care. Pharmacists recommend patient follow-up (follow-ups), providing a counselling environment, collaboration with physicians, expansion of pharmacy services. IMPACTS ON PRACTICE: Raising awareness among pharmacists can be achieved through initiatives like the 'SMART Pharmacist Program', aimed at enhancing the provision of person-centric pharmaceutical care to patients. Government policies may be required revision to community pharmacists greater authority. Academic institutions could contemplate revising the existing pharmacy education curriculum to emphasise person-centric pharmacy education. Likewise, professional organisations may need to reevaluate the continuing professional development prerequisites for community pharmacists. [ABSTRACT FROM AUTHOR]

This study involves the development and molecular characterization of the isogenic markerless knockout mutant SG Δ purB , a genetically engineered live attenuated strain aimed at controlling Salmonella Gallinarum (SG) infection in poultry. The mutant was generated by deleting the purB gene using λ -Red recombination technology, impairing adenylosuccinate lyase, necessary for purine biosynthesis. An 1,180 bp deletion was engineered within the purB gene, leaving a residual 298 bp genomic scar resulting in a purine auxotrophic mutant. Phenotypically, SG Δ purB showed a 66.5% reduction in growth in LB broth compared to the wild-type strain and failed to grow in minimal media without adenosine. Growth was restored to near wild-type levels with 0.3 mM adenosine supplementation, demonstrating the strain's conditional attenuation. In vivo pathogenicity assessments revealed that oral inoculation of SG Δ purB into 3-day-old chickens at a dose of 2 × 108 CFU resulted in zero mortality, compared to an 80% mortality rate in chickens challenged with the wild-type strain. The SG Δ purB strain exhibited significantly reduced clinical signs and lesion scores, with clinical sign scores dropping from 2.5/3 with the wild-type to 0.4/3 with the Δ purB mutant, and lesion scores decreasing from 2.9/3 to 0.3/3. Additionally, the mutant was efficiently cleared from liver and spleen tissues by 14 days post-inoculation, unlike the wild-type strain, which persisted until the experiment's end on day 21. The SG Δ purB mutant shows potential as a safe alternative for preventing fowl typhoid, highlighting the promise of targeted genetic attenuation in developing effective vaccines for poultry diseases. [ABSTRACT FROM AUTHOR]

The depth and width of drip infiltration play a critical role in designing effective irrigation strategies. However, existing models primarily focus on continuous irrigation and fail to predict wetting patterns under intermittent drip irrigation. This study developed an infiltration model to estimate soil moisture depth and width under intermittent drip irrigation and identified strategies that enhance effective water storage. Indoor soil box simulations were conducted, with continuous drip irrigation as the control. Results showed that intermittent irrigation increased infiltration width and reduced depth, maximizing water storage efficiency. We recommend adopting an intermittent irrigation system with 1.5 h of irrigation followed by a 0.5 h interval, repeated four times. This system increased effective water storage by up to 16.23% compared to continuous irrigation. The proposed method is suitable for sandy loam farmland in southern Xinjiang and can significantly improve water use efficiency in arid regions. [ABSTRACT FROM AUTHOR]

Due to changes in meteorological factors, the instability in the power at the end of the transmission system demands considerable attention. The temperature of the transmission line varies, which has a significant impact on the line parameters. An accurate prediction of line parameters behaviour is necessary to ensure system reliability. The present study is a step towards predicting variations in line parameters with respect to temperature variation. In addition, power loss and voltage drop due to variations in resistance are also predicted. Support Vector Machine (SVM) and ElasticNet, a machine learning algorithm, predict line parameters such as resistance, inductance, capacitance, voltage drop, and power losses. Furthermore, different seasons-based SVM and ElasticNet models for these parameters are considered. Seasons-based models are divided into two types, namely, summer and winter. 220-Kilovolt transmission data and weather information are used as model inputs. Predicted results of transmission line parameters are described in the form of RMSE and MRE. Moreover, the performance results of SVM and ElasticNet are also compared to show better prediction results. The result shows that the minimum prediction error of line parameters are 0.0511, 0.301, 0.426, 0.913, and 0.1501 in RMSE and 4.212, 0.518, 2.888, 0.097, and 0.615 percentages in MRE. This research work may provide technical guidance to transmission line engineers on enhancing the performance of transmission systems. [ABSTRACT FROM AUTHOR]