Anti-transpirant effects on vine physiology, berry and wine composition of cv. Aglianico (Vitis vinifera L.) Grown in South Italy

In viticulture, global warming requires reconsideration of current production models. At the base of this need there are some emerging phenomena: modification of phenological phases; acceleration of the maturation process of grapes, with significant increases in the concentration of sugar musts; decoupling between technological grape maturity and phenolic maturity. The aim of our study was to evaluate the effect of a natural anti-transpirant on grapevine physiology, berry, and wine composition of Aglianico cultivar. For two years, Aglianico vines were treated at veraison with the anti-transpirant Vapor Gard and compared with a control sprayed with only water. A bunch thinning was also applied to both treatments. The effectiveness of Vapor Gard were assessed through measurements of net photosynthesis and transpiration and analyzing the vegetative, productive and qualitative parameters. The results demonstrate that the application of antitranspirant reduced assimilation and transpiration rate, stomatal conductance, berry sugar accumulation, and wine alcohol content. No significant differences between treatments were observed for other berry and wine compositional parameters. This method may be a useful tool to reduce berry sugar content and to produce wines with a lower alcohol content

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Purpose: In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda. Design/methodology/approach: Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda. Findings: The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative. Research limitations/implications: The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities. Practical implications: This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models. Originality/value: This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda

Mechanical harvesting of oil olives by trunk shaker with a reversed umbrella interceptor

Trunk shakers are primarily used for the mechanical harvesting of oil olives in intensive orchards. The objective of this trial was to determine the efficiency of mechanical harvesting of olives with a self-propelled trunk shaker with a reversed umbrella interceptor (model F3, SICMA, Catanzaro, Italy), from adult trees of two autochthonous cultivars, ‘Ortice’ and ‘Ortolana’, growing in southern Italy with 6 × 6 m spacing and trained to the vase system. The main characteristics of the trunk shaker were: an engine power of 77 Kw (105 CV), a very-high-frequency vibrating head (1800-2000 vibrations/min), a self-braking system and a 6-meter diameter umbrella opening. The worksite consisted of two workers, one for maneuvering the harvesting machine, and the other for handling the olives. Mechanical harvesting was carried on 30 November 2006 when the fruits of ‘Ortice’ and ‘Ortolana’ had a weight and detachment force around 2.8 g and 3.1 N and 3.8 g and 4.6 N, respectively, and the fruit drop was around 14% and 10%, respectively. Both cultivars had a good production (26.06 and 21.18 kg/tree). The mechanical harvesting yield (percentage of mechanically harvested olives) was very high, reaching values around 97% in both cultivars. Moreover, the low number of workers, the reduced time for the operation (2.5 min/tree), the good yield/tree and the high quantity of harvested fruit allowed a very high work productivity to be obtained: around 302 kg/h/worker for ‘Ortice’ and 246 kg/h/worker for ‘Ortolana’. The quality of the oils extracted from the harvested olives met the requirements set by European law for extra virgin olive oils. The results indicate that the use of a trunk shaker with a reversed umbrella can be an efficient solution for mechanical harvesting of the ‘Ortice’ and ‘Ortolana’ cultivars in southern Italy

Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness