Biomass energy is abundant, clean, and carbon dioxide neutral, making it a viable alternative to fossil fuels in the near future. The release of syngas from biomass thermochemical treatments is particularly appealing since it may be used in a variety of heat and power generation systems. When a syngas with low tar and contaminants is required, downdraft gasifiers are usually one of the first gasification devices deployed. It is time-consuming and impractical to evaluate a gasification system's performance under multiple parameters, using every type of biomass currently available, which makes rapid simulation techniques with well-developed mathematical models necessary for the efficient and economical use of energy resources. This work attempts to examine, through model and experimentation, how well a throated downdraft gasification system performs when using pretreatment biomass feedstock that has been characterized. For the analyses, peanut shell (PS), a biomass waste easily obtained locally, was used. The producer gas generated with 9 mm PS pellets had a composition of 17.93% H2, 24.43 % CO, 12.47 % CO2, and 1.22% CH4 on a wet basis at the value of 0.3 equivalency ratio and 800 °C gasification temperature. The calorific value was found to be 4.96 MJ/Nm3. The biomass feedstock PS is found to be suitable for biomass gasification in order to produce syngas.

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

The cultivation of vegetables serves as a vital pillar in horticulture, offering an alternative avenue towards achieving economic sustainability. Unfortunately, farmers often lack adequate knowledge on optimizing resource utilization, which subsequently results in low productivity. Furthermore, there has been insufficient research conducted on the comparative profitability and efficient use of resources for pea cultivation. So, the present study was conducted to examine the profitability and resource use efficiency of conventional and organic pea production in Northwestern Himalayan state. Using the technique of purposive sampling, the districts and villages were selected based on the highest area. By using simple random sampling, a sample of 100 farmers was selected, out of which 50 were organic growers and 50 were inorganic growers, who were further categorized as marginal and small. The cost incurred was higher for the cultivation of inorganic vegetable crops, whereas returns and output-input ratio was higher in organic cultivation. The cultivation of peas revealed that the majority of inputs were being underutilized, and there was a need for proper reallocation of the resources, which would result in enhanced production. Further, major problems in the cultivation of vegetable crops were a high wage rate, a lack of organic certification, a shortage of skilled labour and a lack of technical knowledge.

Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT's capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT's role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT's capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI's potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for "brainstormin" in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT's effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.