A survey on modern trainable activation functions

In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest of the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as "trainable", "learnable" or "adaptable" activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constraints the corresponding weight layers.Comment: Published in "Neural Networks" journal (Elsevier

Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification

The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision

Improving classification models with context knowledge and variable activation functions

This work proposes two methods to boost the performances of a given classifier: the first one, which works on a Neural Network classifier, is a new type of trainable activation function, that is a function which is adjusted during the learning phase, allowing the network to exploit the data better respect to use a classic activation function with fixed-shape; the second one provides two frameworks to use an external knowledge base to improve the classification results

Automation in Agriculture: Occupational Trends, Worker Outcomes, and Labor Market Implications

This paper investigates how automation technologies are reshaping agricultural labor, focusing on both labor market dynamics and changes in work organization. Drawing on a systematic literature review of 33 peer-reviewed studies and following the PRISMA protocol, the analysis adopts an inductive approach to extract empirical patterns. Findings reveal a dual transformation: while automation reduces the demand for low-skilled, repetitive labor, it simultaneously generates new opportunities for workers with technical and cognitive skills. The study identifies key risks—displacement, polarization, and digital exclusion for vulnerable groups—alongside potential benefits such as professional upskilling and improved working conditions. By introducing a dual-level thematic framework, Occupational and Worker level, the paper provides a granular understanding of labor impacts across macro and micro dimensions. It offers a critical and interdisciplinary contribution to ongoing debates on the social consequences of technological change, with implications for policy, workforce development, and equitable innovation in the agricultural sector

Acute lobar nephritis in children: Not so easy to recognize and manage

Acute lobar nephritis (ALN) is a localized non-liquefactive inflammatory renal bacterial infection, which typically involves one or more lobes. ALN is considered to be a midpoint in the spectrum of upper urinary tract infection, a spectrum ranging from uncomplicated pyelonephritis to intrarenal abscess. This condition may be difficult to recognize due to the lack of specific symptoms and laboratory findings. Therefore the disease is probably underdiagnosed. Computed tomography scanning represents the diagnostic gold standard for ALN, but magnetic resonance imagine could be considered in order to limit irradiation. The diagnosis is relevant since initial intravenous antibiotic therapy and overall length of treatment should not be shorter than 3 wk. We review the literature and analyze the ALN clinical presentation starting from four cases with the aim to give to the clinicians the elements to suspect and recognize the ALN in children

The technological translation from Industry 4.0 to Precision Agriculture: adoption and perception of Italian farmers

Purpose: This research aims to identify the rate of knowledge, adoption and perceptions of Italian farmers towards Precision Agriculture technologies. Methodology: An online survey was carried out, using the Snowball sampling method, among 755 Italian farmers and involving the main Italian trade associations. Findings: The findings showed that among Italian farmers the technologies related to Monitoring appear to be the best known, adopted and perceived as the most useful; followed by technologies related to Automation and IoT. Managerial implications: Considering the results that emerged from this research, it seems necessary to undertake models of training development paths so that farmers can deepen the themes of technological integration with an orientation towards sustainability. Research limitations: The present research, not being able to be considered exhaustive for the understanding of the phenomenon, aims to be the starting point for future research aimed at a further analysis on the models of diffusion and technological integration. Originality: The models of technological integration for agricultural cultivation techniques are constantly evolving. Through the analysis of knowledge, use and perception of farmers it could be possible to detect new models for the diffusion of technology

Innovation in farming: Drivers of adoption of Precision Agriculture amongst farmers

The recent changes in technological innovations in agriculture in the last decades are radically changing the paradigm of traditional cultivation techniques and these have already been the subject of research for some time. The present study aims to review the new trends of digital technologies adoption in precision agriculture. Starting from theoretical study models such as the Agriculture Knowledge and Innovation Systems (AKIS), the Agricultural Innovation System (AIS) and the new modeling of the Farm Management Information System (FMIS), a review was carried out with the aim of identifying emerging adoption drivers for the implementation of precision agriculture technologies. To do this, 19 papers were analyzed in the period 2018-2021 that included empirical investigations. The results of the survey confirm the new adoption trends where, in addition to the size of the agricultural company, the geographical position and the financial resources, sociodemographic factors are included and above all the new emerging trend linked to environmental benefits

Dietary Intake as a Link between Obesity, Systemic Inflammation, and the Assumption of Multiple Cardiovascular and Antidiabetic Drugs in Renal Transplant Recipients.

Abstract We evaluated dietary intake and nutritional-inflammation status in ninety-six renal transplant recipients, 7.2±5.0 years after transplantation. Patients were classified as normoweight (NW), overweight (OW), and obese (OB), if their body mass index was between 18.5 and 24.9, 25.0 and 29.9, and ≥30 kg/m2, respectively. Food composition tables were used to estimate nutrient intakes. The values obtained were compared with those recommended in current nutritional guidelines. 52% of the patients were NW, 29% were OW, and 19% were OB. Total energy, fat, and dietary n-6 PUFAs intake was higher in OB than in NW. IL-6 and hs-CRP were higher in OB than in NW. The prevalence of multidrug regimen was higher in OB. In all patients, total energy, protein, saturated fatty acids, and sodium intake were higher than guideline recommendations. On the contrary, the intake of unsaturated and n-6 and n-3 polyunsaturated fatty acids and fiber was lower than recommended. In conclusion, the prevalence of obesity was high in our patients, and it was associated with inflammation and the assumption of multiple cardiovascular and antidiabetic drugs. Dietary intake did not meet nutritional recommendations in all patients, especially in obese ones, highlighting the need of a long-term nutritional support in renal transplant recipients

Wearable Technologies in Agriculture 4.0: A Systematic Review of Applications for Worker Safety and Ergonomic Support

Purpose: The adoption of wearable technologies in agriculture is increasing in response to the growing need for solutions that enhance worker safety, monitor health conditions, and improve operational performance. This review investigates the current landscape of wearable devices applied in agricultural settings. The main research question explores how wearable technologies contribute to the prevention of occupational risks and the support of agricultural workers across different farming contexts. Methods: A systematic literature review was conducted according to the PRISMA protocol. A comprehensive search was performed in the Scopus database using a structured Boolean query to identify relevant peer-reviewed studies addressing wearable devices in agriculture with a focus on health, safety, and performance outcomes. The selection process included identification, screening, eligibility assessment, and full-text analysis. A total of 15 studies were included in the final review. Results: The reviewed studies report the use of various wearable technologies, including inertial measurement units, exoskeletons, smart glasses, and environmental sensors. Applications span viticulture, livestock farming, and general field operations. Wearable systems demonstrate high accuracy in posture detection, activity classification, and physiological monitoring. Positive impacts are observed in ergonomic support, fatigue reduction, and situational awareness. However, challenges remain regarding comfort, long-term usability, and validation under real-world conditions. Conclusion: Wearable devices show strong potential in advancing occupational health and operational efficiency in agriculture. Further research should focus on ergonomic optimization, long-term deployment, and integration with digital farm management systems to enable widespread and sustainable adoption

The Link between Food Traceability and Food Labels in the Perception of Young Consumers in Italy

The research analyzed the perception of food traceability among consumers in Italy and the role of food labels in supporting consumer information about food traceability. The components (health, quality, product origin and many others) that are involved in the concept of food traceability were examined and the most important ones were identified. An online survey (n=511 consumers) was carried out in Milan in the north of Italy. Students and employees from the Bocconi University were selected in order to investigate the relevance of food traceability in consumer purchasing decisions. An ordered logit regression was applied.The findings confirm that consumers are interested in various components of food traceability and look for labels that provide information on the product supply chain. The research confirms that traceability is important in the food market and some types of labels on product features (as product sustainability or origin) are associated with it