The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Considering the current climate conjuncture, it is a consensus that green spaces in large contemporary urban areas should be increasingly more numerous and simultaneously more sustainable, being adapted to the edaphoclimatic conditions of the site, and with reduced maintenance costs. In the case of Algarve, where this research is focused, the current and future water availability, assumes a preponderant role in the design of green spaces, where the demands mentioned above can only be achieved if we deviate from conventional landscape practices and develop holistic strategies of management and design of green spaces that integrate different areas of knowledge and not merely aesthetic issues. In this context, this work aims to develop more adapted and resilient landscaping practices to the current and future climatic conditions of the Algarve, thus reinventing the concept of landscaping in the south of Portugal. Thus, it will be of paramount importance to develop more sustainable, resilient and tolerant projects to worsening ecological conditions, particularly limitations associated with water availability. The xeromorphic succulents are a group of plants with mechanisms of tolerance to water stress and with very specific characteristics, being succulence one of the most relevant. Studies on these mechanisms are increasingly frequent, which may prove to be very advantageous in our adaptation to future climatic challenges. In addition, their ornamental potential is enormous, since their bold forms and colours are a veritable sensory explosion, which, combined with their morphological and physiological characteristics, make them the species of choice in the reconversion or creation of xerophytic gardens.

The banana (Musa spp.) is a tropical fruit with excellent sensory characteristics in terms of aroma, flavor and texture, consumed worldwide and exploited in most tropical countries. Green banana flour is rich in flavonoids, which protect the gastric mucosa, has a high content of resistant starch, which acts in the body as a dietary fiber and thus has health benefits, and is an alternative option for bakery products, reducing waste of both the peel and the pulp. The aim of this study was to develop bread formulations with partial substitution of wheat flour with green banana flour (FBV), thus increasing the nutritional, technological and sensory value. 4 formulations, (A), standard sample; (B), bread with 10% FBV; (C), bread with 15% FBV and (D), bread with 20% FBV. Physico-chemical quality was assessed in terms of moisture content by drying at 105ºC, ash by incineration, fat by the Soxhlet method, protein by the biuret method, carbohydrates by difference calculation and calorific value by sum calculation and sensory analysis by affective methods. The data was evaluated using the RStudio 4.2.1 DCC statistical package. There were no significant differences in moisture content, lipids and calorific value. Differences were evident in the ash and protein content. Sensory acceptance of the standard formulation was 82.22%. The results obtained show that green banana flour can be used as a partial substitute for wheat flour to produce breads with functional properties.

This paper is a general summary of Neuralink, a revolutionary technology set to elevate human life and neurology. Neuralink itself is a key testimonial to the evolution of neuroscience and even brain-computer interfaces, otherwise known as BCI. The original few BCI experiments were conducted on monkeys in the 1960s and 70s, in which the experiment itself narrowed down and understood brain function as a general concept [3]. More specifically, "Work on these technologies began in the early 1970s, led by computer science professor J.J. Vidal at UCLA" [12]. Science itself progresses day by day, growing rapidly in recent years, especially in neuroscience, something highlighted as a focal point in the previous statement. Moreover, recently we have seen technology go on a rampant rise in terms of popularity, inventions, and changes to the human lifestyle. The interactions humans had with technology initially developed with wearables or wearable technology, such as Apple Watches, AirPods, and Fitbits, and now they have even prompted advancements in brain-computer interfaces. Technology has had the power to advance science, but now it’s capable of changing the human mind. Going back to Neuralink, it’s a startup that began its initiative in 2016 and was approved by the FDA for clinical trials in May of 2023, ready to create a wave of change in the field of neuroscience [6]. The foremost baffling thing is how this chip plans on being placed in the somatosensory system. The somatosensory system is a part of the brain that deals with motor actions, recognition, and perception, and applying Neuralink in this area should supposedly allow for cures and treatment of amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injuries, epilepsy, autism, depression, schizophrenia, and possibly blindness [9]. Neuralink is deemed to lead to a life-changing future, and with co-founders and investors like Elon Musk, there is a lot to know about this piece of technology.