The agriculture sector is the backbone of Pakistan’s economy, reflecting 26% of its GPD and 43% of the entire labor force. Smart and precise agriculture is the key to producing the best crop yield. Moreover, emerging technologies are reducing energy consumption and cost-effectiveness for saving agricultural resources in control and monitoring systems, especially for those areas lacking these resources. Agricultural productivity is thwarted in many areas of Pakistan due to farmers’ illiteracy, lack of a smart system for remote access to farmland, and an absence of proactive decision-making in all phases of the crop cycle available in their native language. This study proposes an internet of agricultural things (IoAT) based smart system armed with a set of economical, accessible devices and sensors to capture real-time parameters of farms such as soil moisture level, temperature, soil pH level, light intensity, and humidity on frequent intervals of time. The system analyzes the environmental parameters of specific farms and enables the farmers to understand soil and environmental factors, facilitating farmers in terms of soil fertility analysis, suitable crop cultivation, automated irrigation and guidelines, harvest schedule, pest and weed control, crop disease awareness, and fertilizer guidance. The system is integrated with an android application ‘Kistan Pakistan’ (prototype) designed in bilingual, i.e., ‘Urdu’ and ‘English’. The mobile application is equipped with visual components, audio, voice, and iconic and textual menus to be used by diverse literary levels of farmers.

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.

Although the influence of climate on the fire regime is unanimously recognized, most publications and studies on this influence are on a global scale. Therefore, this study aims to demonstrate the role of climate in wildfire incidence at the country and regional scale using multivariate statistical analysis and machine learning methods (clustering and classification algorithms). Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. The results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the wildfire incidence, characterized by two peaks, one in the spring and the other in the summer, are a consequence of the country’s type of climate; and (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

The piezoresistive response of n- and p-type hydrogenated nanocrystalline silicon thin films, deposited by hot-wire (HW) and plasma-enhanced chemical vapor deposition (PECVD) on thermally oxidized silicon wafers, has been studied using four-point bending tests. The piezoresistive gauge factor (GF) was measured on patterned thin-film micro-resistors rotated by an angle θ with respect to the principal strain axis. Both longitudinal (GF_L) and transverse (GF_T) GFs, corresponding to θ = 0° and 90°, respectively, are negative for n-type and positive for p-type films. For other values of θ (30°, 45°, 120°, and 135°) GFs have the same signal as GF_L and GF_T and their value is proportional to the normal strain associated with planes rotated by θ relative to the principal strain axis. It is concluded that the films are isotropic in the growth plane since the GF values follow a Mohr’s circle with the principal axes coinciding with those of the strain tensor. The strongest p-type pirezoresistive response (GF_L = 41.0, GF_T = 2.84) was found in a film deposited by PECVD at a substrate temperature of 250 °C and working pressure of 0.250 Torr, with dark conductivity 1.6 Ω⁻¹cm⁻¹. The strongest n-type response (GF_L =− 28.1, GF_T =− 5.60) was found in a film deposited by PECVD at 150 °C and working pressure of 3 Torr, with dark conductivity 9.7 Ω⁻¹cm⁻¹. A model for the piezoresistivity of nc-Si is proposed, based on a mean-field approximation for the conductivity of an ensemble of randomly oriented crystallites and neglecting grain boundary effects. The model is able to reproduce the measured GF_L values for both n- and p-type films. It fails, however, to explain the transversal GF_T data. Both experimental and theoretical data show that nanocrystalline silicon can have an isotropic piezoresistive effect of the order of 40% of the maximum response of crystalline silicon.

Although the influence of climate on the fire regime is unanimously recognized, most publications are studies on this influence are at the global scale. Therefore, this study aims to demonstrate the role of climate in the incidence of wildfires at the country and regional scale using statistical analysis and machine learning methods. Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. Results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the incidence of wildfires, characterized by two peaks, one in spring and the other in summer, are a consequence of the country's type of climate; and, (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.