Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization.

In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and academics. The initial electronics-development tasks of a smart home project were prompted to the ChatGPT system to find out its capabilities and limitations. We wanted to obtain detailed information on the central processing controller units and the actual sensors usable for the specific project, their specifications and recommendations on the hardware and software design flow additionally. Furthermore, an extensive literature survey was requested to see if the bot could offer scientific papers covering the given topic. It was found that the ChatGPT responded with proper recommendations on controllers. However, the suggested sensor units, the hardware and software design were only partially acceptable, with occasional errors in specifications and generated code. The results of the literature survey showed that non-acceptable, fabricated citations (fake authors list, title, journal details and DOI-Digital Object identifier) were presented by the bot. The paper provides a detailed qualitative analysis, a performance analysis and critical discussion of the aforementioned aspects while providing the query set, the generated answers and codes as supplied data with the goal to give added value to electronics researchers and developers if trying to reach out for the tools in their profession.

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the \"per animal\" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.
Precision technologies are revolutionizing animal agriculture by enhancing the management of animal welfare and productivity. To fully realize the potential benefits of precision livestock farming (PLF), the development and application of digital technologies are needed to facilitate the responsible and sustainable intensification of livestock production over the next several decades. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring audibility in value chains while assuaging concerns associated with labor shortages. In this paper, we analyze the multilayered network of sensors, actuators, communication, and analytics currently in use in PLF. We analyze the various aspects of sensing, communication, networking, and intelligence on the farm leveraging dairy farms as an example system. We also discuss the potential implications of advancements in communication, robotics, and artificial intelligence on the security and welfare of animals.

Outdoor social participation in the school playground is crucial for children\'s socio-emotional and cognitive development. Yet, many children with disabilities in mainstream educational settings are not socially included within their peer group. We examined whether loose-parts-play (LPP), a common and cost-effective intervention that changes the playground play environment to enhance child-led free play, can promote social participation for children with and without disabilities.
Forty-two primary school children, out of whom three had hearing loss or autism, were assessed for two baseline and four intervention sessions. We applied a mixed-method design, combining advanced sensors methodology, observations, peer nominations, self-reports, qualitative field notes and an interview with the playground teachers.
Findings indicated for all children a decrease during the intervention in social interactions and social play and no change in network centrality. Children without disabilities displayed also an increase in solitude play and in the diversity of interacting partners. Enjoyment of LPP was high for all children, yet children with disabilities did not benefit socially from the intervention and became even more isolated compared with baseline level.
Social participation in the schoolyard of children with and without disabilities did not improve during LPP in a mainstream setting. Findings emphasize the need to consider the social needs of children with disabilities when designing playground interventions and to re-think about LPP philosophy and practices to adapt them to inclusive settings and goals.

The deployment of sensors is the first issue encountered when microclimate monitoring is planned in spaces devoted to the conservation of artworks. Sometimes, the first decision regarding the position of sensors may not be suitable for characterising the microclimate close to climate-sensitive artworks or should be revised in light of new circumstances. This paper fits into this context by proposing a rational approach for a posteriori deployment of microclimate sensors in museums where long-term temperature and relative humidity observations were available (here, the Rosenborg Castle, Copenhagen, Denmark). Different statistical tools such as box-and-whisker plots, principal component analysis (PCA) and cluster analysis (CA) were used to identify microclimate patterns, i.e., similarities of indoor air conditions among rooms. Box-and-whisker plots allowed us to clearly identify one microclimate pattern in two adjoining rooms located in the basement. Multivariate methods (PCA and CA) enabled us to identify further microclimate patterns by grouping not only adjoining rooms but also rooms located on different floors. Based on these outcomes, new configurations about the deployment of sensors were proposed aimed at avoiding redundant sensors and collecting microclimate observations in other sensitive locations of this museum.

For patients suffering from neurodegenerative disorders, the behavior and activities of daily living are an indicator of a change in health status, and home-monitoring over a prolonged period of time by unobtrusive sensors is a promising technology to foster independent living and maintain quality of life. The aim of this pilot case study was the development of a multi-sensor system in an apartment to unobtrusively monitor patients at home during the day and night. The developed system is based on unobtrusive sensors using basic technologies and gold-standard medical devices measuring physiological (e.g., mobile electrocardiogram), movement (e.g., motion tracking system), and environmental parameters (e.g., temperature). The system was evaluated during one session by a healthy 32-year-old male, and results showed that the sensor system measured accurately during the participant\'s stay. Furthermore, the participant did not report any negative experiences. Overall, the multi-sensor system has great potential to bridge the gap between laboratories and older adults\' homes and thus for a deep and novel understanding of human behavioral and neurological disorders. Finally, this new understanding could be utilized to develop new algorithms and sensor systems to address problems and increase the quality of life of our aging society and patients with neurological disorders.

Nanoporous ultrathin films, constituted by a slab less than 100 nm thick and a certain void volume fraction provided by nanopores, are emerging as a new class of systems with a wide range of possible applications, including electrochemistry, energy storage, gas sensing and supercapacitors. The film porosity and morphology strongly affect nanoporous films mechanical properties, the knowledge of which is fundamental for designing films for specific applications. To unveil the relationships among the morphology, structure and mechanical response, a comprehensive and non-destructive investigation of a model system was sought. In this review, we examined the paradigmatic case of a nanoporous, granular, metallic ultrathin film with comprehensive bottom-up and top-down approaches, both experimentals and theoreticals. The granular film was made of Ag nanoparticles deposited by gas-phase synthesis, thus providing a solvent-free and ultrapure nanoporous system at room temperature. The results, bearing generality beyond the specific model system, are discussed for several applications specific to the morphological and mechanical properties of the investigated films, including bendable electronics, membrane separation and nanofluidic sensing.

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.

Clinical safety findings remain one of the reasons for attrition of drug candidates during clinical development. Cardiovascular liabilities are not consistently detected in early-stage clinical trials and often become apparent when drugs are administered chronically for extended periods of time. Vital sign data collection outside of the clinic offers an opportunity for deeper physiological characterization of drug candidates and earlier safety signal detection. A working group representing expertise from biopharmaceutical and technology sectors, US Food and Drug Administration (FDA) public-private partnerships, academia, and regulators discussed and presented a remote cardiac monitoring case study at the FNIH Biomarkers Consortium Remote Digital Monitoring for Medical Product Development workshop to examine applicability of the biomarker qualification evidentiary framework by the FDA. This use case examined the components of the framework, including the statement of need, the context of use, the state of the evidence, and the benefit/risk profile. Examination of results from 2 clinical trials deploying 510(k)-cleared devices for remote cardiac data collection demonstrated the need for analytical and clinical validity irrespectively of the regulatory status of a device of interest, emphasizing the importance of data collection method assessment in the context of intended use. Additionally, collection of large amounts of ambulatory data also highlighted the need for new statistical methods and contextual information to enable data interpretation. A wider adoption of this approach for drug development purposes will require collaborations across industry, academia, and regulatory agencies to establish methodologies and supportive data sets to enable data interpretation and decision-making.

The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty application in the meat industry. This study aimed to determine the optimal dilution level of meat extract for e-tongue evaluations and also to develop three standardized meat extraction methods. For practicality, the developed methods were applied to detect low levels of meat adulteration using beef and pork mixtures and turkey and chicken mixtures as case studies. Dilution factor of 1% w/v of liquid meat extract was determined to be the optimum for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w chicken in turkey and pork in beef with linear discriminant analysis accuracies (LDA) of 78.13% (recognition) and 64.73% (validation). Even higher LDA accuracies of 89.62% (recognition) and 68.77% (validation) were achieved for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w of pork in beef. Partial least square models could predict both sets of meat mixtures with good accuracies. Extraction by cooking was the best method for discriminating meat mixtures and can be applied for meat quality evaluations with the e-tongue.