AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats.

A significant proportion of the world's agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental data and transmit it once a day to a server via a NB-IoT network. In addition, the sensor nodes use individual, retrainable and updatable machine learning algorithms to assess the risk level in the crop every 30 min. If a risk is detected, environmental data and the risk level are immediately sent. Additionally, the system enables two types of notification: email and flashing LED, providing online and offline risk notifications. As a result, the system was deployed in a real-world environment and the power consumption of the sensor nodes was characterized, validating their longevity and the correct functioning of the risk detection algorithms. This allows the farmer to know the status of their crop and to take early action to address these threats.

Climate change and government policy: Fresh insights from complexity theory.

Governments worldwide are increasingly concerned about ensuring a balance between economic and environmental well being. Global economies, particularly developing ones, emphasize the importance of achieving escofriendly growth to maintain the levels of the ecological footprint while achieving higher economic growth. The ecological footprint is a comprehensive indicator of environmental degradation. It is used to assess the state of the environment because it reflects the impact of all human activities on nature. This study contributes to the literature by offering a novel analytical approach for solving complex interactions of ecological footprint antecedents, advancing the theoretical reasoning behind how government policy combines to explain the ecological footprint from some G7 countries (France, Italy, Japan, United Kingdom, and Germany) from 1996 to 2020. To establish a composite score of environmental footprint, we used complexity theory as well as fuzzy set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA). Our analysis revealed that low expenditures on environmental protection and waste management, low taxes on transport, and high energy use are sufficient conditions to be included in the causal configurations for a high ecological footprint. Additionally, the sufficient solution, which has the highest coverage score that produces a low ecological footprint relies on high expenditure on environmental protection and high taxes on transportation. In this framework, Japan, Italy, and France have more effective government policies in terms of reducing the ecological footprint.

Advanced System-on-Chip Field-Programmable-Gate-Array-Powered Data Acquisition System for Pixel Detectors.

Particle detector systems require data acquisition systems (DAQs) as their back-end. This paper presents a new edge-computing DAQ that is capable of handling multiple pixel detectors simultaneously and was designed for particle-tracking experiments. The system was designed for the ROC4SENS readout chip, but its control logic can be adapted for other pixel detectors. The DAQ was based on a system-on-chip FPGA (SoC FPGA), which includes an embedded microprocessor running a fully functional Linux system. An application using a client-server architecture was developed to facilitate remote control and data visualization. The comprehensive DAQ is very compact, thus reducing the typical hardware load in particle tracking experiments, especially during the obligatory characterization of particle telescopes.

Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy.

Current interest in Fourier lightfield microscopy is increasing, due to its ability to acquire 3D images of thick dynamic samples. This technique is based on simultaneously capturing, in a single shot, and with a monocular setup, a number of orthographic perspective views of 3D microscopic samples. An essential feature of Fourier lightfield microscopy is that the number of acquired views is low, due to the trade-off relationship existing between the number of views and their corresponding lateral resolution. Therefore, it is important to have a tool for the generation of a high number of synthesized view images, without compromising their lateral resolution. In this context we investigate here the use of a neural radiance field view synthesis method, originally developed for its use with macroscopic scenes acquired with a moving (or an array of static) digital camera(s), for its application to the images acquired with a Fourier lightfield microscope. The results obtained and presented in this paper are analyzed in terms of lateral resolution and of continuous and realistic parallax. We show that, in terms of these requirements, the proposed technique works efficiently in the case of the epi-illumination microscopy mode.

"You Are Not My Type": An Evaluation of Classification Methods for Automatic Phytolith Identification.

Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.

Infrared Thermography Protocol on Reducing the Incidence of Soccer Injuries.

CONTEXT: Infrared thermography has been used to detect skeletal muscle overload and fatigue in athletes, but its use in injury prevention in professional soccer has not been studied to date. OBJECTIVES: To establish a novel injury prevention program based on infrared thermography and to determine its influence on the injury incidence in professional soccer players in the preseason. DESIGN: A cross-sectional, prospective study design was used to compare a conventional injury prevention program (CPP) applied over the first preseason and an infrared thermography injury prevention program (IRTPP) carried out in the following preseason. SETTING: Soccer training ground. PARTICIPANTS: Twenty-four players belonging to a first division soccer team from Spain. MAIN OUTCOME MEASURES: Injury incidences of each player were recorded according to the Orchard Sports Injury Classification System (version 10.0) convention to determine the injury classification, location, and type. RESULTS: The incidence of injuries decreased from 15 injuries in the CPP preseason (0.63 [0.77] injuries per player) to 6 injuries in the second preseason when the IRTPP was applied (0.25 [0.53] injuries per player). The days of absence due to injuries also decreased from the CPP preseason (156 d, 10.4 [11.0] d per injury) to the IRTPP preseason (14 d, 2.3 [2.8] d per injury). The injury severity also decreased from the first preseason to the second preseason, and fewer musculoskeletal injuries in the thigh, hip, and groin were reported. CONCLUSIONS: The implementation of an IRTPP can reduce the presence of injuries by identifying players potentially at risk and as a result, reducing the injury severity and days lost as a consequence.

Low-cost single-pixel 3D imaging by using an LED array.

We propose a method to perform color imaging with a single photodiode by using light structured illumination generated with a low-cost color LED array. The LED array is used to generate a sequence of color Hadamard patterns which are projected onto the object by a simple optical system while the photodiode records the light intensity. A field programmable gate array (FPGA) controls the LED panel allowing us to obtain high refresh rates up to 10 kHz. The system is extended to 3D imaging by simply adding a low number of photodiodes at different locations. The 3D shape of the object is obtained by using a non-calibrated photometric stereo technique. Experimental results are provided for an LED array with 32 × 32 elements.

Integral imaging techniques for flexible sensing through image-based reprojection.

In this work, a 3D reconstruction approach for flexible sensing inspired by integral imaging techniques is proposed. This method allows the application of different integral imaging techniques, such as generating a depth map or the reconstruction of images on a certain 3D plane of the scene that were taken with a set of cameras located at unknown and arbitrary positions and orientations. By means of a photo-consistency measure proposed in this work, all-in-focus images can also be generated by projecting the points of the 3D plane into the sensor planes of the cameras and thereby capturing the associated RGB values. The proposed method obtains consistent results in real scenes with different surfaces of objects as well as changes in texture and lighting.

Blood-based biomarkers of Alzheimer's disease: diagnostic algorithms and new technologies.

New concepts about Alzheimer's disease (AD), considered as a clinical-biological entity, make essential the definition of biomarkers that could be used for the in vivo diagnosis of the disorder before dementia develops. Different types of genetic, biochemical and neuroimaging markers have been described, highlighting some of the changes that occur in the brain during the course of the disease, yet there is little proof of their pathognomonic and diagnostic value. Furthermore, many of the assays used are difficult to perform, the equipment/reagents are expensive or potentially hazardous (e.g.; use of radioactive compounds, CSF extraction). Thus, there is a need to define more suitable and convenient approaches, such as the determination of blood parameters that are easy to obtain and that can be repeated as necessary without contraindications. These data can be used by algorithms that combine specific and non-specific changes to classify patients at different stages of AD and/or distinguish AD from other related diseases with a greater specificity and reliability (> 80%). The blood parameters considered in this review are varied, including: ß-amyloid, tau, apolipoproteins and proteins, as well as the metabolic behavior of blood cells, etc. Among the proteins, cytokines/chemokines and other cell factors related to both neuro-inflammatory and peripheral-inflammatory processes in AD are of prime importance. New technologies to detect and quantify these substances, reasonably priced such as the vibrational spectroscopy, panels of parameters and algorithms to assess the results, would be fundamental for the early AD diagnosis and to define new potential therapies.

Vibrational spectroscopic analysis of peripheral blood plasma of patients with Alzheimer's disease.

Using Raman and infrared spectroscopy, we monitored spectral changes occurring in the blood plasma of patients with Alzheimer's disease (AD) in relation to healthy controls. The protein secondary structure as reflected by amide I band involves ß-sheet enrichment, which may be attributable to Aß peptide formation and to increasing proportion of the globulins that are ß-sheet rich. Likewise, the behavior of the infrared 1200-1000-cm(-1) region and the Raman 980-910- and 450-400-cm(-1) regions can be explained in terms of the said plasma composition change. Further, the 744-cm(-1) Raman band from healthy control plasma shows frequency upshifting in the course of AD, which may be generated by the platelets collected in blood plasma. Linear discrimination analysis and receiver operating characteristic (ROC) analysis have been used to distinguish between patients with AD and age-matched healthy controls with a diagnostic accuracy of about 94%.

Security authentication using phase-encoded nanoparticle structures and polarized light.

Phase-encoded nanostructures such as quick response (QR) codes made of metallic nanoparticles are suggested to be used in security and authentication applications. We present a polarimetric optical method able to authenticate random phase-encoded QR codes. The system is illuminated using polarized light, and the QR code is encoded using a phase-only random mask. Using classification algorithms, it is possible to validate the QR code from the examination of the polarimetric signature of the speckle pattern. We used Kolmogorov-Smirnov statistical test and Support Vector Machine algorithms to authenticate the phase-encoded QR codes using polarimetric signatures.

Human gesture recognition using three-dimensional integral imaging.

Three-dimensional (3D) integral imaging allows one to reconstruct a 3D scene, including range information, and provides sectional refocused imaging of 3D objects at different ranges. This paper explores the potential use of 3D passive sensing integral imaging for human gesture recognition tasks from sequences of reconstructed 3D video scenes. As a preliminary testbed, the 3D integral imaging sensing is implemented using an array of cameras with the appropriate algorithms for 3D scene reconstruction. Recognition experiments are performed by acquiring 3D video scenes of multiple hand gestures performed by ten people. We analyze the capability and performance of gesture recognition using 3D integral imaging representations at given distances and compare its performance with the use of standard two-dimensional (2D) single-camera videos. To the best of our knowledge, this is the first report on using 3D integral imaging for human gesture recognition.

Epidemiology of injuries in First Division Spanish football.

The aim was to examine the injuries sustained by Spanish football players in the First Division and to compare injury-related variables in the context of both competition and training. The injury data were prospectively collected from 16 teams (427 players) using a specific web-based survey during the 2008/2009 season. A total of 1293 injuries were identified (145 were recurring injuries). The overall injury incidence was 5.65 injuries per 1000 h of exposure. Injuries were much more common during competition than during training (43.53 vs. 3.55 injuries per 1000 h of exposure, P < 0.05). Most of the injuries (89.6%) involved the lower extremities, and overuse (65.7%) was the main cause. Muscle and tendon injuries were the most common types of injury (53.8%) among the players. The incidence of training injuries was greater during the pre-season and tended to decrease throughout the season, while the incidence of competition injuries increased throughout the season (all P < 0.05). In conclusion, the results of this study suggest the need for injury prevention protocols in the First Division of the Spanish Football League to reduce the number of overuse injuries in the muscles and tendons in the lower extremities. In addition, special attention should be paid during the pre-season and the competitive phase II (the last four months of the season) in order to prevent training and competition injuries, respectively.

Band selection in spectral imaging for non-invasive melanoma diagnosis.

A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more important role in melanoma detection. All the processing steps were designed taking into account the low number of samples in the dataset, situation that is quite common in medical cases. The training/test sets are built using a Leave-One-Out strategy. SMOTE is applied in order to deal with the imbalance problem, together with the Qualified Majority Voting scheme (QMV). Support Vector Machines (SVM) is the classification method applied over each balanced set. Results indicate that all melanoma lesions are correctly classified, using a low number of bands, reaching 100% sensitivity and 72% specificity when considering nine (out of a total of 55) spectral bands.

Relación entre los factores clínico patológicos y enfermedad ganglionar inguinal en cáncer de pene / Relation between clinical pathological factors and inguinal node disease in pennies cancer

Determinar la relación entre factores clínicopatológicos y enfermedad ganglionar inguinal en cáncer de pene, en los pacientes del Instituto de Oncología “Dr. Miguel Pérez Carreño”. Estudio retrospectivo, descriptivo, transversal, de 76 pacientes con cáncer de pene1995-2011. Un total de 76 pacientes, 20 (26,31%) entre los 51-60 años, siendo el grupo etario con mayor incidencia; el estadio más frecuentemente el IIIB con 23 casos (30,26%), histología predominante carcinoma epidermoide 62 casos (81,57%); la invasión angiolinfática positiva 37 pacientes y 19 casos (51,35%), presentaron metástasis ganglionar inguinal, y 31 casos (45,58%) sin invasión angiolinfática 0% de metástasis. Grado histológico: Los tumores GI 41 de 76 casos (54%), el 29,2% (12 casos) se presentó con metástasis inguinal, los tumores GII y GIII el 77% (17 casos), y 100% (5 casos) respectivamente; en relación al (T), grado histológico y metástasis inguinales, los T1G1 fueron los más frecuentes, con 22 casos, solo 1 de ellos con metástasis, de los T2, los T2G2 fue el de mayor frecuencia con 8 casos, 6 de ellos, con metástasis ganglionar inguinal, los T3G1, estuvo conformado por 10 pacientes, siendo el de mayor número de pacientes, 5 de ellos presentaron metástasis ganglionar inguinal; el resto de T3 y T4 presentaron MT. El grado histológico asociado a la profundidad de invasión y la invasión linfovascular son factores predictivos importantes de enfermedad ganglionar en cáncer de pene.

To determine the relationship between the clinics pathological factors and the presence of inguinal nodal disease in penile cancer in patients view in the Oncology Institute Dr. Miguel Pérez Carreño. Retrospective, descriptive and transversal investigation, with 76 patients with diagnostic of penile cancer view between the years 1995-2011. A total of 76 patients, 20 (26.31%) between 51-60 years old, and the age group with the highest incidence, stage IIIB more frequently with 23 cases (30.26%), squamous cell carcinoma predominant histology 62 cases (81.57%), positive angio lymphatic invasion in 37 patients and 19 (51.35%) had inguinal lymph node metastases, and 31 cases (45.58%) without groin metastases, as to histological grade, tumors GI 41 of 76 cases (54%), 29.2% (12 cases) presented with inguinal metastases, GII and GIII tumors 77% (17 cases), and 100% (5 cases) respectively, in terms of the (T), histological grade and metastatic inguinal T1G1 were the most frequent, with 22 cases, only 1 patient with metastasis, the T2, the T2G2 had the highest frequency of 8 cases 6 of them, with inguinal lymph node metastasis, the T3G1, consisted of 10 patients, with the largest number of patients, 5 of them presented inguinal lymph node metastasis, the rest of T3 and T4 showed metastases. The histological grade associated with the depth of invasion and lymph vascular invasion are important predictors of nodal disease in penile cancer.

Discrimination analysis of blood plasma associated with Alzheimer's disease using vibrational spectroscopy.

In this study we have determined whether Raman and infrared spectroscopy of blood plasma differentiates Alzheimer's disease (AD) from normal aging of healthy controls. Spectroscopic analysis was conducted on blood plasma samples from 8 mild AD, 16 moderate AD, 11 severe AD, and 12 normal elderly control persons using Fourier transform spectrometers and a near-infrared laser beam as excitation source for Raman spectroscopy. Spectra were processed employing discriminant analysis to determine whether band areas and frequency-intensity relationships might reveal biochemical differences associated with AD. Seven spectral biomarkers were identified in the Raman regions of 1700-1600 cm-1 (protein secondary structure), 980-910 cm-1 (protein α-helices), 790-730 cm-1 (protein tertiary structure), and 440-390 cm-1 (protein backbone) and in the infrared regions of 1700-1600 cm-1 (protein secondary structure) and 1150-1000 cm-1 (oxidative stress). This discriminant analysis model differentiated AD from normal aging of elderly control persons with a sensitivity of 89% and specificity of 92%. Moreover, specificity increases to 100% for the detection of mild AD. In summary, our results open the possibility of using this spectroscopic approach as a non-invasive, rapid, and relatively inexpensive procedure for early accurate diagnosis of AD.

Hyperspectral remote sensing of foliar nitrogen content.

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact--it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Multispectral integral imaging acquisition and processing using a monochrome camera and a liquid crystal tunable filter.

This paper presents an acquisition system and a procedure to capture 3D scenes in different spectral bands. The acquisition system is formed by a monochrome camera, and a Liquid Crystal Tunable Filter (LCTF) that allows to acquire images at different spectral bands in the [480, 680]nm wavelength interval. The Synthetic Aperture Integral Imaging acquisition technique is used to obtain the elemental images for each wavelength. These elemental images are used to computationally obtain the reconstruction planes of the 3D scene at different depth planes. The 3D profile of the acquired scene is also obtained using a minimization of the variance of the contribution of the elemental images at each image pixel. Experimental results show the viability to recover the 3D multispectral information of the scene. Integration of 3D and multispectral information could have important benefits in different areas, including skin cancer detection, remote sensing and pattern recognition, among others.