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The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods used. In order to guide researchers interested in working in this area, the most frequently used data repositories are also given. Likewise, it identifies the most clinically relevant results and the current lines developed in the field. Automatic speech analysis, within the Health 4.0 framework, offers the possibility of assessing these patients, without the need for a specific infrastructure, by means of non-invasive, fast and inexpensive techniques as a complement to the current diagnostic methods.

BACKGROUND Each year, approximately 800,000 people die by suicide worldwide, accounting for 1–2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person’s motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge. OBJECTIVE To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services. METHODS Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks. RESULTS We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks < 0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by ~0.4. At least 95% of those presenting these factors were identified as suicide cases. CONCLUSIONS Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects.

The creation of an automated system based on digital image processing of photographs of their wings to identify the genus and species of a bee (Apidae: Meliponini) is proposed. A fast and efficient system of taxonomic classification of stingless bees would be a very valuable contribution, as well as a reference for the study of stingless bees. The fore and hind right wings of workers were placed in an inert medium. The bee samples were collected directly from their nests and from the entomological collection of the Tropical Bee Research Center (CINAT).

This paper proposes the application of measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon’s entropy, Lempel–Ziv complexity and Hurst exponent are extracted from the samples of a database of emotional speech. Then, summary statistics such as mean, standard deviation, skewness and kurtosis are applied on the extracted measures. Experiments were conducted on the Berlin emotional speech database for a three-class problem (neutral, fear and anger as emotional states). Feature selection is accomplished and a methodology is proposed to find the best features. In order to evaluate the discrimination ability of the selected features, a neural network classifier is used. The global success rate is 93.78 ± 3.18 %.

The nature of the interaction of water molecules with the surface of a set of experimentally well-studied alkane−amidethiols adsorbed on a gold(111) surface and its effect on the water contact angle have been studied by carrying out a systematic determination of the topology of electrostatic potential using ab initio density functional theory methods for periodic systems. The obtained results have shown that the water contact angle decreases systematically as the number of minima by surface cell unit and the electrostatic potential magnitude at these minima, and consequently the adsorption strength, increases. Thus, this electrostatic potential magnitude can be used as a measure of the efficiency and ability of a chemical group for wetting of a surface by water.

Binding between water and models of poly(ethylene oxide), (CH2−CH2−O)n, n = 2−40, has been studied using the topographic features of the electrostatic potential, V(r), and standard density functional theory methods. It was found that, in general, the contour around the minima of the oxygen atoms overlap forming a negative-valued spiral coiled around a positive-valued helix. The positive zone defines a helical groove in the O−C−C−O units where minima lone pairs critical points are located. Topological analysis of the water molecule has also suggested that the attractive electrostatic effect between the positive water O−H zone and the negative PEO lone pairs plays an important role in the hydrogen bonding of the PEO−water system. Thus, the V(r) topology predicts a coil of water molecules around the PEO chain forming hydrogen bonding with two sites of ether oxygens. This coil is formed in such a way that more water molecules accumulate on the cavities surrounding the poly(ethylene oxide)'s oxygen atoms where...

In this work, the topology of the electrostatic potential using density functional theory for periodic systems was used to study the nature of the interaction of water with laponite surfaces; an uncharged sheet model was also used. The topological analysis predicts that for uncharged surfaces the adsorption mode is such that the water molecules are adsorbed almost parallel to the surface. For laponite surfaces, where there is a net charge, the adsorption mode involves electrostatic repulsion between the negative lone pairs on the water molecules and the ones on the surface oxygen atoms. As a consequence, the water molecules bind to the surface in a perpendicular and tilted approach, minimizing the repulsive interactions. The advantage of using the topology of the electrostatic potential as an efficient method to describe the electrostatic interactions between adsorbates and surfaces is also discussed.

Juan Jose Fuertes Cebrian1, Carlos Manuel Travieso Gonzalez2 and Valery Naranjo Ornedo1 1Instituto Interuniversitario de Investigacion en Bioingenieria y Tecnologia Orientada al Ser Humano, Universitat Politecnica de Valencia, I3BH/Labhuman 2Instituto Universitario para el Desarrollo Tecnologico y la Innovacion en las Comunicaciones (IDETIC), Departamento de Senales y Comunicaciones, Universidad de Las Palmas de Gran Canaria Spain

This work presents an improvement of the automatic and supervised spider identification approach based on biometric spider web analysis. We have used as feature extractor, a Joint Approximate Diagonalization of Eigen-matrixes Independent Component Analysis applying to a binary image with a reduced size (20×20 pixels) from the colour original image. Finally, we have applied a least square support vector machine as classifier, reaching over 98.15% in our hold-50%-out validation. This system is making easier Biologists’ tasks in this field, because they can have a second opinion or have a tool for this work.

Features representing information about pressure distribution from a static image of a handwritten signature are analyzed for an offline verification system. From gray-scale images, its histogram is calculated and used as "spectrum'' for calculation of pseudo-cepstral coefficients. Finally, the unique minimum-phase sequence is estimated and used as feature vector for signature verification. The optimal number of pseudo-coefficients is estimated for best system performance. Experiments were carried out using a database containing signatures from 100 individuals. The robustness of the analyzed system for simple forgeries is tested out with a LS-SVM model. For the sake of completeness, a comparison of the results obtained by the proposed approach with similar works published using pseudo-dynamic feature for offline signature verification is presented.

The verification of identity is becoming a crucial factor in our hugely interconnected society. Questions such as “Is she really who she claims to be?”, “Is this person authorized to use this facility?” are routinely being posed in a variety of scenarios ranging from issuing a driver’s license to gaining entry into a country. The necessity for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometric systems, described as the science in order to recognize an individual based on his or her physical or behavioural traits, is beginning to get acceptance as a legitimate method in order to determine an individual’s identity. Nowadays, biometric systems have been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In particular, this work presents a non-cooperative identification system based on facial biometric.

Today, advances in Computer Science and the proliferation of computers in modern society are an unquestionable fact. Nevertheless, the continuing importance of orthography and the hand-written document are also beyond doubt. The new technologies permit us to work with online information collecting, but there is still a large quantity of information in our society which requires using algorithms for samples off-line. Security in certain applications requires having biometric systems for their identification; in particular, banking checks, wills, postcards, invoices, medical prescriptions, etc, require the identity of the person who has written them to be verified. The only way to do this is with writer recognition techniques. Furthermore, many hand-written documents are vulnerable to possible forgeries, deformations or copies, and generally, to illicit misuse. Therefore, a high percentage of routine work is carried out by experts and professionals in this field, whose task is to certify and to judge the authenticity or falsehood of handwritten documents (for example: wills) in a judicial procedure. Therefore nowadays research on writer identification is an active field. At present, some software tools enable certain characteristics to be displayed and visualised by experts and professionals, but these experts need to devote a great deal of time to such investigations before they are able to draw up conclusions about a given body of writing. Therefore, these tools are not time-saving and nor do they provide a meticulous analysis of the writing. They have to work with graph paper and templates in order to obtain parameters (angles, dimensions of the line, directions, parallelisms, curvatures, alignments, etc.). Moreover, they have to use a magnifying glass and graph paper in order to measure angles and lines. This research aims to lighten this arduous task.

The bonding of sulfur to surfaces of gold is an important subject in several areas of chemistry, physics, and materials science. Synchrotron-based high-resolution photoemission and first-principles density-functional (DF) slab calculations were used to study the interaction of sulfur with a well-defined Au(111) surface and polycrystalline gold. Our experimental and theoretical results show a complex behavior for the sulfur/Au(111) interface as a function of coverage and temperature. At small sulfur coverages, the adsorption of S on fcc hollow sites of the gold substrate is energetically more favorable than adsorption on bridge or a-top sites. Under these conditions, S behaves as a weak electron acceptor but substantially reduces the density-of-states that gold exhibits near the Fermi edge. As the sulfur coverage increases, there is a weakening of the Au-S bonds (with a simultaneous reduction in the Au --S charge transfer and a modification in the S sp hybridization) that facilitates changes in adsorption site and eventually leads to S-S bonding. At sulfur coverages above 0.4 ML, S(2) and not atomic S is the more stable species on the gold surface. Formation of S(n)(n2) species occurs at sulfur coverages higher than a monolayer. Very similar trends were observed for the adsorption of sulfur on polycrystalline surfaces of gold. The S atoms bonded to Au(111) display a unique mobility/reactivity not seen on surfaces of early or late transition metals.

A simple macrocyclic amine is alkylated by methylene chloride to give a quaternary ammonium chloride salt. When methylene chloride is the solvent, the reaction exhibits pseudo-first-order kinetics, and the reaction half-life at 25.0 degrees C is 2.0 min. The reaction half-life for a structurally related, acyclic amine is approximately 50 000 times longer. Detailed calculations favor a mechanism where the methylene chloride associates with the macrocycle to form an activated prereaction complex. The macrocyclic nitrogen subsequently attacks the methylene chloride with a classic SN2 trajectory, and although the carbon-chlorine bond breaks, the chloride leaving group does not separate from the newly formed cationic macrocycle, such that the product is a tightly associated ion-pair. X-ray crystal structures of the starting amine and the product salt, as well as kinetic data, support this mechanism.