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Efficiency estimation of a propeller behind a vessel’s hull while sailing through ice floes, together with the ship’s resistance to motion, is a key factor in designing the power plant and determining the safety measures of a ship. This paper encloses the results from the experiments conducted at the CEHINAV towing tank, which consisted of analyzing the influence of the concentration at the free surface of artificial blocks, simulating ice, in propeller–block interactions. Thrust and torque were measured for a towed self-propelled ship model through simulated broken ice blocks made of paraffin wax. Three block concentrations of different block sizes and three model speeds were studied during the experimentation. Open-water self-propulsion tests and artificial broken ice towed self-propulsion tests are shown and compared in this work. The most relevant observations are outlined at the end of this paper, as well as some guidelines for conducting artificial ice-towed self-propulsion tests in traditional towing tanks.

In the present study, the feasibility to achieve localized induction heating and debonding of multi-material composite structures is assessed in testing coupons prepared by Automated Fiber Placement (AFP) and extrusion-based additive manufacturing (AM) technologies. Nano-compounds of Polyether-ketone-ketone (PEKK) with iron oxide nanoparticles acting as electromagnetic susceptors have been processed in a parallel co-rotating twin-screw extruder to produce filament feedstock for extrusion-based AM. The integration of nanocomposite interlayers as discrete debonding zones (DZ) by AFP-AM manufacturing has been investigated for two types of sandwich-structured laminate composites, i.e., laminate-DZ-laminate panels (Type I) and laminate-DZ-AM gyroid structures (Type II). Specimens were exposed to an alternating magnetic field generated by a radio frequency generator and a flat spiral copper induction coil, and induction heating parameters (frequency, power, heating time, sample standoff distance from coil) have been investigated in correlation with real-time thermal imaging to define the debonding process window without compromising laminate quality. For the optimized process parameters, i.e., 2–3 kW generator power and 20–25 mm standoff distance, corresponding to magnetic field intensities in the range of 3–5 kA m−1, specimens were effectively heated above PEKK melting temperature, exhibiting high heating rates within the range of 5.3–9.4 °C/s (Type I) and 8.0–17.5 °C/s (Type II). The results demonstrated that localized induction heating successfully facilitated debonding, leading to full unzipping of the debonding zones in both laminate structures. Further insight on PEKK nanocomposites debonding performance was provided by thermal, morphological characterization and non-destructive inspection via X-ray micro-computed tomography at different processing stages. The developed framework aims to contribute to the development of rapid, on-demand joining, repair and disassembly technologies for thermoplastic composites, towards more efficient maintenance, repair and overhaul operations in the aviation sector and beyond.

Enhancing human–robot interaction has been a primary focus in robotic gait assistance, with a thorough understanding of human motion being crucial for personalizing gait assistance. Traditional gait trajectory references from Clinical Gait Analysis (CGA) face limitations due to their inability to account for individual variability. Recent advancements in gait pattern generators, integrating regression models and Artificial Neural Network (ANN) techniques, have aimed at providing more personalized and dynamically adaptable solutions. This article introduces a novel approach that expands regression and ANN applications beyond mere angular estimations to include three-dimensional spatial predictions. Unlike previous methods, our approach provides comprehensive spatial trajectories for hip, knee and ankle tailored to individual kinematics, significantly enhancing end-effector rehabilitation robotic devices. Our models achieve state-of-the-art accuracy: overall RMSE of 13.40 mm and a correlation coefficient of 0.92 for the regression model, and RMSE of 12.57 mm and a correlation of 0.99 for the Long Short-Term Memory (LSTM) model. These advancements underscore the potential of these models to offer more personalized gait trajectory assistance, improving human–robot interactions.

The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing specific parameters such as the Breslow index. However, it remains invasive and may lack effectiveness. Therefore, leveraging mathematical modeling and informatics has been a pursuit of diagnostic methods favoring early detection. Fractality, a mathematical parameter quantifying complexity and irregularity, has proven useful in melanoma diagnosis. Nonetheless, no studies have implemented this metric to feed artificial intelligence algorithms for the automatic classification of dermatological lesions, including melanoma. Hence, this study aimed to determine the combined utility of fractal dimension and unsupervised low-computational-requirements machine learning models in classifying melanoma and non-melanoma lesions. We analyzed 39,270 dermatological lesions obtained from the International Skin Imaging Collaboration. Box-counting fractal dimensions were calculated for these lesions. Fractal values were used to implement classification methods by unsupervised machine learning based on principal component analysis and iterated K-means (100 iterations). A clear separation was observed, using only fractal dimension values, between benign or malignant lesions (sensibility 72.4% and specificity 50.1%) and melanoma or non-melanoma lesions (sensibility 72.8% and specificity 50%) and subsequently, the classification quality based on the machine learning model was ≈80% for both benign and malignant or melanoma and non-melanoma lesions. However, the grouping of metastatic melanoma versus non-metastatic melanoma was less effective, probably due to the small sample size included in MM lesions. Nevertheless, we could suggest a decision algorithm based on fractal dimension for dermatological lesion discrimination. On the other hand, it was also determined that the fractal dimension is sufficient to generate unsupervised artificial intelligence models that allow for a more efficient classification of dermatological lesions.

Background and Objectives: Leukemia, characterized by abnormal leukocyte production, exhibits clonal origin from somatic mutations. Globally, it ranked 15th in cancer incidence in 2020, with higher prevalence in developing countries. In Mexico, it was the ninth most frequent cancer. Regional registries are vital for understanding its epidemiology. This study aims to analyze the prevalence and age-standardized incidence rates of leukemias in a tertiary care hospital in the Mexican Bajio region. Materials and Methods: Leukemia cases from 2008–2018 were analyzed, and 535 medical records were included in this study. The prevalence, distribution, and age-specific incidence rate of different types and subtypes of leukemia were determined according to sex and age groups. Results: Overall, 65.79% consisted of lymphocytic leukemia, 33.64% of myeloid leukemia, and 0.56% of monocytic leukemia. No significant sex-based differences were found, but age-specific patterns were observed. Leukemia distribution by age revealed significant associations. Lymphocytic leukemia dominated in the pediatric population, particularly acute lymphocytic leukemia, while myeloid leukemia shifted towards adulthood. Age-specific incidence patterns showed, first, that lymphocytic leukemia is the most common leukemia in pediatric ages, and second, there is a shift from acute lymphocytic leukemia dominance in pediatric ages to myeloid leukemia incidence in late adulthood, emphasizing nuanced epidemiological dynamics. Conclusions: Acute leukemia cases occurred with high prevalence in our study population, with a high incidence in pediatric and adulthood populations, especially for acute lymphocytic leukemia, showing a (

Electrification using renewable energy sources represents a clear path toward solving the current global energy crisis. In Colombia, this challenge also involves the diversification of the electrical energy sources to overcome the historical dependence on hydropower. In this context, green hydrogen represents a key energy carrier enabling the storage of renewable energy as well as directly powering industrial and transportation sectors. This work explores the realistic potential of the main renewable energy sources, including solar photovoltaics (8172 GW), hydropower (56 GW), wind (68 GW), and biomass (14 GW). In addition, a case study from abroad is presented, demonstrating the feasibility of using each type of renewable energy to generate green hydrogen in the country. At the end, an analysis of the most likely regions in the country and paths to deploy green hydrogen projects are presented, favoring hydropower in the short term and solar in the long run. By 2050, this energy potential will enable reaching a levelized cost of hydrogen (LCOH) of 1.7, 1.5, 3.1 and 1.4 USD/kg-H2 for solar photovoltaic, wind, hydropower and biomass, respectively.

The design of scaffolds to reach similar three-dimensional structures mimicking the natural and fibrous environment of some cells is a challenge for tissue engineering, and 3D-printing and electrospinning highlights from other techniques in the production of scaffolds. The former is a well-known additive manufacturing technique devoted to the production of custom-made structures with mechanical properties similar to tissues and bones found in the human body, but lacks the resolution to produce small and interconnected structures. The latter is a well-studied technique to produce materials possessing a fibrillar structure, having the advantage of producing materials with tuned composition compared with a 3D-print. Taking the advantage that commercial 3D-printers work with polylactide (PLA) based filaments, a biocompatible and biodegradable polymer, in this work we produce PLA-based composites by blending materials obtained by 3D-printing and electrospinning. Porous PLA fibers have been obtained by the electrospinning of recovered PLA from 3D-printer filaments, tuning the mechanical properties by blending PLA with small amounts of polyethylene glycol and hydroxyapatite. A composite has been obtained by blending two layers of 3D-printed pieces with a central mat of PLA fibers. The composite presented a reduced storage modulus as compared with a single 3D-print piece and possessing similar mechanical properties to bone tissues. Furthermore, the biocompatibility of the composites is assessed by a simulated body fluid assay and by culturing composites with 3T3 fibroblasts. We observed that all these composites induce the growing and attaching of fibroblast over the surface of a 3D-printed layer and in the fibrous layer, showing the potential of commercial 3D-printers and filaments to produce scaffolds to be used in bone tissue engineering.

To evaluate the bond strength of different universal adhesives on deciduous tooth dentineand their relationship with the composition and potential of hydrogen (pH). Methods: An in vitromicro push-out test on 150 samples (n = 50) per group per adhesive, namely, Adhese Universal(ADH; Ivoclar Vivadent), Futurabond U (FUT; Voco GmbH) (Test), and Scotchbond Universal withpre-conditioning (SCO; 3M) (Control), to record bond strength (BS) and type of adhesive failure.Results: The results of the different adhesives (megapascals (MPa)) varied, showing no statisticalsignificance. The corresponding averages are in MPa: ADH, 13.66 2.81; FUT, 14.48 2.88; SCO,14.98 3.96. Additionally, the frequency of type of failure was as follows: mixed (60.7%), adhesive(27.3%), and cohesive (12%). Conclusions: SCO, with a pH of 2.7, showed greater resistance tofracture, while FUT, with a pH of 2.3 and no pre-conditioning, approached the same values, being aone-step adhesive. No relationship was found between failure and type of adhesive.

This paper reviews and analyzes the process of the implementation of the circular economy in the European Union (EU) and Spain, and its links to the low-carbon transition. The EU implementation of the circular economy went through several stages. Since the adoption of the first action plan in 1993, the links between the circular economy and carbon transition goals have become intertwined. Recently, the EU approved the framework of new monitoring measures and indicators for the circular economy. The adopted actions have involved the adaptation of European legislation at the national level. Spain has participated in this process since 2017, when the Circular Economy Pact was signed. The subsequent approval of the Spanish Strategy for a Circular Economy in 2020 was developed, considering a close relationship between decarbonization and the circular economy. Lately, the circular economy implementation has been strengthened with the strategic project for economic recovery and transformation, linked to the recovery of COVID-19, and the roadmap for the sustainable management of mineral raw materials, with both documents including actions directly related to transforming the energy system. Data show that Spain has made significant improvements in the circular economy transition. However, Spain’s vulnerability to externalities is greater than that of the EU as a whole.

(1) Background and objectives: Paediatric brain injuries can lead to motor and cognitive deficits. Effective rehabilitation is critical for enhancing independence. While virtual reality (VR) and transcranial direct current stimulation (tDCS) have independently demonstrated beneficial effects on motor and cognitive functions, their combined efficacy and its cognitive effects remain to be explored in this population. We aim to investigate the effects of integrating tDCS with VR training on upper limb (UL) functionality and cognitive outcomes through a triple-blind randomised trial. (2) Methods: Twenty-eight children with hemiparesis secondary to non-progressive brain damage will be randomly allocated into two groups: active anodal tDCS (2 mA) plus UL VR training, and sham tDCS with identical VR training. The tDCS will target M1 of the affected or most affected hemisphere for 20 min, simultaneous to VR training. The following four assessments will be carried out: pre-intervention, post-intervention, and three- and six-months follow-up. (3) Results: This study will explore motor and cognitive outcomes of a motor-based intervention in paediatric brain damage. We hypothesise that the experimental group will show significant improvements in UL function and cognition, enhancing their functional recovery. (4) Conclusions: We propose a multidisciplinary therapeutic approach combining neuromodulation and VR to potentiate functional recovery through enhancing motor and cognitive performance in paediatric brain damage.

Background: Electrodermal activity (EDA) serves as a prominent biosignal for assessing sympathetic activation across various scenarios. Prior research has suggested a connection between EDA and fluctuations in Parkinson’s disease (PD), but its precise utility in reliably detecting these fluctuations has remained unexplored. This study aims to evaluate the efficacy of both basic and advanced analyses of EDA changes in identifying the transition to the ON state following dopaminergic medication administration in individuals with PD. Methods: In this observational study, 19 individuals with PD were enrolled. EDA was continuously recorded using the Empatica E4 device, worn on the wrist, during the transition from the OFF state to the ON state following levodopa intake. The raw EDA signal underwent preprocessing and evaluation through three distinct approaches. A logistic regression model was constructed to assess the significance of variables predicting the ON/OFF state, and support vector machine (SVM) models along with various Neural Network (NN) configurations were developed for accurate state prediction. Results: Differences were identified between the ON and OFF states in both the time and frequency domains, as well as through the utilization of convex optimization techniques. SVM and NN models demonstrated highly promising results in effectively distinguishing between the OFF and ON states. Conclusions: Evaluating sympathetic activation changes via EDA measures holds substantial promise for detecting non-motor fluctuations in PD. The SVM algorithm, in particular, yields precise outcomes for predicting these non-motor fluctuation states.

Tourist activity has strategic importance in the global economy. Nevertheless, the tourism activities are linked to increased emissions, due to the sector’s energy intensity, especially in the transport and commercial sectors. The aim of this study is to analyze the relationship between final energy consumption in the whole economy, the transport, commercial and public services sectors, and the tourist activity in the 15 countries with the highest number of international tourist arrivals in the world, in 2000–2019 period. The Energy–Environment Kuznets Curve (EKC) hypothesis extended with tourism has been tested. Obtained results show non-linear relationships between energy consumption and production levels. There is evidence in favor of the energy EKC hypothesis, and in favor of an inverted N-shaped relationship for transport and commercial and public services sectors. The results also show a positive, increasing relationship between energy consumption and tourist arrivals for the whole economy. However, there is some evidence of the tourism energy EKC hypothesis when considering the transport, commercial and public services sectors. Therefore, economies of scale are observed in these sectors. Accordingly, it is highly advisable to increase the economies of scale, coupled with a greater awareness on the use of renewable energy. Negative relationships are found between energy consumption and tourism receipts. Therefore, it is advisable to establish policies that encourage high quality tourism to control the economy’s energy consumption.

Central nervous system (CNS) infections including meningitis and encephalitis, resulting from the blood-borne spread of specific microorganisms, provoke nervous tissue damage due to the inflammatory process. Moreover, different pathologies such as sepsis can generate systemic inflammation. Bacterial lipopolysaccharide (LPS) induces the release of inflammatory mediators and damage molecules, which are then released into the bloodstream and can interact with structures such as the CNS, thus modifying the blood–brain barrier’s (BBB´s) and blood–cerebrospinal fluid barrier´s (BCSFB´s) function and inducing aseptic neuroinflammation. During neuroinflammation, the participation of glial cells (astrocytes, microglia, and oligodendrocytes) plays an important role. They release cytokines, chemokines, reactive oxygen species, nitrogen species, peptides, and even excitatory amino acids that lead to neuronal damage. The neurons undergo morphological and functional changes that could initiate functional alterations to neurodegenerative processes. The present work aims to explain these processes and the pathophysiological interactions involved in CNS damage in the absence of microbes or inflammatory cells.

Tuberculosis (TB) of the central nervous system (CNS) presents high mortality due to brain damage and inflammation events. The formation and deposition of immune complexes (ICs) in the brain microvasculature during Mycobacterium tuberculosis (Mtb) infection are crucial for its pathobiology. The relevance of ICs to Mtb antigens in the pathogenesis of CNS-TB has been poorly explored. Here, we aimed to establish a murine experimental model of ICs-mediated brain vasculitis induced by cell wall antigens of Mtb. We administered a cell wall extract of the prototype pathogenic Mtb strain H37Rv to male BALB/c mice by subcutaneous and intravenous routes. Serum concentration and deposition of ICs onto blood vessels were determined by polyethylene glycol precipitation, ELISA, and immunofluorescence. Histopathological changes in the brain, lung, spleen, liver, and kidney were evaluated by hematoxylin and eosin staining. Our results evidenced that vasculitis developed in the studied tissues. High serum levels of ICs and vascular deposition were evident in the brain, lung, and kidneys early after the last cell wall antigen administration. Cell wall Mtb antigens induce strong type III hypersensitivity reactions and the development of systemic vasculitis with brain vascular changes and meningitis, supporting a role for ICs in the pathogenesis of TB.

The α-synucleinopathies constitute a subset of neurodegenerative disorders, of which Parkinson’s disease (PD) is the most common worldwide, characterized by the accumulation of misfolded α-synuclein in the cytoplasm of neurons, which spreads in a prion-like manner to anatomically interconnected brain areas. However, it is not clear how α-synucleinopathy triggers neurodegeneration. We recently developed a rat model through a single intranigral administration of the neurotoxic β-sitosterol β-D-glucoside (BSSG), which produces α-synucleinopathy. In this model, we aimed to evaluate the temporal pattern of levels in oxidative and nitrosative stress and mitochondrial complex I (CI) dysfunction and how these biochemical parameters are associated with neurodegeneration in different brain areas with α-synucleinopathy (Substantia nigra pars compacta, the striatum, in the hippocampus and the olfactory bulb, where α-syn aggregation spreads). Interestingly, an increase in oxidative stress and mitochondrial CI dysfunction accompanied neurodegeneration in those brain regions. Furthermore, in silico analysis suggests a high-affinity binding site for BSSG with peroxisome proliferator-activated receptors (PPAR) alpha (PPAR-α) and gamma (PPAR-γ). These findings will contribute to elucidating the pathophysiological mechanisms associated with α-synucleinopathies and lead to the identification of new early biomarkers and therapeutic targets.

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.

Mixed reality presents itself as a potential technological tool for the management of people with musculoskeletal disorders, without having as many adverse side effects as immersive virtual reality. The objective of this study was to explore the possibilities of a mixed-reality game, performing task-oriented cervical exercises compared to conventional therapeutic exercises in sensorimotor outcome measures in asymptomatic subjects. A randomized crossover pilot study was performed with two intervention groups: a mixed-reality group (MRG) and a conventional exercise group (CEG). The cervical joint position error test (CJPET) and deep cervical flexor endurance test (DCFET) were measured as sensorimotor outcomes. Statistically significant differences were found in the pre–post comparison in the DCFET for both groups (MRG: t = −3.87, p < 0.01; CEG: t = −4.01, p < 0.01) and in the extension of the CJPET for the MRG (t = 3.50, p < 0.01). The rest of the measurements showed no significant differences comparing both groups pre- and postintervention (p > 0.05). Mixed reality has apparently the same positive effects as conventional exercises in sensorimotor outcomes in asymptomatic subjects. These results could help in future studies with mixed virtual reality in the management of people with musculoskeletal disorders.

Background and Objectives: The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain–Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.

Monitoring of motor symptom fluctuations in Parkinson’s disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation’s occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56–96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder. This disease is characterized by motor symptoms, such as bradykinesia, tremor, and rigidity. Although balance impairment is characteristic of advanced stages, it can be present with less intensity since the beginning of the disease. Approximately 60% of PD patients fall once a year and 40% recurrently. On the other hand, cognitive symptoms affect up to 20% of patients with PD in early stages and can even precede the onset of motor symptoms. There are cognitive requirements for balance and can be challenged when attention is diverted or reduced, linking a worse balance and a higher probability of falls with a slower cognitive processing speed and attentional problems. Cognitive rehabilitation of attention and processing speed can lead to an improvement in postural stability in patients with Parkinson’s. Methods: We present a parallel and controlled randomized clinical trial (RCT) to assess the impact on balance of a protocol based on cognitive rehabilitation focused on sustained attention through the NeuronUP platform (Neuronup SI, La Rioja, Spain) in patients with PD. For 4 weeks, patients in the experimental group will receive cognitive therapy three days a week while the control group will not receive any therapy. The protocol has been registered at trials.gov NCT04730466. Conclusions: Cognitive therapy efficacy on balance improvement may open the possibility of new rehabilitation strategies for prevention of falls in PD, reducing morbidity, and saving costs to the health care system.

Background: Transcranial direct current stimulation (tDCS) of the motor cortex (M1) produces short-term inhibition of pain. Unihemispheric concurrent dual-site tDCS (UHCDS-tDCS) over the M1 and dorsolateral prefrontal cortex (DLPFC) has greater effects on cortical excitability than when applied alone, although its effect on pain is unknown. The aim of this study was to test if anodal UHCDS-tDCS over the M1 and DLPFC in healthy participants could potentiate conditioned pain modulation (CPM) and diminish pain temporal summation (TS). Methods: Thirty participants were randomized to receive a sequence of UHCDS-tDCS, M1-tDCS and sham-tDCS. A 20 min 0.1 mA/cm2 anodal or sham-tDCS intervention was applied to each participant during three test sessions, according to a triple-blind cross-over trial design. For the assessment of pain processing before and after tDCS intervention, the following tests were performed: tourniquet conditioned pain modulation (CPM), pressure pain temporal summation (TS), pressure pain thresholds (PPTs), pressure pain tolerance, mechanosensitivity and cold hyperalgesia. Motor function before and after tDCS intervention was assessed with a dynamometer to measure maximal isometric grip strength. Results: No statistically significant differences were found between groups for CPM, pressure pain TS, PPT, pressure pain tolerance, neural mechanosensitivity, cold hyperalgesia or grip strength (p > 0.05). Conclusions: Neither UHCDS-tDCS nor M1-tDCS facilitated CPM or inhibited TS in healthy subjects following one intervention session.

Pain is an under-reported but prevalent symptom in Parkinson’s Disease (PD), impacting patients’ quality of life. Both pain and PD conditions cause cortical excitability reduction and non-invasive brain stimulation. Mental representation techniques are thought to be able to counteract it, also resulting effectively in chronic pain conditions. We aim to conduct two independent studies in order to evaluate the efficacy of transcranial direct current stimulation (tDCS) and mental representation protocol in the management of pain in PD patients during the ON state: (1) tDCS over the Primary Motor Cortex (M1); and (2) Action Observation (AO) and Motor Imagery (MI) training through a Brain-Computer Interface (BCI) using Virtual Reality (AO + MI-BCI). Both studies will include 32 subjects in a longitudinal prospective parallel randomized controlled trial design under different blinding conditions. The main outcomes will be score changes in King’s Parkinson’s Disease Pain Scale, Brief Pain Inventory, Temporal Summation, Conditioned Pain Modulation, and Pain Pressure Threshold. Assessment will be performed pre-intervention, post-intervention, and 15 days post-intervention, in both ON and OFF states.

Background: Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.

Background: Brain connectivity has shown to be a key characteristic in the study of both Parkinson’s Disease (PD) and the response of the patients to the dopaminergic medication. Time series analysis has been used here for the first time to study brain connectivity changes during motor activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation and resting-state in 6 non-demented PD patients before and after the administration of levodopa and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric divergence differences among PD patients and controls were analyzed under the assumption of stationarity of the time series. Results: During the motor activation test, PD patients showed an increased correlation coefficient (both hands p < 0.001) and a remarkable increase in coherence in all frequency range compared to the generalized reduction observed in controls (both hands p < 0.001). The Kullback­–Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres was observed to increase in controls (right hand p = 0.01; left hand p = 0.015) and to decrease in PD patients (right hand p = 0.02; left hand p = 0.002) with motor activation. Conclusions: Our results suggest that the oscillatory activity of the different cortex areas within healthy brains is relatively independent of the rest. PD brains exhibit a stronger connectivity which grows during motor activation. The levodopa mitigates this anomalous performance.

Within the framework of the COP21 (Conference of the Parties) agreement, Algeria submitted its Intended Nationally Determined Contribution pledging to reduce carbon emissions by at least 7% by 2030. However, it will be a difficult task to reach this target as total final energy consumption has increased 32% from 2010 to 2014, with the major energy increases being related to electricity use in the residential sector. In this context, the relationship between residential electricity consumption and income is analyzed for Algeria in the period 1970–2013, by estimating a residential electricity consumption per capita demand function which depends on GDP per capita, its squared and cubed terms, the electricity prices, and the goods and services imports. An extended Autoregressive Distributed Lag model (ARDL) was adopted to consider the different growth patterns registered in the evolution of GDP. The estimate results show that the relationships between electricity use and GDP (in per capita terms) present an inverted N-shape, with the second turning point having been reached. Therefore, promoting growth in Algeria could be convenient to reduce the electricity consumption, as a higher income level may allow the use of more efficient appliances. Additionally, renewable energies may be adequate to increase the electricity production in order to cover the increasing residential demand.