The dynamical processes of living systems are characterized by the cooperative interaction of many units. This claim enables us to portray the embryo-fetal development of the central and peripheral nervous systems in terms of assemblies of building blocks. We describe how the structure and arrangement of nervous fibers is - at least partially - dictated by biophysical and topological constraints. The far-flung field of soft-matter polymers/nematic colloids sheds new light on the neurulation in mammalian embryos, suggesting an intriguing testable hypothesis: the development of the central and peripheral nervous systems might be correlated with the occurrence of local thermal changes in embryo-fetal tissues. Further, we show a correlation between the fullerene-like arrangement of the cortical microcolumns and the Frank-Kasper phases of artificial quasicrystals assemblies. The last, but not the least, we explain how and why the multisynaptic ascending nervous fibers connecting the peripheral receptors to the neocortical areas can be viewed as the real counterpart of mathematical tools such as knot theory and braid groups. Their group structure and generator operations point towards a novel approach to long-standing questions concerning human sensation and perception, leading to the suggestion that the very arrangement and intermingling of the peripheral nervous fibers contributes to the cortical brain activity. In touch with the old claims of D’Arcy Thompson, we conclude that the arrangement and the pattern make the function in a variety of biological instances, leading to countless testable hypotheses.

Cells take decisions on their responses depending on the stimuli received by the surrounding extracellular environment, that provides the essential cues at the micro and the nano-lengthscales required for adhesion, orientation, proliferation and differentiation. In this study, discontinuous microcones on silicon (Si) and continuous microgrooves on polyethylene terephthalate (PET) substrates were fabricated via ultrashort-pulsed laser irradiation at various fluences, resulting in microstructures with different roughness and geometrical characteristics. The topographical models attained were specifically developed to imitate the guidance and alignment of Schwann cells for oriented axonal regrowth, towards nerve regeneration. At the same time, positive replicas of the silicon microstructures formed were successfully reproduced, via soft lithography, on the biodegradable polymer poly(lactide-co-glycolide) (PLGA). The anisotropic continuous (PET) and discontinuous (PLGA replicas) microstructured polymeric substrates were assessed in terms of their influence on the Schwann cells responses. It is shown that the developed micropatterned substrates enable control over the cellular adhesion, proliferation and orientation and are thus useful to engineer cell alignment in vitro. This property could be potentially useful in the fields of neural tissue engineering and for dynamic microenvironment systems that simulate in vivo conditions.

Intraoperative load sensors have been developed with the aim of balancing the soft tissue in the knee during Total Knee Replacements (TKRs). Literature has shown success in soft tissue bal-ancing however, concerns regarding the accuracy of these sensors remain. For this reason, pre-vious research has attempted to create robust sensors for intraoperative use. The design of the sensor in this research has aimed to address the concerns outlined in the literature to create a new and novel smart-sensor for soft-tissue balancing. This new intraoperative load sensor included design features to increase accuracy while artificial intelligence allowed for comprehensive sensing across the entirety of the sensor, providing unparalleled insight during the operation. Notably, the sensor was designed to withstand loads of at least 450 N, ensuring robust perfor-mance of the sensor. To optimize the design process while minimizing cost, Finite Element Analysis was employed. This approach ensured the design features fulfilled their function while maintaining structural integrity to withstand loads. Moreover, the novelty of using training data from simulated data will reduce the iterative process of the labor-intensive collection of training data for the artificial intelligence while reducing human error associated with it. Results from the evaluation demonstrated the ability of this design to successfully bridge the current gap in the market by fulfilling all essential design criteria established within the literature. This innovative smart-sensing tool will in turn enhance patient outcomes and alleviate financial burdens to pa-tients and the healthcare systems by reducing the need for early revision surgeries associated with improper joint tension.

The formation of the required AC voltage level is currently one of the most important problems. An unstable voltage level can lead to the failure of domestic and industrial equipment, also to the appearance of a lighting equipment flicker effect, which can adversely affect the operating mode. In this regard, the development of AC voltage regulators that perform the function of stabilizers has become relevant. In turn, new topologies should be energy efficient and reliable, this can be achieved by reducing the number of semiconductor elements, which in turn will reduce losses and increase efficiency. In addition to voltage stabilization, AC voltage regulators have found application as soft-start devices for asynchronous motors, and have become competitors to frequency converters. The power level can vary from units to tens of kilowatts where such devices can be used. This paper provides a review of AC voltage regulators, which can be used both as soft starters and AC voltage stabilizers. The analysis of the circuits was carried out and the efficiency was evaluated through their total harmonic distortions (THD), power factor and efficiency coefficient. Experiments were also performed for a number of circuits, where their operating modes were evaluated. Eventually, all circuits were divided into full number of switches with two or more AC switches per phase, with a reduced number of switches, where one AC switch per phase and with low number of switches – with one complementary pair of AC switches for all three phases.

Tree resin, which in addition to its widespread use in industry is also a macroergic component which has not yet been used for energy purposes. The main goal of this work is to determine the energy content of the resin of spruce, pine and larch and wood components – pulp and turpentine. The combustion heats of resin (MJ/kg) from each timber was determined calorimetrically. The energy values of tree resin (>38.0 MJ.kg-1) were 2.2 and 2.4 times higher than that of bleached and unbleached cellulose, the highest value was recorded for the turpentine (>39.0 MJ.kg-1). The best way of resin tapping is the America method, providing 5 kg resin ha-1 yr-1. The resin quantity tapped was raised at least 3 times applying stimulant, its production cost compared to other feedstocks was the cheapest. The tree resin can be applied as a good means to mitigate global warming and consequently dampen climate change. One tonne of tree resin burned instead of coal spares the atmosphere by 5.0 Mt CO2 since it is environmentally neutral. As resin heating values approach to the lower end of ones of liquid hydrocarbon fuels, greater efforts are needed to examine the most efficient energy use of resin.

The effect of cooling rate on the magnetorheological response was investigated for magnetic hydrogels consisting of carrageenan and carbonyl iron particles with a concentration of 50 wt.%. For magnetic gels prepared by natural cooling, the storage moduli at 0 and 50 mT were 3.7×104 Pa and 5.6×104 Pa, respectively, and the change in the modulus was 1.9×104 Pa. For magnetic gels prepared by rapid cooling, the storage moduli at 0 and 50 mT were 1.2×104 Pa and 1.8×104 Pa, respectively, and the change in the modulus was 6.2×103 Pa, which was 3.1 times higher than that for the magnetic gel of rapid cooling. The critical strains, where G‘=G“ on the strain dependence of the storage modulus, for magnetic gels of natural cooling and rapid cooling were 0.023 and 0.034, respectively, indicating that the magnetic gel of rapid cooling has a hard structure compared to that of natural cooling. Oppositely to this, the change in the storage modulus at 500 mT for the magnetic gel of rapid cooling was 1.6×105 Pa, which was 2.5 times higher than that of natural cooling. SEM images revealed that many small aggregations of carrageenan network were found in the magnetic gel of natural cooling and continuous phases of carrageenan network with large sizes were found in the magnetic gel of rapid cooling. It was revealed that, magnetic particles in the magnetic gel of rapid cooling can move and form a chain structure at high magnetic fields by breaking the restriction from the continuous phases of carrageenan.

In oral pathology, the appearance of a large ulcerated lesion may suggest a malignant process that requires an early diagnosis given the uneasiness of the patient and his or her family. Finding lingual carcinoma in children is, however, extremely rare We present the case of a 13-year-old girl who came to the oral medicine clinic, referred by her orthodontist, with a large round lesion on the ventral side of the tongue (15 x 12 mm) that did not heal, after three weeks of evolution and after eliminating the existing traumatic factors (orthodontic appliance in the lower jaw) and treatment with topical gel based on PVP (polyvinyl pyrrolidone), glycyrrhetinic acid and hyaluronic acid (Aloclair PLUS®). The objective of this article is analyse the possible causes and provide a review of the literature of a large ulcerated lesions.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To tackle this problem, this study proposes a foggy weather detection method, YOLOv5s-Fog, based on the YOLOv5s framework. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer, SwinFocus. Additionally, this research incorporates decoupled head into the model and replaces the conventional non-maximum suppression method with Soft-NMS. Experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

The paper proposes a human explainable artificial intelligence approach for mapping the status of environmental phenomena from multisource geo data. It is both knowledge and data driven: it exploits remote sensing expert’s knowledge to define the contributing factors from which partial evidence of the environmental status can be computed. Furthermore, it aggregates the partial evidences to compute a map of the environmental status by adapting to a region of interest through a learning mechanism exploiting Volunteered Geographic Information (VGI), both from in situ observations and photointerpretation. The approach is capable to capture the specificities of local context as well as to cope with the subjectivity and incompleteness of expert’s knowledge. The proposal is exemplified to map the status of standing water areas (i.e. water bodies and river, human driven or natural hazard flooding) by considering satellite data and geotagged observations. Results of the validation experiments were performed in three areas of Northern Italy, characterized by distinct ecosystems. Results of the proposed methodological framework showed better performances than traditional approaches based on single spectral indexes thresholding. The use of expert’s knowledge, possibly imprecise/uncertain and incomplete, the need of few ground truth data for learning, and finally the explainability of learned rules are the distinguishing characteristics of the proposal with respect to traditional machine learning methods.

Precise classification of sarcomas is crucial to drive optimal clinical management. In this prospective, multicenter, observational study within the Hellenic Group of Sarcoma and Rare Cancers (HGSRC) we assessed the effect of expert pathology review, coupled with the application of molecular diagnostics, on diagnosis and management of sarcoma patients. Newly diagnosed sarcoma patients were addressed by their physicians to one of the two sarcoma pathologists of HGSRC for histopathological diagnostic assessment. RNA next-generation sequencing was per-formed to all samples, using a platform targeting 86 sarcoma gene fusions. Additional molecular methods were performed on the opinion of the expert pathologist. Therefore, the expert pathologist provided a final diagnosis based on the histopathological findings and, when necessary on molecular tests. In total, 128 specimens from 122 patients were assessed. Among the 119 cases in which there was a preliminary diagnosis by a non-sarcoma pathologist, there were 37 modifications in diagnosis (31.1%) by the sarcoma pathologist, resulting in 17 (14.2%) modifications in management. Among the 110 cases in which molecular tests were performed, there were 29 modifications in diagnosis (26.4%) through the genomic results, resulting in 12 (10.9%) modifications in management. Our study confirms that expert pathology review is of utmost importance for the optimal sarcoma diagnosis and management and should be assisted by molecular methods in selected cases.

In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.

This paper proposes a model based on gene expression programming for predicting discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F=V/√(gy)) and vertex angle () dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with GEP. The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts the discharge coefficient with an average relative error of approximately 2.5% in such a manner that the predicted values have less than 5% relative error in the worst model.

The clinical requirement for a good esthetic result for immediate implant placement is the absence of dehiscence in the anterior facial alveolar bone. In the presence of a dehiscence, it is recommend-ed to use a connective tissue graft in addition to immediate implant placement or to change to ear-ly implant placement. However, the literature focusing on dehiscence is scarce, and the influence of different placement times and combined use of connective tissue graft on postoperative esthetics in cases with dehiscence is unclear. Therefore, we quantitatively evaluated the pre-extraction de-hiscence morphology and postoperative changes in the facial tissue of implants in three groups: immediate implant placement (Group I), immediate implant placement with connective tissue graft (Group IC), and early implant placement (Group E). Fifty-two implants were obtained (20 in Group I, 16 in Group IC, and 16 in Group E). A wider dehiscence increases the risk of soft tissue regression, which was one reason for choosing early implant placement. A combination of imme-diate implant placement and connective tissue graft, or early implant placement, tended to result in less soft tissue regression due to the thicker postoperative facial soft tissue volume.

In this article, the effect of substitution of Zr and Ga in Dy2Fe17 prepared by arc melting technique are studied. The substitution of Zr and Ga in Dy2Fe17 was found to have an important effect on their structure and magnetic properties. The Rietveld analysis conformed that the crystalline system wereTh2Ni17 structure. Lattice parameters a (Å) and c (Å), unit cell volume (Å3), bonding distance (Å) were calculated by using Rietveld analysis. The unit cell volume of Dy2Fe17-xZrx and Dy2Fe16Ga1-xZrx increase linearly with the Zr and Ga substitution. The substitution of Zr and Ga are limited up to x=1 in order to avoid the decrease in saturation magnetization. The Curie temperature (Tc) of Dy2Fe17-xZrx and Dy2Fe16Ga1-xZrx are found to be Zr content dependent. The Curie temperature is found to be increasing first and then decrease for higher Zr content. The maximum curie temperature was observed 510K at x = 0.75 for Dy2Fe17-xZrx and 505.1 K at x = 0.5 for Dy2Fe17-xZrx which are102 K and 97 K higher than Dy2Fe17. The room temperature Mössbauer analysis shows the decrease in average hyperfine field and increase in isomer shift with Zr doping.

The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be replaced by additions, which are more energy-efficient. However, its ``Hard Reset" mechanism for the firing activity would ignore the difference among membrane potentials when the membrane potential is above the firing threshold, causing information loss. Meanwhile, quantifying the membrane potential to 0/1 spikes at the firing instants will inevitably introduce the quantization error thus bringing about information loss too. To address these problems, we propose to use the ``Soft Reset" mechanism for the supervised training-based SNNs, which will drive the membrane potential to a dynamic reset potential according to its magnitude, and Membrane Potential Rectifier (MPR) to reduce the quantization error via redistributing the membrane potential to a range close to the spikes. Results show that the SNNs with the ``Soft Reset" mechanism and MPR outperform their vanilla counterparts on both static and dynamic datasets.

Soft Rot Pectobacteriaceae (SRP) bacteria as Pectobacterium and Dickeya are phytopathogenic agents responsible of blackleg disease on several crop as potato, affecting the yield and depressing the seed production quality. However, neither conventional nor biocontrol product are available on the market to control this disease. In this study Pseudomonas PA14H7, a bacteria isolated from potato rhizosphere, was selected as a potential antagonist agent against Dickeya. In order to understand the mechanism involved in this antagonism we managed to identify the main active(s) molecule(s) produced by PA14H7. In that way, cell-free supernatant (CFS) of PA14H7 cultures were extracted and analyzed by LC-MS, GC-MS and NMR. We have correlated biological activity in extracted CFS-PA14H7 to the presence of 7-hydroxytropolone (7-HT) complexed with iron. Even if this molecule is produced by other Pseudomonas and mostly known for its antibacterial and antifungal activities, it is the first description of its involvement as an effective molecule against pectinolytic bacteria. We have synthesized 7-HT in order to determine the real amount produced by PA14H7 and to compare its activity at the same concentration with that of CFS-PA14H7. This study aims to understand the origin of the antagonist activity of PA14H7 as a potential biocontrol agent against potato blackleg.

The human body contains 60-70% of water, depending on age. As a body fluid, it is not only a medium in which physical and chemical processes take place, but it is also one of the active mediators. Water is the richest substance with non-covalent hydrogen bonds. Water molecules, by themselves (in vacuum), are diamagnetic, but when organized into clusters, become diamagnetic or paramagnetic. Also, biomolecules (DNA, collagen, clathrin, and other proteins) have non-covalent hydrogen bonds in their structure. The interaction, as well as signal transmission, between water and biomolecules is achieved through the vibrations of covalent and non-covalent hydrogen bonds, which determine the state and dynamics of conformational changes of biomolecules. Disruptive conformational changes of biomolecules, cells, and tissues lead to their dysfunctionality, so they are a frequent cause of many disorders and diseases. For example, the rearrangement of hydrogen bonding due to mitochondrial disease mutation in cytochrome bc1 disturbs heme bH redox potential and spin state. In order to prevent and repair the dysfunctional conformational changes, a liquid substance was developed based on the second derivative of the C₆₀ molecule (SD-C₆₀), which has classical and quantum properties. Characterization of SD-C₆₀ by UV-VIS-NIR, FTIR, TEM, and AFM/MFM was done and it is shown that SD-C₆₀ water layers generate vibrations with near-zero phase dispersion which are transmitted through the Fibonacci’s water chains to biomolecules. In comparison to previously published SD-C₆₀ derivate (3HFWC, size until 10 nm, and 1-5 water layers), improved formulation (3HFWC-W, size 10-25 nm, and 6-9 water layers) showed multiplied cytotoxic activity against melanoma cell lines of different aggressiveness. Apart from this, the mode of action was preserved and based on induction of senescence rather than cell death. Importantly, high selectivity toward malignant phenotype was detected. Observed effects can be ascribed to a machinery of hydrogen bonds, which are generated in SD-C₆₀ and transmitted through water to biomolecules. This approach may open a new field in science and healthcare - a “water-based nanomedicine”.

Seeing atomic configuration of single organic nanoparticles at sub-Å spatial resolution by transmission electron microscopy has been so far prevented by the high sensitivity of soft matter to radiation damage. The difficulty is related to the need to irradiate the particle with a total dose of few electrons/Å2, not compatible with the electron beam density necessary to search the low-contrast nanoparticle, to control its drift, to finely adjust the electro-optical conditions and particle orientation, and to finally acquire an effective atomic resolution image. On the other hand, the capability to study individual pristine nanoparticles, such as proteins, active pharmaceutical ingredients, and polymers, with peculiar sensitivity to the variation of the local structure, defects, and strain, would provide advancements in many fields, including materials science, medicine, biology, and pharmacology. Here we report direct sub-ångström resolution imaging at room temperature of pristine unstained crystalline polymer-based nanoparticles. This result is obtained by combining low-dose in-line electron holography and phase-contrast imaging on state-of-the-art equipment, providing an effective tool for the quantitative sub-angstrom imaging of soft matter.

Objective: CAD/CAM generated ceramic implant abutments have recently attracted interest due to their superior customization possibilities and aesthetic advantages. Despite their widespread clinical use, little information is currently available on their surface topography, however. The transmucosal portion of the abutment shoulder is of particular interest, as it ideally supports soft tissue but minimizes mechanical plaque retention. The aim of this in vitro study was to topographically characterize the trans- and subgingival roughness of CAD/CAM zirconia abutments from different manufacturers and compare them with zirconia stock abutments. Material and Method: The surface topography of eight CAD/CAM zirconia implant abutments (tests) and two prefabricated zirconia stock abutments (controls) was determined using focus variation microscopy. Two points on the abutment shoulder were subjected to profilometric examination. 2D and 3D parameters of roughness were obtained and compared. Results: The surface roughness of all the test abutments exceeded the recommended threshold of Ra = 0.2 µm and therefore exhibited an increased risk of mechanical plaque retention. Obvious differences in surface structure were apparent, allowing conclusions to be drawn about the manufacturing method and subsequent reworking processes. Conclusion: Manually reworking the trans- and submucosal area of the investigated CAD/CAM zirconia abutments appears necessary to fulfil the conditions for optimal surface topography. The Sa value as arithmetic mean, taking the maximum height (Sz value) and surface excess (Sdr) into account, is an essential parameter for assessing the surface topography of implant abutments.

The aim of this work is a comprehensive study of the effect of variable atomic composition and structural-phase state of (CoFeZr)x(MgF2)100-x nanocomposites (NCs) on their nonlinear electronic and magnetic/magneto-optical properties. Micrometer-thick nanocomposites layers on the glass substrates were obtained by ion-beam sputtering of a composite target in the argon atmosphere in a wide range of compositions x=9-51at.% The value of the resistive percolation threshold xper=34 at.%, determined from the concentration dependences of the electrical resistance of NCs, coincides with the beginning of nucleation of metallic nanocrystals CoFeZr in MgF2 dielectric matrix. The absolute value of maximum magnetoresistance of NCs is 2.4 % in a magnetic field of 5.5 kOe at x=25 at.%, up to the percolation threshold. Two maxima appear in the concentration dependences of magneto-optical transversal Kerr effect, one of which at x = 34 at.% corresponds to the formation of CoFeZr alloy nanocrystals of a hexagonal structure, and the second one at x=45 at.% corresponds to the phase transition of nanocrystals from a hexagonal to a cubic body-centered structure. The magnetic percolation threshold in (CoFeZr)x(MgF2)100-x system at xfm=34 at.% with the appearance of a hysteresis loop and a coercive force of Hc≈ 8 Oe, coincides with the resistive percolation threshold xper=34 at.%. Concentration dependence of the coercive force showed that at low contents of metallic alloy x<30 at.% NCs are superparamagnetic (Hc=0). With an increase of the alloy content, in the region of magnetic and resistive percolation thresholds NCs exhibit a magnetically soft ferromagnetic character and do not change it far beyond the percolation threshold with the maximum value of the coercive force Hc< 30 Oe. Decrease in magnetic order and soft ferromagnetic are influenced by the diamagnetic phase FeF2 that we discovered at the interface of magnetic nanocrystals CoFeZr with a dielectric matrix MgF2 by the Mössbauer spectroscopy.

The Wind Energy Conversion System (WECS) plays an inevitable role across the world. In particular, the attention for Permanent Magnet Synchronous Generators (PMSGs) connected with wind farm is popular. This paper deals with the literature review that describes the recent advances, progresses and innovatory trends on PMSGs for WECS. Comparison between geared and direct-driven conversion systems and the classification of electrical machines used in WECS are discussed. A detailed analysis on the design aspects considering various topologies of PMSGs are encompassed in the literature. The PMSG design and optimization problems are solved by field computation techniques and optimized by using Soft Computing (SC) techniques .The three-dimensional, finite element software platform for the analysis and design of PMSGs is discussed. This paper also deals with the interdisciplinary modeling, analysis, and optimization of PMSG using Finite Element Analysis (FEM) and SC techniques. Finally, PMSGs are reviewed and compared for further exploration.

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

Abstract: Pasteurella spp. is a gram-negative bacterium that is part of the oral and upper respira-tory tract microbiota of many animals such as canids and felines. In humans it can produce pa-thology primarily associated with animal bites or scratches. In addition, members of this genus, and especially Pasteurella multocida, may also be involved in systemic infections, mainly in im-munocompromised patients in close contact with pets. The aim of this study is to analyse the prevalence and clinical characteristics of infections caused by Pasteurella spp. as well as the sensitivity profile of these isolates obtained in the Microbiology laboratory of the Hospital Can Misses (Ibiza, Ibiza and Formentera Health Area, ASEF) in the period from January 2013 to December 2018. The group of islands of Ibiza and Formentera is known as the Pitiusas Islands. Retrospective descriptive study in which all Pasteurella spp. isolates were analysed in the Microbiology Laboratory of Hospital Can Misses from 1 January 2013 to 31 De-cember 2018. Bacterial identification was performed by Vitek 2 automated biochemical test panel (bioMérieux, Spain) and antimicrobial susceptibility by E-test or antibiotic discs in Mueller-Hinton blood agar, interpreted based on European Committee on An-timicrobial Susceptibility Testing (EUCAST) standards, 2022 version. Medical records were systematically reviewed by collecting demographic data of infected patients, comorbidities, epidemiological data and clinical features of the infection. A total of 22 isolates of Pasterurella spp. were obtained from 22 different patients, 62.8% female, from three different species: 18 P. multocida, 2 P. canis and 2 P. pneumo-tropica. Most isolates came from soft tissue infection samples: 7 wound exudates and 5 abscess material. The antibiotics tested with the highest sensitivity profile were gen-tamicin and cefepime (100% sensitivity). The species most commonly isolated in our environment is P. multocida. Close contact with companion animals, especially cats and dogs, is the main risk factor for Pasteurella spp. infections, although in many cases such contact cannot be established. So Pasteurella spp. infection should be considered a zoonosis in possible emergency, especially in rural areas such as Pitiusas Islands.

This paper presents a control algorithm for soft-switching series LC converters. The conventional voltage-to-voltage controller is split into a master and a slave controller. The master controller implements constant-current-constant-voltage (CCCV) control, required for demanding applications, i.e. lithium battery charging or laboratory power supplies. It defines the set-current for the open-loop current slave controller, which generates the PWM parameters. The power supply achieves fast large-signal responses, e.g. from 5 V to 24 V, where 95% of the target value is reached in less than 400 µs. The design is evaluated extensively in simulation and on a prototype. A consensus between simulation and measurement is achieved.

University-Business partnership for collaborative curriculum design, development and delivery is possibly the less explored dimension of University Business Collaboration (UBC). Even if some models for curricular design and development might be available, scarce information exists on how the partnership is constructed, the new curricula are designed, developed and how they are implemented. This article intends to present and discuss the experience obtained during a three-year European funded Project, namely Think4Jobs. This project exemplifies the significance and benefits of UBC in the design, development and delivery of curricula that meet the evolving demands of the labour market while promoting Critical Thinking (CT) as a foundational 21st century skill to contribute to graduates' employability. Think4Jobs project brought together a multidisciplinary team of researchers and business organisations from five European countries (Germany, Greece, Lithuania, Portugal and Romania) with interests in promoting and developing CT and mitigating eventual competence gaps. The project's success was attributed to key practices, including defining a common conceptualization of CT, employing participatory co-design, and providing common training for university and business partners. Clear objectives, explicit roles, effective communication, and ongoing evaluation further enhanced the collaboration. Experiential learning, real-work problems, and case studies reinforced the curricula, bridging the gap between academia and the labour market. By embracing these insights, future UBC initiatives can empower graduates with the necessary skills to stand out in an ever-changing labour market, contributing to enhanced education and successful careers.

With the rise of internet technology and the growth of mobile communications, the global economy has undergone a significant transformation, shifting towards digital and soft markets. As a result, great efforts have been made to establish virtual marketplaces that provide customers with an exceptional shopping experience. One crucial aspect of this new paradigm is marketing, which has led to the emergence of a new stage known as E-marketing. In E-marketing, understanding customer behavior is of utmost importance. Marketing teams and companies are eager to gain insights into how different customer segments react to their products. Marketing strategies are now developed based on customer information such as financial status, location, age, gender, and more. As a result, the abundance of customer data generated by these new commerce technologies has surpassed the capabilities of traditional data analysis methods. To extract valuable knowledge from this vast amount of data, data mining technologies have come into play. Techniques such as clustering, classification, and prediction are utilized to mine marketing data and uncover hidden patterns and trends. The impact of data mining on E-marketing has been the subject of analysis in this study. A questionnaire-based review was conducted to collect the necessary data, which was then analyzed using SPSS 23 software. The study's results demonstrate that data mining can significantly enhance the performance of E-marketing by providing intelligent and efficient predictions of customer behaviour. Moreover, this technology offers similar advantages for end-customers as well. By leveraging the power of data mining, companies can gain valuable insights, tailor their marketing efforts, and ultimately provide better products and services to their customers in the digital marketplace.

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomato and plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and K-means clustering method was used to adjust more appropriate anchor size than manual setting to improve detection accuracy. A variety of data augmentation techniques were used to train the network. The test results showed that compared with the traditional Faster R-CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of precise targeting pesticide application system and automatic picking device.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

The Golgi apparatus (GA) dysfunctions in Parkinson’s Disease (PD), neurodevelopmental disorders (NDDs), cancer, and organelle structural biology (OSB) can provide insights into therapeutic targets, gene therapy, and drug design. Primary defects and fragmentation within the GA are implicated in a wide range of neurodegenerative diseases. GA defects typically result in mislocation of proteins, accumulation of undegraded proteins, and impaired glycosylation of proteins. Inhibition of vesicular trafficking by α-synuclein (aSyn) may affect the dopamine-producing neurons and neuromodulators. GA regulates apoptosis during pathological mechanisms of neurological diseases and could provide new avenues in treatments through translation research. PD patients bearing the hereditary E46K disease mutation manifest the clinical picture of parkinsonism. How do we provide high resolution nanoimages of the GA during disease to capture dysfunction? Could we visualize the aSyn traffic jam between vesicles in the organelles ER and GA? OSB is emerging as a field as more technology advances and is more accessible. Structural studies of the GA will advance the field of neurological disease forward with an in depth understanding of dysfunction, fragmentation, and defects. Discoveries of the GA in PD, NDDs, and cancer would break new ground and provide translational medicine data of these diseases. Future research could be visualizing high angle annular dark field-STEM (HAADF-STEM) tomograms, cryogenic electron tomography (cryo-ET), multiplex correlative light and electron microscopy (cryo-CLEM), nanobody-assisted tissue immunostaining for volumetric EM (NATIVE) and using soft X-ray tomography (SXT) and computational reconstruction of the GA.

Scaffold guided breast tissue regeneration (SGBTR) can both transform reconstructive and cosmetic breast surgery. Implant-based surgery is the most common method. However, there are inherent limitations as there is replacement of tissue rather than regeneration. Regenerating autologous soft tissue has the potential to provide a more like-for-like reconstruction with minimal morbidity. Our SGBTR approach regenerates soft tissue by implanting additively manufactured bioresorbable scaffolds filled with autologous fat graft. A pre-clinical large animal study was conducted implanting 100 ml breast scaffolds (n=55) made from medical-grade polycaprolactone in 11 minipigs for 12 months. Various treatment groups were investigated where immediate or delayed autologous fat graft, as well as platelet rich plasma were added to scaffolds. Computed Tomography and Magnetic Resonance Imaging was performed on explanted scaffolds to determine volume and distribution of regenerated tissue. Histological analysis was performed to confirm tissue type. At 12 months we were able to regenerate and sustain a mean soft tissue volume of 60.9 4.5 ml (95% CI) across all treatment groups. There was no evidence of capsule formation. There were no immediate or long-term post-operative complications. In conclusion, we were able to regenerate clinically relevant soft tissue volumes utilizing SGBTR in a pre-clinical large animal model.

One of the frequently used classes of the sparse reconstruction algorithms is based on the iterative shrinkage/thresholding procedure, where the thresholding parameter controls a trade-off between the algorithm accuracy and the execution time. In order to avoid this trade-off, we propose using a fast intersection of confidence interval method in order to adaptively control the threshold value through the reconstruction algorithm iterations. We have upgraded the two-step iterative shrinkage thresholding algorithm with a such procedure, improving its performance. The proposed algorithm, denoted as the FICI-TwIST, along with a few selected state-of-the-art sparse reconstruction algorithms have been tested on the classical problem of image recovery by emphasizing the image sparsity in the discrete cosine and the discrete wavelet domain. Furthermore, we have derived a single wavelet transformation matrix which avoids wrapping effects achieving significantly faster execution times when compared to more traditional function based transformation. The obtained results indicate competitive performance of the proposed algorithm, even in cases where all algorithm parameters have been individually fine-tuned for best performances.

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

There exists an implicit potential limitation in every physical discoveries that has been implemented and understood. However, the limitations can be bounded within a safe limit to prevent any constructing theory to be free from errors. As, it’s the inert nature of the humans, to go far beyond the scope of experimental findings in order to pursue any studies with the sole help of logical reasoning and mathematics, the argument can be prevailed in the form of WEAK Clampdown Effect & STRONG Clampdown Effect. More, the theories are constructed out of physical nature, more the theory gets hypothetical without any finding evidence, but that does or doesn’t actually justify the phenomenon, that too with the more increment of KARDASHEV Scale, more moderate ways of experimentation got developed curbing down the limitations within the human limit of ‘ERRORS’, that does can be neglected by approximation. Relationship being cross-judgmental on the basis of the computational limits and calculation accuracy, leading to a soft singularity, as a warning, that if computer powers cannot be checked on the basis of error approximations, then this may lead to the hitting of a hard singularity, that in phase with the forbidden gap (or after the optimum limit that arises at the core constraints of nature) to prevent the computation being carried off with respect to super-intelligence machines that are cognitive capability oriented future computers responsible for self growth & reproduction with more improvement algorithm, restricting all forms of humanity & constraints the human growth by virtue of limiting capacities of the humans as compared to computers.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

Despite a global push in the development and implementation of widespread alternative energy use, significant disparities exist across given nation-states. These disparities reflect both technical and economic factors, as well as the social, political, and ecological gaps between how communities see energy development and national/global policy goals. Known as the “local-national gap,” many nations struggle with fostering meaningful conversations about the role of alternative energy technologies within communities. Mitigation of this problem first requires understanding the distribution of existing alternative energy technologies at the local level of policymaking. Using the State of Georgia, U.S.A. as a case study, we present a model for analyzing how existing adoption trends enable/limit conversation at the scale of local governance (i.e., county governments). Leveraging existing work on the Gini Coefficient as a metric for measuring energy inequity, we argue these tools can be applied to analyze where gaps exist in ongoing solar adoption trends. As we demonstrate, communities that adopt solar tend to be concentrated in a few counties, indicating existing conversations are limited to a circumscribed set of social networks. This information and the model we demonstrate can enable focused qualitative analyses of existing solar trends, not only amongst high-adoption areas but within communities where little to no adoption has occurred.

We report a small footprint cost-effective isothermal rapid DNA amplification system, with integrated microfluidics for automated sample analysis and detection of SARS-CoV-2 in human and environmental samples. Our system measures low-level fluorescent signal in real-time during amplification, while maintaining the desired assay temperature, on a low power, portable system footprint. A unique soft microfluidic chip design was implemented to mitigate thermocapillary effects and facilitate optical alignment for automated image capture and signal analysis. The system-on-board prototype coupled with the LAMP primers designed by BioCoS, is sensitive enough to detect viral loads of SARS-CoV-2 in a broad concentration range corresponding to a threshold cycle range of 16 to 39. Clinical specimens were tested at Kingston Health Sciences using a clinically validated PCR assay and variants were determined using whole genome sequencing.

The necessity to reduce the effect of the greenhouse phenomenon imposed the turn of the food packaging technology to the use of biobased raw materials decreasing the carbon dioxide fingerprint. In this direction alginates derived from brown algae species are of the most promising biobased biopolymers, used for development of edible active coatings, capable to protect food from oxidation/bacterial spoilage. In this study sodium alginate, which was plasticized with glycerol and mixed with a biobased thymol/natural halloysite nanohybrid, was used to develop novel edible active coatings. Active nanocomposite coatings developed by mixing of pure halloysite with sodium alginate/glycerol matrix were produced for comparison purposes. X-Ray and Fourier Transform Infrared Spectroscopy indicated higher compatibility of thymol/halloysite nanohybrid with sodium alginate/glycerol matrix compared to the relevant of pure halloysite. This increased compatibility led to higher tensile properties, water/oxygen barrier properties, and total antioxidant activity. All coating samples were applied as edible active coatings for “tsalafouti” spreadable soft cheese, exhibited a reduction of mesophilic microbial population of more than one log10(cfu/g) unit comparing with uncoated cheese. Moreover, the reduction of mesophilic microbial population was increased by increasing halloysite and thymol content which indicates the sodium alginate/glycerol/thymol/halloysite as promising edible active coatings for “tsalafouti”.

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Electrically conductive adhesives (ECA’s) are used widely to replace or reinforce lead soldering or conductive metal components in electronic packaging applications such as die attachment, solderless interconnections, component repair, display interconnections, and heat dissipation. Their conductive behavior as well as the associated behaviors such as heat conduction and mechanical behavior (strength, rigidity, deformation and viscoelastic behavior, which may be affected by moisture ingression) are affected by adhesive film thickness, volume fraction, size and shape of the conductive filler, as well as uncured base adhesive viscosity, substrate and filler surface treatment and the applied pressure (during bonding and during conduction). The adhesive resistivity decreases precipitously above a characteristic filler volume fraction called the percolation threshold. In general, micron-sized metal fillers mixed in an adhesive (often an epoxy) resulting with different film thicknesses exhibit thickness thresholds for transition from three-dimensional conductivity to two-dimensional conductivity with considerable increases in thickness-direction (z-axis) resistivity when the film thicknesses are smaller than these threshold values. Recently, the use of conductive nanoparticles allowed decreases in percolation threshold levels as well as increases in mechanical strength and durability of ECA’s. Most ECA’s are supplied in liquid or paste form in varying viscosities and therefore, the method of their application also affects their performance. This work intends to provide an understanding of these effects on conduction behavior in (usually high-priced) equipment in which they are used.

Protein immobilization for the functionalization of particles is used in various applications, including biosensors, lateral-flow immunoassays (LFIA), bead-based assays, and others. Common methods for the quantification of bound protein are measuring protein in the supernatant before and after coating and calculating the difference. This popular approach has the potential for a significant overestimation of the amount of immobilized protein since layers not directly bound to the surface (soft protein corona) are usually lost during washing and handling. Only the layer directly bound to the surface (hard corona) can be used in subsequent assays. A simplified amino acid analysis method based on acidic hydrolysis and RP-HPLC-FLD of tyrosine and phenylalanine (aromatic amino acid analysis, AAAA) is proposed to directly quantify protein bound to the surface of gold nano- and latex microparticles. The results are compared with indirect methods such as colorimetric protein assays, such as Bradford, bicinchoninic acid (BCA), as well as AAAA of the supernatant. For both particle types, these indirect quantification techniques show a protein overestimation of up to 1700% compared to the direct AAAA measurements. In addition, protein coating on latex particles was performed both passively through adsorption and covalently through EDC/sulfo-NHS chemistry. Our results showed no difference between the immobilization methodologies. This finding suggests that usual protein determination methods are no unambiguous proof of a covalent conjugation on particles or beads.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation