Polytechnic University of Catalonia

In order to keep pace with the speed of the new era, modified patterns from the past can help generate faster and more reliable solutions, along with new ideas in the urban design process. Meanwhile, the urban traditional forms serve as a valuable knowledge source, offering guidance for contemporary designers. Iran's traditional bazaars represent urban complexes with a history dating back to the formation of cities. Endowed with both physical and functional flexibility, these bazaars have grown and evolved, enduring long lives to reach the present age. This article delves into the reasons behind the adaptation ability of these historical urban complexes employing the morphological method. The analysis focuses on the bazaars’ structure, anatomy, and diversity. Consequently, the established physical patterns of Iran's traditional bazaars unveil the key to their remarkable resilience against various physical changes over time. The results derived from this research can help better preserve these historical phenomena as they face the threat of isolation every day. Additionally, lessons from these adaptable patterns hold relevance not only for the bazaar's future but also for urban design within the Iranian context and beyond.

The NRAS-mutant subset of melanoma is one of the most aggressive and lethal types associated with poor overall survival. Unfortunately, a low understanding of the NRAS-mutant dynamic behavior has lead...

Traumatic brain injuries (TBIs) pose a significant health concern among the elderly population, influenced by age-related physiological changes and the prevalence of neurodegenerative diseases. Understanding the biomechanical dimensions of TBIs in this demographic is vital for developing effective preventive strategies and optimizing clinical management. This comprehensive review explores the intricate biomechanics of TBIs in the elderly, integrating medical and aging studies, experimental biomechanics of head tissues, and numerical simulations. Research reveals that brain atrophy in normal aging occurs globally at annual rates of -0.2% to -0.5%, with distinct regional variations, while neurodegenerative diseases such as Alzheimer's, Parkinson's, and multiple sclerosis exhibit higher rates. These differences underscore the importance of considering varying brain atrophy rates in the context of TBIs among the elderly. Experimental studies demonstrate age-related changes in the mechanical properties of critical head tissues, increasing vulnerability to head injuries. Numerical simulations provide insights into the biomechanical response of the aging brain to traumatic events, aiding in injury prediction and preventive strategy development tailored to the elderly. Integrating biomechanical principles into clinical practice shows promise for optimizing preventive healthcare and improving outcomes for elderly individuals affected by TBIs and neurodegenerative conditions. Future studies should refine biomechanical models and simulations to better represent aging complexities, conduct longitudinal studies on biomechanical changes in elderly populations, and leverage imaging and computational advancements for more accurate head injury assessments.

Position calibration in the deep sea is typically done by means of acoustic multilateration using three or more acoustic emitters installed at known positions. Rather than using hydrophones as receivers that are exposed to the ambient pressure, the sound signals can be coupled to piezo ceramics glued to the inside of existing containers for electronics or measuring instruments of a deep sea infrastructure. The ANTARES neutrino telescope operated from 2006 until 2022 in the Mediterranean Sea at a depth exceeding 2000 m. It comprised nearly 900 glass spheres with 432 mm diameter and 15 mm thickness, equipped with photomultiplier tubes to detect Cherenkov light from tracks of charged elementary particles. In an experimental setup within ANTARES, piezo sensors have been glued to the inside of such – otherwise empty – glass spheres. These sensors recorded signals from acoustic emitters with frequencies from 46545 to 60235 Hz. Two waves propagating through the glass sphere are found as a result of the excitation by the waves in the water. These can be qualitatively associated with symmetric and asymmetric Lamb-like waves of zeroth order: a fast (early) one with $\boldsymbol{v_e \approx 5\,{\textbf {mm}}/\mu \text {s}}$ and a slow (late) one with $\boldsymbol{v_\ell \approx \,2\,{\textbf {mm}}/\mu \text {s}}$. Taking these findings into account improves the accuracy of the position calibration. The results can be transferred to the KM3NeT neutrino telescope, currently under construction at multiple sites in the Mediterranean Sea, for which the concept of piezo sensors glued to the inside of glass spheres has been adapted for monitoring the positions of the photomultiplier tubes.

The sustainable synthesis of urea from ammonia (NH3) and carbon dioxide (CO2) using ultraporous permanently polarized hydroxyapatite (upp‐HAp) as catalyst has been explored as an advantageous CO2‐revalorization strategy. As the simultaneous activation of N2 and CO2 (single‐step) demands an increase of the reaction conditions, we have re‐visited the industrial two‐step Bazarov reaction. upp‐HAp has been designed as a stable multifunctional catalyst capable of promoting both CO2 and NH3 adsorption for their subsequent C−N bond formation. Herein we report the synthesis of 1 mmol/gcat of urea with a selectivity of 97 % under strictly mild conditions (95–120 °C and 1 bar of CO2; without applying any electrical currents or UV irradiation) which represents an efficiency of ~2 % and ~30 % with respect to the NH3 and CO2 content, respectively. The study of the NH3 content, products adsorbed in the catalyst, presence of intermediates and temperature of the reaction allows unveiling the great potential of upp‐HAp as a green catalyst for sustainable Bazarov reactions. Results suggest that the double‐step approach could be more advantageous for both synthesizing urea and as a CO2‐revalorization strategy, which in turn promotes the development of specific technologies for the independent synthesis of green NH3.

The gig economy has been explored recently in the media through videos, films, and series. Similarly, different video games have shown the ideology, values, and mechanisms that govern the gig economy. This article applies six mechanisms of algorithmic control at work to achieve a dual objective: to analyze how platform workers experience algorithmic control and to examine the extent to which video games, as a medium for raising critical awareness, reflect these workers’ experiences. We analyzed interviews with 42 platform workers in different sectors and six video games that address this topic. Our findings reveal that these games consistently mirror the dynamics and experiences arising from platform control. Furthermore, through specific elements of their meaningfulness (such as narrative, rules systems, and mechanics, among others), video games simulate situations and processes, sometimes opaque to workers, portraying the reality of the gig economy more explicitly and transparently.

We present a semi-automated image processing method, the continuous maximum gradient (CMG) method, for identifying the air–water interface in side-view digital images of unidirectional water waves in a glass-walled laboratory wave flume. In a manner similar to Canny edge detection, CMG exploits gradients in pixel intensity to identify the free surface, but also enforces an additional streamline constraint. This latter step is necessary to exclude signals from other features, such as wave gauges and water droplets on the glass, which also exhibit large intensity gradients. To demonstrate the performance and accuracy of CMG, we first compare its detection results with independent wave gauge measurements. The maximum difference in total spectral variance was found to be approximately 4%, while quantitative error metrics from a regression analysis yielded an $R^{2}$ value of 0.997 for the surface elevation time-series. We also compare the CMG detection results with imagery data from existing literature where excellent visual agreement is observed, confirming the broad applicability of the CMG method. The employment of CMG facilitates free surface measurements at a very high resolution (order of millimeters) which is essential for capturing the spatio-temporal wave-field evolution and obtaining instantaneous measurement of local wave shape.

In this paper, we present an algorithm that addresses the challenge of dividing a workspace among multiple UAVs. The workspace can be any convex or non-convex polygon and may contain holes of various shapes that represent no-fly zones. The UAVs can be heterogeneous, with different levels of autonomy, speed, and range. The goal of the workspace division is to obtain areas whose sizes are best matched to the capabilities of the UAVs while maximizing compactness. The algorithm decomposes the polygon representing the workspace into a triangular grid, followed by an iterative process of accumulating adjacent triangles while maximizing the compactness of the resulting regions. The performance of the algorithm and the quality of the partitions generated by the algorithm are compared to existing methods. Results show that this approach outperforms others in several metrics, achieving a 5% to 10% improvement in compactness, while maintaining reasonable performance, with a time overhead of up to approximately two seconds when splitting a polygon into ten parts.

With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system. Here, we propose a novel hybrid methodology for wind speed and solar irradiance forecasting. The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. By integrating a non-linear convergence parameter and the dimension learning hunting approach, the performance of WOA can be drastically enhanced, leading to premature convergence, enhanced population variety, and effective global search. The final prediction outcome is obtained by summing the individual reconstructed sub-series prediction outcomes. To evaluate its efficacy, the proposed model is compared to five well-established models. The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and stability, thus confirming that a hybrid forecasting model approach combining an efficient decomposition method with a simplified but efficient parameter-optimized neural network can enhance its accuracy and stability.

This study explores the characterization and application of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) synthesized from organic residues, specifically milk and molasses. Six PHBV samples with varying 3-hydroxyvalerate (3HV) content (7%, 15%, and 32%) were analyzed to assess how 3HV composition influences their properties. Comprehensive characterization techniques, including NMR, FTIR, XRD, DSC, TGA, and tensile-stress test, were used to evaluate the molecular structure, thermal properties, crystalline structure, and mechanical behavior. Selected PHBV samples were fabricated into nanofibrous scaffolds via electrospinning, with uniform fibers successfully produced after parameter optimization. The electrospun scaffolds were further analyzed using DSC, GPC, and SEM. Biological evaluations, including cytotoxicity, in vitro drug release, and antibacterial activity tests, were also conducted. The results indicate that the electrospun PHBV scaffolds are biocompatible and exhibit promising properties for biomedical applications such as tissue engineering and drug delivery. This study demonstrates the potential of using organic residues to produce high-value biopolymers with tailored properties for specific applications.

Genetically modified maize tolerant to broad-spectrum herbicides may greatly alter weed flora composition, abundance and therefore affect organisms of higher trophic levels, including herbivore and detritivore arthropods and their natural enemies. This three-year study measured the effects on arthropods of an intensive use of broad-spectrum herbicides in comparison with one application of conventional pre-emergence herbicide. Numbers of arthropods were measured by three techniques: visual counts on plants, catches in pitfall and yellow sticky traps. Weed density was much higher in conventional treatment in the first year, showed significant difference in the second year, but was no significant difference in the third year. Counts of arthropod taxa were significantly different only in the first year in the two kinds of weed management systems. In visual counts Cicadellidae and Aphididae among herbivores, the two main generalist predators, Orius spp. and Araneae, and the family Coccinellidae were more abundant on plants treated twice with glyphosate. In pitfall there were higher records in glyphosate-treated plots for Myriapoda but the opposite was seen for Carabidae counts. The yellow sticky traps catches were higher in the glyphosate-treated plots for Cicadellidae and Mymaridae, and lower for Thysanoptera. Most of the significant differences found between herbicide regimes disappeared when abundances of weeds (monocotyledons and dicotyledons) were introduced into the analysis as covariates; this finding signals weed abundance as the main cause of arthropod abundance alteration. However, only a drastic alteration of weed abundance causes significant changes in arthropod densities.

The Gulf of Guayaquil (GG) is the most important tropical estuarine system of the eastern coast of South America, receiving an average water flow of about 1 650 m³ s⁻¹ from a river basin of approximately 33 700 km². The city of Guayaquil surrounds the inner coastal lagoon of the Estero Salado (ES) that empties into the GG. This coastal lagoon is of high social, food production, and environmental importance for the city and the GG. However, there is limited high quality data on metal pollution in this zone, no recent information on Hg, and the extent to which sediment metal pollution extends into the GG is presently unknown. As, Cd, Pb, and Hg were analysed in surface sediments from the urban zone and gave average concentrations of 32.3, 2.08, 41.9, and 0.12 mg kg⁻¹ (dry weight), respectively. Additionally, data were obtained for the first time for the El Morro Channel, south of the ES in the GG, which is expected to be a relatively pristine zone; average As, Cd, Pb and Hg concentrations were 6.6, 0.22, 7.9 and 0.02 mg kg⁻¹ (dry weight), well below concentrations seen in the urban ES zone. Estimates of the geo-accumulation index for metal pollution, using the El Morro data as background values, were 1.7 (As), 2.7 (Cd), 1.8 (Pb) and 2.0 (Hg), making the ES class II and a moderately polluted estuary for As, Hg and Pb, but class III and “moderately to heavily polluted” for Cd. If the lowest concentrations of the EM samples are taken the ES is class III for As, IV for Hg and Pb, and V for Cd; id est, the ES would classify as a heavily to extremely polluted estuary regarding these metals. These data show the metal concentrations increase significantly as the main conurbation of Guayaquil is approached from offshore, indicating a strong anthropogenic source of metals from the city, with anticipated negative environmental impacts.

Breathing rate is a crucial vital sign for evaluating well-being and identifying underlying diseases related to the respiration system. This paper presents a fully stretchable triangular loop antenna-based sensor for real-time respiration monitoring. This antenna is integrated into a commercially available T-shirt, which is made of stretchable conductive ink printed on a Thermoplastic polyurethane (TPU) substrate. The sensing mechanism is influenced by shifts in the resonance frequency of a triangular loop antenna sensor, which occur due to thoracic and abdominal deformation during the breathing cycle of inspiration and expiration. The proposed system captures breathing patterns by detecting shifts in the resonance frequency, which are continuously recorded in real-time via Matlab. The Vector Network Analyzer (VNA) was connected to a remote PC via a LAN interface to store the breathing data on a PC host, facilitating data transfer over TCP/IP through the same LAN interface. The proposed antenna-based sensor stands out for its lightweight structure making it convenient to embed in clothing, and its ability to provide continuous monitoring of different breathing patterns including Eupnea, Bradypnea, Tashypnea, Ataxic, Sighing, and Biot’s breathing. The proposed system was tested through experimental measurements, and the obtained results matched well with the standard breathing patterns provided by the World Health Organization.

BACKGROUND With growing concerns over the adverse effects of animal‐derived products on health, animal welfare and the environment, the rising popularity of plant‐based foods underscores the importance of understanding consumer preferences and determining acceptance. The present study takes the form of a case study that utilized innovative legume‐based flours to develop multiple gnocchi products. The Becker–DeGroot–Marschak (BDM) mechanism as an auction method was employed to elicit consumers’ willingness to pay (WTP) following a hedonic evaluation test involving 127 Spanish consumers. RESULTS The findings indicate that a majority of consumers exhibit a high level of environmental concern, coupled with increased trust in, as well as perceptions of, the benefits of consuming plant‐based products. However, they demonstrated moderate attitudes with regard to plant‐based products. Notably, product sample tasting had a negative impact on consumers’ WTP for legume‐based gnocchi. The respondents' education level, income, financial situation, government support, environmental concerns, perceived risks, flavor and color significantly influenced consumers’ WTP. CONCLUSION The present study offers initial insights into consumer attitudes and WTP for legume‐based products in Spain. The findings are of relevance for producers and marketers aiming to promote environmentally‐sustainable food production and consumption. They may also play a pivotal role in facilitating the successful introduction and sale of such plant‐based products in the Spanish market. Going forward, addressing any limitations of this study and exploring further research avenues will be crucial for refining our understanding of consumer behavior in this context. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques. This study introduces a novel methodology for partially synthetic tabular data generation, designed to reduce the reliance on sensor measurements and ensure secure data exchange. Using the UMAP (Uniform Manifold Approximation and Projection) visualization algorithm to transform the original, high-dimensional reference data set into a reduced-dimensional space, we generate and validate synthetic values for incomplete data sets. This approach mitigates the need for extensive sensor readings while addressing data privacy concerns by generating realistic synthetic samples. The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. Furthermore, our results demonstrate superior performance in comparison to state-of-the-art imputation techniques. This work makes a dual contribution by not only proposing an innovative method for synthetic data generation, but also studying and establishing a formal framework to understand and solve synthetic data generation and imputation problems in sensor-driven environments.