In this work, we report the magnetocaloric effect (MCE) in a quantum dot corresponding to an electron interacting with an antidot, under the effect of an Aharonov-Bohm flux subjected to a parabolic confinement potential. We use the Bogachek and Landman model, which additionally allows the study of quantum dots with Fock-Darwin energy levels for vanishing antidot radius and flux. We find that the Aharonov-Bohm flux (AB-flux) strongly controls the oscillatory behaviour of the MCE, thus acting as a control parameter for the cooling or heating of the magnetocaloric effect. We propose a way to detect AB-flux by measuring temperature differences.

This present work explores the performance of a thermal-magnetic engine of Otto type, considering as a working substance an effective interacting spin model corresponding to the q− state clock model. We obtain all the thermodynamic quantities for the q = 2, 4, 6, 8 cases in a small lattice size (3×3 with free boundary conditions) by using the exact partition function calculated from the energies of all the accessible microstates of the system. The extension to bigger lattices was performed using the mean-field approximation. Our results indicate that the total work extraction of the cycle is highest for the q=4 case, while the performance for the Ising model (q=2) is the lowest of all cases studied. These results are strongly linked with the phase diagram of the working substance and the location of the cycle in the different magnetic phases present, where we find that the transition from a ferromagnetic to a paramagnetic phase extracts more work than one of the Berezinskii–Kosterlitz–Thouless to paramagnetic type. Additionally, as the size of the lattice increases, the extraction work is lower than smaller lattices for all values of q presented in this study.

In this work, we report the magnetocaloric effect (MCE) in two systems of non-interactive particles, the first corresponds to the Landau problem case and the second, the case of an electron in a quantum dot subjected to a parabolic confinement potential. In the first scenario, we realize that the effect is totally different from what happens when the degeneration of a single electron confined in a magnetic field is not taken into account. In particular, when the degeneracy of the system is negligible, the magnetocaloric effect cools the system, while in the other case, when the degeneracy is strong, the system heats up. For the second case, we study the competition between the characteristic frequency of the potential trap and the cyclotron frequency to find the optimal region that maximizes the ΔT of the magnetocaloric effect, and due to the strong degeneration of this problem, the results are in coherence with those obtained for the Landau problem. Finally, we consider the case of a transition from a normal MCE to an inverse one and back to normal as a function of temperature. This is due to the competition between the diamagnetic and paramagnetic response when the electron spin in the formulation is included.

In this paper, we revisit the q-states clock model for small systems. We present results for the thermodynamics of the q-states clock model from $q=2$ to $q=20$ for small square lattices $L \times L$, with L ranging from $L=3$ to $L=64$ with free-boundary conditions. Energy, specific heat, entropy and magnetization are measured. We found that the Berezinskii-Kosterlitz-Thouless (BKT)-like transition appears for $q>5$ regardless of lattice size, while the transition at $q=5$ is lost for $L<10$; for $q\leq 4$ the BKT transition is never present. We report the phase diagram in terms of $q$ showing the transition from the ferromagnetic (FM) to the paramagnetic (PM) phases at a critical temperature T$_1$ for small systems which turns into a transition from the FM to the BKT phase for larger systems, while a second phase transition between the BKT and the PM phases occurs at T$_2$. We also show that the magnetic phases are well characterized by the two dimensional (2D) distribution of the magnetization values. We make use of this opportunity to do an information theory analysis of the time series obtained from the Monte Carlo simulations. In particular, we calculate the phenomenological mutability and diversity functions. Diversity characterizes the phase transitions, but the phases are less detectable as $q$ increases. Free boundary conditions are used to better mimic the reality of small systems (far from any thermodynamic limit). The role of size is discussed.

We study the performance of a classical and quantum magnetic Otto cycle with a quantum dot as a working substance using the Fock-Darwin model with the inclusion of the Zeeman interaction. Modulating an external/perpendicular magnetic field, we found in the classical approach an oscillating behavior in the total work that is not perceptible under the quantum formulation. Also, we compare the work and efficiency of this system for different regions of the Entropy, $S(T,B)$, diagram where we found that the quantum version of this engine always shows a reduced performance in comparison to his classical counterpart.

Beyond the usual ferromagnetic and paramagnetic phases present in spin systems, the usual q-state clock model, presents an intermediate vortex state when the number of possible orientations q for the system is equal to 5 or larger. Such vortex states give rise to the Berezinskii-Kosterlitz-Thouless (BKT) phase present up to the XY model in the limit q→∞. Based on information theory, we present here an analysis of the classical order parameters plus new short-range parameters defined here. Thus, we show that even using the first nearest neighbors spin-spin correlations only, it is possible to distinguish the two transitions presented by this system for q greater than or equal to 5. Moreover, the appearance at relatively low temperature and disappearance of the BKT phase at a rather fix higher temperature is univocally determined by the short-range interactions recognized by the information content of classical and new parameters.

Objective: To investigate fundamental movement skills (FMS) and physical activity differences among Hispanic and non-Hispanic White elementary school children. Materials and Methods: This study included 194 children aged between 4 and 11 in kindergarten, 2nd, and 5th grades. For evaluable participants, t-tests were used to test potential differences in fitness or movement skills. The characteristics of Hispanic and non-Hispanic White were compared using the p-value to detect statistical differences in anthropometric, FMS, and fitness characteristics. To identify any noticeable differences, the means and standard deviations of each group were compared. Results: There were significant group differences across the participants in age, height, Body Mass Index (BMI), BMI percentile, arm circumference, and waist circumference. In addition, there were significant differences between Hispanic and Non-Hispanic White in catch, run, dodge, punt, and two-hand strike (all p’s <0.05). Lastly, there were significant differences between Hispanic and Non-Hispanic White in pull-up, push-up, and sit-up (all p’s <0.05). Conclusion: Future studies should focus on decreasing obesity, increasing fitness, and FMS in elementary school children. Particularly, with Hispanic children whose FMS is negatively affected at such a young age, leading to eventual implications for their physical fitness and health in the future.

Digital twins are an emerging technology that could be harnessed for the digitalization of the industry. Steel industry systems contain a large number of electro-hydraulic components, as proportional valves. An input-output model for a novel water proportional cartridge valve is derived from physical modelling based on fluid mechanics, dynamics, and electrical principles. The valve is a two-stage valve with two 2/2-way water proportional valves as the pilot stage and a marginally stable poppet-type cartridge valve as the main valve. The orifice equation, which is based on Bernoulli principles, is approximated by a polynomial which makes parameter estimation easier and makes modelling possible without measuring pressure of the varying control volume situated in the pilot stage part of the valve. Data were collected when the valve was operating as a component in a closed-loop system as part of a press-mill machine in a steel manufacturing plant. Parameter estimation was made by convex optimization with physical constraints to overcome problems caused by poor system excitation. For comparison, a simple linear model was derived and the least squares method used for parameter estimation. A thorough estimation of the parameters relative errors is presented. The model contains 5 parameters related to the design parameters of the valve. The modelled position output is in good agreement with experimental data for training and test data. The model can be used for real time monitoring of the valve’s status by the model parameters. One of the model parameters varies linearly with production cycles. Thus, aging of the valve can be monitored.

As the global population grows, the demand for food increases and puts a strain on food production systems and agricultural productivity, causing soil degradation. Soil Quality Indexes (SQIs) have been developed to maintain and improve soil quality. However, due to the variety of soils and SQIs, analyzing and comparing results has been historically difficult. Therefore, in this study, we carried out a systematic review with meta-analysis focused on soil quality studies of agricultural soils under intensive agriculture using the unified weighted additive SQI methodology (SQIU). We analyzed 65 quality observations obtained from 22 studies. Chemical indicators were the most prevalent in the SQIUs, followed by physical and biological indicators. Conventional soil management had negative effects on soil quality (–7.55%). From the factors analyzed, the minimum database had a significant effect on the soil quality results, but not the number of indicators that made up the SQIU. The SQIU made up of chemical-biological indicators (CB) presented negatively overestimated measurements of soil quality (–32.53%), exaggerating the damage to the analyzed soils. The indicators that correlated most strongly with the size of the effect on agricultural soil quality were the cation exchange capacity (CEC), carbon to nitrogen ratio (C/N), and microbial biomass carbon (MBC). The SQIU is a feasible tool to interpret the quality of agricultural soils around the world, as it makes it possible to obtain a simple and generalized view of soil conditions.

Androgen receptor signaling is crucial for prostate cancer growth and is positively regulated in part by intratumoral CYP3A5. As African American (AA) men often carry the wild type CYP3A5 and express high level of CYP3A5 protein, we blocked the wild type CYP3A5 in AA origin prostate cancer cells and tested its effect on androgen receptor signaling. q-PCR based profiler assay identified several AR regulated genes known to regulate AR nuclear translocation, cell cycle progression and cell growth. CYP3A5 processes several commonly prescribed drugs and many of these are CYP3A5 inducers or inhibitors. In this study, we test the effect of these commonly prescribed CYP3A5 inducers/inhibitors on AR signaling. The results show that the CYP3A5 inducers promoted AR nuclear translocation, downstream signaling and cell growth whereas CYP3A5 inhibitors abrogated them. The observed changes in AR activity is specific to alterations in CYP3A5 activity. Both the inducers tested demonstrated increased cell growth of prostate cancer cells, whereas the inhibitors showed reduced cell growth. Further, characterization and utilization of the observation that CYP3A5 inducers and inhibitors alter AR signaling may provide guidance to physicians prescribing CYP3A5 modulating drugs to treat comorbidities in elderly patients undergoing ADT, particularly AA.

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

Carbon footprint mitigation actions have become a key issue for companies involved in urban freight transport. The electrification of the delivery fleets is one of the crucial sustainable actions carried out. To this end, it is necessary to explore several fleet renewal strategies over a finite planning horizon evaluating different types of electric powertrains for light commercial vehicles. The primary goal of this purpose-built analysis is to assist and boost the fleet managers' decisions when transitioning to alternative-fuelled vans, intending to maximize cost savings and reduce greenhouse gas emissions inventory. The model presented in this work is a Multi-Objective Linear Programming hybrid analysis taking into account the vehicle and energy supply infrastructure costs and the emissions reported in each inventory scope following the Greenhouse Gas Protocol sustainability guidelines. From this perspective, the conducted research offers a novel approximation to fleet management problems enabling a break-down cost analysis and an evaluation of the annual emissions company report. The comprehensive numerical simulations carried out over different study scenarios in Spain demonstrate that the optimization approach does not only show effective fleet renewal strategies but also identifies critical factors that impact the fleet's competitiveness, offering valuable insights for fleet managers and policymakers to meet carbon footprint reductions in a cost-effectively way.

Knowledge of the motivations of managers to practice Corporate Social Responsibility (CSR) is a critical issue for those who promote its adoption. The understanding of these reasons is complicated by the fact that there are different ways of defining this responsibility, raising the question of whether there is any relationship between reasons for adopting it and how to define it. To address this issue, a classification of these reasons was constructed and related to a classification of the different ways of defining CSR. A self-administered questionnaire was applied to a non-probability sample of Social Responsibility managers, which included indicators for both classifications. It was found that these managers present all types of motives, with proactive outweighing reactive motives and that there is a significant degree of correlation between the ways of defining CSR and the reasons for doing so. This suggests that managers present coherence in their objectives (motives) and means (practices of Social Responsibility.

In the last decade, the genetic engineering world has been shaken up by a relatively new genetic editing tool based on RNA-guided Nucleases (RGNs): the CRISPR/Cas9 system. Since the first report in 1987 and its characterization in 2007 as a bacterial defense mechanism, the interest and research on this system have grown exponentially. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system that makes it possible to genetically modify almost any organism. There are diverse approaches regarding the refinement of these modifications, such as constructing more accurate nucleases, understanding the cellular context and facing the epigenetic conditions, or re-designing guide RNAs (gRNAs). Considering the critical importance for the correct CRISPR/Cas9 systems performance, our scope will emphasize in the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting their computational architecture and gRNA characteristics evolution through the years. Our study concisely explains the computational approaches that use machine learning techniques, deep neural networks, and large datasets of gRNA/target interactions to make possible both predictions and classifications directed to design, optimize, and create promising gRNAs suitable for future gene therapies.

Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop evapotranspiration. A scale system to monitor crop evapotranspiration was developed consisting of an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADC). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak.com IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. The results from the experiments showed that the IoT scale system could accurately measure crop evapotranspiration using the weighing lysimeter technique.

The world is facing an energy crisis. Governments are seeking to provide universal energy access and guarantee energy security while trying to mitigate climate change. One possible solution is energy transitions towards low carbon energy systems. Among other things (physical infrastructure, public policy and regulatory enablers and knowledge and capacities) changes in the energy systems require a well informed and participative citizenship. Within this context the concept of energy literacy appears. Energy literacy is the understanding of how energy is generated, transported, stored, distributed and used, awareness about its environmental and social impacts and the knowledge to use it efficiently in the different sectors of the economy. This paper provides a systematic literature review in the Web of Science’s Core Collection. Most of the work done around energy literacy addresses its evaluation among different groups, particularly students at different levels, and the construction, application and evaluation of tools for improving energy literacy. Other frequently studied issues are the influence of energy literacy in decision making, its drivers and conceptual research about the topic. Energy enables citizens to effectively contribute to energy efficiency and sustainable development, nevertheless energy literacy is not strongly correlated to energy consumption habits.

Based on recent contributions regarding the treatment of unsteady hydraulic conditions into the state-of-the-art of scour literature, the theoretically derived probability distribution of bridge scour is introduced. The model is derived assuming a rectangular hydrograph shape with a given duration, and random flood peak following a Gumbel distribution. A model extension for a more complex flood event is also presented, assuming a synthetic exponential hydrograph shape. The mathematical formulation can be extended to any flood-peak probability distribution. The aim of the manuscript is to move forward the current approaches adopted for the bridge design coupling hydrological, hydraulic, and erosional models in a mathematical closed form.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

The demographic history of anatomically modern humans (AMH) involves multiple migration events, population extinctions and genetic adaptations. As genome-wide data from complete genome sequencing becomes increasingly abundant and available even from extinct hominins, new insights of the evolutionary history of our species are discovered. It is currently known that AMH introgressed with archaic hominins once they left the African continent. Current out of African human genomes carry fragments of archaic origin. This review focuses on the fitness consequences of archaic interbreeding in current human populations. We discuss new insights and challenges that researchers face when interpreting the potential impact of introgression on fitness and testing hypotheses about the role of selection within the context of health and disease.

The effect of the addition of maleated polyethylene (MAPE) to compounds of natural rubber (NR) and Teline monspessulana flour (TMF) previously mercerized was investigated. Two factors were analyzed: A. concentration of MAPE with five levels 2; 4; 6; 8; 10 phr (parts per hundred rubber), B. concentration of TMF with two levels 25 and 40 phr. The effect of MAPE on compatibility between NR and HTM was evaluated by tensile testing the compounds. The mixing was performed in a laboratory scale mill. The test tubes were obtained by cutting or die-cutting crosslinked peroxide sheets, these were obtained during the compression molding process. Analysis indicate that the MAPE coupling agent improved the compatibility between HTM and NR, this effect was evidenced by the values of tensile strength and elongation at break. However, the gel content determination indicates that the addition of 10 phr of MAPE crosslinking decreases due to competition with coupling reaction MAPE - HTM.

The objective of this research is to develop an experimental-theoretical analysis about the influence of the cooling medium and the geometry of the welding bead profile in fatigue life and associated parameters with structural integrity of welded joints. A welded joint with cruciform geometry is considered using SMAW, plates in structural steel ASTM A36 HR of 8 mm of thickness and E6013 electrode input. A three-dimensional computational model of the cruciform joint was created using the finite element method. For this model, the surface undulation of the cord and differentiation in the mechanical properties of the fusion zone were considered, the heat-affected zone (HAZ) and base material, respectively. In addition, an initial residual stress field which was established experimentally was considered. The results were a set of analytical expressions for the weld magnification factor Mk. It was found that values for the latter decrease markedly in function of the intensity of the cooling medium used in the post welding cooling phase, mainly due to the effect of the residual compressive stresses. The obtained models of behavior of the weld magnification factor are compared with the results from other researchers with some small differences, mainly due to the inclusion of the cooling effect of the post weld and the variation of the leg of the weld bead. The obtained analytical equations in the present research for Mk can be used in management models of life and structural integrity for this type of welded joint.

Corneal biomechanics is a hot topic in ophthalmology. The biomechanical properties of the cornea (BMPs) have important implications in the management and diagnosis of corneal diseases such as ectasia and keratoconus. In addition, the characterization of BMPs is crucial to model the predictability of a corneal surgery intervention, the outcomes of refractive surgery or follow-up of corneal diseases. The biomechanical behavior of the cornea is governed by viscoelastic properties that allow, among other structural implications, the damping of excess of intraocular pressure and reduce damage to the optic nerve. Currently, the most versatile and complete methods to measure corneal viscoelasticity are based on air-puff corneal applanation. However, those methods lack the ability to directly measure corneal viscosity. The aim of this work is to propose a new methodology based on the analysis of corneal air-puff measurements through the Standard Linear Solid model (SLSM) to provide analytical expressions to separately calculate the elastic and time-dependent (corneal retardation time and viscosity) properties. The results shown the mean values of elasticity (E), viscosity (Ƞ) and corneal retardation time (τ) in a sample of 200 young and healthy subjects. The influence of elasticity and viscosity on viscoelasticity, high-order corneal aberrations and optical transparency is investigated. Finally, the SLSM fed back from experimental E and Ƞ values was employed to compare the creep-relaxation response between normal, an ocular hypertension patient and Ortho-K user.

The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

In this review, we aimed to understand the relationship between SARS-CoV-2 infection and preeclampsia severity in infected pregnant women. Pregnant women with SARS- CoV-2 infection have been shown to have a significantly increased risk of maternal death, ICU admission, preterm delivery, and stillbirth compared with those without infection. The risk of preeclampsia also increases in pregnant women infected with SARS-CoV-2, particularly in those with severe symptoms. We emphasize the im-portance of specialized clinical management to prevent poor pregnancy outcomes in this situation. The association between COVID-19 and preeclampsia (PE) is likely due to multiple mechanisms, including the direct effects of the virus on trophoblast function and the arterial wall, acute atherosis, local inflammation leading to pla-cental ischemia, exaggerated inflammatory responses in pregnant women, SARS-CoV-2-related myocardial injury, cytokine storm, and thrombotic microan-giopathy. Emphasis has been placed on the potential impact of COVID-19 on pregnant women, specifically in relation to thrombotic complications, placental dysfunction, and cardiovascular dysfunction. Undoubtedly, one of the diagnostic tools to differentiate between COVID-19-induced preeclampsia-like syndrome and true preeclampsia is the use of biomarkers, such as the sFlt-1/PlGF ratio. We wish to highlight the potential for COVID-19-induced myocardial injury in pregnant women and the associated in-crease in maternal mortality rate. Vaccination against COVID-19 in the general population and in pregnant women in particular, drastically reduced the severity of the disease. There is an urgent need to continue the follow-up of these women and their children to detect the appearance of sequelae of the disease or peristent COVID 19

The applicability of the distance aggregation problem has attracted the interest of many authors. Motivated by this fact, in this paper we face the modular quasi-(pseudo-)metric aggregation problem. We characterize those functions that allow merging a collection of modular quasi-(pseudo-)metrics into a single one. Specifically, a description of such functions in terms of triangle triplets is given and, in addition, the relationship between modular quasi-(pseudo-)metric aggregation functions and modular (pseudo-)metric aggregation functions is discussed. Such characterizations are illustrated with appropriate examples. A few methods to construct modular quasi-(pseudo-)metrics are yielded. Several properties of modular quasi-(pseudo-)metric aggregation functions are explored and used to develop quick tests for discarding candidate functions to aggregate modular quasi-(pseudo-)metrics. Moreover, a characterization of those modular quasi-(pseudo-)metric aggregation functions that preserve modular quasi-(pseudo-)metrics is also provided. Furthermore, the relationship between modular quasi-(pseudo-)metric aggregation functions and quasi-(pseudo-)metric aggregation functions is studied in such a way that significative differences are displayed.

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet's climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDI). We studied a 40-year period (1976-2015) using annual and seasonal time scales. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican meteorological service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grides of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

This study explores into a comprehensive examination of an optical cavity system that integrates Raman and Yb-doped gain media, with a focus on understanding their interactions. The research implies a characterization of each gain medium within the cavity while subjecting them to diverse co-pumping conditions with the other. When the Raman-lasing cavity is co-pumped by exciting the Yb-doped section, the resulting composite laser exhibits significant threshold reductions and there is an optimal co-pumping regime that enhances energy transfer from pump to Stokes. As for the complementary cavity, where the Yb-doped gain is influenced by the co-pumped Raman gain, at moderate pump powers a light-controlling-light behavior phenomenon arises. Within this regime, the 1064-nm signal suppresses the Yb-generated 1115-nm signal, suggesting potential applications in intracavity optical modulation. For higher pump levels, a cooperative effect emerges whereby both lasers mutually enhance each other. Minor variations in the primary 974-nm pump power, even by just a few milliwatts, result in significant capabilities for switching or modulating the Stokes signal. Under these conditions of mutual enhancement, the hybrid optical system validates notable improvements regarding energy transfer efficiency and threshold reduction. This research provides valuable insights into the intricate dynamics of optical cavity systems and reveals promising avenues for applications in advanced optical modulation technologies.

This study proposes a methodology for identifying time intervals in which time-of-use (ToU) energy tariffs can be set. This is achieved by analyzing the variability in electric power demand and esti-mating the potential for demand flexibility. The proposed methodology is as follows: first, data preprocessing is performed, focusing on the organization and cleaning of individual consumption records. Subsequently, the data are processed and classified by using consumption variability analysis and clustering techniques. Then, representative clusters are selected and time intervals are analyzed to identify periods of high electric power consumption variability. This methodology was tested with real data from smart meters in the Colombian electrical system, in which time intervals with potential for settinh ToU tariffs were ultimately identified. This work promotes the implementation of demand response strategies that enhance sustainability and facilitate the transition to a more resilient energy landscape both nationally and internationally. The findings are expected to contribute to the formulation of tariff schemes that encourage con-scious changes in electric energy consumption patterns.

With the rising prevalence of obesity, Non-Alcoholic Fatty Liver Disease (NAFLD) now affects 20-25% of the global population. NAFLD, a progressive condition associated with oxidative stress, can result in cirrhosis and liver cancer in 10% and 3% of patients suffering NAFLD, re-spectively. Therapeutic options are currently limited, emphasizing the need for novel treatments. In this study, we examined the potential of activating the transcription factor NRF2, a crucial player in combating oxidative stress, as an innovative approach to treating NAFLD. Utilizing a CRISPR-engineered human cell line, we were able to monitor the expression of heme oxygenase-1 (HMOX1), an NRF2 target, using a Nanoluc luciferase tag. Our model was validated using a known NRF2 activator, after which we screened 1200 FDA-approved drugs, unearthing six compounds (Disulfiram, Thiostrepton, Auranofin, Thimerosal, Halofantrine, and Vorinostat) that enhanced NRF2 activity and antioxidant response. These compounds demonstrated protec-tive effects against oxidative stress and lipid accumulation in vitro. Our study underscores the utility of CRISPR tagging with Nanoluc luciferase in identifying potential NRF2 activators, pav-ing the way for potential NAFLD therapeutics.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common one has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

DNA methylation and other epigenetic factors are important in the pathogenesis of late-onset Alzheimer’s disease (LOAD). Methylenetetrahydrofolate reductase (MTHFR) gene mutations occur in most elderly patients with memory loss. MTHFR is critical for production of S-adenosyl-L-methionine (SAM), the principal methyl donor. A common mutation (1364T/T) of the cystathionine-γ-lyase (CTH) gene affects the enzyme that converts cystathionine to cysteine in the trans-sulfuration pathway causing plasma elevation of total homocysteine (tHcy) or hyperhomocysteinemia – a strong and independent risk factor for cognitive loss and AD. Other causes of hyperhomocysteinemia include aging, nutritional factors, and deficiencies of B vitamins. We emphasize the importance of supplementing vitamin B₁₂ (methylcobalamin), vitamin B₉ (folic acid), vitamin B₆ (pyridoxine), and SAM to patients in early stages of LOAD.

Inherited retinal dystrophies (IRDs) are a global problem that is largely unaddressed, especially in Africa. Black indigenous Africans are rarely represented in research that develop genetic tests and genetic therapies for IRDs yet their genomes are more diverse. The aim of this literature review is to synthesize information on the IRD genetic research conducted among indigenous black Africans to identify challenges and opportunities for progress. PubMed was searched to identify empirical publications reporting the genetic analysis of IRDs among indigenous Africans. A total of 10 articles were selected for the review. Based on the information in the articles, the main genetic testing methods in use include next-generation, whole exome, and Sanger sequencing. The main IRDs characterized by the genetic tests include retinitis pigmentosa, Leber Congenital Amaurosis, Stagardt disease, and cone dystrophy. Examples of implicated genes include MERTK, GUCY2D, ABCA4, and KCNV2 for the four IRDs respectively. Research activities on the genetics of IRDs are generally scanty in Africa. Even in South Africa and North Africa where some research activities were noted, only a few indigenous black Africans were in the study cohorts. There is an urgent need for genetic research on IRDs, especially in East, Central, and West Africa.

Excessive extracellular histones can damage cells despite their importance in DNA structure and gene regulation. We previously showed the harmful effect of histone 3.3 (H3.3) and its acetylated form (AcH3.3) in chronic obstructive pulmonary disease (COPD), but further research is needed to understand the in vivo underlying mechanisms. To investigate this, we administered a single dose of recombinant histones via intra-tracheal instillation in C57BL/6 mice and analyzed the outcomes after 48 hours. Both rH3.3 and rAcH3.3 effectively reached the bronchial and alveolar space. Lung histology revealed severe immune cell infiltration, rupture, damage, and thickening of many alveolar walls. Micro-CT scans showed macroscopic structure changes with a decreased tissue volume to total volume ratio. The administration of rH3.3 and rAcH3.3 induced lung apoptotic activity, as indicated by increased levels of cleavage caspase 3 and 9. Moreover, elevated plasma levels of pro-inflammatory cell mediators, including TNF-alpha, IL-6, MCP-3, and CXCL-1, were observed. The pathological changes and pro-inflammatory cytokine response were more pronounced in animals treated with rAcH3.3, which also exhibited leukocytosis and lymphocytosis. These findings emphasize the crucial role of extracellular H3.3 acetylation in inducing cytotoxicity and a robust, acute inflammatory response similar to what occurs in the lungs of COPD patients.

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problem. These algorithms have been compared with the traditional ones algorithms and have demonstrated to be superior in complex problems. This paper attempts to describe the algorithms based on nature, that are used in fuzzy clustering. We briefly describe the optimization methods, the most cited nature-inspired algorithms published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new are of nature and bio-inspired optimization of fuzzy clustering.

Chondroitin is a glycosaminoglycan that has gained widespread use in nutraceuticals and pharmaceuticals, mainly for treating osteoarthritis. Traditionally, it has been extracted from animal cartilage but recently, biotechnological processes have emerged as a commercial alternative to avoid the risk of viral or prion contamination and offer a vegan-friendly source. Typically, these methods involve producing the chondroitin backbone using pathogenic bacteria and then modifying it enzymatically through the action of sulfotransferases. Despite the challenges of expressing active sulfotransferases in bacteria, the use of eukaryotic microorganisms is still limited to a few works using Pichia pastoris. To create a safer and efficient biotechnological platform, we have constructed a biosynthetic pathway for chondroitin production in S. cerevisiae as a proof-of-concept. Up to 125 mg/L and 200 mg/L of intracellular and extracellular chondroitin were produced, respectively. Furthermore, as genome-scale models are valuable tools for identifying novel targets for metabolic engineering, a stoichiometric model of chondroitin-producing S. cerevisiae has been developed and used in optimization algorithms. Our research has yielded several novel targets, such as uridine diphosphate(UDP)-N-acetylglucosamine pyrophosphorylase (QRI1), glucosamine-6-phosphate acetyltransferase (GNA1) or N-acetylglucosamine-phosphate mutase (PCM1) overexpression that might enhance chondroitin production, which will guide future experimental research to develop more efficient host organisms for the biotechnological production process.

Tetranychid outbreaks have been detected since 2016 in southern highbush blueberries (SHB); however, it was not until 2019 when the southern red mite (SRM), Oligonychus ilicis (Acari: Tetranychidae) was confirmed as the pest causing severe bronzing and stunting, in multiple Florida and Georgia commercial blueberry plantings. There is little known about the management of SRM in SHB and only three miticides (fenazaquin, fenpyroximate, and acequinocyl), have recently been registered for use in SHB between 2019 and 2020. Similarly, there is no knowledge regarding the existence of natural enemies of SRM in SHB. This is the first report of naturally occurring predatory mites (Amblyseius sp. and Neoseiulus ilicis) associated with SRM in SHB. Predatory mites were recorded in treated bushes after evaluating the performance of seven miticides used to manage SRM populations including spiromesifen, acequinocyl, sulfur, sulfur + molasses, bifenazate, fenpyroximate, and fenazaquin. Miticide efficacy was rated based on the number of SRM recorded on collected leaves and plant damage ratings using an arbitrary index (from 0= no bronzing to 4= 100% bronzing). Additionally, the presence or absence of predatory mites per sample was recorded. Fenpyroximate used as the standard miticide, significantly reduced mite numbers seven days after application, as well as acequinocyl and fenazaquin. Only plants treated with fenpyroximate or fenazaquin showed significantly less bronzing compared with the control plants. Overall, fenpyroximate and fenazaquin demonstrated the best performance for managing O. ilicis on SHB and safe to naturally occurring predatory mites. Lastly, the level of growers’ awareness regarding SRM was assessed using surveys in 2020 to design adequate educational materials available to the grower community.

A software defined radio (SDR) is a communication system that makes use of components that can be configured through software, in contrast to traditional systems where these components are variable through hardware, these radio devices are much more versatile, this article describes the factors that must be considered when implementing a communication system based on Software Defined Radios (SDR), in order to reduce the attenuation factors and thus obtain the maximum distance for a transmission of data effectively in the UHF band. The calculations made for the first Fresnel zone and for the design of the Ground Plane type antennas used in the transmission/reception stages of the x40 bladeRF platforms are also presented. The tests were carried out at the facilities of the Huarangal Nuclear Center of the Peruvian Institute of Nuclear Energy, obtaining favorable results that allow ratifying the versatility and performance of the SDRs.

PHA synthases (PhaC) are grouped into four classes based on the kinetics and mechanisms of reaction. The grouping of PhaC enzymes into four classes is dependent on substrate specificity, according to the preference in forming short chain length (scl) or medium chain length (mcl) polymers: class I, class III, and class IV produce scl-PHAs depending on propionate, butyrate, valerate and hexanoate precursors, while class II phaC synthesize mcl-PHAs based on the alkane (C6 to C14) precursors. PHA synthases of class I, in particular PhaC_Cs from Chromobacterium USM2 and PhaC_Cn/RePhaC1 from Cupriavidus necator/R. eutropha, have been analysed and the crystal structures of the C-domains have been determined. PhaC_Cn/RePhaC1 was also studied by small angle X-ray scattering (SAXS) analysis. Models have been proposed for dimerization, catalysis mechanism, substrate recognition and affinity, product formation and product egress route. The assays based on amino acid substitution by mutagenesis have been useful to validate the hypothesis on the role of amino acids in catalysis and in accommodation of bulky substrates, for the synthesis of PHB co-polymers and medium chain length-PHA polymers with optimized chemical properties.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired in the visual cortex, are characterized by their hierarchical learning structure, which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

Extensive genome structure variation, such as copy number variations (CNVs) and presence/absence variations, is the basis for the remarkable maize genetic diversity; however, the effect of CNVs on maize herbivory defense remains largely underexplored. Here, we report that the naturally occurring duplication of the maize 9-lipoxygenase gene, ZmLOX5, leads to increased resistance of maize to herbivory by fall armyworms (FAW). Previously, we showed that ZmLOX5-derived oxylipins are required for defense against chewing insect herbivores and identified several inbred lines, including Yu796, that contained duplicated CNVs of ZmLOX5, referred to as Yu796-2×LOX5. To test whether introgression of the Yu796-2×LOX5 locus into the herbivore-susceptible B73 background that contains a single ZmLOX5 gene is a feasible approach to increase resistance, we generated a series of near-isogenic lines that contained either 2, 1 or 0 copies of the Yu796-2×LOX5 locus in the B73 background by six back crosses (BC6). Droplet digital PCR (ddPCR) confirmed the successful introgression of the Yu796-2×LOX5 locus in B73. The resulting B73-2×LOX5 inbred line displayed increased resistance against FAW associated with increased expression of ZmLOX5, increased wound-induced production of its primary oxylipin product, the α-ketol, 9-hydroxy-10-oxo-12(Z),15(Z)-octadecadienoic acid (9,10-KODA), and the downstream defense hormones regulated by this molecule, 12-oxo-phytodienoic acid (12-OPDA) and abscisic acid (ABA). Surprisingly, wound-induced JA-Ile production was not increased in B73-2×LOX5, resulting from the increased JA catabolism. Furthermore, B73-2×LOX5 displayed enhanced drought tolerance likely due to increased ABA and 12-OPDA content. Taken together, this study revealed that the duplicated CNV of ZmLOX5 quantitively contributes to maize antiherbivore defense and presents proof-of-concept evidence that the introgression of naturally occurring duplicated CNVs of a defensive gene into the productive, but susceptible, crop varieties is a feasible breeding approach for enhancing plant resistance to herbivory and tolerance to abiotic stress.

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

Labyrinth seals are commonly used in turbomachinery in order to control leakage flows. Flutter is one of the most dangerous potential issues for them, leading to High Cycle Fatigue (HCF) life considerations or even mechanical failure. This phenomenon depends on the interaction between aerodynamics and structural dynamics; mainly due to the very high uncertainties regarding the details of the fluid flow through the component, it is very hard to predict accurately. In 2014, as part of the E-Break research project funded by the European Union (EU), an experimental campaign regarding the flutter behaviour of labyrinth seals was conducted at Centro de Tecnologias Aeronauticas (CTA). During this campaign, three realistic seals were tested at different rotational speeds, and the pressure ratio where the flutter onset appeared was determined. The test was reproduced using a linearized uncoupled structural-fluid methodology of analysis based on Computational Fluid Dynamics (CFD) simulations, with results only in moderate agreement with experimental data. A procedure to adjust the CFD simulations to the steady flow measurements was developed. Once this method was applied, the matching between flutter predictions and the measured data improved, but still some discrepancies could be found. Finally, a set of simulations to retain the influence of the external cavities was run, what further improved the agreement with testing data.

Soil-borne Trichoderma spp. have been extensively studied for their biocontrol activities against pathogens and growth promotion ability with plants. However, the beneficial effect of Trichoderma on inducing resistance against insect herbivores has been considered only recently. Among diverse Trichoderma species, consistent with previous reports, we showed that root colonization by T. virens triggered induced systemic resistance (ISR) to leaf-infecting fungal pathogens. Whether T. virens induces ISR to insect pests has not been tested before. In this study, we investigated whether T. virens affects jasmonic acid (JA) biosynthesis and defense against fall armyworm (FAW) and western corn rootworm (WCR). Unexpectedly, the results showed that T. virens colonization of maize seedlings grown in autoclaved soil suppressed wound-induced production of JA, resulting in reduced resistance to FAW. Similarly, the bacterial endophyte Pseudomonas chlororaphis 30-84 suppressed systemic resistance to FAW due to reduced JA. Further comparative analyses of the systemic effects of these endophytes when applied into sterile or non-sterile field soil showed that both T. virens and P. chlororaphis 30-84 triggered ISR against hemibiotrophic fungal pathogen Colletotrichum graminicola in both soil conditions, but only suppressed resistance to FAW in sterile soil. In contrast to the effect on FAW defense, T. virens colonization of maize roots suppressed WCR larvae survival and weight gain. This is the first report suggesting the potential role of T. virens as a biocontrol agent against WCR.

Polyhydroxyalcanoates (PHAs) are biodegradable polymers synthesized in cytoplasmic granules in bacteria, such as Cupriavidus necator (Ralstonia eutropha), Alcaligenes latus, Pseudomonas spp., Comamonas spp. and other species. PHAs accumulation occurs in response to stress conditions, i.e. under high carbon and low nitrogen (24:1 ratio). PHA can be synthesized using recombinant microorganisms (provided with the operon phbA/phbB/phbC), escaping the constrains of nutrient request, except addition of high amount of sugar (glucose, lactose, fructose). In this study; E. coli was genetically modified for PHB production in biofermentors. The production of PHA at industrial scale requires a continuous supplementation of fermentable sugars to support the availability of nutrients and to assess the level of the exponential growth phase; since sugars are required either for bacterial growth either for PHA synthesis and energy storage. The biofermentors need to be run in automated system. Sensors are used at many points in fermentators; in the evaluation of parameters: consumption of sugars; cell density; quantification of PHB synthesis. The need of operational control during the fermentation has prompted us to application of three measurements; one unit linked to a Nanodrop to evaluate OD; one linked to a reaction chamber to measure sugars consumed by enzyme based fluorescence detection; and one for bacteria Nile Blue staining and fluorescence intensity reads. The growth of bacteria on three different plant by-products was monitored and PHB production in four days using a banana by-product feed was optimised. These detectors will make possible to exploit the full potential of bioreactors optimizing the time of use and maximizing the number of bacteria synthesizing PHA.

A hallmark of Alzheimer’s disease (AD) are the proteinaceous aggregates formed by the amyloid-beta peptide (Aβ) that is deposited inside the brain as amyloid plaques. The accumulation of aggregated Aβ may initiate or enhance pathologic processes in AD. According to the amyloid hypothesis any agent that has the capability to inhibit Aβ aggregation and/or destroy amyloid plaques represents a potential disease modifying drug. In 2023, a humanized IgG1 monoclonal antibody (lecanemab) against the Aβ soluble protofibrils was approved by US FDA for AD therapy thus providing compelling support to the amyloid hypothesis. To acquire a deeper insight on the in vivo Aβ aggregation, various animal models, including aged herbivores and carnivores, non-human primates, transgenic rodents, fish and worms are widely exploited. This review is based on the recent data obtained using transgenic animal AD models and presents experimental verification of the critical role in Aβ aggregation seeding of the interactions between zinc ions, Aβ with the isomerized Asp7 (isoD7- Aβ) and the α4β2 nicotinic acetylcholine receptor.

This review describes the positive effects of growth hormone on the cardiovascular system. We analyze why the vascular endothelium is a real internal secretion gland, whose inflammation is the first step for developing atherosclerosis, as well as the mechanisms by which GH acts on the vascular endothelium improving its dysfunction. We also report how GH acts on coronary arterial disease and heart failure, and on peripheral arterial disease inducing the generation of new collateral vessels able to bypass a major artery occlusion. We include some preliminary data from a trial in which GH or placebo is given to elder people suffering from critical limb ischemia, showing the effects of the hormone on plasma markers of inflammation, and stating that the administration of GH in short periods of time is safe and effective even in diabetic patients. We also analyze how Klotho may have strong relationships with GH, inducing, after being released from the damaged vascular endothelium, the pituitary secretion of GH to repair the damaged tissue. Lastly, we show how GH induces wound healing by increasing the blood flow to the ischemic tissue. In summary, we postulate that short-time GH administration is useful for treating cardiovascular diseases.

We present a very computationally light and fast approximation algorithm and then verify it with genetic algorithm and simulated annealing. We show that our algorithm is on par with GA and SA in terms of output produced while having a tightly bounded time complexity. Our algorithm works best when there is a strong positive correlation between the reliability of a component and its cost. We present two algorithms with the same essence. One of them is system cost bounded and the other is target reliability bounded. Our proposed algorithm works on a subsystem level redundancy instead of component level redundancy.

Airworthiness certification requires proof of structure strength, which is performed generally through building block approach. To achieve this, representative intermediate scale experiments generated by test benches are in general needed, additionally to material characterization at coupons scale and structure testing at large scale. The VERTEX test bench can generate combined loading of tension/compression-shear-pressure on structural elements and was modelled with Finite Elements to perform virtual testing representative of its intermediate-scale specificity. The numerous bolted joints of the bench were modelled and their behavior identified in previous tests, so the model could quantitatively estimate the transfer function of the bench, which is the relation between the displacements imposed by the jacks and the resulting loads on a given sample. The VERTEX model was identified to represent load shapes and amplitudes on a training set, and was later confronted to a validation set of tests of tension and shear. A model with ideal boundary conditions was also developed for comparison but it failed to predict some load shape specificities and did not give any indication of loading amplitude. Application cases of the developed model are shown to assess a range of virtual testing possibilities.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015 TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of the production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015, TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

The present study aims to measure the technical efficiency and establish core factors effecting rice production in Cambodia. Four-years dataset generated from the central government document “Profile on Economics and Social” of entire 25 provinces between 2012-2015 and the stochastic production frontier model (SFA) was applied. The results indicated that the level of output (quantity) of Cambodian rice production varied according to the different level of capital investment in agricultural machineries, total rice actual harvested area, and technically fertilizers application within provinces. Furthermore, evidence revealed the overall mean efficiency of rice production is 78.4% implies that there is still room to further improve technical efficiency by given the same level of inputs and technology. More importantly, the findings revealed that irrigation, production technique and amount of agricultural supporting staffs are being as the most important influencing factors of rice production’s technical efficiency in Cambodia. In conclusion, present study strongly recommends the development of irrigation systems and good water management practices to be considered and bring into more effective actions by the central government as well as related agencies for improving rice production in Cambodia in addition to capital investment and improving technical skills of supporting staffs and rural farmers.

Diffuse optical tomography (DOT) technology enables a differentiation between oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the sensory and motor cerebral gyri, resulting in greater sensitivity for cerebral activation compared to functional magnetic resonance imaging (fMRI). Here, we introduce a novel approach where functional regions of interest (ROIs) are created based on the specific signal behavior observed in DOT measurements, in contrast to the conventional use of structural-ROIs obtained from anatomical information. The generation of cerebral activation maps involves using the general linear model (GLM) to compare the outcomes obtained from both the functional and structural ROI approaches. DOT-derived maps are then compared with maps derived from fMRI datasets considered the gold standard for assessing functional brain activity. The results obtained demonstrate the effectiveness of employing functional ROIs to improve the spatial location of functional activations in the sensory and motor cerebral gyri by leveraging the neural synchronization data provided by DOT. Furthermore, this methodology simplifies data processing in animal models, where anatomical differences compared to the human head can pose challenges. By incorporating functional ROIs prior to GLM application, this study offers enhancements to DOT analysis techniques and broadens its applicability in both human and animal models.

Cotton is the most ubiquitous and profitable fiber with diverse industrial and domestic applications. Grown in over 100 countries, it has a global market value of about $40 billion and employs over 350 million people from fields to textile mills, contributing about 7% of total labor-force recruitment in developing economies. Cotton has an indeterminate growth habit and an extensive tap root system affected by soil physicochemical and environmental conditions. There is a consensus among experts that conventional cotton cultivation still has a long way to attain sustainability, which is essential if cotton will maintain its competitive edge over other natural and synthetic fibers like hemp, polyesters, and rayon. Despite several efforts already committed to growing cotton sustainably, sustainability has eluded cotton cultivation globally because of the intense farm inputs (freshwater, pesticides, and heavy-duty equipment) needs, especially in developing economies. Some of the technological advancements towards achieving the goal of sustainable cotton cultivation include the development of new varieties, improved irrigation and mulching and precision agriculture techniques, application of remote sensing and Unmanned Aerial Systems (UAS) combined with image processing, and the introduction of autonomous and multi-purpose robotic platforms for growing and harvesting cotton. This review attempts to evaluate the successes already achieved by stakeholders in moving cotton towards sustainable production and identify areas where efforts are still needed to reach sustainable production and improved profitability goals for cotton with projections for future research directions.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

ZnO films with Ti atoms incorporated (TZO) in a wide range (0-18 at. %) have been grown by reactive co-sputtering on silicon and glass substrates. The influence of the titanium incorporation in the ZnO matrix on the structural and optical characteristics of the samples has been determined by Rutherford backscattering spectroscopy (RBS), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). The results indicate that the samples with low Ti content (< 4 at. %) exhibit the wurtzite-like structure, with the Ti+4 ions substitutionally incorporated into the ZnO structure, forming Ti-doped ZnO films. In particular, very low concentration of Ti (<0.9 at. %) leads to a significant increase of the crystallinity of the TZO samples. Higher Ti contents give rise to a progressive amorphization of the wurtzite-like structure so samples with high Ti content (≥18at. %), displays an amorphous structure indicating the XPS analysis a predominance of Ti-O-Zn mixed oxides. The energy gap, obtained from absorption spectrophotometry, increases from 3.2 eV for pure ZnO films to 3.6 eV for those with the highest Ti content. Ti incorporation in the ZnO samples below 0.9 at. % rises both, the blue (380 nm) and green (550 nm) bands of the photoluminescence (PL) emission, thereby indicating a significant improvement of PL efficiency of the samples.

Metaheuristic algorithms are widely used as optimization methods, due to their global exploration and exploitation characteristics, which obtain better results than a simple heuristic. The Stochastic Fractal Search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic-stochastic diffusion property, an improved Dynamic Stochastic Fractal Search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

The increasing number of registered road crashes involving cyclists during the last decade, and the high proportion of road crashes resulting in severe injuries and fatalities among cyclists constitutes a global issue for community health, urban development, and sustainability. Nowadays, the incidence of many risk factors for road crashes of cyclists remains largely unexplained. Given the importance of this issue, the present study has been conducted with the aim of determining relationships between infrastructural, human factors and safety outcomes of cyclists. Objectives: This study aimed, first, to examine the relationship between key infrastructural and human factors present in cycling, bicycle-user characteristics, and their self-reported experience with road crashes. And second, to determine whether a set of key infrastructural and human factors may predict their self-reported road crashes. Methods: For this cross-sectional study, a total of 1064 cyclists (38.8% women, 61.2% men; M = 32.8 years of age) from 20 different countries across Europe, South America and North America, participated in an online survey composed of four sections: demographic data and cycling-related factors, human factors, perceptions on infrastructural factors, and road crashes suffered. Results: The results of this study showed significant associations between human factors, infrastructural conditions and self-reported road crashes. Also, a logistic regression model found that self-reported road crashes of cyclists could be predicted through variables such as age, riding intensity, risky behaviors, and problematic user/infrastructure interactions. Conclusions: The results of this study suggest that self-reported road crashes of cyclists are influenced by features related to the user and their interaction with infrastructural characteristics of the road.

Revealing the spatial distribution mechanism of the coupling effect between soil water and salt is essential for managing saline-alkali farmland. This study utilized the multifractal method to analyze soil water and salt under varying water content levels in arid saline-alkali farmland. Soil samples were collected on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after a rainfall event. The findings showed that a significant decrease in soil water content, contrasting with an increase in soil salt content throughout the soil layers post-rainfall. As water content decreased, the spatial variability of soil water initially increased from S1-3 and then decreased, while the spatial variability of soil salt decreased. The spatial distribution between soil water and salt exhibited a high correlation at S3-4 (with relative water content of soil ranging from 0.52 to 0.75) due to their coupling effect. However, soil salt was unevenly leached by rainfall at high water content levels (S1-2) and precipitated at low water content levels (S5), resulting in low spatial variability correlations between soil water and salt. This study elucidated the coupling process of soil water and salt, identifying their spatiotemporal distribution mechanism in dryland agricultural areas.

The microstructure of the coating during hot-dip galvanizing under industrial conditions of two structural steels, a low-silicon ASTM A36 steel and a high-silicon Q345B steel, both of commercial quality, have been characterized for industrial-relevant times. For both cases it is noted that the formation of the Fe-Zn phases begins in early stages during the heating of the steel, a situation in which all the phases are in the solid state. The characterization was carried out through traditional metallographic techniques including SEM-EDS and DRX equipment. The evolution in time of the microstructure of both steels is examined, being able to observe that the mechanism by which silicon accelerates the formation of Fe-Zn phases in galvanizing is related to the change in the energy of the interfaces caused by a silicon accumulation in the liquid phase in contact with the ζ layer. These results provided us with a deeper understanding of the phase evolution in the hot-dip galvanizing of high silicon steels.

In this paper, a system of searching for optimal paths is developed and concreted on a FPGA. It is based on a memristive emulator, used as a delay element, by configuring the test graph as a memristor network. A parallel algorithm is applied to reduce computing time and increase efficiency. The operation of the algorithm in Matlab is checked beforehand and then exported to two different Intel FPGAs: a DE0-Nano board and an Arria 10 GX 220 FPGA. In both cases reliable results are obtained quickly and conveniently, even for the case of a 300x300 nodes maze.

Winding design methods have been a subject of research for many years of the past century. Many methods have been developed, each one characterized by some advantages and some drawbacks. Nowadays, the star of slots is the most widespread design tool for electrical machine windings. In this context, this paper presents a simple and effective procedure to determine the distribution of the EMF stars and of the winding configuration in all possible typologies of electrical machines equipped with symmetrical windings. Moreover, this procedure can also be easily implemented in a computer program in order to perform automated winding designs for rotating electrical machines. Several examples are provided in order to validate the proposed procedure.

More than 20 years after the discovery of the first anelloviruses (AVs), they still remain a mysterious group of viruses. To date, there is no clear understanding of their impact on the host organism, although information is emerging about their participation in various pathologies. However, it is well known that AVs are an important component of the human virome. In the current study we assessed the abundance of AVs in viromes from four nasal and pharyngeal samples from children with respiratory symptoms using a metagenomic approach. It is worth noting that three patients had known respiratory viruses and one did not have any. Regarding AVs, they were detected in all samples, namely Torque teno mini virus (TTMV). The greatest abundance and diversity of AVs (Torque teno virus (TTV), TTMV and Torque teno midi virus (TTMDV)) were shown in a diseased patient with an absence of respiratory viruses, which may indicate the potential involvement of AVs in the maintenance of respiratory symptoms. An activated immune system to combat the main pathogens, against the background of which the number of AVs decreases, is discussed as a possible reason for the reduced diversity of anellome in other patients. We also compared the results of the metagenomic approach and qPCR for major respiratory viruses, which were in agreement. In addition, metagenomic sequencing made it possible to obtain additional data on viral genomes in order to establish their taxonomic identification and characterize individual viral profiles in patients from the Siberian part of Russia.

Electrospun ultrathin fibers based on binary compositions of polylactide (PLA) and poly(ε-caprolactone) (PCL) with different ratios of polymer components from 0/100 to 100/0 wt. % were studied. Using a combination of thermal (DSC) and relaxation (EPR) measurements, the effect of biopolymers content on the characteristics of the crystal structure of PLA and PCL and the diffusive rotational mobility of the stable TEMPO radical in the amorphous regions of PLA/PCL compositions was shown. It was revealed that after PLA and PCL blending, significant changes in the degree of crystallinity of PLA, PCL segmental mobility, sorption capacity of the radical as well as activation energy of rotational diffusion in the amorphous regions of PLA/PCL composition are observed. The characteristic region of biopolymers’ composition from 50/50 to 30/70 % PLA/PCL blends ratio was found, where the inversion transition of PLA from dispersive medium to dispersive phase where an inversion transition is assumed when the continuous medium of the PLA transforms into a discrete phase. The performed studies made it possible for the first time to carry out a comprehensive analysis of the effect of the system component ratio on the structural and dynamic characteristics of the PLA/PCL film material at the molecular level.

Due to the increased use of memristors, and its many applications, the use of emulators has grown in parallel to avoid some of the difficulties presented by real devices such as variability and reliability. In this paper, we present a memristive emulator designed using a Switched Capacitor (SC), this is, an analog component/ block and a control part or block implemented using stochastic computing (SCo) and therefore fully digital. Our design is thus a mixed signal circuit. Memristor equations are implemented using stochastic computing to generate the control signals necessary to work with the controllable resistor implemented as switched capacitor.

This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. In this case, the mutation parameter is dynamically moved during the evolution process by using a shadowed and general type-2 fuzzy systems. The main idea of this work is to make a performance comparison between using shadowed and general type 2 fuzzy systems as controllers of the mutation parameter in differential evolution. The performance is compared with the problem of optimizing fuzzy controllers for a D.C. Motor. Simulation results show that general type-2 fuzzy systems are better when higher levels of noise are considered in the controller.

The Romani are the main European ethnic minority. The Romani people’s situation of social vulnerability and their difficulties in accessing the health system, make their health indicators worse than those of the non-Romani population. The present study will delve into the health beliefs and experiences with health services and their professionals through the perspectives of Romani women. In this qualitative study, 16 women of different ages were interviewed in a city located in the South of Spain. Four themes emerged from the analysis of data: the construction of the identity of Romani women, difficulties in life, health and disease beliefs and barriers in the access to the health system. We conclude that every project for the improvement of the health of the Romani community must take into account the active participation of Romani women and must consider the principles of Cultural Safety, by delving into the intercultural training of health professionals and addressing the social determinants of health which affect the Romani collective.

Background: The objective of the present study was to evaluate the formation of biofilm in bone patellar tendon bone grafts (BPTB grafts) and to compare it to the formation of biofilm formation in quadrupled hamstring anterior cruciate ligament grafts (4xHt graft). Methods: A descriptive in vitro study was conducted. One 4xHt graft and one BPTB graft were prepared. They were then contaminated with a strain of S. epidermidis. Later, a quantitative analysis was carried out by means of microcalorimetry and sonication with plating. Additionally, a qualitative analysis was carried out by means of electron microscopy. Results: No significant differences were found between the bacterial growth profiles of 4xHt graft and BPTB graft in microcalorimetry and colony counting. In the samples analyzed with electron microscopy, no specific biofilm growth pattern was identified upon comparing BPTB graft to 4xHt graft. Conclusions: There were no significant differences at either the quantitative or qualitative level when comparing bacterial growth in BPTB graft to that in 4xHt graft. Therefore, the presence of sutures in 4xHt graft cannot be established as a predisposing factor for increased biofilm growth in this in vitro study.

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers, which include the fluid-structure interaction. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids, and then introduced as a variable of the stiffness matrix during the structural analysis. The materials used were non-linearly modeled using an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor can be expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC) where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

The ability of S. aureus to infect humans and cause a wide range of infections so efficiently is a consequence of the many variant virulence factors that it can produce. Superantigens (SAgs) make up a group of them and include staphylococcal enterotoxins (SE). The main objective of this study was to detect enterotoxin gene cluster (EGC) in S. aureus isolates that caused infections in Paraguayan children in the year 2017, through an observational and cross-sectional study. The detection of the EGC including the seg, sei, sem, sen, seo, and seu genes, was carried out by end-point PCR. 181 S. aureus isolates were collected, most of them caused skin and soft tissue infections (SSTI, n=140, 77%) and came from children under 6 years 50% (n=90). At least one SE gene was detected in 96% (174/181) of isolates and 24% (44/181) carried the entire EGC. An association between methicillin resistance and seg, sei, seo, and seu gene carriage (p ≤0.05) was observed. A high prevalence of EGC was detected in S. aureus isolates collected from infected children attending the main health care centers in Paraguay, its importance is due to the potential of these toxins to aggravate staphylococcal infections.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

“De-[Constructing] Growth” is offered as a deeper and more useful conceptualization that avoids the negative connotations of, and resistance to, “degrowth” by decoupling profit from unsustainable consumption, production, and inequality.

Proprotein convertases activate various envelope glycoproteins and participate in cellular entry of many viruses. We recently showed that the convertase furin is critical for the infectivity of SARS-CoV-2. This study investigated the implication of the two cholesterol-regulating convertases SKI-1 and PCSK9 in SARS-CoV-2 entry. We used cell-to-cell fusion assays in HeLa cells and pseudoparticle entry into Calu-3 cells. SKI-1 increases cell-to-cell fusion by enhancing the activation of SREBP-2, whereas PCSK9 reduces cell-to-cell fusion by promoting the cellular degradation of ACE2. Metalloprotease activation is sensitive to enhanced cholesterol levels resulting from SKI-1-activated SREBP-2 that leads to enhanced S2’ formation. However, high metalloprotease activity results in S2’ shedding into a new C-terminal fragment (S2”), leading to reduced cell-to-cell fusion. Indeed, S-mutants that increase S2’’ formation, abolish S2’ and cell-to-cell fusion, as well as pseudoparticles entry, indicating that the formation of S2’’ prevents SARS-CoV-2 cell-to-cell fusion and entry. We next demonstrated that PCSK9 enhanced the cellular degradation of ACE2, thereby reducing cell-to-cell fusion. However, different from the LDLR, a canonical target of PCSK9, the C-terminal CHRD domain of PCSK9 is dispensable for the PCSK9-induced degradation of ACE2. Molecular modeling suggested binding of ACE2 to the Pro/Catalytic domains of mature PCSK9. Thus, both cholesterol-regulating convertases SKI-1 and PCSK9 can modulate SARS-CoV-2 entry via two independent mechanisms.

Millions of people worldwide have an inadequate intake of selenium (Se) and zinc (Zn), and agronomic biofortification may minimise these problems. To evaluate the efficacy of combined foliar Se and Zn fertilisation in bread making wheat (Triticum aestivum L.), foliar Se (as NaSeO4) and soil and foliar Zn applications (as ZnSO4·7H2O) were tested individually and in all combinations. A 2-year field experiment was established in southern Spain under semiarid Mediterranean conditions, by following a split-split-plot design. The study year (2017/2018, 2018/2019) was considered as the main-plot factor, soil Zn application (50 kg Zn ha-1, nil Zn) as a subplot factor, and foliar application (nil, 10 g Se ha-1, 8 kg Zn ha-1, 10 g Se ha-1 + 8 kg Zn ha-1) as a sub-subplot factor. The best treatment to increase both Zn and Se concentration in both straw, 12.3- and 2.7-fold respectively, and grain, 1.2- and 4.1-fold respectively was the combined foliar application of Zn and Se. This combined Zn and Se application also increased on average the yield of grain, main product of this crop, by almost 26%. Therefore, bread-making wheat seems to be a very suitable crop to be used in biofortification programs with Zn and Se to alleviate their deficiency in both, people when using its grain and livestock when using its straw.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

An exploding demand for processing capabilities related to the emergence of the IoT, AI and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path, but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations; four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the SC framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

The paper presents a method to assess the effect of electrolyte additives on the energy capacity of Pb-acid batteries. The method applies to any chemically unreactive additive, including suspensions and gels. The approach is thermodynamically based and it leads to the definition of a region of admissible concentrations –the battery’s admissible range– where the battery can undergo an indefinite number of charge/discharge cycles without suffering permanent damage. An experimental procedure to determine this range is presented. The obtained results provide a way to assess the potential of electrolyte additives to improve the energy capacity of Pb-acid batteries. The same results also provide a means to determine the additive concentration that produces the maximum energy capacity increase of the battery. The paper closes with an example of application of the proposed approach to a practical case.

Background: The practice of self-medication is common worldwide and is an important component of medical self-care. However, the practice can be dangerous and is linked to various risks for the patients and the entire community. This study aimed to estimate the prevalence and factors associated with self-medication among Bangladeshi adults. Methods: A cross-sectional survey was conducted between April and June 2021 among Bangladeshi adults (aged &gt;19 years) using convenient sampling. A total of 1320 data were collected through face-to-face interviews using a standardized questionnaire. Multivariable logistic regression analysis was used to identify factors associated with self-medication practice (SMP). Results: Overall, 41% of adults in our survey reported SMP. The most common illnesses that prompted SMP were common cold/flu (66.4%), gastric problems (65%), and headache (64.4%). The most frequent reasons for SMP were to get better services (30.59%), perceive SMP without side effects (23.28%), and save time with effectiveness (14.56%). Potential risk factors included 10 years (AOR = 1.91; 95% CI: 1.04 - 3.50) and &gt;12 years of schooling (AOR = 5.03; 95% CI: 2.27 - 11.15), being a businessman (AOR = 4.64; 95% CI: 1.74 - 12.37), having ≤6 family members (AOR = 2.13; 95% CI: 1.40 - 3.24), being a member of a social group (AOR = 1.53; 95% CI: 1.10 - 2.12), a health status check after every six months (AOR = 1.52; 95% CI: 1.08 - 2.13), and current ill-health (AOR = 1.41; 95% CI: 1.06 - 1.87). Protective factors identified included ≤30 years of age (AOR = 0.40; 95% CI: 0.17 - 0.93), and practice of modern (AOR = 0.39; 95% CI: 0.22- 0.69) and herbal (AOR = 0.45; 95% CI: 0.21- 0.97) healing types. Conclusion: More than one-third of the study participants reported practicing self-medication. Increasing the community’s awareness of the extreme consequences of SMP and not just the average experience might sway individuals away from SMP, and implementing strict jurisdiction could be a way to minimize the SMP.

Biodiversity has become an issue of global anxiety over the past decades due to its rapid decline worldwide. Bangladesh as one of the most densely populated countries in the world is no more exception. The country, although, was once very rich in biodiversity, during the last few decades as a consequence of the rapid reduction in forest area, urbanisation, habitat modification, unsustainable natural resources use and collection and overall climate change it has decreased alarmingly. Of late, the government, as a signatory of various regional and international conservation treaties, has taken various initiatives to improve country’s dwindling biodiversity. This paper reviews the present situation of biodiversity in Bangladesh, management trends and major causes of biodiversity loss. A separate statutory body is fundamental to ensure conservation, sustainable use and equitable sharing of benefits arisen from biodiversity in the country.

To improve efficacy and safety of sunscreen emulsions, UV‐filter skin permeation could be kept as low as possible by reducing UV‐filter content or changing emulsion composition. In this work, different lipid phase ratio (12, 14 and 16% w/w) were assessed for their ability to affect the technological and sensory properties of O/W emulsions in which bemotrizinol (BMTZ), a broad spectrum sunscreen agent, was incorporated free or loaded into nanostructured lipid nanocarriers (NLC) to reduce its release from the vehicle and, hence, its skin permeation. The following technological properties were evaluated in vitro by suitable methodologies: spreadability, viscosity, pH, occlusion factor, BMTZ release, sun protection factor (SPF). Sensory attributes were assessed by trained panelists in three different phases: before/during pick‐up, rub‐in, after application. Raising the lipid phase ratio led to an increase of viscosity and to a corresponding drop of spreadability while the incorporation of BMTZ‐loaded NLC determined a decrease of occlusion factor and an increase of SPF. No BMTZ release was observed from all emulsions. Sensory attributes were mainly affected by lipid phase ratio. These results suggest that lipid phase ratio and BMTZ incorporation into NLC could contribute to determine technological and sensory properties of O/W emulsions.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

Dendroclimatology has gained relevance during the XXI century. We analyze the state of-the-art of dendroclimatology in Latin America during the past 28 years (1990 to 2019), identifying the current state and recent advances in the application of dendroclimatology in this region. We carried out a systematic review in ScienceDirect, Web of Science, and Scopus databases using Boolean operators to logically connect the keywords “dendrochronology,” “dendroclimatology,” “trend,” “climatic variability,” and “climatic variability trend”, for each country. Dendroclimatological research conducted in the region focused on climatic reconstruction and the evaluation of new tree species with dendrochronological potential, which increase in 2010, then there was a gap between 1995 and 1996, later increase to present. Dendroclimatological studies in Latin America have been mainly developed in temperate climate zones (82.4 %) followed by tropical or subtropical areas (17.6 %). Dendroclimatological research in Latin America has provided advances in the study of climate variability by defining response functions of tree rings to climate. The generated information allows for a better understanding of the spatial and temporal dynamics of climatic variability and about its effects on ecosystems and society. We also call for increased dendroclimatological research based on subtropical and tropical forests limited studied until today.

This study introduces an innovative optimization method to identify the optimal configuration of a sparse symmetric 2D array for applications in security, particularly multistatic imaging. Utilizing Genetic Algorithms (GA) in a sophisticated optimization process, the research focuses on achieving the most favorable antenna distribution while mitigating the common issue of secondary lobes in sparse arrays. The main objective is to determine the ideal configuration from specific design parameters, including hardware specifications such as number of radiating elements, minimum spacing, operating frequency range, and image separation distance. The study employs a cost function based on the the point spread function (PSF), the system response to a point source, with the goal of minimizing the secondary lobe levels and maximizing their separation from the main lobe. Advanced simulation algorithms based on Physical Optics (PO) are used to validate the presented methodology and results.

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults the use of vibrations or sound generally offers better results in the accuracy of the detection although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has much more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal

In this study, we address the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on very-large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

One of the most important defining characteristics of groundwater quality is pH as it fundamentally controls the amount and chemical form of many organic and inorganic solutes in groundwater. Groundwater data are frequently characterized by a wide degree of variability of the factors which possibly influence pH distribution. For this reason, it is challenging to link the spatio-temporal dynamics of pH to a single environmental factor by the ordinary least squares regression technique of the conditional mean. In this study, quantile regression was used to estimate the response of pH to nine environmental factors (As, Cd, Fe, Mn, Pb, turbidity, electrical conductivity, total dissolved solids and nitrates). Results of 25%, 50%, 75% quantile regression and ordinary least squares (OLS) regression were compared. The standard regression of the conditional means (OLS) underestimated the rates of change of pH due to the selected factors in comparison with the regression quantiles. The effect of arsenic increased for sampling locations with higher pH values (higher quantiles) likewise the influence of Pb and Mn. However, the effects of Cd and Fe decreased for sampling locations in higher quantiles. It can be concluded that these detected heterogeneities would be missed if this study had focused exclusively on the conditional means of the pH values. Consequently, quantile regression provides a more comprehensive account of possible spatio-temporal relationships between environmental covariates in groundwater. This study is one of the first to apply this technique on groundwater systems in sub-Saharan Africa. The approach is useful and interesting and has broad application for other mining environments especially tropical low-income countries where climatic conditions can drive rapid cycling or transformations of pollutants. It is also pertinent to geopolitical contexts where regulatory; monitoring and management capacities are weak and where mining pollution of groundwater largely occur.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The growing awareness of the biological effects of artificial light on humans has stimulated ample research. It is now widely accepted that asynchrony between artificialand natural light-dark cycles can elicit severe detrimental health effects. New research has been devoted to lighting solutions that dynamically change their color to mimic spectral changes of daylight and to account for human needs. However, in some situations, the visual properties of light must be preserved: For example professional TV video editors and shift workers who must work under standardized lighting conditions to do color correction in post-production. We have investigated the possibility to tune circadian effects using white lights that are spectrally different but nonetheless have similar color coordinates thus appear as a similar white tone. Our simulation results indicate that it is possible to modulate circadian light effects by combining LEDs for neutral white (4000 K), a widely used white tone for indoor lighting in europe. The results also show that the solutions combining single-color LEDs do however not meet the quality criteria from the visual point of view because their color rendering ability decays to unacceptable low levels. Combining narrowband LEDs with a broadband white LED improves the color rendering quality and we show how far circadian light effects can be tuned according to common theoretical models. The aim is to reflect daylight situations with artificial lighting, thus having a high melatonin suppression in the morning and low melatonin suppression in the evening. As a consequence our aim is to show what maximum and minimum circadian effect is possible with the same set of LEDs.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

A total of 473 metabolites were identified from 4 sour cherry genotypes using UPLC-TOF-MS. Untargeted metabolomics revealed the dominant chemical groups present in sour cherries. PCA showed that the diversity in sour-cherry metabolites was due to the genotype differences indicating iditol, malic acid, chlorobenzene, 2-mercaptobenzothiazole, and pyroglutamic acid as the predominant contributors. The variable importance in the projection (VIP &gt; 1.0) in partial least-squares–discriminant analysis described 20 biomarker metabolites, representing the cherry metabolome profiles. A heatmap of Pearson’s correlation analysis between the 20 biomarker metabolites and antioxidant activities identified seven antioxidant determinants that displayed the highest correlations with different types of antioxidant activities. This study of correlating metabolomics and antioxidant activities elucidated that the higher nutritional value and biological functions of select sour cherry genotypes can be useful for the development of nutraceutical and functional foods.

Meningitis is a severe disease associated with death in children under five with highest rates of infections under age of one. Vaccines for Neisseria meningitides and Haemophilus influenza are used to prevent the main causative agents of meningitis. Administration of H. influenzae type b (Hib) vaccine is recommended at 2, 4 and 6 months with a booster dose at 18 months. N. meningitidis has two commercially available vaccines, the pure polysaccharide is recommended at 24 months meanwhile the protein-conjugated vaccines at 12 months. We sought in this study to examine if coadministering the vaccines for the two main meningitis causing bacteria might be synergistic as a preliminary step towards the possibility of shuffling immunization schedule. So, we coadministered Hib vaccine with commercially available vaccines either quadrate (ACWY) polysaccharide meningococcal (Men) or conjugated meningococcal (Nim) vaccines in Balb/C mice (n = 6/group) and compared to each vaccine administered separately and controls. Thirty-five days post immunization, we measured specific antibodies titers. Hib vaccine increased Men antibody titers significantly for serotypes Y and W. When Hib vaccine was coadministrated with Nim, antibody titer for Y, W and A significantly increased. For serotype C, there was no significant difference in antibody titers among immunized groups. As for effect of meningococcal vaccines on Hib, Men significantly increased Hib antibody titers while Nim had no effect. Collectively, our data suggested that coadministration of Hib and Men or Nim vaccines was safe and had synergistic effect on immune responses elicited to both vaccines. Further studies are needed before immunization schedule modifications. Such immunization schedule recommendation should provide better protection against this life-threatening disease in young children.

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap smear samples to identify potentially cancerous cells primarily falls on pathologists, a task known to be exceptionally challenging and time-consuming. This paper proposes a solution to address the shortage of pathologists for cervical cancer screening. It leverages the OpenAI-GYM API to create a deep reinforcement learning environment utilizing liquid-based Pap smear images. By employing the Proximal Policy Optimization algorithm, autonomous agents navigate Pap smear images, identifying cells with the aid of rewards, penalties, and accumulated experiences. Furthermore, the use of a pre-trained convolutional neuronal network like Res-Net54 enhances the classification of detected cells based on their potential for malignancy. The ultimate goal of this study is to develop a highly efficient, automated Papanicolaou analysis system, ultimately reducing the need for human intervention in regions with limited pathologists.

Three-dimensional matrices are a new strategy used to tackle type I diabetes; a chronic metabolic disease characterized by the destruction of beta pancreatic cells. Type I collagen is an abundant extracellular matrix (ECM), component that has been used to support cell growth. However, pure collagen possesses some difficulties including low stiffness and strength, and high susceptibility to cell-mediated contraction. Therefore, we developed a collagen hydrogel with a poly(ethylene glycol) diacrylate (PEGDA) interpenetrating network (IPN), functionalized with vascular endothelial growth factor (VEGF) to mimic the pancreatic environment for the sustenance of beta-pancreatic cells. We analyzed the physicochemical characteristic of the hydrogels and found that they were successfully synthesized. The mechanical behavior of the hydrogels improved with the addition of VEGF, and the swelling degree and the degradation were stable over time. In addition, it was found that 5 ng/mL VEGF-functionalized collagen/PEGDA IPN hydrogels sustained and enhanced viability, proliferation, respiratory capacity and functionality of beta pancreatic cells. Hence, this is a potential candidate for future preclinical evaluation, which may be favorable for diabetes treatment.

Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their small size is generally an issue in terms of yield, efficiency, and purity, for large-scale individual sand fly DNA extractions when using traditional methods. Even though this can be circumvented with commercial kits, these are generally cost-prohibitive for developing countries. We encountered these limitations when analysing parasite infection in Lutzomyia spp. by PCR [1] and, for this reason, we evaluated various modifications on a previously published protocol ([2] and Acardi personal communication). The most significant variation was the use of a different lysis buffer [3] to which added Ca2+ (buffer TESCa), because this ion protects proteinase K against autolysis, increases its thermal stability, and could have a regulatory function for its substrate-binding site [4]. Individual sand fly DNA extraction success was confirmed by amplification reactions using internal control primers that amplify a fragment of the cacophony gene [5,6]. To the best of our knowledge, this is the first time a lysis buffer containing Ca2+ has been reported for the extraction of DNA from sand flies.

The modern civil engineering and construction sector requires collaborative work environments, learning and trust among all parties involved, qualities that are absent in the Peruvian reality. This research, which is based on an extensive literature review, investigates this challenge. The study reflects upon (i) the current situation of public works procurement in Peru and (ii) the New Engineering Contract (NEC). Comparisons are presented between the characteristics, documentation and roles of these two systems, with the purpose of understanding and representing the advantages, disadvantages and possibilities of integrating tools of NEC4 Engineering and Construction Contract to the Peruvian State Contracting Law (Ley de Contrataciones del Estado: LCE). The research is validated through the case study of a high impact road infrastructure project in the city of Arequipa, Peru; which revealed five main negative impacts on good contractual management and, also, facilitated an initial assessment of challenges and improvement opportunities in public infrastructure procurement. Thus, contribution is made to closing the knowledge gap regarding the implementation of NEC4 ECC in public sector works.

Wheat is widely grown in the Mediterranean region, where plants frequently face water shortages. Recently, more attention to rationalizing irrigation water has been increased due to the adoption of sustainable agricultural systems. Therefore, two field trials were conducted at the Agriculture Research Station of Nubaria, Egypt, in two consecutive seasons (2017/2018 and 2018/2019) to examine three irrigation regimes' effects, i.e., 60, 80, and 100% of crop evapotranspiration (ETc) combined with foliar ascorbic acid (AsA) or vitamin B2 (VB2) on yield and water productivity of wheat in two separate experiments. A split-plot design was used with three replicates. Irrigation treatments occupied the main plots, while the subplot in the first experiment was placed by ascorbic acid (AsA) and vitamin B2 in the second experiment. Results indicated that 100% ETc produced higher wheat yield and its attributes and insignificantly surpassed 80% ETc. Compared to the unsprayed plots, the foliar application of 6 mM ascorbic acid or 2.0 mM vitamin B2 significantly increased grain yield, components, and water use efficiency. The interaction between irrigation regimes (100% ETc or 80% ETc) and foliar application of 6.0 mM ascorbic acid or 2.0 mM vitamin B2 resulted in the highest values with no significant difference. Foliar application of ascorbic acid or vitamin B2 saved 20% of irrigation water while yielding a reduction that did not exceed 10%. The biplot graph of treatments x traits (TT) was a useful statistical tool for examining the impact of treatments on yield and its characteristics and identifying the links between these factors. Results suggest that riboflavin or ascorbic acid may have a potential role as an effective antioxidant for enhancing the tolerance for drought stress in wheat.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.