This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

Water stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate buffer solution (PBS) showed the formation of a gel with β-sheet features of silk II. Electrical measurements on SF indicate that calcium ions are primarily involved in the conductivity mechanism. By exploiting the water solubility properties of silk II and Ca2+ ion transport phenomena and its trapping effect over water molecules, SF provides piezoresistive and piezocapacitive properties to cement mortars, thus enabling self-sensing of mechanical strain which is quite attractive in structural health monitoring applications. SF/cement-based composite introduces the capacitive gauge factor that surpasses the traditional resistive gauge factor reported in the literature by threefold. Cyclic voltammetry measurements demonstrated SF cement mortars possess memcapacitive behavior for positive potentials around +5 V, attributed to an interfacial charge build-up modulated by the SF concentration and the working electrode. Electrical square-biphasic excitation combined with cyclic compressive loads revealed memristive behavior during the unloading stages. These findings along with the availability and the sustainability of SF pave the way for the design of novel multifunctional materials, particularly for applications in masonry and concrete structures.

Autonomous Underwater Vehicles (AUVs) are highly efficient tools for underwater exploration, capable of carrying various payloads to perform specific missions as needed. To ensure the success of these missions and the spatiotemporal accuracy of sampled data, integrated navigation systems and appropriate filtering algorithms are essential. These systems process and merge motion data to provide accurate positioning information. The most commonly used algorithms in integrated navigation systems are Kalman filter and its nonlinear derivatives. Of these, the ensemble Kalman filter (EnKF) is particularly notable for its ability to handle nonlinear systems by incorporating Monte Carlo methods. This study proposes an enhanced adaptive EnKF algorithm to improve the smoothness and accuracy of the filtering process. Instead of the conventional Gaussian distribution, this algorithm employs a Laplace distribution to construct the system state vector and observation vector ensembles, enhancing stability against non-Gaussian noise. Additionally, the algorithm dynamically adjusts the number of vector members in the ensemble using adaptive mechanism by specifying thresholds during filtering, to adapt the requirements of real-world observational settings. Using measured data from field trials, including DVL, GPS, and electronic compass data, we examine the effects of various parameter settings on the algorithm's performance. Optimal parameter settings are identified to fine-tune and verify the filtering algorithm as well as its adaptive capability. The results demonstrate that the enhanced adaptive EnKF algorithm exhibits superior filtering performance in terms of both accuracy and smoothness compared to the conventional EnKF and EKF algorithms. This indicates the significant advantages of our proposed algorithm for AUV navigation.

This article study was developed based on the relationship between access to justice and the instrumentality of the legal process, aiming to assess the effectiveness of the constitutional principle enshrined in Article 5, item XXXV: “the law shall not exclude any injury or threat to a right from the consideration of the Judiciary” (BRAZIL, 2008, p.27). To achieve this goal, the concept of justice was examined, as well as the definition and evolution of access to justice within legal science. In light of these concepts, it became possible to establish a connection between access to justice and the instrumentality of the legal process. This analysis reveals that the State has not yet achieved sufficient efficiency in providing support to its less favored citizens in facilitating access to justice.

Interspecific competition between mosquito larvae may affects adult vectorial capacity, potentially reducing disease transmission. It also influences population dynamics, cannibalistic and predatory behaviors. However, knowledge of interspecific competition between Ae. aegypti and Anopheles species is limited. The study examined interspecific competition between Ae. aegypti larvae and either An. arabiensis, An. gambiae, or An. funestus on individual fitness in semi-field settings. The experiments involved density combinations of 100:100, 200:0, and 0:200 (Ae. aegypti: Anopheles), reared with and without food, in small habitat (8.5 cm height × 15 cm diameter) with 0.5 litre and medium habitats (15 cm height × 35 cm diameter) with 1 litre of water. The first group received Tetramin® fish food (0.02 g), while the second group was unfed to assess cannibalism and predation. While, interspecific competition affected both genera, Anopheles species experienced greater effect, with reduced survival and delayed development, compared to Ae. aegypti. The mean wing lengths of all species were significantly small in small habitats in mixed population (p < 0.001). The presence of food reduced cannibalism and predation compared to its absence. These interactions have implications for diseases transmission dynamics and can serve as biological indicators to signal the impacts of vector control interventions.

In response to a perceived epidemic of coronary heart disease, Ancel Keys introduced the lipid-heart hypothesis in 1953 which asserted that high intakes of total fat, saturated fat, and cholesterol lead to atherosclerosis and that consuming less fat and cholesterol, and replacing saturated fat with polyunsaturated fat, would reduce serum cholesterol and consequently the risk of heart disease. Keys proposed an equation that would predict the concentration of serum cholesterol (DChol.) from consumption of saturated fat (DS), polyunsaturated fat (DP), and cholesterol (DZ): ΔChol. = 1.2(2ΔS − ΔP) + 1.5ΔZ. However, the Keys equation conflated natural saturated fat and industrial trans-fat into a single parameter and considered only linoleic acid as the polyunsaturated fat. This ignored the widespread consumption of trans-fat and its effects on serum cholesterol and promoted an imbalance of omega-6 to omega-3 fatty acids in the diet. Numerous observational, epidemiological, interventional, and autopsy studies have failed to validate the Keys equation and the lipid-heart hypothesis. Nevertheless, these have been the cornerstone of national and international dietary guidelines which have focused disproportionately on heart disease and much less so on cancer and metabolic disorders, which have steadily increased since the adoption of this hypothesis.

Requirements elicitation from multiple human sources involves information validation and conflicts resolution to deal with uncertainty inherent in the process. Most requirements analysis methods focus on expressing the requirements and ignore the uncertainty inherent in the process of requirements elicitation. This paper build on the work presented by the author in [1,10], where a method for requirements elicitation from multiple sources was presented. This paper gives an overview of the method and presents detailed case studies. The cases studies were designed to illustrate different aspects of the Source Control Method and to determine its feasibility and its practical utility as a problem investigation and information validation technique.

Objectives: Cardiogenic shock [CS] is associated with a high-mortality. Suitable patients maybe successfully bridged using newer intravascular micro-axial left-ventricular assist devices [M-LVAD] for recovery or determination of definitive therapy. Methods: Between January-2020 and July-2024, 107 patients underwent placement of M-LVAD for CS. The cohort was divided into 4 groups based on their destination; group-1: 34 patients [32%] receiving transplant; group-2: 25 patients [23%] receiving durable LVAD; group-3: 42 patients [39%] bridging from post-cardiotomy CS [PCCS]; and group-4: 6 patients [5.6%] bridging decision/recovery [these were excluded from analysis]. Multivariable logistic-regression [MLR] and Cox-regression [MCR] models identified predictors of early-hospital and late mortality, with data reported as odds ratios [ORs], and hazard ratios [HRs], respectively. P<0.05 was statistically significant. Results: Complications included device-malfunction [n=6, 6%], gastrointestinal bleed [n=9, 9%], stroke [n=11, 11%]; long-term hemodialysis [n=21, 21%]. Early-hospital mortality included 13 patients [13%]: 2 in group-1, 1 in group-2 and 10 in group-3 [p=0.02]. In the MLR model, the category of cardiogenic shock requiring M-LVAD placement was statistically significant [OR=4.7 (0.9-24), P=0.05]. Patients were followed for up to 4.5-years, and 23 deaths occurred; group-1: 3 patients, group-2: 5 patients, and group-3: 15 patients [p=0.019]. At 4.5-years, actuarial survival was 90.7±5.1% in group-1, 79.2±8.3% in group-2, and 62.8%±7.7% in group-3 [P=0.01]. In the MCR model, M-LVAD category [HR=3.63 (1.03-12.9) P=0.04], and long-term postoperative dialysis [HR=3.9 (1.6-9) P=0.002], emerged as statistically significant predictors of long-term mortality. Conclusions: In cardiogenic shock, our mid-term outcomes demonstrate good survival with M-LVADs as bridge to transplant and durable LVAD, and reasonable survival as bridge to recovery following cardiotomy, reduced ECMO usage and early ambulation/rehabilitation.

Outlier detection is a critical technique across various domains, including statistics, data science, machine learning, and finance. Outliers, data points that differ significantly from the majority, can indicate errors, anomalies, or even new insights. This article provides an in-depth exploration of the primary techniques used to detect outliers, categorized into statistical methods, machine learning-based approaches, and proximity-based methods. We discuss the advantages, limitations, and specific use cases of each technique, highlighting their applicability to different types of datasets. The goal is to equip practitioners with a better understanding of how to identify and handle outliers effectively in real-world data analysis.

Introduction: Lumbar radiculopathy, frequently triggered by disc herniation, affects around one in 50 young adults (30-55 ages), leading to significant impacts on quality of life. When conservative treatments fail to alleviate symptoms, surgical intervention such as discectomy is often pursued. Technological advancements have led to various minimally invasive techniques, promising improved outcomes. Our aim was to compare clinical outcomes of different techniques. Methods: We conducted a systematic search in PubMed using "lumbar disc hernia" and the meta-analysis filter. Inclusion criteria comprised meta-analyses focusing on lumbar disc herniation treatments, published in English. A rigorous selection process adhering to PRISMA guidelines ensured high-quality evidence extraction. Results: From 28,171 initial records, 31 meta-analyses involving 87,852 patients were included. Heterogeneity across the literature presented challenges, notably in study populations, surgical techniques, and research designs. Among the techniques evaluated, full-endoscopic lumbar discectomy (FELD) emerged as superior in various perioperative parameters, including Visual Analog Scale (VAS) scores, Oswestry Disability Index (ODI), operation time, and complications. Conversely, automated percutaneous discectomy (AUTD) consistently exhibited poorer outcomes, suggesting limited effectiveness compared to other techniques. Conclusion: Despite advancements, the heterogeneity observed in the literature underscores the need for standardized approaches in minimally invasive spinal surgery research. FELD stands out as a preferred technique, offering favorable outcomes across multiple parameters. Conversely, techniques like AUTD raise concerns regarding efficacy. Future research should aim for uniformity in methodologies to facilitate accurate comparisons and guide clinical decision-making.

This study presents a novel approach to automatic generation of cultural and context-specific science assessments for K-12 education using generative AI (GenAI). We first developed a GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework that establishes the relationship between CRSciA and GenAI, by incorporating key cultural tenets such as indigenous language, Indigenous knowledge, ethnicity/race, and religion. The CRSciA framework along with dynamic prompt strategies were used to develop the CRSciA-Generator model within the OpenAI platform. The CRSciA-Generator allows users to automatically generate assessments tailored to students’ cultural and contextual needs. In a pilot comparison test between the CRSciA-Generator and the base GPT 4o (with standard prompt), the models were tasked with generating CRSciAs that aligned with the Next Generation Science Standard on predator and prey relationship for students from Ghana, the USA, and China. The results showed that the CRSciA-Generator output assessments incorporated more tailored culturally and context assessment items for each specific group with examples, such as traditional stories of lions and antelopes in Ghana, Native American views on wolves in the USA, and Taoist or Buddhist teachings on the Amur tiger in China than the standard prompt out within the base GPT 4o. However, due to the background information provided, the CRSciA-Generator overgeneralized its output focusing on broad national contexts, treating entire countries as culturally homogenous and neglecting the subcultures. Therefore, we recommend that teachers provide detailed background information about their students when using the CRSciA-Generator. Additionally, we believe the pilot test did not fully validate the model’s efficacy, and future studies involving human experts’ review are recommended to evaluate the cultural and contextual validity of the generated assessments. We also suggest empirical studies in diverse contexts to further test and validate the model’s overall effectiveness.

Breast cancer is the most commonly diagnosed cancer in women in both developed and developing countries. Due to the increasing alarms in the detection of cases every year, it is considered a priority to evaluate new molecules as potential antitumoral agents. Among these molecules, cationic peptides have emerged as promising therapeutic agents with a membrane-based mechanism of action very hard to counteract by the cancer cells. This biophysical study seeks to offer preliminary insights into how NA-CATH:ATRA-1-ATRA-1, a synthetic cationic peptide derived of the peptide NA-CATH, isolated from the species Natra aja or Chinese cobra, is a potential antitumoral compound at the membrane level on breast cancer cell lines. The interaction of the NA-CATH-ATRA-1-ATRA-1 with two lipid systems representative of tumoral and non-tumoral membranes was followed by differential scanning calorimetry and Fourier-transform infrared spectroscopy. The results showed a strong interaction of NA-CATH-ATRA-1-ATRA-1 with the lipids present in the eukaryotic membranes, especially phosphatidylserine, suggesting a mechanism based on the electrostatic interaction of the peptide with the anionic lipids of the tumoral cancer membranes. The biophysical studies are essential to understand the mechanism of action of a potential pharmaceutics.

Data privacy is a critical concern in health informatics, where the management of sensitive patient information requires robust and secure database systems. This study aims to explore the data privacy challenges faced in health informatics and conduct a comparative analysis of popular Database Management Systems (DBMS), including MySQL, MongoDB, PostgreSQL, and Oracle, with a focus on their effectiveness in safeguarding patient data. Through a review of existing literature, case studies, and interviews with database administrators, the research identifies common privacy threats such as data breaches and unauthorized access. It examines the security features of each DBMS, such as encryption methods, access controls, and user authentication mechanisms. The findings reveal variations in the privacy protection capabilities of different DBMS, highlighting the trade-offs between security, performance, and usability. The study provides recommendations for healthcare organizations in selecting a DBMS that balances privacy with operational needs and offers insights for policymakers to improve regulatory frameworks in health data security. This research contributes to the ongoing discourse on enhancing data privacy in health informatics and suggests directions for future studies to integrate emerging technologies for more secure health data management.

Diabetic kidney disease (DKD) is regarded as a silent assassin due to its delayed signs. It is a progressive illness that can appear in various ways, from mild to severe. The severity of DKD must be determined in order to make educated treatment decisions and understand the hazards that the patient may encounter. DKD is one of the primary causes for death and morbidity among noncommunicable illnesses, affecting 10% to 15% of the global population. Timely prediction of DKD is expected to improve patients' health and reduce mortality. This study aims to predict DKD using machine learning models like Decision Trees, Random Forest, Logistic Regression, Bagging Tree Model and Support Vector Machine, to help in timely detection and cure of this deadly disease. Clinical data required for model training were taken from Kaggle's public repository and additional samples of 50 individuals were collected from local hospital, Coimbatore, Tamil Nadu. Meta-heuristic algorithms based on Swarm Intelligence, such as Particle Swarm Optimization, Ant Colony Optimization, and Firefly Optimization, were employed to optimize the selected models. The main aim of this study is to compare the predictions of different models and identify models that best agree with the given scenario.

The main objective of the present study is to demonstrate the possibility of actively influencing the position of the bow shock wave and the main parameters of supersonic flow over a blunt body by organizing a gas discharge near the front surface, in the region between the body and the bow shock wave. The research is carried out using both experimental and numerical methods. The working gas was xenon. It is shown that the steady bow shock wave stand-off distance along with the current and power of the discharge, is associated with the change in the adiabatic index of the plasma created by the discharge, which, in turn, is determined by the plasma parameters, such as the degrees of nonequilibrium and the degree of ionization. It is shown that the adiabatic index with the power supplied to the impact zone in the range of 30-120 kW can both increase and de-crease in the range of 1.25-1.288. The study of the discharge created plasma zone was conducted and the correspondence between the discharge current and power, and the average parameters in the plasma zone created by the discharge is presented. A good agreement between the numerical and experimental data was shown. The results may be useful in developing control systems for high-speed civil aircrafts.

Parametric quantile regression is illustrated for the one parameter new unit Rayleigh distribution called Median Based Unit Rayleigh distribution (MBUR) distribution. The estimation process using re-parameterized maximum likelihood function is highlighted with real dataset example. The inference and goodness of fit is also explored.

Background/Objectives: Post traumatic growth (PTG) has the potential to draw positive consequences from trauma. Hence, the interest of finding ways to promote PTG. Research has identified an attentional bias towards positive resilience-related words (e.g., “persistence”, “purpose”) in university students who report high PTG after experiencing adversities. Although people can respond to these experiences by showing low PTG, this bias seems to help with their struggle by making purposeful contents more accessible. Therefore, boosting attentional bias towards positive resilience-related words could help people with low PTG. Methods: In this study, the participants were thirty-six university students who had experienced bullying before entering university. Using a Stroop emotional task, they identified the color of resilience and neutral words, either positive or negative, before and after being submitted to transcranial direct current stimulation. Stimulation was targeted at the right temporal area involved in intentionality processing. Results: In the anodal condition, the results support a stimulation effect on the resilience attentional bias that could benefit participants with low PTG. A significant moderation of approach motivation for this effect was also found. Specifically, only when participants had medium or high approach motivation, did stimulation boost the attentional bias in students with low PTG. Conclusions: These results support that tDCS stimulation in this brain area is effective in enhancing resilience attentional bias in low PTG students. However, for this effect to occur it is necessary to have approach motivation, which is motivation related to goals.

This paper presents a comprehensive review of the transformative impact of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DT), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM), and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development.

This article explores the use of vegetation indices (VIs) in remote sensing for estimating standing wood volume and volume increments in forest ecosystems. The study investigates the correlations between various VIs and wood volume across different forest stand age classes, focusing on the effectiveness of commonly used indices such as Squared Ratio Simple Red Edge (SQSR) and Red-Edge Ratio Vegetation Index (RERVI), as well as other typical indicators including Enhanced Vegetation Index (EVI), Red-Edge EVI (RE-EVI), and Green Normalized Difference Vegetation Index (GNDVI). Additionally, the analysis considers a pair of indicators based on blue and near-infrared channels, resembling the Blue-normalized Difference Vegetation Index (BNDVI). The research reveals that quadratic and power-law models of SQSR and RERVI demonstrate higher correlations (>0.40) with wood volume, especially in younger forest stands (>0.80). Furthermore, the study highlights the potential of NDII and NDWI indices for estimating volume increments in certain classes of forest stands (0.70). However, correlations vary across different forest stand classes, suggesting the need for further investigation into the reliability of VIs for monitoring wood volume and volume increments in forest ecosystems. Overall, this research contributes to the understanding of the applicability of VIs in remote sensing for forest management and ecosystem monitoring.

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.

Aim: To evaluate the impact of applying alternative diagnostic criteria for gestational diabetes mellitus (GDM) during the COVID-19 pandemic on GDM prevalence, obstetrical and perinatal outcomes, and costs, as compared to the standard diagnostic method. Methods: A cohort of pregnant individuals undergoing GMD screening with the alternative GDM method, which uses plasma glucose (fasting or non-fasting) and HbA1c, was compared with a cohort of pregnant individuals undergoing the standard GDM screening method. Both cohorts were obtained from 6 hospitals across Catalonia, Spain, from April 2020 to April 2022. The primary outcome was large for gestational age rate at birth. Secondary outcomes were composite adverse outcomes including pregnancy complications, delivery complications, and neonatal complications. Cost differences between screening methods were also evaluated. A similar analysis was performed in the subgroup diagnosed with GDM. Results: Data were collected from 1,543 pregnant individuals in the standard screening group and 2,197 in the alternative screening group. The standard screening group had a higher GDM diagnostic rate than the alternative screening group (10.8% vs 6.9%, respectively; p<0.0001). The primary outcome (large for gestational age rate) was similar between groups: 200/1543 (13.0%) vs 303/2197 (13.8%). The adjusted OR for this outcome was 1.74 (95% CI: 0.74-4.10). An adjusted analysis showed no differences between groups in composite adverse outcomes for pregnancy complications (OR: 1.11; 95% CI: 0.91-1.36), delivery complications (OR: 0.95; 95% CI: 0.75-1.19), and neonatal complications (OR: 1.28; 95% CI: 0.94-1.75). Among individuals diagnosed with GDM, the large for gestational age rate was similar between groups: 13/166 (7.8 %) vs 15/151 (9.9 %). The OR adjusted for this outcome was 1.24 (95% CI: 0.51-3.09). An adjusted analysis showed no differences in composite adverse outcomes for pregnancy complications (OR: 1.57; 95% CI: 0.84-2.98), delivery complications (OR: 1.21; 95% CI: 0.63-2.35), and neonatal complications (OR: 1.35; 95% CI: 0.61-3.04). The mean cost (which included expenses for consumables, equipment and personnel) of the alternative screening method was 46.0 euros (22.3 SD), as compared to 85.6 euros (67.5 SD) for the standard screening method. Conclusions: In this Spanish population during the COVID-19 pandemic, GDM prevalence was lower in the alternative screening group than in the standard screening group. After adjusting for GDM risk factors, outcomes related to obstetrics, delivery, and neonatal complications were comparable between both groups. Finally, the alternative screening method was cheaper than the standard screening method.