Search Results (588)

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck. Full article

Background: Semaglutide and Liraglutide are medications in the Glucagon-like peptide-1 agonists (GLP-1 RAs) class used to manage type 2 diabetes mellitus and obesity in Saudi Arabia. Although the 1.0 mg once weekly dosage of Semaglutide does not have a labeled indication for the management of obesity, many believe that this dosage is more effective than the 3.0 mg once daily Liraglutide dosage for the management of both diabetes and obesity. Objective: To compare the effectiveness of the dosage of 1.0 mg of Semaglutide administered once weekly versus 3.0 mg of Liraglutide administered once daily in controlling HbA1c levels, promoting weight loss, and evaluating their financial implications among obese patients in Saudi Arabia using real-world data. Methods: A retrospective review of Electronic Medical Records (EMRs) from January 2021 to June 2024 was conducted on patients prescribed Semaglutide or Liraglutide for at least 12 months. Exclusion criteria included pre-existing severe conditions (e.g., cardiovascular disease, stroke, or cancer) and missing baseline data. The primary outcomes assessed were changes in HbA1c, weight, and direct medical costs. Results: Two hundred patients (100 patients on the 1.0 mg once weekly dose of Semaglutide and 100 patients on the 3.0 mg once daily dose of Liraglutide) of those randomly selected from the EMRs met the inclusion criteria and were included in the analysis. Of the 200 eligible patients (65.5% female, mean age 48.54 years), weight loss was greater with Semaglutide (−8.09 kg) than Liraglutide (−5.884 kg). HbA1c reduction was also greater with Semaglutide (−1.073%) than Liraglutide (−0.298%). The use of Semaglutide resulted in lower costs of USD −1264.76 (95% CI: −1826.82 to 33.76) and greater reductions in weight of −2.22 KG (95% CI: −7.68 to −2.784), as well as lower costs of USD −1264.76 (95% CI: (−2368.16 to −239.686) and greater reductions in HbA1c of −0.77% (95% CI: −0.923 to −0.0971) in more than 95% of the cost effectiveness bootstrap distributions. Conclusions: Semaglutide 1.0 mg weekly seems to be more effective and cost-saving in managing prediabetes, diabetes, and obesity compared to Liraglutide 3.0 mg daily. Future studies should examine these findings using a more representative sample and a robust study design. Full article

Urban green spaces are increasingly recognized as important habitats for pollinators, and wildflower seed mixes marketed as pollinator-friendly are gaining popularity, though their actual conservation value remains poorly understood. This study provides the first systematic screening of commercially available seed mixes in Portugal, evaluating their taxonomic composition, origin, life cycle traits, and potential to support pollinator communities. A total of 229 seed mixes were identified. Although these have a predominance of native species (median 86%), the taxonomic diversity was limited, with 91% of mixes comprising species from only one or two families, predominantly Poaceae and Fabaceae, potentially restricting the range of floral resources available to pollinators. Only 21 seed mixes met the criteria for being pollinator-friendly, based on a three-step decision tree prioritizing native species, extended flowering periods, and visual diversity. These showed the highest percentage of native species (median 87%) and a greater representation of flowering plants. However, 76% of all mixes still included at least one non-native species, although none is considered invasive. Perennial species dominated all seed mix types, indicating the potential for the long-term persistence of wildflower meadows in urban spaces. Despite their promise, the ecological quality and transparency of the seed mix composition remain inconsistent, with limited certification or information on species origin. This highlights the need for clearer labeling, regulatory guidance, and ecologically informed formulations. Seed mixes, if properly designed and implemented, represent a largely untapped yet cost-effective tool for enhancing the pollinator habitats and biodiversity within urban landscapes. Full article

Background: Medication adherence is essential for managing chronic conditions, while non-adherence remains a widespread issue, leading to poorer health outcomes and higher healthcare costs. This study aimed to identify key adherence barriers, explore their relationship with patient satisfaction, and assess their impact on overall well-being among ambulatory patients in Albania. Methods: A cross-sectional study was conducted in three public urban health centers in Vlora, Albania, between November 2024 and January 2025. A total of 80 ambulatory patients were recruited using convenience sampling. Data were collected through face-to-face interviews using validated questionnaires, including the Adherence Barriers Questionnaire (ABQ), the Patient Satisfaction with Nursing Care Quality Questionnaire (PSNCQQ), and the Patient Health Questionnaire (PHQ-9) for depression screening. Results: The study included 80 ambulatory patients (mean age 66.7 years; 48.7% female), predominantly diagnosed with diabetes (42.5%) and rheumatic diseases (36.3%). All participants reported at least one adherence barrier, with 92.5% experiencing multiple barriers. The most common were financial burden (91.3%) and fear of side effects (77.5%). A significant positive correlation was found between adherence barriers and depression severity (ρ = 0.518, p < 0.0001), while patient satisfaction did not significantly influence adherence barriers (ρ = −0.217, p = 0.053) or depression severity (ρ = −0.004, p = 0.969). Multiple regression analysis showed that higher depression severity (p = 0.0049) was significantly associated with greater adherence barriers, while postgraduate education was associated with fewer barriers (p = 0.0175). Conclusions: Financial burden, fear of side effects, and psychological distress are key barriers to adherence among Albanian ambulatory patients. Although there are limitations inherent to the cross-sectional design and modest sample size, our findings highlight the potential benefit of routine mental health screening, targeted financial support, and improved patient education on medication management within primary care. These insights may help inform future research and interventions aimed at enhancing adherence and overall well-being. Patient satisfaction did not significantly impact adherence or depression. Targeted interventions focusing on financial support, mental health care, and patient education are needed to improve adherence and patient well-being. These findings underscore the need for integrated mental health and adherence support strategies within routine primary care services. Full article

This study highlights waste bread (WB) as a novel, cost-effective, and nutrient-rich substrate for microbial growth, offering a sustainable alternative to conventional media. As a renewable resource, WB promotes the circular economy by reducing food waste and encouraging biotechnological innovation. The incorporation of WB into microbial culture media enhanced the growth of various reference strains (E. coli, E. faecalis, P. aeruginosa, and S. aureus), with at least a two-fold increase compared to conventional Luria-Bertani (LB) medium. Moreover, combining 2% WB with diluted LB (1/10) reduced medium costs by up to 90%. Furthermore, it was confirmed that 1% WB can effectively replace starch during the screening of amylolytic strains. Applying a fractional factorial design, the production of amylase by Bacillus sp. BSS (Amy-BSS) was enhanced 15-fold. An analysis of the Pareto diagram revealed that WB was the most significant factor. Additionally, Amy-BSS was applied to hydrolyze polysaccharides in WB, enabling the generation of high-value-added products in food processing. This hydrolysis process yielded 4.6 g/L of fermentable sugars from 1% WB. Evaluating the economic feasibility of WB valorization into value-added products elucidates potential pathways for cost reduction and enhanced environmental sustainability, thereby positioning WB as a viable tool for sustainable development. Full article

Improper stakeholder practices are considered a primary driver of food loss. This study aims to investigate the consequences of pre- and post-harvest practices on extending the shelf life of agro-food products, identifying which practices yield the highest marginal returns for quality. Using Fractional Regression Models (FRM) and Ordinary Least Squares (OLS), the research analyzed data from 343 Egyptian grape farmers and intermediaries. Key findings at the farmer level include significant food loss reductions through drip irrigation (13.9%), avoiding maturity-accelerating chemicals (24%), increased farmer-cultivated area (6.1%), early morning harvesting (8.7%), and improved packing (13.7%), but delayed harvesting increased losses (21.6%). For intermediaries, longer distances to market increased losses by 0.15%, while using proper storage, marketing in the formal markets, and using an appropriate transportation mode reduced losses by 65.9%, 13.8%, and 7.9%, respectively. Furthermore, the interaction between these practices significantly reduced the share of losses. The study emphasizes the need for increased public–private partnerships in agro-food logistics and improved knowledge dissemination through agricultural extension services and agri-cooperatives to achieve sustainable food production and consumption. This framework ensures robust, policy-actionable insights into how stakeholders’ behaviors influence postharvest losses (PHL). The findings can inform policymakers and agribusiness managers in designing cost-efficient strategies for reducing PHL and promoting sustainable food systems. Full article

Electromagnetic absorbers combining ultrathin profiles with robust absorptivity across wide incidence angles are essential for applications such as stealth applications, wireless communications, and quantum computing. Traditional designs, including Salisbury screens, typically require thicknesses of at least a quarter-wavelength ($\lambda /4$), restricting their use in compact systems. While metamaterial absorbers (MMAs) offer reduced thicknesses, their absorptivity generally decreases under oblique incidence conditions. Here, we introduce an adjoint optimization-based inverse design method that merges the ultrathin advantage of MMAs with the angle-insensitive characteristics of Salisbury screens. By leveraging the computational efficiency of the adjoint method, we systematically optimize absorber structures as thin as $\lambda /20$. The optimized structures achieve absorption exceeding 90% at the target frequency (7.5 GHz) and demonstrate robust performance under oblique incidence, maintaining over 90% absorption up to 50°, approximately 80% at 60°, and around 70% at 70°. Comparative analysis against particle swarm optimization further highlights the superior efficiency of the adjoint method, reducing the computational effort by approximately 98%. This inverse design framework thus provides substantial improvements in both the performance and computational cost, offering a promising solution for advanced electromagnetic absorber design. Full article

Resistive angle sensors are widely used due to their simple signal conditioning circuits and high cost-effectiveness. This paper presents a resistive angle sensor based on a rotary potentiometer, designed to offer a measurement range of 180° for low-cost angle measurement in industrial automation and electromagnetic interference (EMI)-sensitive applications. The sensor features a specially designed signal conditioning circuit and mechanical housing. Experimental results show that it exhibits excellent linearity and temperature stability over a wide temperature range of −20 °C to 60 °C, with a zero-temperature drift of approximately 0.004°/°C. For the nonlinearity and hysteresis caused by unavoidable friction and manufacturing tolerances between the transmission mechanism and rotary potentiometer, an adaptive linear neuron (ADALINE) technique based on the α-least mean square (α-LMS) algorithm was implemented for software compensation. The results show that the percentage nonlinearity error was reduced from the original 4.413% to 0.182%, and the percentage hysteresis error was decreased from the original 4.061% to 0.404%. The research results of this paper offer valuable insight for high-precision resistive angle sensors. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

Urban ventilation corridors play a critical role in improving wind environments, mitigating the urban heat island (UHI) effect, and enhancing urban climate resilience. Traditional Computational Fluid Dynamics (CFD) methods offer high accuracy in simulating wind fields but are computationally intensive and inefficient for large-scale, multi-scenario urban planning tasks. To address this limitation, this study proposes a morphology-driven, machine learning-based framework for ventilation corridor identification. The method integrates Lattice Boltzmann Method (LBM) simulations, neighborhood-based feature normalization, and a random forest regression model to establish a predictive relationship between morphological indices and wind speed distributions under prevailing wind conditions. Input features include raw and log-transformed LBM values, neighborhood-normalized indicators within multiple radii (100–2000 m), and porosity statistics. The model is trained and validated using CFD-simulated wind speeds, with the dataset randomly divided into training (80%), validation (10%), and testing (10%) subsets. The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. Primary corridors are closely aligned with large rivers and lakes, while secondary corridors are shaped by arterial roads and localized open spaces. Compared with conventional approaches such as FAI classification, Least Cost Path (LCP), and circuit theory models, the proposed framework offers higher spatial resolution and better alignment with the CFD results while significantly reducing computational cost. This study demonstrates the feasibility of using morphological and data-driven approaches to support efficient and scalable urban ventilation analysis, providing valuable guidance for climate-responsive urban design. Full article

This work presents the validation of a low-cost measurement system based on an open-ended coaxial SMA (SubMiniature version A) probe for the characterization of complex permittivity in the microwave frequency range. The system combines a custom-fabricated probe, a vector network analyzer, and a dedicated software application that implements three analytical models: capacitive, radiation, and virtual transmission line models. A comprehensive experimental campaign was carried out involving pure polar liquids, saline solutions, and biological tissues, with the measurements compared against those obtained using a high-precision commercial probe. The results confirm that the proposed system is capable of delivering accurate and reproducible permittivity values up to at least 6 GHz. Among the implemented models, the radiation model demonstrated the best overall performance, particularly in biological samples. Additionally, reproducibility tests with three independently assembled SMA probes showed normalized deviations below 3%, confirming the robustness of the design. These results demonstrate that the proposed system constitutes a viable alternative for cost-sensitive applications requiring portable or scalable microwave dielectric characterization. Full article

Unplanned dilution is a critical challenge in underground mining, directly affecting operating costs, resource recovery, stope stability and operational safety. This study presents an empirical–statistical framework that integrates the Mathews–Potvin stability graph, the Equivalent Linear Overbreak/Slough (ELOS) metric, and a site-specific linear calibration to improve dilution prediction in sublevel stoping operations. A database of more than 65 stopes from a Brazilian underground zinc mine was analyzed and classified as cable-bolted, non-cable-bolted, or self-supported. Planned dilution derived from the Potvin graph was compared with actual ELOS measured by cavity-monitoring surveys. Results show a strong correlation between cable-bolted/supported stopes (r = 0.918), whereas non-cabled/unsupported and self-supported stopes display lower correlations (r = 0.755 and 0.767). Applying a site-specific linear calibration lowered the mean absolute dilution error from 0.126 m to 0.101 m (≈20%), with the largest improvement (≈29%) occurring in self-supported stopes where the unadjusted graph is least reliable. Because the equation can be embedded in routine stability calculations, mines can obtain more realistic forecasts without abandoning established empirical workflows. Beyond geotechnical accuracy, the calibrated forecasts improve grade-control decisions, reduce unnecessary waste haulage, and extend resource life—thereby enhancing both the efficiency and the accessibility of mineral resources. This research delivers the first Brazilian case study that couples Potvin analysis with ELOS back-analysis to generate an operational calibration tool, offering a practical pathway for other sites to refine dilution estimates while retaining the simplicity of empirical design. Full article

Arsenic contamination in water poses a significant global health risk, necessitating efficient and sustainable remediation strategies. Arsenic contamination affects groundwater in at least 106 countries, potentially exposing over 200 million people to elevated levels, primarily through contaminated drinking water. Among the most affected regions, Bangladesh remains a critical case study, where widespread reliance on shallow tubewells has resulted in one of the largest mass poisonings in history. Bio-based nanomaterials have emerged as promising solutions due to their eco-friendly nature, cost-effectiveness, and high adsorption capabilities. These nanomaterials offer a sustainable approach to arsenic remediation, utilizing materials like biochar, modified biopolymers, and bio-based aerogels, which can effectively adsorb arsenic and other pollutants. The use of environmentally friendly nanostructures provides a potential option for improving the efficiency and sustainability of arsenic remediation from groundwater. This review explores the mechanisms underlying arsenic remediation using such nanomaterials, including adsorption, filtration/membrane technology, photocatalysis, redox reactions, complexation, ion exchange, and coagulation–flocculation. Despite their potential, challenges such as scalability, stability, and regeneration hinder widespread application. We discuss recent advancements in material design, surface modifications, and hybrid systems that enhance performance. Finally, future perspectives are highlighted, including the integration of these bio-derived systems with smart sensing technologies, sustainable water-treatment frameworks, smart design, and life-cycle integration strategies, particularly for use in resource-constrained regions like Bangladesh and other globally impacted areas. Full article

Healthcare providers performing aerosol-generating procedures (AGPs) face significant infection risks, emphasizing the critical need for effective aerosol containment systems. In this study, we developed and validated a negative pressure chamber enhanced with an innovative aerodynamic cap structure designed to optimize aerosol containment. Initially, computational fluid dynamics (CFD) simulations were performed to evaluate multiple structural improvement ideas, including air curtains, bidirectional suction, and aerodynamic cap structures. Among these, the aerodynamic cap was selected due to its superior predicted containment performance, practical feasibility, and cost-effectiveness. The CFD analyses employed realistic transient boundary conditions, precise turbulence modeling using the shear stress transport (SST) k–ω model, and detailed droplet evaporation dynamics under realistic humidity conditions. A full-scale prototype incorporating the selected aerodynamic cap was fabricated and evaluated using physical polyalphaolefin (PAO) particle leakage tests and biological aerosol validation with aerosolized Bacillus subtilis. For the physical leakage tests, the chamber opening was divided into nine sections, and the aerosol dispersion was tested in three distinct directions: ceiling-directed, toward the suction hole, and opposite the suction hole. These tests demonstrated significantly stabilized airflow and substantial reductions in aerosol leakage, consistently maintaining containment levels below the critical threshold of 0.3%, especially under transient coughing conditions. The biological aerosol experiments, conducted in a simulated emergency department environment, involved aerosolizing bacteria continuously for one hour. The results confirmed the effectiveness of the aerodynamic cap structure in achieving at least a one millionth (10⁻⁶) reduction in the aerosolized bacterial leakage compared to the control conditions. These findings highlight the importance and effectiveness of advanced CFD modeling methodologies in accurately predicting aerosol dispersion and improving containment strategies. Although further studies assessing the structural durability, long-term operational ease, and effectiveness against pathogenic microorganisms are required, the aerodynamic cap structure presents a promising, clinically practical infection control solution for widespread implementation during aerosol-generating medical procedures. Full article

The process of conceptualisation and prototyping of electric energy products is demanding due to the need for a multifaceted approach to product design. This task becomes even more complex during sustainable development, within which supporting techniques are sought. Energy conversion products such as electric motorcycles require special attention due to their impact on energy efficiency, environmental emissions, and operating and production costs. The research gap refers to the lack of a model to aggregate these aspects simultaneously. The objective of the research was to develop a CQ-LCA model (Cost–Quality–Life Cycle Assessment) supporting the creation of alternative product solutions and their evaluation in terms of the following: (i) environmental impact in the life cycle (LCA), (ii) quality, and (iii) production and/or purchase costs. The model was developed in seven main stages and tested for electric motorcycles and their ten prototypes, which are examples of modern products that convert electrical energy into mechanical energy. Using the EDAS method, the quality of electric motorcycle prototypes was calculated. Then, by the LCA method according to ISO 14040, the CO₂ emissions were estimated and modelled adequately to quality change. Next, by the parametric model based on the static method and the cost value function, including the nominal least squares method, the cost was estimated adequately to quality and environmental change. The model provided a qualitative and quantitative interpretation of electric motorcycle prototypes (CQ-LCA), allowing for the consideration of product characteristics, such as engine power, charging time, and battery capacity, but also environmental impacts and costs. The originality is the provision of a multi-aspect morphological analysis, after which different scenarios of product solutions. The model can be useful for various commonly used energy-converting products. Full article