Limited Capacity Research Articles

Associations of hypertension with clinical and instrumental characteristics in the ukrainian cohort of patients hospitalized for COVID-19

long COVID-19 syndrome causes sustained limitations in activity and work capacity in a significant proportion of convalescents, the risk of which is higher among patients who required hospitalization. Phenotyping of COVID-19 survivors based on the most common comorbidities’ role in shaping the risks of the acute phase endpoints and development of post-covid syndrome can be used to improve the efficacy of predicting the course of the disease. To study the associations of hypertension (HT) with the clinical features of acute COVID-19 in hospitalized patients, severity of residual functional disorders and pulmonary affection by the time of discharge. Material and methods. 221 hospitalized patients with COVID-19 (age 53,4 ± 13,6 years, 53% female) were examined 1-2 days before discharge. The study protocol included interview and analysis of medical documentation to retrospectively extract the anamnestic data and clinical features of the acute phase; lung ultrasound, reactive hyperemia test of the brachial artery, and a 6-minute walk test. Results. The most frequently identified comorbidities in the study cohort were HT, obesity, and type 2 diabetes, which were present in 40%, 38%, and 10% of participants, respectively. Hypertensive patients were older (57,8 ± 11,8 vs 50,7 ± 13,9 years, р < 0,001), had higher BMI (31,7 ± 5,3 vs 31,7 ± 5,3 кг/м2, р < 0,001), higher prevalence of obesity (59% vs 25%, p < 0,001) and type 2 diabetes (21% vs 2%, p < 0,001) compared to normotensive participants, but were characterized by comparable laboratory indices, parameters of pulmonary parenchyma affection (by CT scan and lung ultrasound), endothelial function, SpO2 levels during the acute phase of the disease and by the time of discharge, as well as age- and anthropometric parameters-adjusted 6-minute walk distance (67,4 ± 10,5 % vs 69,5 ± 13,6 %, p > 0,05). Hypertensive patients hospitalized for COVID-19 were characterized by older age, higher prevalence of obesity and type 2 diabetes compared to normotensive participants but did not differ in their laboratory profile (including markers of systemic inflammation, prothrombotic activation, renal function, and liver damage), levels of pulmonary affection by CT and lung ultrasound, and endothelium-dependent vasodilatation in a reactive hyperemia test of the brachial artery. The observed decrease in the 6-minute walk distance in patients with HT was explained by differences in age and anthropometric parameters.

Enhanced metal-free photocatalyst performance by synergistic Coupling of internal magnetic field and piezoelectric field

Graphitic carbon nitride (g-C3N4) is a promising metal-free photocatalyst; however, its high carrier recombination rate and insufficient redox capacity limit its degradation effect on antibiotics. In order to overcome these shortcomings, the photocatalytic activity is improved by regulating the spin polarization state, constructing the internal electric field, and applying the external piezoelectric field. In this paper, the chlorine-doped and nitrogen-deficient porous carbon nitride composite carbon quantum dots (Nv-Cl/UPCN@CQD) has been synthesized successfully. The doping position of chlorine and spin polarization properties are verified by DFT calculation. The key intermediates *O2− and *OOH for the synthesis of reactive oxygen species were detected by in-situ infrared testing, which promotes the production of •O2− and H2O2. The degradation rate constant of Nv-Cl/UPCN@CQD for removal of tetracycline is 8.45 times higher than that of g-C3N4. The active oxygen production and degradation efficiency of piezoelectric photocatalysis under the synergistic effect of intense stirring and vis-light irradiation are much higher than those of photocatalysis and piezoelectric catalysis, and the conversion of H2O2 to •OH is promoted by piezoelectric field. This paper provides a reliable way to improve the performance of piezoelectric photocatalysts by adjusting their energy band, electronic structure and piezoelectric force.

Unrevealing the potential of aquatic macrophytes for phytoremediation in heavy metal-polluted wastewater

Aquatic macrophytes including Eichhornia crassipes, Pistia stratiotes, and Lemna minor have shown promise in phytoremediation for wastewater treatment and heavy metal removal. This approach may reduce pollution. However, plant metabolic rate and ability to break down organic compounds or tolerate heavy metals are limited. Biotechnology can significantly improve these issues. Scientists can alter plants to improve phytoremediation using genetic engineering. Direct gene transfer allows certain genes to be introduced into plants, giving them advantages. Introducing genes involved in metal absorption, transport, and detoxification into plants may increase their ability to store and survive heavy metals. Introducing genes that encode enzymes that break down organic pollutants can also improve decomposition. Transgenic plants with improved metabolic pathways, biomass yield, and stress tolerance have been created using biotechnology. These advances benefit phytoremediation. Selecting acceptable plant species for genetic modification is crucial. Aquatic macrophytes can produce a lot of biomasses, grow quickly, and absorb nutrients, making them excellent for genetic engineering in phytoremediation. In general, biotechnology can improve phytoremediation by overcoming plant capacity restrictions. The ecological and regulatory effects of introducing genetically modified organisms into the environment must be considered. The safe and sustainable use of genetically modified plants for phytoremediation requires collaboration between biotechnologists, environmental scientists, and legislators.

The effectiveness of community health worker training, equipping, and deployment in reducing COVID-19 infections and deaths in rural Western Kenya: A comparison of two counties.

COVID-19 and other pandemics remain significant threats to population health, particularly in rural settings where health systems are disproportionately weak. There is a lack of evidence on whether trained, equipped, and deployed community health workers (CHWs) can lead to significant reductions in COVID-19 infections and deaths. Our objective was to measure the effectiveness of deploying trained and equipped CHWs in reducing COVID-19 infections and deaths by comparing outcomes in two counties in rural Western Kenya, a setting with limited critical care capacity and limited access to COVID-19 vaccines and oral COVID-19 antivirals. In Siaya, trained CHWs equipped with thermometers, pulse oximeters, and KN95 masks, visited households to convey health information about COVID-19 prevention. They screened, isolated, and referred COVID-19 cases to facilities with oxygen capacity. They measured and digitally recorded vital signs at the household level. In Kisii county, the standard Kenya national COVID-19 protocol was implemented. We performed a comparative analysis of differences in CHW skills, activity, and COVID-19 infections and deaths using district health information system (DHIS2) data. Trained Siaya CHWs were more skilled in using pulse oximeters and digitally reporting vital signs at the household level. The mean number of oxygen saturation measurements conducted in Siaya was 24.19 per COVID-19 infection; and the mean number of temperature measurements per COVID-19 infection was 17.08. Siaya CHWs conducted significantly more household visits than Kisii CHWs (the mean monthly CHW household visits in Siaya was 146,648.5, standard deviation 11,066.5 versus 42,644.5 in Kisii, standard deviation 899.5, p value = 0.01). Deploying trained and equipped CHWs in rural Western Kenya was associated with lower risk ratios for COVID-19 infections and deaths: 0.54, 95% CI [0.48-0.61] and 0.29, CI [0.13-0.65], respectively, consistent with a beneficial effect.

Variable, sometimes absent, but never negative: Applying multilevel models of variability to the backward crosstalk effect to find theoretical constraints

When performing two tasks at the same time, the congruency of the second task's features influences performance in the first task. This is called the backward crosstalk effect (BCE), a phenomenon that influences both theories of binding and of dual-task capacity limitations. The question of whether the BCE is found in all participants at all times is relevant for understanding the basis of the effect. For example, if the BCE is based on strategic choices, it can be variable, but if it is automatic and involuntary, it should never vary in whether it is present or not. Variability in observed BCE sizes was already documented and discussed when the group average effect was first reported (Hommel, 1998). Yet the theories discussed at the time did not motivate a more direct analysis of this variability, nor did the readily available statistical tools permit it.Some statistical approaches recently applied in cognitive psychology allow such a variability-focused analysis and some more recent theoretical debates would benefit from this as well. We assessed the variability of the BCE as well as a BCE-like free-choice congruency effect by applying a Bayesian multilevel modeling approach to the data from a dual-tasking experiment. Trials consisted of a two- and a four-response task. We manipulated which task was presented first and whether the response to the four-choice task was free or forced choice.RT data were best predicted by a model in which the BCE is zero in part of the population and drawn from a normal distribution truncated at zero (and thus always positive) in the rest of the population. Choice congruency bias data were best predicted by a model assuming this effect to be drawn from a normal distribution truncated at zero (but, in contrast to the RT data, without the subset of the population where it is zero).The BCE is not an inflexible and universal phenomenon that is directly linked to an inherent structural trait of human cognition. We discuss theoretical constraints implied by these results with a focus on what we can infer about the traits of the factors that influence BCE size. We suggest that future research might add further major constraints by using multi-session experiments to distinguish between-person and within-person variability. Our results show that the BCE is variable. The next step is understanding along which axes it is variable and why it varies.

A Mixed-integer Linear Programming Model for Solving Multi-item Uncapacitated Lot-sizing Problems with Storage Capacities Limitations: A Case Study of an Apparel Manufacturing Company

This study aimed to use a mixed-integer linear programming model to solve the multi-item uncapacitated lot size problem with storage capacity limitations for apparel manufacturing. In real-world operations, sometimes the storage capacity of cloth rolls can be insufficient at any given time. This would be because the sum of the remaining cloth rolls from the previous period and the receiving cloth rolls from the existing period would be a vast number. Furthermore, the extra cloth on the rolls would have to be carried and kept in the production line. Consequently, this caused the late issue of the distribution of the material, the loss of the cloth rolls, and the failure to count the cloth rolls. As a result, we conducted a multi-item cloth roll replenishment plan to meet the production line's demand and keep all the inventory and receiving cloth rolls. Using mixed-integer linear programming, the multi-item lot size of each period and the sum of the cloth, ordering, and holding costs was calculated, including the number of occupied cloth racks that did not exceed the number of available cloth racks in the cloth storage. Then, this result was compared with the calculation of a mixed-integer linear programming model based on the procurement policies of a case study company. This study used OpenSolver and GAMS/CPLEX software to calculate the solutions. The results found that in using the procurement policy, we could provide the maximum number of occupied cloth racks, about 368 racks, or 2.78 times the racks on the available shelves of the cloth storage. The result of the proposed model indicated that the number of occupied cloth racks to be installed did not exceed the number of available racks on the shelves. The total cost of the model of the procurement policy was less than the proposed model by about 0.26 percent because ordering occurred only about 25 times.

Working Memory Capacity of ChatGPT: An Empirical Study

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory.

Learning to Learn Better Visual Prompts

Prompt tuning provides a low-cost way of adapting vision-language models (VLMs) for various downstream vision tasks without requiring updating the huge pre-trained parameters. Dispensing with the conventional manual crafting of prompts, the recent prompt tuning method of Context Optimization (CoOp) introduces adaptable vectors as text prompts. Nevertheless, several previous works point out that the CoOp-based approaches are easy to overfit to the base classes and hard to generalize to novel classes. In this paper, we reckon that the prompt tuning works well only in the base classes because of the limited capacity of the adaptable vectors. The scale of the pre-trained model is hundreds times the scale of the adaptable vector, thus the learned vector has a very limited ability to absorb the knowledge of novel classes. To minimize this excessive overfitting of textual knowledge on the base class, we view prompt tuning as learning to learn (LoL) and learn the prompt in the way of meta-learning, the training manner of dividing the base classes into many different subclasses could fully exert the limited capacity of prompt tuning and thus transfer it power to recognize the novel classes. To be specific, we initially perform fine-tuning on the base class based on the CoOp method for pre-trained CLIP. Subsequently, predicated on the fine-tuned CLIP model, we carry out further fine-tuning in an N-way K-shot manner from the perspective of meta-learning on the base classes. We finally apply the learned textual vector and VLM for unseen classes.Extensive experiments on benchmark datasets validate the efficacy of our meta-learning-informed prompt tuning, affirming its role as a robust optimization strategy for VLMs.

Development of a three-stage hierarchical model for quick calculating stair evacuation time of high-rise building coupled with simulation analysis

Calculating the evacuation time during staircase evacuation is essential to evaluate the security conditions of high-rise buildings under emergency scenarios. A three-stage hierarchical model for calculating emergency evacuation time is constructed by combining discrete state update rules and continuous forms in computation to investigate the evacuation process of pedestrians in multi-floor buildings in emergency circumstances. Combined with simulation analysis, the model considers the capacity constraint of stairwell and congestion effects and calculates the evacuation time hierarchically. The model's validity is verified by the existing evacuation simulator for a hypothetical building case study, coupled with a comprehensive computational comparison. Calculations on entire and separate evacuation times are explored considering different stairway capacity restrictions and geometric features. Computational results demonstrated that this model is effective and may be regarded as a valid tool for staircase evacuation time calculation in the context of high-rise buildings.

Developmental Changes in Executive Control: Investigating Dual-Task Interference During Motor and Central Cognitive Processing with the Overlapping Task Paradigm

ABSTRACT Previous studies demonstrated that dual-task impairments are higher in children than in young adults. A previous study systematically assessed the sources of these larger dual-task impairments by identifying age-related differences in capacity limitations during dual-task processing. Capacity limitations in central cognitive processes were present in both age groups, while perceptual capacity limitations were larger in children than in young adults. However, the literature lacks further confirmations and explanations for increased dual-task impairments in children. Moreover, this previous study was not informative regarding age-related differences in potential capacity limitations during central cognitive processing in relation to limitations during motor processing. Therefore, in a German context, the present study investigated children (N = 32) and young adults (N = 32) by combining visual (Task 1) and auditory (Task 2) sensorimotor tasks, separated by a varying stimulus onset asynchrony, in dual tasks of the psychological refractory period (PRP) type. Performance differences were assessed by manipulations of task difficulty in Task 1. The results replicated the finding of increased dual-task impairments in children compared with young adults. Furthermore, the manipulations in the PRP dual task resulted in similar effects in both age groups. Thus, these findings confirmed the presence of capacity limitations during central cognitive processing, while there were no processing limitations in motor processing between children and young adults.

Advanced oxidation of polycyclic aromatic hydrocarbons in tropical soil: Self-catalytic utilization of natural iron contents in an oxygenation reactor supported with persulfate

The catalysts derived from natural iron minerals in the advanced oxidation process offer several advantages. However, their utilization in soil remediation is restricted due to the presence of soil impurities, which can inhibit the catalytic activity of these minerals. The soils in tropical regions exhibit lower organic matter content, limited cation exchange capacity, and are non-saline, this enhances the efficiency of utilizing natural iron minerals from tropical soil as a catalyst. In this regard, the catalytic potential of naturally iron-bearing tropical soil was investigated to eliminate phenanthrene (PHE), pyrene (PYR), and benzo[α]pyrene (B[α]P) using an oxygenated reactor supported with persulfate (PS). The system showed an efficient performance, and the removal efficiencies under the optimum conditions were 81 %, 73 %, and 86 % for PHE, PYR, and B[α]P, respectively. This indicated that the catalytic activity of iron was working efficiently. However, there were changes in the soil characteristics after the remediation process such as a significant reduction in iron and aluminum contents. The scavenging experiments demonstrated that HO• had a minor role in the oxidation process, SO4•− and O2•− emerged as the primary reactive species responsible for the effective degradation of the PAHs. Moreover, the by-products were monitored after soil remediation to evaluate their toxicity and to propose degradation pathways. The Mutagenicity test showed that two by-products from each PHE and B[α]P had positive results, while only one by-product of PYR showed positive. The toxicity tests of oral rat LD50 and developmental toxicity tests revealed that certain PAHs by-products could be more toxic from the parent pollutant itself. This study represents a notable progression in soil remediation by providing a step forward in the application of the advanced oxidation process (AOP) without requiring additional catalysts to activate oxidants and degrade pollutant PAHs from the soil.

Seismic fragility and vulnerability assessment of a multi-span irregular curved bridge under spatially varying ground motions

Multi-span reinforced concrete (RC) curved box-girder bridges are commonly designed to facilitate traffic flow at highway interchanges. The Aksemsettin Viaduct (henceforth, A Viaduct for brevity) in Istanbul, Turkey, is an eleven-span interchange bridge with a total length of 596.8 m. Located in a high seismicity zone, the A Viaduct is designed with a curved deck, multiple bearings that have different isolation mechanisms at different bents and directions, ten rectangular columns with unequal heights, and a mix of pile foundations and spread footings. The significant length of the viaduct crossed by eleven spans also makes it susceptible to varying ground motion excitations at different foundations. To evaluate the effects of the degree of modeling detail and analysis complexity on the estimated seismic performance, the present study conducts a comprehensive fragility assessment of the specimen viaduct under various ground motion excitation schemes. First, a three-dimensional finite element model is developed with detailed simulations for the deck, columns, bearings, foundations, and abutment components. To enable different ground motion excitations at each foundation, 57 sets of spatially varying ground motions are simulated by considering the realistic surface topography and soil stratigraphy at the bridge site. Cyclic pushover analyses are performed along multiple loading directions to develop the direction-dependent capacity limit state models for hollow rectangular columns. Subsequently, a demand-capacity ratio method is utilized to develop reliable fragility models for bridge columns. Component- and system-level fragilities of the A Viaduct are then assessed under uniform versus multi-support excitations, vertical motions, and ground motions with varying incidence angles. To further capture the seismic damage discrepancies of the same components at different locations, seismic repair cost ratios of the A Viaduct are assessed when subjected to uniform and multi-support excitations. This study highlights the significance of considering multi-support excitations to achieve more realistic seismic fragility and loss estimates for multi-span long curved highway bridges.

Integrating economic load dispatch information into the blockchain smart contracts based on the fractional-order swarming optimizer

The modern power generation systems are increasing their reliance on high penetrations of distributed energy resources (DERs). However, the optimal dispatching mechanisms mainly rely on central controls which receive the load demand information from the electricity utility providers and allocate the electricity production targets to participating generating units. The lack of transparency and control over the DER fuel inputs makes the physical power purchase agreements (PPAs) a cumbersome task. This research work proposes an innovative fractal moth flame optimization (FMFO) approach to tackle the problem of integrated load dispatch (ILD). The proposed methodology provides a mechanism to integrate the information of the proposed optimizer, i.e., FMFO into the smart contracts enabled by the blockchain technology. This problem entails the allocation of loads to power-generating units in a manner that minimizes the total generation cost in a decentralized manner. To improve the efficiency of dispatch operations in the presence of a substantial integration of wind energy, this study proposes a novel framework based on the principles of fractal heritage, drawing inspiration from the classical MFO method. To assess the effectiveness and adaptability of the algorithm suggested, various non-convex scenarios in the context of optimization for ILD are considered. These scenarios incorporate valve-point loading effects (VPLEs), capacity limitations, power plants with multiple fuel options, and the presence of stochastic wind (SW) power uncertainty, following a Weibull distribution. The findings demonstrate exceptional performance in terms of minimizing fuel generation costs compared to traditional algorithms.

Bifunctional MNPs@UIO-66-Arg core-shell-satellite nanocomposites for enrichment of phosphopeptides.

A facile and mild method based on self-assembled lysozyme (LYZ) to fabricate bifunctional MNPs@UIO-66-Arg core-shell-satellite nanocomposites (CSSNCs) is reported for the high-efficiency enrichment of phosphopeptides. Under physiological conditions, LYZ rapidly self-assembled into a robust coating on Fe3O4@SiO2 magnetic nanoparticles (MNPs) with abundant surface functional groups, which effectively mediate heterogeneous nucleation and growth of UIO-66 nanocrystals. Well-defined MNPs@UIO-66 CSSNCs with stacked pores, showing high specific surface area (333.65 m2 g- 1) and low mass transfer resistance, were successfully fabricated by fine-tuning of the reaction conditions including reaction time and acetic acid content. Furthermore, the UIO-66 shells were further modified with arginine to obtain bifunctional MNPs@UIO-66-Arg CSSNCs. Thanks to the unique morphology and synergistic effect of Zr-O clusters and guanidine groups, the bifunctional MNPs@UIO-66-Arg CSSNCs exhibited outstanding enrichment performance for phosphopeptides, delivering a low limit of detection (0.1 fmol), high selectivity (β-casein/BSA, mass ratio 1:2000), and good capture capacity (120 mg g- 1). The mechanism for phosphopeptides capture may attribute to the hydrogen bonds, electrostatic interactions, and Zr-O-P bonds between phosphate groups in peptides and guanidyl/Zr-O clusters on bifunctional MNPs@UIO-66-Arg CSSNCs. In addition, the small stacking pores on the core-shell-satellite architecture may selectively capture phosphopeptides with low molecular weight, eliminating interference of other large molecular proteins in complex biological samples.

Exogenous Collagen Crosslinking is Highly Detrimental to Articular Cartilage Lubrication.

Healthy articular cartilage is a remarkable bearing material optimized for near-frictionless joint articulation. Because its limited self-repair capacity renders it susceptible to osteoarthritis (OA), approaches to reinforce or rebuild degenerative cartilage are of significant interest. While exogenous collagen crosslinking (CXL) treatments improve cartilage's mechanical properties and increase its resistance to enzymatic degradation, their effects on cartilage lubrication remain less clear. Here, we examined how the collagen crosslinking agents genipin (GP) and glutaraldehyde (GTA) impact cartilage lubrication using the convergent stationary contact area (cSCA) configuration. Unlike classical configurations, the cSCA sustains biofidelic kinetic friction coefficients (μk) via superposition of interstitial and hydrodynamic pressurization (i.e., tribological rehydration). As expected, glutaraldehyde- and genipin-mediated CXL increased cartilage's tensile and compressive moduli. Although net tribological rehydration was retained after CXL, GP or GTA treatment drastically elevated μk. Both healthy and "OA-like" cartilage (generated via enzymatic digestion) sustained remarkably low μk in saline- (≤0.02) and synovial fluid-lubricated contacts (≤0.006). After CXL, μk increased up to 30-fold, reaching values associated with marked chondrocyte death in vitro. These results demonstrate that mechanical properties (i.e., stiffness) are necessary, but not sufficient, metrics of cartilage function. Furthermore, the marked impairment in lubrication suggests that CXL-mediated stiffening is ill-suited to cartilage preservation or joint resurfacing.

A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power

AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch.

Sustainable waste collection optimization using electric vehicles

Solid waste management is a crucial municipal service directly influencing urban livability. Optimizing refuse collection routes enhances service efficiency and reduces costs. The recent introduction of fully electric refuse trucks, boasting features like zero emissions, low noise, and lower operating costs compared to diesel trucks, holds promise. However, their limited autonomy presents challenges that need addressing before widespread deployment. This study proposes a realistic formulation for the refuse collection routing problem with electric trucks, introducing electromobility constraints never considered before in related works. The goal is to find optimal routes covering all collection points while minimizing travel distance within vehicle battery and capacity limits. An accurate energy consumption model is presented, accounting for route elevation, vehicle speed, waste collection operations, and regenerative braking. Genetic algorithms are used to find solutions to the problem, as well as the well-known Clarke and Wright heuristic for comparison purposes. The algorithms are tested on six diverse instances reflecting real-world scenarios in New York City (United States) and Puerto Real (Spain). Results indicate the genetic algorithm’s superiority, yielding solutions with reduced travel distance and consumption while requiring fewer routes compared to the state-of-the-art heuristic.

Studying the Improvement of Solar Collector Mechanism with Phase Change Materials

This study delves into the integration of phase change materials (PCM) in solar thermal collector systems to address this challenge. By incorporating nano encapsulated PCMs, researchers have mitigated concerns surrounding PCM leakage, revolutionizing the potential of solar collector systems to elevate energy efficiency, diminish carbon emissions, and yield manifold benefits. This article comprehensively investigates the design and utilization of solar phase change energy storage devices and examines the transformative impact of employing nano-coated phase change materials (Nano capsules) to augment solar collector performance. The integration of paraffin-based PCM and the insulation of the collector system have been crucial in optimizing heat retention and operational efficacy. The composition of the PCM involves a balanced blend of octadecane phase-change particles and water as the base fluid, designed to maximize thermal performance. Analysis of the experimental findings demonstrates the dynamic thermal behavior of the nano encapsulated phase change material, revealing distinctive temperature profiles about fluid dynamics and absorbent characteristics. Notably, the study emphasizes the nuanced trade-offs associated with the conductivity and melting temperature of the Nano encapsulated PCM, yielding valuable insights into energy storage capacity limitations and thermal performance variations throughout diurnal cycles. Central to the investigation, the optimal nanoparticle proportion is elucidated, shedding light on its pivotal role in modulating PCM performance. Furthermore, findings underscore the complex interplay between nanoparticle volume fraction and thermal fluid temperature, providing critical perspectives on optimizing PCM-enhanced solar collector systems.

A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities.

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies.

Is Africa Ready to Use Phycoremediation to Treat Domestic Wastewater as an Alternative Natural Base Solution? A Case Study

This review outlines the potential of phycoremediation as a natural, cost-effective solution for domestic wastewater treatment in Africa, particularly focusing on its application in less densely populated and rural areas. The urgency of improving sanitation access, a key objective in both the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030), is underscored by the fact that half of Africa’s population suffers from diseases linked to inadequate water and sanitation facilities. South Africa, a focal point of this study, faces significant challenges in wastewater management. These include the limited capacity of wastewater treatment plants to handle the burgeoning wastewater volumes due to population growth, unregulated discharges causing fluctuating pollution levels, and high operational costs leading to improper sludge disposal and odor issues. Compounding these problems are frequent power outages, financial constraints impacting wastewater treatment plant operations and maintenance across Africa, and a lack of skilled personnel to manage these facilities.