Background: The increasing prevalence of Mycoplasma genitalium infections with macrolide resistance causing high azithromycin failure rates is a major concern internationally. In response to this challenge, diagnostics that simultaneously detect M. genitalium and genetic markers for macrolide resistance enable the therapy to be individually tailored, i.e. to implement resistance-guided therapy (RGT). Objectives: This study aimed to evaluate patient treatment outcomes of M. genitalium therapy guided by a macrolide-resistance assay in Bulgaria; Methods: Consecutively referred M. genitalium infection cases (n=17) were analyzed for macrolide-resistance mutations (MRMs) and specific antimicrobial treatment was recommended accordingly (MRMs-negative infections received azithromycin and MRMs-positive received moxifloxacin). Treatment outcome based on test-of-cure was recorded and treatment failure rates and time to microbiological cure were compared with treatment outcomes in patients treated before the implementation of RGT; Results: Among patients given RGT (n=17), the overall treatment failure rate was 1/17 (5.9%). This was significantly lower than the rate (47.6%) observed in patients treated pre-RGT (p=0.002). The time to microbiological cure was 29.4 days (CI 24.5 – 34.3) compared to 45.2 days (CI 36.5 – 53.7) pre-RGT (p=0.001); Conclusions: Implementation of M. genitalium diagnostics with macrolide-resistance detection improved treatment outcomes in Bulgaria with significantly lower treatment failure rates and reduced time to microbiological cure. In light of limited treatment options and concerns about their decreasing efficacy in response to misuse and overuse, diagnostic macrolide-resistance assay is critical to direct appropriate first-line treatment, to maintain the efficacy of antimicrobial treatment (antibiotic stewardship) and to minimize the spread of antimicrobial resistance.

The capability of the complex fuzzy sets plays a valuable role to resolve many real life problems. In this paper, we present the compositions of complex fuzzy relations by using the idea of implication operators and max-product compositions of complex fuzzy relations and illustrate these compositions with concrete examples. The converse of these newly invented triangular compositions in terms of compositions of the converse relations are also defined. We also study the interactions with union and intersection. The main goal of this article to present a new technique to enhance medical diagnostic model that can assist to increase the features of healthcare systems. We utilize these compositions to determine the diseases of patients on the basis of intensity of symptoms.

Low-density parity-check (LDPC) codes form part of the IRIG-106 standard and have been successfully deployed for the Telemetry Group version of shaped-offset quadrature phase shift keying (SOQPSK-TG) modulation. Recently, LDPC code solutions have been proposed and optimized for continuous phase modulations (CPMs), including pulse code modulation/frequency modulation (PCM/FM) and the multi-h CPM developed by the Advanced Range TeleMetry program (ARTM CPM). These codes were shown to perform around one dB from the respective channel capacities of these modulations. In this paper, we consider the effect of random puncturing and shortening of these LDPC codes to further improve spectrum efficiency. We perform asymptotic analyses of the ARTM0 code ensembles and present numerical simulation results that affirm the robust decoding performance promised by LDPC codes designed for ARTM CPM.

Two-phase evaporative spray cooling technology can significantly reduce power consumption in data centre cooling applications. However, the literature lacks an established methodology for assessing the overall performance of such evaporation systems. The current study develops a Lagrangian-Eulerian computational fluid dynamics modelling approach to examine the functionality of these two-phase evaporative spray cooling systems. To replicate a modular system, a hollow spray cone nozzle with Rosin-Rammler droplet size distribution is simulated in a turbulent convective natural-air environment. The model was validated against the available experimental data from the literature. A new formulation for the coefficient of performance (COP) is derived to assess the performance of the system. Parametric studies of geometric, flow and climate parameters, namely, domain length, droplet size, water mass flow rate, temperature, and humidity were performed with a specific focus on the climatic conditions in Dublin, Ireland. The findings indicate that at elevated temperatures and low humidity, evaporation results in a bulk temperature reduction of up to 12℃. The efficiency of the evaporation system showed a systematic improvement with a reduction in the droplet size and the mass flow rate. A close to 100 % evaporation rate was achieved in comparison to only a 1 % evaporation rate when the spray Sauter mean diameter (SMD) is reduced to 8-15 μm from 292 μm. It was concluded that the utilization of a fine droplet spray nozzle provides an effective solution for the reduction in water consumption (97 % in our case) for data centres, whilst concomitantly augmenting the proportion of evaporation.

Physical literacy (PL) is increasingly recognised as essential to fostering lifelong physical activity (PA) engagement, particularly when nurtured in early childhood. However, there remains limited understanding of how key stakeholders in early years (EY) education perceive, value, and implement a PL informed approach. This exploratory study aimed to investigate a variety of EY stakeholders' knowledge and beliefs regarding PL and PA in relation to 3-5-year-olds. A concurrent mixed-methods approach was used, incorporating semi-structured expert interviews (n=11), focus groups (n=22), and a survey (n=210) to gather both qualitative and quantitative data. Thematic analysis was applied to identify key themes, and survey data was analysed to complement and triangulate qualitative findings. Findings revealed variation in stakeholders' awareness of PA recommendations, confusion over terminology, and the influence of environmental and policy constraints on practice. Stakeholders acknowledged the importance of PL but expressed uncertainty about the connection between academic theory and practical application. Resource limitations, conflicting priorities, and lack of training and policy support emerged as key barriers. Feedback on an educational PL-EY model was generally positive, emphasising its potential educational value while underscoring the need for clearer guidance and support. Given the pivotal role of EY education in shaping children’s behaviours, health, and well-being, this study highlights the necessity of a holistic approach to interventions, strong stakeholder involvement, and evidence-based practices to foster PL in EY children and raises critical questions about what effective interventions to develop PL in this age group should consider and look like.

Post-exercise recovery is fundamental to optimizing athletic performance, focusing on muscle repair, glycogen replenishment, rehydration, and reducing exercise-induced inflammation. This review examines the evolving landscape of recovery nutrition, highlighting the transition from conventional supplements—such as protein, carbohydrates, creatine, and branched-chain amino acids (BCAAs)—to functional foods rich in bioactive compounds. Emerging evidence supports the efficacy of functional foods like tart cherry juice, turmeric, and omega-3 fatty acids in mitigating oxidative stress and inflammation, thus accelerating recovery. Additionally, probiotics and prebiotic-rich foods are gaining recognition for enhancing gut health, promoting nutrient absorption, and strengthening immune function, which are crucial in recovery processes. The concept of personalized nutrition, guided by genetic and metabolic profiling, is explored as a promising approach to tailor recovery strategies to individual physiological needs. Continued research into the long-term effects of supplements, the role of functional foods, and nutrient interactions is essential for developing more comprehensive and individualized recovery protocols. Integrating functional foods with personalized nutrition offers a holistic strategy to enhance recovery, optimize performance, and promote long-term health in athletes.

Cardiac ischemia-reperfusion injury (IRI) occurs when blood flow is restored to the myocardium after a period of ischemia, leading to oxidative stress and subsequent myocardial cell damage, primarily due to the accumulation of reactive oxygen species (ROS). In our previous research, we identified that miR-25 is significantly overexpressed in pressure overload-induced heart failure, and its inhibition improves cardiac function by restoring the expression of SERCA2a, a key protein involved in calcium regulation. In this study, we aimed to investigate the role of miR-25 in the context of ischemia-reperfusion injury. We found that miR-25 was markedly upregulated under hypoxic conditions in both in vitro and in vivo models. Through in silico analysis, we identified Sestrin3 (SESN3), an antioxidant protein known for its protective effects against oxidative stress, as a novel target of miR-25. Based on these findings, we hypothesized that inhibiting miR-25 would restore Sestrin3 expression, thereby reducing ROS-induced myocardial cell damage and improving cardiac function. To test this hypothesis, we employed two model systems: a hypoxia/reoxygenation (H/R) stress model using H9c2 myoblasts and a surgically induced ischemia-reperfusion injury mouse model. Our results demonstrated that the use of miR-25 inhibitors significantly improved cardiac function and reduced myocardial damage in both models through the restoration of SESN3 expression. In conclusion, our findings suggest that targeting miR-25 may serve as a novel therapeutic modality to alleviate oxidative damage in the heart.

Mergers and acquisitions (M&A) represent pivotal moments for businesses, with the potential to unlock growth, synergies, and market power. However, the success of such transactions is heavily dependent on the robustness of the contractual agreements that define them. Poorly designed contracts introduce numerous risks that can derail the benefits of the deal, leading to financial losses, legal disputes, or operational inefficiencies. This work examines the core risks associated with inadequate contract design in M&A, supported by academic research.

Single crystal nickel-based superalloys are extensively used in turbine blade applications 1 due to their superior creep resistance compared to their polycrystalline counterparts. With the high 2 creep resistance, High Cycle Fatigue (HCF) and Low Cycle Fatigue (Single crystal nickel-based 3 superalloys are extensively used in turbine blade applications due to their superior creep resistance 4 compared to polycrystalline counterparts. As a result, fatigue becomes the primary failure mechanism 5 in such applications. Specifically, High Cycle Fatigue (HCF) and Low Cycle Fatigue (LCF) are of 6 significant concern under these conditions. This study investigates the fatigue life prediction of 7 CMSX-4 using a combined crystal plasticity and lifetime assessment model. The constitutive crystal 8 plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while 9 a modified Chaboche damage model is used for lifetime assessment, incorporating anisotropy by 10 focusing on cleavage stresses on active slip planes. Both qualitative and quantitative data obtained 11 from HCF experiments on single crystal superalloys with notched geometry were used to validate 12 the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy 13 of lifetime predictions across varying temperatures by analyzing the stress-strain data obtained from 14 simulations. The integration of crystal plasticity, damage mechanics, and ANNs resulted in accurate 15 predictions of fatigue life and crack initiation points under complex loading conditions of single 16 crystal superalloys.

Biofilm-associated infections caused by Candida species represent a significant clinical challenge due to their inherent resistance to antifungal therapies. This study evaluates the antifungal efficacy of trans-Cinnamaldehyde, a natural compound derived from Cinnamomum cassia, in comparison to the widely used antifungal agent Nystatin, against both planktonic and biofilm forms of Candida albicans, Candida glabrata, and Candida tropicalis. Biofilms were established in 96-well polystyrene plates and treated with various concentrations of trans-Cinnamaldehyde and Nystatin. The Minimum Biofilm Inhibitory Concentration (MBIC) and planktonic minimum inhibitory concentration (PMIC) were determined using the MTT assay and Scanning Electron Microscopy (SEM) was employed to assess structural changes in biofilms. The results demonstrated that Nystatin exhibited superior antifungal efficacy across all tested species, particularly in biofilm inhibition, with MBIC50 values as low as 0.0015 mg/mL for C. albicans and C. glabrata. In contrast, trans-Cinnamaldehyde showed moderate antifungal activity with MBIC50 values of 0.16 mg/mL for C. albicans and C. glabrata and higher resistance observed in C. tropicalis (MBIC50 of 0.32 mg/mL). SEM analysis revealed significant biofilm disruption in C. albicans following exposure to trans-Cinnamaldehyde, but C. tropicalis exhibited more resilient biofilm structures. Overall, while Nystatin remains the gold standard for antifungal treatment, trans-Cinnamaldehyde presents potential as an adjunctive treatment, particularly for biofilm-related infections in C. albicans and C. glabrata. Further research is required to explore synergistic effects between trans-Cinnamaldehyde and conventional antifungal agents and to optimize its therapeutic application in clinical settings. This study reused measurement results data from our previous publication to include in tables and charts.

Wearing masks to protect against communicable diseases is an effective tool used in many countries affected by the COVID-19 pandemic. Antibacterial activity, antibacterial efficiency, microbial purity and breathability properties of medical disposable masks are very important. Ag is most commonly applied to antimicrobial textiles, but several studies are known about Ag nano-particles. In this work three antimicrobial additives were used. One of them Rudolf GmBH's RUCO-BAC AGP with silver chloride (AgCl) active ingredient also has antimicrobial activity. Four compositions of the binders with antimicrobial additives were prepared and applied to one-layer nonwoven PP material using screen printing, knife over roll coating and laminating machine methods. The influence of the binder antimicrobial polymer coating on the breathability and antibacterial activity of the nonwoven PP material was evaluated. Results had showed that the composition of the polyacrylic acid binder Tubicoat Thickener LP had the least effect on their breathability and samples with the RUCO-BAC AGP formulation had showed the best antimicrobial response. Therefore, further studies of the antimicrobial coating composition were performed using the binder Tubicoat Thickener LP and antimicrobial additives of different chemical nature and concentration - RUCO-BAC AGP, BI-OME AM10 and Nano Ag aqueous dispersion. Based on the microbiological and air permeability results of the samples of the one-layer nonwoven material with coating, the samples of two-layers and three-layers of medical mask model were prepared. Microbiological studies have shown that three-layered medical mask model with RUCO-BAC AGP composition in the middle layer, in both sides of the model has antibacterial efficiency against three pathogens (E. Coli, K. Pneumoniae, an S. Aureus). The performance of this medical mask model has been found to meet requirements for type I medical masks according to the EN 14863 standard. Study have shown that the microbial purity of the mask model is CFU/g <3.

Industrial products e-commerce refers to the specific application of the e-commerce concept in the field of industrial products transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industry literature such as policies, reports and standards related to industrial products e-commerce contains abundant and crucial information. Through systematical analysis of the literature information, it is beneficial to explore and grasp the development characteristics and trends of the industrial products e-commerce. To study the characteristics and development status of industrial products e-commerce in China, literature comprising 18 policy documents, 10 industrial reports and 5 standards is deeply analyzed by employing text mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial products commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the results of word frequency statistics are visually displayed. Subsequently, the feature set is obtained by combining the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in word2vec, and the feature words are transformed into word vectors in the multi-dimensional space. The k-means algorithm is used to cluster the feature words into groups. Moreover, the latent dirichlet allocation (LDA) method is also utilized to further group and discover the features. The research results based on text mining provide supportive decisions for uncovering the development characteristics and trends of industrial products e-commerce in China.

Indoleamine 2,3-dioxygenase 1 (IDO1) and IDO2, originated from gene duplication before vertebrate diver-gence. While IDO1 has a well-defined role in immune regulation, the biological role of IDO2 remains unclear. Discovered in 2007, IDO2 is located near the IDO1 gene. Because of their high sequence similarity, IDO2 was initially thought to be a tryptophan (Trp)-degrading enzyme like IDO1. Differently from what expected, IDO2 displays extremely low catalytic activity toward Trp. Nevertheless, many studies, often contradictory, tried to demonstrate that IDO2 modulates immune responses by catabolizing Trp into kynurenine, a hypothesis un-convincing and, let’s say, linked to the first life of IDO2. Here, we review its “new life”, the one in which IDO2 has a functional role beyond Trp metabolism. The IDO2’s evolutionary persistence across species, despite be-ing almost inactive as an enzyme, suggests it has some relevant biological importance. IDO2 expression in human normal cells is poor, but significant in various cancers, with two prevalent SNPs. Overall, the com-parison of IDO2 to IDO1 as a Trp-degrading enzyme may have led to misunderstandings about IDO2’s true physiological and pathological roles. New insights suggest that IDO2 might function more as a signaling molecule, particularly in cancer contexts, and further studies could reveal its potential as a target for cancer therapy.

With the rising awareness of environmental issues, concerns about pollution and global warming have gained importance, and electricity generation costs continue to increase. As a result, research on energy recovery using thermoelectric modules has attracted attention in recent years. Given the lack of significant breakthroughs in thermoelectric materials, improving the structure of thermoelectric modules has become a key direction for enhancing efficiency. Common thermoelectric module types include flat plate (Flat Plate TEG), annular (Annular TEG), flexible (Flexibility TEG), segmented, and cascaded designs. Factors influencing thermoelectric performance include the number, shape, height, contact area of thermoelectric elements, temperature difference, copper plate length, contact resistance, and the geometric arrangement of the elements. This study is based on the commercially available TGM1-127-1.0-0.8 thermoelectric module. By simulating the output performance of both flat plate and annular thermoelectric module structures under identical conditions—such as total volume of thermoelectric material, shape, and height of the thermoelectric legs, the total contact area with the heat source, temperature differential, and total volume of the connecting copper plate—we observed that the number of thermoelectric elements not only determines the open-circuit voltage but also influences the output power. The trend of output power remains consistent under varying temperature differences, regardless of changes in the load resistance.

The gut microbiome known as the “hidden organ” in humans has received wide attention due to its pivotal role in human health, influencing various physiological functions and metabolic processes. Multiple factors influence the gut microbiome and diet emerging as a prominent determinant. Increasing evidence verifies different dietary patterns modulate gut microbial composition and its functionality. Asia, home to the world's largest population, showcases immense diversity, reflected in its complex dietary patterns influenced by tradition and modernization. A plethora of research evidence proves Asian diet confers beneficial effects on maintaining and improving health through favorable alteration of gut microbiome. Yet up to date, no comprehensive review exists on the influence of the Asian diet on gut microbiome. This review critically examines published data on dietary components from Eastern, Southeastern, Southern, Western and Central Asian diet and their interaction with the human gut microbiome across healthy and diseased states. It identifies strengths and limitations in Asian gut microbiome research. Asia experiences rapid dietary transitions demanding further investigation into their effects on the gut microbiota. However, evidence proves traditional Asian diets hold promise for developing personalized dietary interventions and therapeutics to promote gut health and prevent chronic diseases by modulating gut microbiome.

Antimicrobial resistance (AMR) is a worsening global health crisis that has arisen due to antibiotic overuse and misuse. Antibiotic resistance renders standard treatments less effective, making it difficult to control some infections, thereby increasing morbidity and mortality. Medicinal plants are sustainable treatment options that are becoming increasingly important as antibiotics lose their efficacies. The present study evaluates the antibacterial activity of aqueous, methanolic and ethyl acetate extracts prepared using Terminalia bellirica and Terminalia chebula fruit against six bacterial pathogens using disc diffusion and broth microdilution assays. The aqueous and methanol extracts of T. bellirica and T. chebula showed substantial zones of inhibition (ZOIs) against S. aureus and MRSA. The activity against those bacteria was determined to be strong, with minimum inhibitory concentrations (MIC) ranging from 94 µg/mL to 392 µg/mL. Additionally, the T. bellirica methanolic extract showed noteworthy antibacterial activity against E. coli and an ESBL E. coli strain (MIC values of 755 µg/mL for both). The aqueous T. bellirica and T. chebula extracts also inhibited K. pneumoniae growth (MIC values of 784 µg/mL and 556 µg/mL respectively), whilst the methanolic extracts of these plants also inhibited ESBL K. pneumoniae growth (MIC values of 755 µg/mL and 1509 µg/mL respectively). Eighteen additive interactions were observed when extracts were combined with reference antibiotics. Notably, strong antagonistic interactions occurred when any of the extracts were mixed with polymyxin B. All extracts were subjected to LC-MS analysis, revealing several interesting flavonoids and tannins, including 6-galloylglucose, 1,2,6-trigalloyl-β-D-glucopyranose, 6-O-[(2E)-3-phenyl-2-propenoyl]-1-O-(3,4,5-trihydroxybenzoyl)-β-D-glucopyranose, propyl gallate, methyl gallate, sanguiin H4, hamamelitannin, pyrogallol, gallic acid, ellagic acid, chebulic acid, and chebuloside II. All extracts were nontoxic when tested in brine shrimp assays. This lack of toxicity, combined with their antibacterial activities suggests that these plant species may be promising sources of antibacterial compound(s) that warrant further study.

Groundwater pollution in intensive horticultural areas is becoming an increasingly important problem. Therefore, this study evaluates nitrogen (N) fertilization in open field processing pepper crop in Extremadura, Spain. Previous research on the effect of N on sweet peppers grown in greenhouses is abundant, but data on outdoor cultivation, especially considering variety and site influences, are lacking. Field trials were conducted in 2020, 2021 and 2022 to determine the opti-mum N fertilizer rate for processing peppers, with the aim of reducing environmental impacts such as nitrate leaching while maintaining crop yields. Different N rates were applied in the trials, ranging from no N fertilizer to 300 kg N/ha. Meas-urements included crop yield, biomass, intercepted photosynthetically active radiation (PAR), and canopy cover. The study also developed a critical nitrogen curve (CNC) to determine the minimum N concentration required for optimal growth. The results showed that applying more than 200 kg N/ha did not significantly increase yield. Nitrogen-free treatments resulted in earlier fruit maturity, concentrating the harvest and reducing waste. In addition, excessive N application led to envi-ronmental problems such as groundwater contamination due to nitrate leaching. The study concludes that outdoor pepper crops in this region can achieve optimal yields with lower N rates (around 120 kg N/ha) compared to current practices, thereby reducing environmental risks and fertilizer costs. It also established relationships between biomass, canopy cover, and N uptake to improve fertilization strategies. These data support future crop modelling and sustainable fertili-zation practices.

The Electric Fish Optimization (EFO) Algorithm is inspired by the predation behavior and communication of weak electric fish. It is a novel meta-heuristic algorithm that attracts researchers because it has few tunable parameters,high robustness,and strong global search capabilities. Nevertheless, when operating in complex environments, the EFO algorithm encounters several challenges including premature convergence, susceptibility to local optimum, and issues related to passive electric field localization stagnation. To address these challenges, this study introduces an Adaptive Electric Fish Optimization Algorithm Based on Standstill Label and Level Flight (SLLF-EFO). This hybrid approach incorporates the Golden Sine Algorithm and Good Point Set Theory to augment the EFO algorithm’s capabilities, employs a variable step size Levy flight strategy to efficiently address passive electric field localization stagnation problems, and utilizes a standstill label strategy to mitigate the algorithm’s tendency to fall into local optimum during the iterative process. By leveraging multiple solutions to optimize the EFO algorithm, this framework enhances its adaptability in complex environments. Experimental results from benchmark functions reveal that the proposed SLLF-EFO algorithm exhibits improved performance in complex settings, demonstrating enhanced search speed and optimization accuracy This comprehensive optimization not only enhances the robustness and reliability of the EFO algorithm but also provides valuable insights for its future applications.

FER zeolites has a unique framework structure and acid distribution, and is widely studied as a catalyst for reactions such as n-butene skeletal isomerization and dimethyl ether carbonylation. The BAS located in the 10-MR of FER zeolites serves as the active site for the isomerization reaction of n-butene skeletal to produce isobutene. In contrast, the acid site found in the 8-MR is prone to side reactions, such as polymerization and cracking, which can adversely affect the selectivity of isobutene. This study prepared five types of FER zeolites using different method, inducing Na-form or H-form crystal seeds without organic structure directing agents (OSDAS), using pyridine alone as a template, and combining the seeds with pyridine. The differences in the structure and acid distribution of the five zeolites were investigated, as well as their catalytic performance for the the skeletal isomerization of n-Butene. Experiments and characterization results showed that under hydrothermal synthesis conditions, the FER-PY+SH zeolites synthesized by using both H-form zeolites seeds and pyridine exhibited the highest aluminum concentrations at T1 and T3 positions, along with the greatest BAS of 10-MR. This unique composition contributed to the highest selectivity of isobutene. The FER-PY+SH catalyst was continuously used for 720 hours at 350 ℃, 0.1 MPa, and n-butene mass space velocity of 2.0 h^-1 for three recycles on 2160 h. During this period, the conversion of n-butene over 39%, while the selectivity of isobutene exceeded 95%.The catalyst exhibited excellent stability and activity.

In the present research, it has been tried to use the applied research bases in the field of business management with marketing orientation. Since there were wide and diverse spectrums in the field of economic activities in Isfahan province and Isfahan city, the model of economic sectors was used to categorize these activities. In this regard, the specific goals of the research in all five sectors of the model of economic sectors, drawing and prioritizing the components (indices) of the 7P marketing mix model in each of these economic sectors by the related individuals and members of subgroups of guilds and non-governmental organizations in Isfahan province, have been formed, specified and determined. Collecting data for processing and answer the question of this research, after designing a questionnaire in accordance with the AHP model standard, by spending a period of 2 months from September 2022 and ended in November 2022 , from the statistical population has been done.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc, an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 meters in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

Predicting the thermophysical properties of designed materials at the nanoscale is crucial. However, this remains a significant challenge despite numerous efforts by researchers and scientists. This paper presents a unified thermodynamic model to study the size and shape-dependent cohesive energy, as well as the melting temperature of semiconductor nanosolids, including InSb, ZnSe, CdS, and CdSe. The model provides insight into the thermal stability of nanosolid materials by considering the combined impact of size, shape, and packing density. The finding indicates that smaller nanoparticles exhibit a lower cohesive energy compared to larger ones. Our unified thermodynamic model yields results consistent with experimental values and demonstrates a closer agreement than those from the universal liquid drop model of size-dependent cohesive energy, which includes a shape factor of α = 2/3 for nanowires. According to the results, the melting temperature of nanoparticles drops as their size decreases. Below a diameter of 5 nm, this trend becomes more noticeable. In the nano-scale domain, the nanoparticle's shape also matters greatly. In particular, compared to tetrahedral nanoparticles, nanowires show a lesser reduction in melting temperature. Also, up to a diameter of roughly 10 nm, the melting temperatures of these nanoparticles rise with particle size. The search results show that, especially in the < 5 nm size range, the size and shape of nanoparticles significantly affect their melting point. The models accurately predict the relationship between particle size and melting point for various semiconductor nanomaterials, aligning with experimental findings. Interestingly, the information points to a potential preference for these nanomaterials to form nanowire structures in the < 5 nm size range. Our research offers helpful insights into the thermophysical properties of these semiconducting materials, which may have significant implications for their use in a diversity of technological applications.

This study explores how the Household Dietary Diversity Score (HDDS) and spatial visualization can inform food governance in Chile, focusing on socio-demographic and geographical determinants affecting food consumption patterns. A national household database (n=4,407) provided by the “Family Support Program of Food Self-Sufficiency” (FSPFS) of the Ministry of Social Development and Family was analyzed. The findings revealed that Chilean vulnerable households were led mostly by women (86.6%), with an age average of 55.9 15.6 years old, versus 68.9 12.9 years in the case of men. The intake frequency analysis showed that dairy, fruits, and vegetables were below the recommended values in at least half of the households, and that fats and sugars were above recommended levels. Regarding the HDDS [0-189], the national average was of 91.4 ± 20.6 and was significantly influenced by the number of minors in the households, water access, food access issues, and residing in the Zona Sur. Finally, the spatial visualization showed that the Zona Central had higher consumption of fruits and vegetables, while the extreme zones Norte Grande and Zona Austral showed higher intakes of fats and sugars. These findings emphasize the importance of leveraging data insights like HDDS and spatial visualization to enhance food security and inform food governance strategies.

Sustainable water resource management in Bangladesh's Gumti River Basin faces challenges from climate change, urbanization, and population growth. This study aimed to calibrate and validate the HEC-HMS model for the Gumti River to enhance flood prediction and water resource planning. The model was set up using precipitation, temperature, and discharge data from 2019-2021, with SRTM elevation data for catchment delineation. Manual calibration was performed for 2019-2020, followed by validation for 2021, using parameters such as Muskingum routing and SCS Curve Number. Model performance was evaluated using R², Nash-Sutcliffe efficiency (NSE), RSR, and PBIAS metrics. Results showed satisfactory to good performance, with R² values of 0.64 and 0.68 for calibration and validation, respectively. NSE values were within the satisfactory range, while PBIAS indicated very good performance with slight underestimation. The study concludes that the calibrated HEC-HMS model provides a reliable tool for simulating the Gumti River's hydrology, though there's room for improvement. Integration with flood frequency analysis, water quality monitoring, and climate change projections is recommended for comprehensive water resource management, alongside the implementation of Sustainable Water Resource Management principles and community engagement strategies.

The Sustainable Development Center of the University of Brasilia is one of the modernist buildings that make up the Darcy Ribeiro campus. The architectural project contains several recommendations for the execution of the flat roof waterproofing system, as well as details for rainwater runoff and drainage, which reveals a certain concern with watertightness. The research seeks to identify the relationship between the pathological manifestations recognized on the roof and the semicircular shape of the building, assessing the state of conservation through the use of damage maps as an analysis tool. The article is based on a field survey using aerophotogrammetry with a drone, the application of vector drawing software for graphic representation and discussion of the possible causes, agents and mechanisms of degradation at work. The results show the importance of mechanical protection for the good performance of the waterproofing system, as well as the need for correct sizing of expansion joints in order to absorb and relieve the stresses caused by hygrothermal variations. The methodology incorporated proved to be effective and economical in diagnosing and monitoring pathological manifestations, making it possible to plan maintenance actions that extend the useful life and preserve the intrinsic characteristics of building systems.

A simple protocol for preparation of O-acylated enol form - (R/S)-ethyl-2-acetoxy-4-phenyl-4H-chromene-3-carboxylate 5 was presented. The compound was characterized by 1H-, 13C-and DEPT135 NMR spectra, including {1H,1H} COSY, {1H,13C} HSQC, {1H,13C} HMBC and 2D-NOESY spectra. The preferred regioselectivity for O-acylation of 3,4-dihydrocoumarin 5 in the presence of substituent in 4-th position in the chroman ring and accounting the steric hindrance of the ester group in 3-th place was confirmed.

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected through SAR techniques. Two models, generalized linear model (GLM) and random forest (RF), were used to predict the mass of residues using interfer-ometric variables (phase, amplitude and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R2 of 0.48 for GLM and 0.13 for RF), with acceptable bias and RMSE. We concluded that SAR data are feasible to predict the mass of harvesting residues and the findings could lead to improved monitoring and management of forest residues, contributing to sustainable forestry practices and enhanced bioenergy resource utilization.

To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault type recognition rates when optimizing Variational Mode Decomposition (VMD) and Support Vector Machine (SVM) parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization (GTO) algorithm. The GTO algorithm is enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm is used to optimize the parameters of VMD and SVM for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes show that the proposed method achieves a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.

The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.

The microbial communities are closely related to the overall health and quality of soil, but studies on microbial ecology in apple-pear orchard soils are limited. In the current study, 28 soil samples were collected from three apple-pear orchards in northeastern China, and the composition and structure of fungal and bacterial communities were investigated by high-throughput sequencing. Molecular ecological network showed that the keystone taxa of bacterial communities were Actinobacteria, Proteobacteria, Gemmatimonadetes, Acidobacteria, Nitrospirae, and Chloroflexi, and the keystone taxa of fungal communities was Ascomycota. Mantel tests showed that soil texture and pH were important factors shaping soil bacterial and fungal communities, and soil water soluble organic carbon (WSOC) and nitrate nitrogen (NO3--N) were also closely related to soil bacterial communities. Canonical correspondence analysis (CCA) and variation partition analysis (VPA) analysis revealed that geographic distance, soil texture, pH, and other soil properties could explain 10.55%, 13.5%, and 19.03% of the overall variation of bacterial communities, and they explained 11.61%, 13.03%, and 20.26% of the overall variation of fungal communities, respectively. The key stone taxa of bacterial and fungal communities in apple-pear orchard soils and their strong correlation with soil properties could provide useful clues toward sustainable management of orchards.

Microalgae are photosynthetic microorganism able to receive light from sunlight and external light sources for their growth. Despite the simplicity of cultivation and their widespread presence, the growth of microalgae is affected by several factors, light intensity being a crucial one as it is linked to external environmental conditions. On the other hand, microalgae cultivation in wastewater with high nitrogen content has become a promising alternative as a low cost nutrients substrate method. However, effluents may contain many compounds, making therefore difficult the assessment of light intensity on cells growth profile and especially on nutrients assimilation rate. The aim of this study was the examination of the effect of intensity of light source on the growth and nutrient uptake rates by a freshwater microalgae, Chlorella sorokiniana in the effluent from an anaerobic digestion plant, with nitrogen content reaching up to 5 g/L. Microalgae were cultivated using three light intensities (1,500, 5,000 and 12,000 lx) in 10% digestate mixtures in water. Samples were collected and analyzed for the presence of microalgae cells, while aqueous phase samples were subjected to the analysis of nitrogen and phosphorous content. Biomass productivity followed the light intensity pattern: the highest the light the highest the cells growth. A similar pattern was observed in the reduction rate of nitrogen and phosphorous concentrations; however, nutrients reduction rate per gram of microalgae were mainly associated to biomass growth rate, indicating that the effect of light intensity was significant for biomass growth, without however changing the main nutrients assimilation pathway. In addition, results indicated that under high light intensity, microalgae might change their metabolism towards formation of lipids against nitrogenous compounds.

Access to reliable water supply is critical for sustainable development and public health, yet many peri-urban and rural communities in the developing world face challenges such as poor maintenance, faulty infrastructure, and inadequate governance of water services, hindering the UN’s Sustainable Development Goals (SDG). In Emina-Boadi, a small town in Kumasi, Ghana, residents increasingly rely on boreholes due to substandard service from the Ghana Water Company Limited (GWCL). This shift highlights concerns about water reliability, quality, and affordability, impacting public health and hindering progress toward SDG 6. This case study assesses water service levels using key performance indicators (KPIs) such as quantity, quality, affordability, accessibility, and reliability, focusing on sources like boreholes, hand pumps, piped water, hand-dug wells, rainwater, and surface waters. The study aims to delineate the roles of different water providers, understand community dynamics, and evaluate contributions to rural development and SDG 6 through stakeholder interviews and questionnaires. Leveraging the WASHCost framework, which aims to improve planning methods and provision of water and sanitation services by addressing the lack of cost information in rural and peri-urban areas, and the Joint Monitoring Programme (JMP) framework, which monitors progress toward global targets related to water, sanitation, and hygiene, the study categorizes indicators and draws connections among them. Findings reveal a significant preference for borehole services, with 87% usage and an 83% reliability rate compared to 13% for GWCL. Daily water consumption averages 60.75 liters per person, with on-premises access improving water collection efficiency to 7 minutes. Dissatisfaction with GWCL services highlights the need for improved delivery aligned with community needs and SDG targets. Adopting sustainable management practices, enhancing infrastructure, and improving governance are crucial for ensuring safe and affordable water access for all residents of Emina-Boadi and similar communities.

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images, annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIB) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision (mAP@0.5) of 98.5%. Detailed analysis of the model's performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as thick line and star crack, the model maintained high performance with accuracies of 94% and 96%, respectively. The recall-confidence and precision-recall curves further demonstrate the model's robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production.

Stage IV grade C localized periodontitis (pre-puberal localized aggressive periodontitis/LPP), an extremely rare form of periodontal disease, occurs in otherwise healthy individuals (no signs of dental plaque/calculus) due a hyper-aggressive auto-immune response to high periodontopathic bacteria levels. Methods: A 4-year-old Caucasian girl with unusual high mobility of temporary lower left canine and localized gingival inflammation was misrecognized by multiple clinicians (initially diagnosed with hypophosphatasia, genetic and metabolic disorders, all turning negative), over a period of 4-6 month, despite initial radiographs showing clear pathognomonic signs. LPP diagnostic was made by the last clinician, but by now the tooth was lost. Similar inflammation signs appeared around the lower temporary right canine. X-ray examination showed similar bone and periodontal loss as previously seen, while periodontopathic bacteria tested highly positive. The patient received both mechanical cleaning and ten days systemic antibiotic treatment (Augmentin and Metronidazole). Results: Two months after, inflammation signs disappeared, with periodontal regeneration radiologically present, and only small periodontopathic bacteria precursors concentrations. Conclusions: Despite initial periodontal loss, an adequate treatment can keep under control an LPP disease. Moreover, bone and periodontal regeneration appears if periodontopathic bacteria scores are kept lower, showing the importance of fast adequate diagnostic and treatment.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumour cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analysed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

Improving photobioreactor performance for microalgae cultivation has attracted many researchers over the past years. One of the primary challenges associated with existing photobioreactors is the light penetration. An effective photobioreactor design should maximize light penetration, ensuring uniform illumination throughout the reactor. This study aims to assess the impact of light intensity on microalgal growth from the perspective of energy efficiency and productivity in two photobioreactors. A novel cactus-like and a cylindrical photobioreactor were designed and fabricated using 3D printing technology. These two photobioreactors were used to cultivate two strains of microalgae. The novel photobioreactor achieved a maximum biomass productivity of 0.99 g/L/d and a maximum energy efficiency of 0.3139 g/d/kWh. The cylindrical photobioreactor reached a maximum biomass productivity of 0.74 g/L/d and 0.2199 g/d/kWh of energy efficiency. The increase in biomass productivity can be linked to enhancements in the photobioreactor's surface-to-volume ratio and better light utilization.

The process of oxygenic photosynthesis is primarily driven by two multiprotein complexes known as photosystem II (PSII) and photosystem I (PSI). PSII facilitates the light–induced reactions of water–splitting and plastoquinone reduction, while PSI functions as the light–driven plastocyanin-ferredoxin oxidoreductase. In contrast to the highly conserved structure of PSII among all oxygen–evolving photosynthetic organisms, the structures of PSI exhibit remarkable variations, especially for photosynthetic organisms that grow in special environments. In this review, we make a concise overview of the recent investigations of PSI from photosynthetic microorganisms including prokaryotic cyanobacteria and eukaryotic algae from the perspective of structural biology. All known PSI complexes contain a highly conserved heterodimeric core; however, their pigment compositions and peripheral light-harvesting proteins are substantially flexible. This structural plasticity of PSI reveals the dynamic adaptation to environmental changes for photosynthetic organisms.

GABAA receptors are channel proteins crucial to mediating neuronal balance in the CNS. The structure of GABAA receptors allows for multiple binding sites and is key to drug development. Yet, the formation mechanism of the receptor’s distinctive pentameric structure is still unknown. This study aims to investigate the role of three predominant subunits of the human GABAA receptor in the formation of protein pentamers. Through purifying and refolding the protein fragments of the GABAA receptor α1, β2, and γ2 subunits, homopentamers were visualised with negative staining and Cryo-EM. To aid the analysis, AlphaFold2 was used to compare the structures. Results show that α1 and β2 subunit fragments successfully formed homo-oligomers, particularly homopentameric structures, while the predominant heteropentameric GABAA receptor was also replicated through the combination of the three subunits. However, it is still unclear whether γ2 subunits can form homopentamers or their main purpose is to bring together the α1 and β2 subunits to form GABAA receptors. Nevertheless, this study will lead to a deeper understanding of the atomic structure of GABAA receptors, especially in the formation mechanism of complex protein structures, and hopefully pave the way to the development of novel therapeutics for neuropsychiatric diseases.

Understanding the dynamics of land use and land cover (LULC) change in today's context of rapid urbanization is critical for sustainable urban planning and environmental conservation. Therefore, the research aim is to understand the LULC changes in Chattogram Metropolitan Area and to assess the policy implications of these changes for sustainable urban development. Through a comparative analysis of LULC maps for two periods (1997–2007; 2007–2017), we investigated the transformation of a detailed planning zone before and after detailed area plan (DAP) approval. Using a mixed-method approach integrating quantitative data with policy analysis, we elucidated the impacts of urbanization trends on local ecosystems—specifically the conversion of forest cover and waterscape use. The findings reveal a significant conversion of forested and waterscape areas to urban and peri-urban landscapes, highlighting the urgent need for sustainable planning interventions that focus on these peri-urban areas. The study further critiques the DAP's effectiveness, revealing a disconnect between regional policy implementation and local outcomes. This research proposes a validated, scalable framework for urban master plans that adapts to the complex socio-ecological contexts of rapidly urbanizing regions.

This paper reviews the requirements for the production, quality, and quality assurance of solid recovered fuels (SRF) that are increasingly used for the substitution of fossil fuels in the indus-trial utilities. The economical, technological and environmental aspects should be considered before using SRF as an alternative fuel. In general, the most important factors determining the commercial production of SRF referred to the calorific values and their available amounts adopted in the starting materials. Furthermore, the low-melting impurities and undesired constituents/elements, including chlorine, sulfur, potassium and sodium, may pose negative impacts on air pollutant emission and slagging & fouling in the process utilities. In this regard, the vent emitted from the industrial utilities must comply with the effluent standards of station-ary sources like particulate, sulfur oxides, nitrogen oxides, heavy metals (e.g., lead, cadmium, and mercury) and dioxins. For these reasons, this work will summarize the legal or regulatory requirements under development in Asian countries (i.e., Japan, South Korea, and Taiwan) and European Union (EU). Finally, the outlook of solid recovered fuel (SRF) in the waste manage-ment and industrial sectors will be addressed to echo the sustainable development goals (SDGs).

Starfish are keystone species as predators in benthic ecosystems, but when population outbreaks occur this can have devastating consequences ecologically. Furthermore, starfish outbreaks and invasions can have adverse impact economically by impacting shellfish aquaculture. Addressing the imperative need to proactively mitigate starfish invasions requires comprehensive research on their behavior and the underlying mechanisms of outbreaks. This review scrutinizes the historical patterns of outbreaks among diverse starfish species across various regions, delineates the factors contributing to the proliferation of Asterias amurensis in Chinese waters, articulates preventive and remedial strategies, and outlines the potential for sustainable utilization of starfish.

Appropriate animal models of human diseases are a cornerstone in the advancement of science and medicine. To create animal models of neuropsychiatric and neurobehavioral diseases such as ASD necessitates the development of sufficient neurobehavioral measuring tools to translate human behavior to expected measurable behavioral features in animals. Indeed, at least in rodents, adequate neurobehavioral and neurological tests have been developed. Since ASD is characterized by a number of specific behavioral trends with significant severity, animal models of autistic-like behavior have to demonstrate the specific characteristic features, namely impaired social interactions, and communication, restricted, repetitive behavioral patterns with association to several additional impairments such as somatosensory, motor and memory impairments. Thus, an appropriate model must show behavioral impairment of a minimal number of neurobehavioral characteristics using an adequate number of behavioral tests. The proper animal models enable the study of ASD like-behavior from the etiologic, pathogenetic and therapeutic aspects. From the etiologic aspects, models have been developed by the use of immunogenic substances like polyinosinic-polycytidylic acid (PolyIC), Lipopolysaccharide (LPS) and propionic acid or other well-documented immunogens or pathogens, like mycobacterium tuberculosis. Another approach is the use of chemicals like valproic acid, Polychlorinated biphenyls (PCBs), organophosphate pesticides - chlorpyrifos (CPF) and others. These substances were administered either prenatally, generally after the period of major organogenesis, or, especially in rodents, during the early postnatal life. In addition, using modern genetic manipulation methods, genetic models have been created of almost all human genetic diseases that are manifested by autistic like behavior (i.e. fragile X, Rett syndrome, SHANK gene mutation Neuroligin genes and others). Ideally, we should not only evaluate the different behavioral modes affected by the ASD-like behavior but also assess the severity of the behavioral deviations by an appropriate scoring system, as applied to humans.

Semi-Interlocking Masonry (SIM) represents an innovative building system developed at the Centre for Infrastructure Performance and Reliability at The University of Newcastle, Australia. This system employs a unique approach to interlocking mortar-less engineered masonry panels constructed from SIM units. These units are designed to offer substantial energy dissipation capacity, primarily attributed to the friction occurring on the sliding bed joints between the units within the panel during seismic events. The primary aim of this study is to evaluate the viability of incorporating semi-interlocking masonry as infill panels in the construction of multi-storey buildings across diverse geographical locations with varying seismic conditions in Australia. To assess the feasibility of SIM panels in different conditions (according to Australian Standard AS1170) in Australia, a comprehensive analysis using Strand7 is conducted on a three-storey structure incorporating traditional masonry infill panels and SIM panels. The comparison of displacement and base shear capacities is carried out in response to seismic events across various scenarios in different locations within Australia. The insights gained from this comparative analysis contribute valuable information regarding the viability of implementing SIM as a contemporary construction material.

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

Quaking aspen (Populus tremuloides Michx.) landscapes are valued for their biodiversity, water retention, fire mitigation, aesthetics, and recreation opportunities. Across North America, some aspen populations are experiencing population declines as they face uninhibited ungulate browsing, drought, fire suppression, insects, disease, and inappropriate management. Increased human development and recreational use within aspen landscapes can serve as additive stressors, though there is a dearth of literature examining these elements. At a popular recreational area in Utah, USA, identifying the cause of apparent decline within a larger aspen community is complicated by development upstream and recreation-related activities. We sought to: 1) assess the overall condition of the aspen at the site, 2) understand key variables that influence aspen conditions, and 3) elucidate how aspen fitness varies across the site. We collected data from forty-five plots using established aspen sampling methods, including ungulate presence, tree characteristics, soil chemistry, and environmental descriptors. Results suggest that a combination of higher levels of browsing and elevated soil sodium may be causing premature mortality and limiting aspen recruitment in a portion of the study area. These findings will inform future management at this site, as well as similar recreational forest settings experiencing compound stressors.

The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being “shared” between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour, before considering future directions in the field.

This study explored differences in student-athletes’ symptoms of depression, anxiety, and stress pre- to post-COVID-19 pandemic. The WHO reported a 25% increase in depression and anxiety rates worldwide with young people disproportionately affected. Student-athletes face many stressors related to their sporting and academic feats but what is not known is how the COVID-19 pandemic affected their experiences of mental illness. A multiple cohort cross-sectional study design was employed, and data collected using physical and online surveys. Participants (M age = 19.98 years, SD = 1.50) were recruited from UK universities (N = 807; 427 pre-pandemic cohort, 380 post-pandemic cohort). Results revealed statistically significant differences in mean depression, anxiety, and stress scores between cohorts. Scores for the post-pandemic cohort were significantly higher than pre-pandemic, suggesting a worsening of symptom severity. Distributions of student-athletes across categories of symptom severity also worsened for depressive and anxiety symptoms post-pandemic and were skewed towards more severe categories. Symptoms of de-pression, anxiety, and stress were a concern pre-pandemic. Rates are higher in the post-pandemic cohort, suggesting a worsening of symptoms. This data adds to evidence on student-athletes’ symptoms of mental illness by exploring a UK sample and comparing scores pre- and post-pandemic.

CO2 emission activity of xylotrophic fungi response to an increase in temperature in the range of 10-30 °C on the example of pure dikaryotic cultures of Fomes fomentarius s. str., F. inzengae, Fomitopsis betulina, F. pinicola, Phellinus igniarius was analyzed. Emission activity was assessed by the difference in CO2 concentration in 0.5-liter exposure chambers with Petri dishes with mycelium growing on agar at the beginning of exposure and an hour later using a Gasmet DX-4030 FTIR spectrometer (Gasmet Technologies Oy, Finland), error measurements ±50 ppm. Specific (μg CO2/cm2/h) and total (μg CO2/h) emission activity and its relationship with temperature and size (area) of the mycelium were assessed. It has been shown that in the range of 10-30 °C, the specific and total CO2 emission activity of the mycelium is closely and positively related to temperature. Specific emission, which is an indicator of the respiratory activity of the mycelium, does not depend on its size; its only driver is temperature, the relationship with which is linear: an increase in temperature by 10 °C causes an increase in the specific emission activity of the mycelium by 1.7 times. The total CO2 emission activity, which is an indicator of the total amount of CO2 emitted, is directly proportional to the specific emission activity and the size of the mycelium. In the range of 10-30 °C, an increase in temperature causes an almost equal increase in both the specific emission activity of the mycelium (Q10 1.7) and its growth (Q10 1.5) and causes an exponential increase in the total emission of CO2. This must be taken into account when predicting CO2 emissions from woody debris under climate change, as it could potentially contribute to accelerating climate change.

As technology advances, the field of electrical and computer engineering continuously demands the introduction of innovative new tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. This research addresses an identified gap in technology-augmented education capabilities and researches the integration of Virtual Reality (VR) technology with real-time electronic circuit simulation to enable and enhance the visualization of non-observable concepts such as voltage distribution and current flow within these circuits. In this paper, we describe the development of our immersive educational platform, which makes understanding these abstract concepts intuitive and engaging. This research also involved the design and development of a VR-based circuit simulation environment. By leveraging VR’s immersive capabilities, our system enables users to physically interact with electronic components, observe the flow of electrical signals, and manipulate circuit parameters in real-time. Through this immersive experience, learners can gain a deeper understanding of fundamental electronic principles, transcending the limitations of traditional two-dimensional diagrams and equations. Furthermore, this research focuses on the implementation of advanced and novel visualization techniques within the VR environment for non-observable electrical and electromagnetic properties, providing users with a clearer and more intuitive understanding of electrical circuit concepts. Examples include color-coded pathways for current flow and dynamic voltage gradient visualization. Additionally, real-time data representation and graphical overlays are researched and integrated to offer users insights into the dynamic behavior of circuits, allowing for better analysis and troubleshooting.

Greek roots: "phos" (φῶς) - means "light" and "dynamis" (δύναμις) - means "force" or "power". Photodynamic therapy (PDT) is an innovative treatment method based on the ability of photosensitizers to produce reactive oxygen species after exposure to light of a specific wavelength range, either in the visible or near infrared range. This process results in damage to pathological cells, such as cancer cells, while minimizing the impact on healthy tissues. PDT is a promising direction in the treatment of many diseases, with particular emphasis on the fight against cancer and other diseases asso-ciated with excessive cell growth. The power of light contributed to the creation of phototherapy, whose history dates back to ancient times. It was then noticed that some substances exposed to the sun have a negative effect on the body, while others have a therapeutic effect. This work provides a detailed review of photodynamic therapy, from its origins to the present day. It is surprising how a seemingly simple beam of light can have such a powerful healing effect, which is used not only in dermatology, but also in oncology, surgery, microbiology, virology, and even dentistry. However, despite promising re-sults, photodynamic therapy still faces many challenges. The availability of this thera-py is limited due to the small number of centers offering this type of treatment and the high costs, which may be unaffordable for many patients. Moreover, photodynamic therapy requires further research and improvement.

Confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the influencing factors and practical strategies of urban carbon emissions and urban vibrancy, respectively, the unclear mutual nexus between them and the development strategy of collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and building carbon emission data, we have developed a reproducible method to estimate total carbon emission and emission intensity per capita. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each typology based on geographical location and the requirements of urban development policies of these four combination typologies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

In the ground-based infrared optical remote sensing system, the target signal is very weak due to the dynamic strong light background and the movement of small and small targets. In order to improve the limit detection capability, the background suppression and target enhancement methods are required to be more suitable for this scene. To solve this problem, we first analyze the image features in the current scene, and propose a more perfect point target and noise model. Then, we propose a new WY distribution function based on the Fermi Dirac distribution function, and propose an anisotropic filtering method based on this function, and further suppress the back-ground through the difference results of two steps. Then, based on the distribution function, we further designed an energy concentration accumulation strategy in 9 scale directions, through which the SNR of the target is effectively improved and the suppression ability of the background is enhanced. Through quantitative and qualitative experimental analysis, compared with the same type of algorithms, the proposed method has better robustness against extremely weak targets and dynamic backgrounds.

Over the last decade, a lot of research has been conducted on memristors. Most of this research focusses on using memristors for Artificial Intelligence (AI) applications and to fabricate non-volatile memory, but also the security aspects of memristors have been examined. The current study summarises and compares five reviews on the security aspects of memristors. These reviews cover two different perspectives: (1) security applications of memristors such as Physical Unclonable Functions (PUFs) or True Random Number Generators (TRNGs), and (2) potential threats when using memristors to train and store neural networks. The comparison of the reviews reveals that different sets of studies are included in the reviews and different characterisations of the studies are provided. This shows that different perspectives are necessary to get a comprehensive overview of the security aspects of memristors. By synthesising the perspectives of different reviews, this study helps to get such an overview.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

In this paper, the feasibility and potential assessment (PA) of wind power in six different areas MehalMeda, Eneware, DebreBerhan, AlemKetema, GundoMekel, and Majet are investigated. The data is collected from the National Metrological Agency (NMA) in Addis Ababa and from the districts of the case study areas and analyzed using MATLAB and HOMER software. Using the data collected at 10 meter height, wind direction using a wind rose, the wind frequency distribution, and wind turbine characteristics have been analyzed. Moreover, to obtain a more realistic wind potential study, the wind speed at a 10 meter height is extrapolated to a height of 50 meter in order to determine wind speed, wind power density, and wind energy density. The average wind speed of the zone at 10m and 50m heights are 1.58 m/s and 1.99 m/s, 3.06 m/s and 3.85 m/s, 4.75 m/s and 5.98 m/s, 2.29 m/s and 2.88 m/s, 1.01 m/s and 1.27 m/s and 4.07 and 5.12m/s at Alemketema, Debreberhan, Eneware, Gundemeskel, Majete and Mehalmeda respectively. The extrapolated result shows that the maximum wind speed and power densities at a height of 50 meter for Eneware, MehalMeda and Debre Berhan are 10.4 m/s, 689 W/m2, 8.9m/s, 429 W/m2 and 6.3m/s, 152.2 W/m2 respectively. The energy densities at a height of 50 meter for AlemKetema, DebreBerhan, Eneware, GundoMekel, Majete, and MehalMeda are 181.06 kWh/m2*yr , 1332.83 kWh/ m2*yr, 6078.91 kWh/ m2*yr, 857.76 kWh/ m2*yr, 58.78 kWh/ m2*yr, and 3939.42 kWh/ m2*yr respectively. As per the international standard for wind power and wind speed classification, considering the extrapolated height, Eneware, MehalMeda, and DebreBerhan have feasible wind potential for both small-scale and large-scale wind farm implementation, whereas Majete, AlemKetema, and GundoMeskel of the North Shewa Zone in the Amhara region have poor wind potential.

The fatty acid composition, biophenol, tocopherol, sterol and triterpenic dialcohol content and composition of the autochthonous Slovenian olive varieties 'Buga', 'Črnica' and 'Drobnica' were studied during a three-year period with the aim to valorise the characteristics of the three olive varieties. Standardized and accredited analytical methods were applied. The results of the investigation showed that the highest average amount of oleic acid (75.75%) was determined in oils of the variety 'Črnica', followed by 'Drobnica' (72.06%) and 'Buga' (68.73%). All the three varieties are a good source of total biophenols ('Buga' 616 mg/kg, 'Drobnica' 569 mg/kg and 'Črnica' 427 mg/kg) and α-tocopherol ('Buga' 378 mg/kg, 'Drobnica' 279 mg/kg and 'Črnica' 243 mg/kg). 'Buga' and 'Drobnica' are characterised by high amounts of total sterols, 2468 mg/kg and 2391 mg/kg, respectively, 'Črnica' oils, in comparison, showed a lower average value of total sterols (1351 mg/kg). The acquired data of the autochthonous Slovenian olive varieties 'Buga', 'Črnica' and 'Drobnica' are very important to valorise the characteristics of the produced oils. The studied varieties showed a great potential for further cultivation and valorisation of the traditional olive oil production of the region and are important for preserving the biodiversity and tradition of the territory.

In Mexico there is a deficit in the production of pine resin, because it depends on natural forests. Therefore, it is necessary to select provenances and phenotypes of potential species such as P. oocarpa. The objective was to determine the difference between provenances and the variation of resin components and quality, as well as the effect of geographic and climatic factors. Resin from five provenances was collected from southern Mexico. The percentage of rosin, turpentine and water was obtained, as well as the acidity and saponification index. P. oocarpa resin had 80.94 % rosin, 7.7 % turpentine and 11.49 % water. The saponification and acidity index was 125.47 and 117.49 mg KOH.g_1, respectively. All variables showed differences (p≤ 0.0001) between provenances. The provenance contributed between 6.44 and 11.71 % to the total variance, the error contributed between 88.29 and 93.56 %. Geographic and climatic variables only had an effect on the percentage of turpentine; the correlation was negative with altitude and longitude, but positive with temperature and precipitation. The results allow defining seed collection sites for resin plantations and orienting the selection for a P. oocarpa improvement program.

The chestnut tree (Castanea sativa Mill.) is gaining importance in the mountainous regions of southern Europe due to the high value of its fruits. It is essential to establish effective cultivation protocols, considering that this species is still relatively understudied. In this study, we present the outcomes of the initial establishment of a chestnut orchard conducted through a nutrient omission trial for four years. The treatments included a fertilization plan with nitrogen, phosphorus, potassium, and boron (NPKB), the control, and four other treatments corresponding to the omission of each nutrient (-NPKB, N-PKB, NP-KB, NPK-B). The -NPKB and NP-KB treatments showed significantly lower trunk circumferences and canopy volumes compared to the other treatments. The NPK-B treatment resulted in the lowest fruit production, with a total accumulated yield (2020-2022) of 0.56 kg tree–1, a value significantly lower than that of NPKB (1.12 kg tree–1) and N-PKB (1.19 kg tree–1). The assessment of nutrient concentrations in the leaves revealed plants with deficient levels of B and K in treatments that did not receive these nutrients. Conversely, N levels in the leaves in the -NPKB treatment fell within the sufficiency range (20 to 28 g kg–1). This suggests that the sufficiency range should be adjusted to higher values, given the treatment's effect on tree growth. It was also observed that the -NPKB treatment led to lower soil organic matter compared to the other treatments, likely due to reduced herbaceous vegetation development under the canopy, leading to decreased organic substrate deposition in the soil. The main findings of this study are that N and K were crucial elements for the optimal growth of chestnut trees, while B played a significant role in fruit production.

Graph visualization has evolved significantly over time, offering various methods to effectively represent complex data structures. This study introduces a novel force-directed algorithm for 3D data visualization, specifically tailored for commonly used datasets in linked data visualization. Leveraging the power of graph theory and methods, the algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations, which often necessitate advanced user interaction. By utilizing 3D space and the Unity Engine, the research successfully visualizes data and develops interactive visualizations, overcoming limitations of basic force-directed implementations. The main contribution of presented research is in the force-directed algorithm with springs and controlled placement as a visualization technique that combines the use of springs and attractive forces to stabilize a graph in a 3D environment. The combination of these two techniques also helps prevent the formation of a single large cluster within the graph, enhancing the overall visualization quality.

The mechanisms underlying the establishment of asymmetric structures during development remain elusive. The wing of Drosophila is asymmetric along the Anterior-Posterior (AP) axis, despite that its growth signals that drive its growth are presumably symmetric. Here, we investigated the contribution of cell recruitment, a process that drives wing fate differentiation in Drosophila, to the asymmetric shape and pattern of the adult wing. Genetic impairment of cell recruitment results in a significant reduction in adult wing asymmetry, primarily affecting the region between longitudinal vein 5 and the wing margin (L5-M). Morphometric analysis reveals that the lack of cell recruitment results in a more symmetric wing with respect to the AP, suggesting a contribution of cell recruitment to the establishment of asymmetry in the adult wing. A close examination of the expression of the wing selector gene vestigial (vg) in wing disc does not support this hypothesis. However, the circularity of the Vg pattern significantly increase in recruitment-impaired wing disc, suggesting that while the decrease of the L5-M region in the adult wing may not be attributed to cell recruitment, it could contribute to the overall asymmetry of the adult wing. We conclude that cell recruitment, a widespread mechanism that participates in growth and patterning of several developing systems, could drive, at least partially, the asymmetric shape of the Drosophila wing.

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age, as a simple indicator of water quality in the network, is of great interest in both design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper firstly analyses the factors with biggest influence in the tanks water age, to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet have been presented. Also, a real tank behaviour has been characterised through field measurements and Epanet simulations in order to determine the best fitted mixing model.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

Tuberculosis (TB) remains a global health threat, necessitating innovative strategies for control and prevention. This comprehensive review explores the Mycobacterium tuberculosis Lysine Exporter (LysE) gene, unveiling its multifaceted roles and potential uses in controlling and preventing tuberculosis (TB). As a pivotal player in eliminating excess L-lysine and L-arginine, LysE contributes to the survival and virulence of M. tuberculosis. This review synthesizes findings from different electronic databases and includes 13 studies focused on the LysE of M. tuberculosis. The research unveils that LysE can be a potential drug target, a diagnostic marker for TB, and a promising candidate for vaccine development. The absence of LysE in the widely used BCG vaccine underscores its uniqueness and positions it as a novel area for TB prevention. In conclusion, this review underscores the significance of LysE in TB pathogenesis and its potential as a drug target, diagnostic marker, and vaccine candidate. The multifaceted nature of LysE positions it at the forefront of innovative approaches to combat TB, calling for sustained research efforts to harness its full potential in the global fight against this infectious disease.

Hepatitis C virus (HCV) is a major public health threat associated with serious clinical consequences worldwide. The WHO reports an estimated 58 million people globally living with chronic hepatitis C virus (HCV) infection, with 1.5 million new cases and over 350,000 deaths annually due to HCV-related diseases. Despite the existence of multiple diagnostic methods, the accessibility of diagnosis and treatment is often hindered across the continent by limited resources, insufficient healthcare infrastructure, and low public awareness of Hepatitis C virus Hepatitis C virus infection is a major public health concern in African countries, which have the highest prevalence rates of HCV in the world (1–26%). Egypt, African country which had the highest prevalence of Hepatitis C virus infection in the world. Its national hepatitis C elimination initiative emerges as an exemplary case of success an having screened over 60 million individuals, and treated more than 4 million, thereby markedly diminishing both the prevalence and incidence of the disease. The strategies employed by Egypt offer critical insights and adaptable frameworks for other African Countries facing analogous challenges. The Aim of this Review is to highlight the triumphs and hurdles of Egypt's approach, while also providing strategic guidance for African Countries to accelerate the journey towards the eradication of hepatitis C.

This study aimed to review the dietary risk factors for root caries prevalence in older adults, from socially active people to frail patients. A comprehensive search strategy was used to select studies from PubMed and Scopus databases. Two evalua-tors performed data extraction, screening, and quality assessment independently. Only studies written in English were included. Caries is very common in the elderly, as there are several aggravating factors such as the decrease of the ability to self-care and, by extension, insufficient oral hygiene, a carious diet, limited exposure to fluoride, xero-stomia, gingival recession, limited access to dental care. Root caries is prevalent in the elderly due to gingival recession and root exposure to the oral environment. Dietary risk factors significantly affect root caries prevalence in older adults, including high intake of sugars and an alteration of their composition preference in this age group. Caries risk appears more significant in frail, standardized patients who feed with even softer food or supplements.

The increasing availability of longitudinal data (repeated numerical observations of same units at different times) requires the development of flexible techniques to automatically detect common errors in such data. Besides obvious and easily identifiable cases, such as missing or out-of-range data, large longitudinal dataset often present problems not easily traceable by the techniques used for generic datasets. In particular, elusive and baffling problems are i) inversion of one or more values from one unit to another; ii) anomalous jumps in the series of values, iii) errors in the timing of the values due to a recalculation operated by the data providers to compensate previous errors. This work proposes a statistical-mathematical approach based on a system of indicators that is able to capture the complexity of the described problems by working at the formal level, regardless of the specific meaning of the data. The proposed approach identifies suspect erroneous data and is applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing Total academic staff, that is one of the most important variables, used in empirical analysis as proxy of size and also considered by policy makers at European level.

In the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework establishes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and results of knowledge application are presented, aimed at preparing students to excel in a future where AI will be omnipresent and indispensable.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

Giant unilamellar vesicles (GUVs) are membrane model used to study membrane properties. Electroformation is one of the methods used to produce GUVs. During electroformation protocol dry lipid film is formed. Drying of the lipid film induce Cholesterol (Chol) demixing artifact, in which Chol forms anhydrous crystals, where these crystals does not participate in formation of vesicles. This leads to a lower Chol concentration in the vesicle bilayers compared to the Chol concentration in the initial lipid solution. To address this problem, we propose novel electroformation protocol that includes rapid solvent exchange, plasma cleaning and spin-coating methods to produce GUVs. We tested the protocol focusing on vesicles with high Chol content using different spin-coating durations and vesicle types deposition. Additionally, we compared the novel protocol using completely dry lipid film. The optimal spin-coating duration for vesicles made out of phosphatidylcholine/Chol mixture was 30 s. Mulitlamellar vesicles (MLVs), large unilamellar vesicles (LUVs) obtained by extrusion of MLVs through 100 nm membrane pores and LUVs obtained by extrusion of previously obtained LUVs through 50 nm membrane pores were deposited on electrode for 1.5/1 Chol/phosphatidylcholine lipid mixture and the results were compared. Electroformation using all three deposited vesicle types gave a high GUVs yield, but deposition of LUVs obtained by extrusion of MLVs through 100 nm membrane pores gave the most reproducible results. Using deposition of these LUVs, we produced GUVs with high yield for 6 different Chol concentrations (from 0 % to 71.4%). Using protocol that includes dry lipid film GUVs yield was lower and Chol demixing artifact was induced, proving that the lipid film should never undergo drying when Chol content is high.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

Nanocrystalline silicon oxide (nc-SiOx:H) is a multipurpose material with its varied application in solar cells as a transparent front contact, intermediate reflector, back reflector layer and even tunnel layer for passivating contacts, owing to easy tailoring of its optical properties. In this work, we systematically investigate the influence of gas mixture (SiH4, CO2, PH3 and H2), RF power and process pressure on the optical, structural and passivation properties of thin n-type nc-SiOx:H films prepared in an industrial, high throughput, plasma-enhanced chemical vapor deposition (PECVD) reactor. We provide a detailed description of the n-type nc-SiOx:H material develop-ment using various characterization techniques (SEM, EDX, Raman spectroscopy, and spectro-scopic ellipsometry) with a focus on the relationship between the material properties and the pas-sivation they provide to n-type c-Si wafers characterized by their effective carrier lifetime (τeff). Further, we also outline the parameters to be kept in mind while developing different n-type nc-SiOx:H layers for different solar cell applications. We report a tunable optical gap (1.8 – 2.3 eV) for our n-type nc-SiOx:H films as well as excellent passivation properties with τeff up to 3.5 ms (iVoc ~ 715 mV) before annealing. Oxygen content plays an important role in determining the crystallinity and hence passivation quality of the deposited nanocrystalline silicon oxide films.

China's cotton production has shown a concentration to the advantageous regions in recent years. Based on the relevant data of cotton production in China, the study measures the cotton production agglomeration levels of 16 major cotton-producing provinces from 1978 to 2020, summarizes the spatiotemporal evolution characteristics of cotton production agglomeration, and analyzes the factors influencing cotton production agglomeration using spatial econometric models. The results indicate that: Firstly, since the opening-up and reform, China's cotton production agglomeration levels have shown an overall upward trend, exhibiting periodic dynamic changes over time, and the spatial pattern reveals a dynamic regional gradient. Secondly, there is spatial correlation among the production scales of cotton in different provinces, with a trend resembling a "U" shape, and the agglomeration form shows a low-level convergence. Thirdly, technological level, policies, substitute industrial products and foreign trade are identified as important factors. The cotton production in each province is spatially correlated through these factors, and there is also a presence of spillover effects. In conclusion, the government should prioritize considerations of various influencing factors to formulate tailored support policies, promote large-scale production to enhance technological levels, and focus on policies aimed at improving the quality of cotton.

Global warming may increase the potential evapotranspiration (Etp), thereby affecting the amount of clean water resources available in a region. In this study, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 emission scenarios are corrected by using 8 bias correction methods. The trend of Etp in Yangzi River Delta in the 21st century is projected by using 4 Etp calculation formulas. The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that, relative to the reference period (1971~2000), the annual mean daily Etp in Yangzi River Delta will increase by 0.14~0.17mm/d during the period 2021~2050 and by 0.21~0.41mm/d during the period 2061~2090, respectively. During 2021~2050, the uncertainty of Etp increase projections is dominantly contributed by the main effects of GCM (63%) and Etp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of emission scenario (34%) and Etp calculation formula (18%). The increase projections of Etp are generally reliable and robust during the two projection periods.

In this study, the olive pomace was considered because of its importance in the environment. So, this study carried out to estimate the total nitrogen content and the fatty acids profile of olive pomace. The olive pomace samples were collected from Irbid, Jarash, Ajloun and Mafraq during the harvesting season 2014 to study the biochemical characteristics of fatty acid composition and the total nitrogen (TN) content. The total nitrogen content was determined by standard Kjeldahl method and the fatty acid profile was detected by gas chromatography (GC). The results show that the total nitrogen content ranged from (0.39 ± 0.0) to (0.62 ± 0.02) with statistically significant difference suggesting that the composition of matrices and their percentage may be responsible for composition of amendments. The calculated total protein percentage ranged between (2.43 ± 0.00) to (3.87 ± 0.17). Also, 13 different fatty acids were quantitatively profiled and quantitated. Oleic acid (C 18:1) was found to be the highest percentage of all other fatty acids and ranged between (59.03%) and (63.81%), moreover the C 18:1/C 18:2 (oil quality) was calculated and C.V% showed variation meaning that nutritional implication could affect the oxidative stability of oils. In conclusion, OP by-product could give a sustainable and alternative-cheap source for fertilizers, pharmaceutical industries, cosmetics and other industries.

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

This paper addresses a new micromechanical model to predict biaxial tensile moduli of plain weave fabric (PWF) composites by considering the interaction between the orthogonal interlacing strands. The two orthogonal yarns in micromechanical unit cell (UC) were idealized as the curved beams with a path depicted by using sinusoidal shape functions. The biaxial tensile moduli of PWF composites were derived by means of the minimum total complementary potential energy principle founded on micromechanics. The biaxial tensile tests were respectively conducted on the RTM-made EW220/5284 PWF composites at five biaxial loading ratios of 0, 1, 2, 3 and ∞ to validate the new model. The predictions from the new model were compared with experimental data and good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed model. Using the new model, the biaxial tensile moduli of plain weave fabric (PWF) composites could be predicted based only on the properties of basic woven fabric.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

Mass hauling operations play central roles in construction projects. They typically use many haulers that consume large amounts of energy and emit significant quantities of CO₂. However, practical methods for estimating the energy consumption and CO₂ emissions of such operations during project planning are lacking. This paper presents a detailed model for estimating the energy consumption and CO₂ emissions of mass haulers that integrates the mass hauling plan with a set of predictive equations. The mass hauling plan is generated using a planning program such as DynaRoad in conjunction with data on the productivity of selected haulers and the amount of material to be hauled during cutting, filling, borrowing, and disposal operations. This plan is then used as input for estimating the energy consumption and CO₂ emissions of the selected hauling fleet. The proposed model will help planners to assess the energy and environmental performance of mass hauling plans, and to select hauler and fleet configurations that will minimize these quantities. The model was applied in a case study, demonstrating that it can reliably predict energy consumption, CO₂ emissions, and hauler productivity as functions of the hauling distance for individual haulers and entire hauling fleets.

Wildfires have always been a part of the history of Mediterranean forests. However, forest regeneration after a wildfire is not certain. It depends on many factors, some of which may be influenced by land management activities. Failure of regeneration will cause a regime shift in the ecosystem, reducing the provision of ecosystem services and ultimately leading to desertification. How can we increase Mediterranean forests’ resilience to fire? To answer this question, we did a literature review, investigating chains of processes that allow forests to regenerate (which we label “regeneration mechanisms”), and assessed the impact of selected management practices documented in the WOCAT database on the regeneration mechanisms. We identified three distinct regeneration mechanisms that enable Mediterranean forests to recover, as well as the time frame before and after a fire in which they are at work, and factors that can hinder or support resilience. The three regeneration mechanisms enabling a forest to regenerate after a fire consist of regeneration (1) from a seed bank; (2) from resprouting individuals; and (3) from unburned plants that escaped the fire. Management practices were grouped into four categories: (1) fuel breaks, (2) fuel management, (3) afforestation, and (4) mulching. We assessed how and under what conditions land management modifies the ecosystem’s resilience. The results show that land management influences resilience by interacting with resilience mechanisms before and after the fire, and not just by modifying the fire regime. Our analysis demonstrates a need for adaptive – i.e. context- and time-specific – management strategies.

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.

Background/Objectives: The interest in performing Total Ankle Arthroplasty (TAA) to address end-stage ankle osteoarthritis (OA) is continuously growing. Sports activity plays an important role in our world. There is sparse literature regarding return sports activity following TAA. Levels and types of sports in TAA are rarely reported. The purpose of this prospective case series study is to investigate sports activity in ankle osteoarthritis (OA) and TAA in terms of rate, frequency and type as well as clinical outcomes with a minimum 2 years of follow-up after surgery. Methods: 103 patients (105 implants, 52 female, and 51 male), mean age 60.5 years (range, 23-84 years) with end-stage ankle OA were treated using a three-component, uncemented, mobile-bearing VANTAGE Total Ankle System. The mean follow-up was 2.9 years (range, 2-5 years). Visual Analogic Scale Pain Score (VAS, 0-10 points), Ankle Dorsiflexion/Plantarflexion (DF/PF) Range of Motion (ROM; degrees), functional American Orthopedic Foot and Ankle Society (AOFAS) Ankle/Hindfoot Score (0-100 points), Subjective Patients' Satisfaction Score (0-10 points), Sports Activity Rate, Sports Frequency Score, and Sports Type were assessed. Results: Mean preoperative VAS Pain Score was 6.7 points (range, 3-10 points) and postoperative 0.2 points (range, 0-3 points) (p < 0.001). The mean DF/PF ROM was 24.9° (range, 0°-60°) pre-operative and was 52.9° post-operative (range, 15°-85°) (p < 0.001). The mean preoperative functional AOFAS Ankle/Hindfoot Score was 39.5 points (range, 4-57 points) and postoperative 97.8 points (range, 75-100 points) (p < 0.001). Mean postoperative Subjective Patients’ Satisfaction Score was 9.7 points (range, 7-10 points). The preoperative Sports Activity Rate was 31.1%, postoperative 85.4% (p < 0.001). All the groups exhibited substantial Sports Frequency Score increases (p < 0.001). The most practiced sports were hiking, biking, fitness, and swimming. Conclusions: Total Ankle Arthroplasty (TAA) is an effective treatment for end-stage ankle OA. TAA allows a noteworthy increase in sports activity. This research offers important sports insights to patients with ankle OA and TAA.

Abstract Introduction: Despite the determination to succeed in eliminating neglected tropical diseases, these have been increasing within certain endemicities, particularly in depriving region. Objective: This study aimed to assess the relative burden of neglected tropical diseases in rural and urban slum settings in Eastern Ethiopia. Methods: A comparative cross-sectional study was conducted from February 25 to April 30, 2024 with a multistage random sampling technique used. A survey tool with the integration of the spectrum was employed. Data were entered using Epi-Data version 3.1 and analyzed using STATA 16. Descriptive statistics and multivariable logistic regression analysis was used to identify the risk factors and the level for statistical significance was declared at p-value < 0.05 with 95 % CI. Result: The response rate was 93 % and the mean age of house heads was 31.6 (Range; 18. 63) with 5.7 average family size. The overall prevalence of NTDs among study participants was 22.6 % (95% CI; 20.2, 25.2); urban slum it was 15 % (95% CI; 12.2, 18.4) while it was 29.8 % (95% CI; 26, 33.9) in rural. Though there is no statistically significant difference, the relative prevalence of NTDs in rural to urban was two. The multivariable analysis provided that the risk factors associated with NTDs were government employment (AOR: 12.5 (1.17, 132.4)), good income (AOR: 0.13 (.03, 0.57)), Child playing with soil (AOR: 5.8 (1.52, 22.0)); child hand washing at critical times ( AOR: 11.2 (1.7, 74.9)), presence of flies in the child faces (AOR: 20.3 (4.7, 88.6)) and type of drinking water sources (AOR: 0.11 (0.02, 0.73)). Conclusion: the burden of NTDs was moderate in the study area dominated by STH, Trachoma, and scabies. Socio economic and WASH factors were found to be associated with NTDs. Collaborated efforts among stakeholders are crucial to prevent and control NTDs.

Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater Based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations. When dividing weekly data points based on whether the seven-day average daily new COVID-19 cases were above a certain threshold, the resulting clustering model could differentiate between weeks with surges in clinical reports and periods between them with an 83.3% accuracy rate. Moreover, the clustering model provided satisfactory forecasts one week (79.2% accuracy) and two weeks (72.9%) into the future. However, the prediction of the weekly average of new daily cases was limited (R2 = 0.452, MAPE = 180.2%), likely because of insufficient dimensionality in the database. Overall, while simple, WBS-supported models can provide relevant insights for decision-makers during epidemiological outbreaks, regression algorithms for prediction using low-dimensionality datasets can still be improved.

It is expected that two-dimensional (2D) metal nitrides (MNs), consisting of the elements of the 13th group of the periodic table and nitrogen, namely aluminium nitride (AlN), gallium nitride (GaN), indium nitride (InN) and thallium nitride (TlN) have enhanced physical and mechanical properties, due to a honeycomb graphene-like atomic arrangement, characteristic to these compounds. The basis for the correct design and improved performance of nanodevices and complex structures based on 2D MNs of the 13th group is the understanding of the mechanical response of their components. In this context, a comparative study to determine the elastic properties of metal nitride nanosheets was carried out making use of the nanoscale continuum modelling (or molecular structural mechanics) method. The differences in the elastic properties (surface Young´s and shear moduli, and Poisson´s ratio) found for 2D 13th group MNs are attributed to the bond length of the respective diatomic hexagonal lattice. The results obtained contribute to a benchmark in the evaluation of mechanical properties of AlN, GaN, InN and TlN monolayers by analytical and numerical approaches.

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of skin cancer, with an increasing global incidence. While most cases are successfully treated with surgical excision, a subset can metastasize, leading to significant morbidity and mortality. Current staging systems based on clinical and histopathological features have shown limitations in accurately predicting metastatic risk. This review examines the role of gene expression profiling (GEP), particularly the 40-gene expression profile (40-GEP) test, in improving risk stratification and management of cSCC. We assess the prognostic value of the 40-GEP test, its integration with current staging systems, and its impact on clinical decision-making. Recent studies suggest that incorporating GEP results with traditional staging methods can enhance the identification of high-risk patients, potentially leading to more personalized treatment strategies. The review also explores the challenges of implementing GEP in routine clinical practice, including cost-effectiveness considerations and the need for standardization. Finally, we discuss the implications for future cSCC management and highlight areas for further research. As molecular profiling techniques continue to evolve, GEP represents a promising approach to optimizing care for cSCC patients, aligning with the growing emphasis on personalized medicine in oncology.

Species of the genus Kalanchoe have a long history of therapeutic use in ethnomedicine linked to their remarkable medical properties. One of them is Kalanchoe blossfeldiana succulents occurring in tropical regions. Despite the great interest in this plant, there are no reports about its therapeutic effects on the skin. In this study, the antioxidant properties and skin permeation of a topical hydrogel containing K. blossfeldiana ethanol extract was assessed. Additionally, the content of active compounds in the K. blossfeldiana extract was evaluated by UHPLC-MS and HPLC-UV. The extract was analysed with three antioxidant assays: ABTS, DPPH and FRAP. Furthermore, antielastase and antihialuronidase properties of the tested extract were assessed. The ex vivo penetration studies were performed using the Franz diffusion cells. The obtained results show that antioxidant properties of K. blossfeldiana extract were similar to ascorbic acid, while antielastase and antihialuronidase tests indicated strong antiaging and anti-inflammatory activity (IC50 was 26.8 ± 0.13 and 77.31 ± 2.44 µg/mL, respectively). Moreover, active ingredients contained in K. blossfeldiana extract penetrated through human skin and accumulate in it. In conclusion, the hydrogel containing K. blossfeldiana extract can be considered as an interesting and new alternative to dermatologic and cosmetic preparations.

Background/Objectives: Pityriasis rosea (PR) is a self-limiting exanthematous disease associated with the endogenous reactivation of human herpesvirus (HHV)-6 and HHV-7. Classically, the lesions gradually resolve leaving no sequelae. Therefore, the best treatment is reassuring the patient and suggesting a resting period. However, atypical PR cases characterized by extensive, persistent lesions and systemic symptoms may impact the patient’s quality of life, and, therefore, a treatment can be prescribed. There is limited evidence on the comparative effectiveness of pharmacological treatments for PR, therefore we performed a network meta-analysis to compare these interventions. Methods: Overall, 12 randomized control trials (RCTs) were identified. The outcomes were itch resolution and rash improvement. Results were expressed as risk ratio (RR) and 95% confidence interval (CI). We also calculated the relative ranking of the interventions for achieving the aforementioned outcomes as their surface under the cumulative ranking (SUCRA). Results: On network meta-analysis, only oral steroids and the combination of oral steroids+antihistamine resulted significantly superior to the placebo in terms of itch resolution (RR 0.44, CI 0.27-0.72 and RR 0.47, CI 0.22-0.99). Oral steroids resulted as the best treatment (SUCRA 0.90) for itch resolution. In terms of rash improvement, only acyclovir and erythromycin resulted significantly superior to placebo (RR 2.55, CI 1.81-3.58 and RR 1.69, CI 1.23-2.33), and acyclovir outperformed all the other tested interventions. Consequently, acyclovir ranked as the best intervention (SUCRA score 0.92). Conclusions: Acyclovir represents the best option in PR patients with extensive, persistent lesions or systemic symptoms. Steroids and antihistamines seemed the best treatment for itch resolution.

This study provides a detailed numerical assessment of the productivity of single-slope solar stills across key cities in the Caspian region, including Aktau, Atyrau, Astrakhan, Makhachkala, Baku, Tehran, and Turkmenbashi. Employing a mathematical model based on heat balance equations and the 4th-order Runge-Kutta method, we simulated the distillation process under various climatic conditions. The results reveal that productivity is significantly influenced by geographic location and local meteorological conditions. Tehran demonstrated the highest productivity across all seasons, with values of 1.75 × 10³ kg/(m²·year) and an efficiency of 0.53, due to its optimal solar irradiation and ambient temperature. In contrast, Atyrau and Makhachkala exhibited lower productivity, particularly in colder months, highlighting the effect of ambient temperature on solar still efficiency. The analysis identified optimal water depth at 2 cm and insulation thickness between 4 and 9 cm for enhancing productivity in continental climates like Aktau. Additionally, the lowest cost of distilled water was $0.024 per kilogram in Baku. These findings align with existing literature, validating the numerical model's accuracy. Future research will explore integrating solar stills with other renewable and fossil fuel-based technologies.

Energy generation now heavily relies on the field of photocatalysis. Many conventional research topics have been superseded by the conversion of solar energy into chemical or thermal energy for a variety of energy-related applications. The urgent necessity of moving away from non-renewable energy sources is a result of the problem of global warming. In an effort to create the best photocatalysts for effective solar conversion, scientists are investigating a variety of material systems and material combinations. By examining the influence of morphology changes on the photoelectrochemical activity of zinc oxide nanostructures, here electrodeposition was used to create the nanostructures. By using the proper capping agents, it is possible to control the growth rates of different ZnO facets and get well-defined nanostructures and orientation. In order to generate distinct ZnO morphologies, we report here the electrodeposition of ZnO nanostructured thin films in the presence of different capping agents. A zinc nitrate (Zn (NO₃)₂) bath was used to electrodeposit ZnO nanostructures on indium tin oxide glass (ITO) substrate at 70 °C with an applied potential of -1.0 V. Capping agents such as ethylenediamine (EDA) or ammonium fluoride (NH₄F) were added to this zinc nitrate bath. The photoelectrochemical capabilities of the morphology-controlled zinc oxide nanostructures were extensively evaluated and characterised. Thus various ZnO morphologies had an impact on the PEC properties.

Crops obtained by genome editing (GE) technologies represent a notable and disruptive shift from genetically modified or transgenic plants, outpacing public perception and regulatory frame-works. Multiple aspects of GE are contributing to the advancement of modern agriculture, in-fluencing the market structure by introducing specific changes within the existing genetic blue-print of an organism. Generally, the GE toolbox includes distinct technologies able to incorporate site-specific and pre-determined genomic modifications, exploiting well-established genetic modification strategies. For these reasons, GE-derived approaches are considered new plant breeding methods, known as new breeding techniques (NBTs). Particularly, CRISPR/Cas-based technologies represent a considerable improvement over prior techniques, being more precise, accurate and straightforward for targeted gene editing, in a reliable and reproducible way, with numerous applications. CRISPR/Cas system exploitation for non-editing purposes has also been widely documented, ranging from cell imaging to expression regulation and DNA assembly. Recently, CRISPR/Cas systems have been employed for target detection and could be used to ef-ficiently identify multiple organic chemicals. High-sensitivity and high-specificity detection are two main points of the CRISPR/Cas technology, without needing complex expertise or technical pipelines. This manuscript aims to provide an overview of the main advances of CRISPR/Cas-based systems into new frontiers of non-editing. The associated implications (e.g., molecular traceability) and their relative impacts are also presented and discussed.

Background: Despite the tremendous effort in hamstring injury prevention, injury rates in soccer remain elevated. Hypothesis: Hamstring and hip/pelvic isometric strength imbalances may increase the likelihood of hamstring strain injury (HSI) occurrence amongst soccer players.Study design: Case-control study.Methods: In 120 male soccer players (mean age: 20.0 +/- 6.96 years) pre-season maximum isometric strength of the hip adductors, abductors, flexors, and knee flexors were measured, and strength ratios were calculated. Previous injury and anthropometric data were registered. Injury data were collected following the FIFA/UEFA consensus. A multivariate and Machine learning-based Logistic Regression model was employed to estimate the likelihood of players sustaining a hamstring injury.Results: 21 players sustained 32 hamstring injuries. Players with higher abduction strength in the dominant leg had an 18.5% lower risk of sustaining a hamstring injury (OR = 0.818, CI 95% = [(0.695-0.964]). No other significant contributor variables were found to be significant predictors of hamstring injury in the player group (p> 0.05).Conclusion: Isometric strength of the abductor muscles in the dominant limb had a significant association with hamstring injury.Clinical relevance: Our findings indicate the utilization of hip strength measurements as an integral component of the pre-season screening assessment, thereby affirming prior research on the correlation between lumbo-pelvic stability and incidence of hamstring injuries. Consequently, clinicians should contemplate incorporating hip strengthening exercises into regular training sessions, rehabilitation programs, and injury prevention protocols especially indicated when lower hip strength levels are observed.

BReast CAncer gene 1 (BRCA1) and BReast CAncer gene 2 (BRCA2) encode for tumor suppressor proteins which are critical regulators of the homologous recombination (HR) pathway, the most precise and important DNA damage response mechanism. Dysfunctional HR proteins cannot repair double-stranded DNA breaks in mammalian cells, a situation called HR deficiency. Since their identification, pathogenic variants and other alterations of BRCA1 and BRCA2 genes have been associated with an increased risk of developing mainly breast and ovarian cancer. Interestingly, HR deficiency is also detected in tumors not carrying BRCA1/2 mutations, a condition termed “BRCAness”. One of the main mechanisms causing the BRCAness phenotype is the methylation of the BRCA1/2 promoters and this epigenetic modification is associated with carcinogenesis and poor prognosis mainly among patients with breast and ovarian cancer. BRCA1 promoter methylation has been suggested as an emerging biomarker of great predictive significance, especially concerning Poly (ADP-ribose) Polymerase inhibitors (PARP inhibitor-PARPi) responsiveness, along with or beyond BRCA1/2 mutations. However, as its clinical exploitation is still insufficient, the impact of BRCA1/2 promoter methylation status needs to be further evaluated. The current review aims to gather the latest findings about the mechanisms that underline BRCA1/2 function as well as the molecular characteristics of tumors associated with BRCA1/2 defects, by focusing on DNA methylation. Furthermore, we critically analyze their translational meaning and the validity of BRCA methylation biomarkers in predicting treatment response and we suggest a diagnostic pipeline that could be implemented in liquid biopsy to aid precision pharmacotherapy in BRCA-associated tumors.

Plant activators have emerged as promising alternatives to conventional pesticides for crop disease manage-ment due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce systemic acquired resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of developing pathogen resistance. Among the various defensive pathways tar-geted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful devel-opment of commercial activators like benzothiadiazole for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed across multiple levels, from whole-plant bioassays to molecular and transgenic approaches. We expound on the intricate components, biological relevance, and regulatory circuits governing the SA pathway, while criti-cally examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials as-sessing their practical applicability are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent en-deavors in China. Collectively, this comprehensive review aims to synthesize existing knowledge and provide a roadmap for future research toward developing more potent and mechanistically understood plant activa-tors that fortify crop immunity against disease.

Serum biochemical indicators serve as vital proxies that reflect the physiological state and functions of different organs. The genetic parameters and molecular mechanisms underlying serum biochemical indicators of sheep (Ovis aries) have not been well understood. Therefore, the aim of the present study was to identify genetic architecture and genomic loci underlying ten serum biochemical indicators in sheep, including alanine transaminase, aspartate transferase, lactate dehydrogenase, cholesterol, glucose, phosphorus, calcium, creatinine, urea concentrations and total protein levels. We implemented genetic parameter estimations and GWAS for each trait in 422 Akkaraman lambs. Overall, low to moderate heritability estimates were found, in the range 0.14–0.55. Additionally, low to high genetic correlations were observed among traits. In total, 23 SNP loci were associated with serum biochemical indicators leading to 19 genes . These are SPTA1, MGST2, CACUL1, IGFBP7, PARD3, PHB1, SLC15A5, TRIM35, RGS6, NUP93, CNTNAP2, SLC7A11, B3GALT5, DPP10, HST2ST1, NRP1, LRP1B, MAP3K9, ENSOARG00020040484.1 as well as LOC101103187, LOC101117162, LOC105611309 and LOC101118029. To our knowledge, these data provide the first association between SPTA1 and serum cholesterol and between ENSOARG00020040484.1 and serum glucose. The current findings provide a comprehensive inventory of the relationships between serum biochemical parameters, genetic variants, and disease-relevant characteristics. This information may facilitate the identification of therapeutic targets and fluid biomarkers and establish a strong framework for comprehending the pathobiology of complex diseases as well as providing targets for sheep genetic improvement programmes.

In the context of rapid urbanization, the spatiotemporal patterns of socio-economic exposed to high seismic hazard in large cities lacks quantitative assessment, thus, it is difficult to understand the evolution of earthquake catastrophe risk there. In this paper, exposure change rate, and absolute and relatively exposure indices were proposed to quantitatively study the changes of spatiotemporal exposure to high seismic hazard in urban areas of large cities (i.e., permanent residents in urban areas more than 1 million) in Chinese mainland, using a set of multi-temporal population, GDP, and built-up datasets and a seismic hazard zoning map. The results show that, 64 out of 78 large cities are entirely or partially located in high seismically hazardous zones. Although the urban areas of large cities in high seismically hazardous zones (HSHUAs) cover only 1.73% (167177 km²) of mainland China, their share of population, GDP, and built-up land amounted to 18.14%, 30.24% and 24.55%, respectively, in 2015. Unprecedented urbanization has led to a dramatic exposure growth in HSHUAs between 1990 and 2015. The population, GDP, and built-up land in HSHUAs increased by 110 million people (with an average annual growth rate of 2.37%), 273.71 trillion USD(average annual growth rate of 11.15%), and 10593 km2 (average annual growth rate of 2.02%), respectively. HSHUAs experienced much faster population and GDP growth than the whole country, while relatively slower growth in built-up land. Exposure growth in HSHUAs also was varied for different types of large cities. Megacities had a higher proportion of increased GDP and population in HSHUAs, induced by industry and population agglomeration. Whereas, Type II large cities had a higher proportion of increased built-up land in HSHUAs, driven mainly by urban sprawl. Our analysis indicates that, for large cities under rapid urbanization, it is an urgent need to strengthen resilient urban planning, renewal and construction.