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The 2015 seismic events in Nepal highlighted critical challenges in constructing earthquake-resilient, self-built stone masonry houses in rural mountain areas. The Department of Urban Development and Building Construction (DUDBC), however, provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the shortcomings of DUDBC model houses related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study proposes a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: (1) post-disaster problem assessment, (2) need identification, (3) material selection, (4) design development, and (5) validation and implementation. Based on this framework, we proposed a novel gabion building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socio-economic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves local architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G-2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a multi-objective framework for a two-story gabions house that is most suitable for self-built resilient homes that preserves the local architecture and socio-economic conditions while providing structural integrity and safety.

Field pea, a key pulse crop for sustainable agriculture, faces significant production challenges due to drought, exacerbated by extreme climatic changes. This study evaluated 17 field pea plant introductions (PIs) and two commercial varieties under greenhouse conditions to assess their performance, determine the relationships between agronomic traits and grain yield (GY), and identify genotypes with stable yields under drought stress. Two water treatments were applied: 100% field capacity for well-watered (WW) and 60% field capacity for water deficit (WD) conditions, with drought stress imposed 21 days after planting. Significant genotypic variation was observed under both conditions. Water deficit significantly reduced GY, the number of pods per plant (NPP), plant height (PH), aboveground dry vegetative biomass (ADVB), and days to maturity (DTM) while increasing stomatal density on both adaxial (SD.ADX) and abaxial leaf surfaces (SD.ABX). Traits associated with GY in WW, such as NPP, PH, ADVB, and SD.ADX, showed stronger correlations under WD, with DTM being significantly associated with GY only in WD. Top-performing genotypes in both conditions exhibited higher pod numbers, increased aboveground dry vegetative biomass, late maturity, and lower adaxial stomatal density. Notably, PI 272215 was identified as a top performer under both conditions, with an 88% yield stability index. PI 180702 demonstrated comparable performance to PI 272215 but with no yield loss under the same conditions. These findings can be used for future field pea breeding programs aimed at developing drought-tolerant field pea varieties.

The present research focuses on optimizing 5G base station deployment and visualization, addressing the escalating demands for high data rates and low latency. The study compares the effectiveness of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimizer (GWO) in both Urban Macro (UMa) and Remote Macro (RMa) deployment scenarios that overcome the limitations of the current method of 5G deployment, which involves adopting Non-Standalone (NSA) architecture. Emphasizing population density, the optimization process eliminates redundant base stations for enhanced efficiency. Results indicate that PSO and GA strike the optimal balance between coverage and capacity, offering valuable insights for efficient network planning. The study includes a comparison of 28 GHz and 3.6 GHz carrier frequencies for UMa, highlighting their respective efficiencies. Additionally, the research proposes a 2.6 GHz carrier frequency for Remote Macro Antenna (RMa) deployment, enhancing 5G Multi-Tier Radio Access Network (RAN) planning and providing practical solutions for achieving infrastructure reduction and improved network performance in a specific geographical context.

Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively.

Plant height is a critical biophysical trait indicative of plant growth and developmental conditions and is valuable for biomass estimation and crop yield prediction. This study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial system (UAS) imagery. This study was conducted in a field with a sub-surface irrigation system in Lubbock, Texas, between 2022 and 2023. Images using the DJI Phantom 4 RTKs were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) at different growth stages. The resulting images depicted six scenarios of UAS altitudes and camera angles. The derived plant height was subsequently calculated as the vertical difference between the apical region of the plant and the ground elevation. Linear regression compared UAS-derived heights to manual measurements from 96 plots. Lower altitudes (40 m) outperformed higher altitudes (80 m) across all dates. For the early season (4 July 2023), the 40 m altitude had r2 = 0.82–0.86 and RMSE = 2.02–2.16 cm compared to 80 m (r2 = 0.66–0.68, RMSE = 7.52–8.76 cm). Oblique angles (45°) yielded higher accuracy than nadir (90°) images, especially in the late season (24 October 2022) results (r2 = 0.96, RMSE = 2.95 cm vs. r2 = 0.92, RMSE = 3.54 cm). These findings guide optimal UAS parameters for plant height measurement.

RNA virus-induced excessive inflammation and impaired antiviral interferon (IFN-I) responses are associated with severe disease. This innate immune response, also referred to as “dysregulated immunity” is caused by viral single-stranded RNA (ssRNA)- and double-stranded-RNA (dsRNA)-mediated exuberant inflammation and viral protein-induced IFN antagonism. However, key host factors and the underlying mechanism driving viral RNA-mediated dysregulated immunity are poorly defined. Here, using viral ssRNA and dsRNA mimics, which activate toll-like receptor 7 (TLR7) and TLR3, respectively, we evaluated the role of viral RNAs in causing dysregulated immunity. We observed that murine bone marrow-derived macrophages (BMDMs), when stimulated with TLR3 and TLR7 agonists, induced differential inflammatory and antiviral cytokine response. TLR7 activation triggered a robust inflammatory cytokine/chemokine induction compared to TLR3 activation, whereas TLR3 stimulation induced significantly increased IFN/IFN stimulated gene (ISG) response relative to TLR7 activation. To define the mechanistic basis for dysregulated immunity, we examined cell-surface and endosomal TLR levels and downstream mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-kB) activation. We identified significantly higher cell-surface and endosomal TLR7 levels compared to TLR3, which were associated with early and robust MAPK (p-ERK1/2, p-P38, and p-JNK) and NF-kB activation in TLR7-stimulated macrophages. Furthermore, blocking EKR1/2 and NF-kB activity reduced TLR3/7-induced inflammatory cytokine/chemokine levels, whereas only ERK1/2 inhibition enhanced viral RNA mimic-induced IFN/ISG responses. Collectively, our results illustrate that high cell-surface and endosomal TLR7 expression and robust ERK1/2 activation drive viral ssRNA mimic-induced excessive inflammatory and reduced IFN/ISG response and blocking ERK1/2 activity would likely mitigate viral-RNA/TLR-induced dysregulated immunity.

Resilience-based decision-making for urban water distribution systems (WDSs) is a challenge when WDS sensing data contain incomplete or missing values. This study investigated the impact of missing data imputation on a WDS resilience evaluation depending on missing data percentages. Incomplete datasets for the nodal pressure of the C-town WDS were developed with 10%, 30%, and 50% missing data percentages by manipulating a true dataset for normal operation conditions produced using EPANET. This study employed multiple imputation methods including classification and regression trees, predictive mean matching, linear regression regarding model error, and linear regression using projected values. Then, resilience values were evaluated and compared using unimputed and imputed datasets. An analysis of performance indicators based on NRMSE, NMAE, NR-Square, and N-PBIAS revealed that higher missing-data percentages led to increased deviation between the true and imputed datasets. The resilience evaluation using unimputed datasets produced significant deviations from the true resilience values, which tended to increase as the missing data percentages increased. However, the imputed datasets substantially contributed to reducing the deviations. These findings underscore the contributions of data imputation to enhancing resilience evaluation in WDS decision-making and suggest insights into advancing a resilience evaluation framework for urban WDSs with more reliable data imputation approaches.

The importance of dietary calcium intake in bone metabolism has been well established; however, it is still less investigated in health-related components, especially addressing muscle performance. This study aims to compare dietary calcium intake and its relationship with cardiovascular and muscular performance in young athletes (Lacrosse, Baseball, and soccer players). In this cross-sectional study, 95 participants (ages 18 to 30) participated during two visits to the Human Performance Laboratory. Participants completed body composition, lower and upper body muscle performance, cardiorespiratory protocol, and questionnaires related to dietary calcium intake. One-way ANOVA was used to determine the difference in the calcium intake between groups. The Pearson Correlation Coefficient was used to investigate the relationship between dietary calcium intake and muscle performance variables. Further, linear regression was used to assess the predictive value of calcium variables on overall muscle performance. Lacrosse players had significantly higher calcium intake than Baseball and soccer players (p < 0.05). There was a significant positive correlation between dietary calcium intake and the number of push-ups (r = 0.28; p = 0.03). CIBW and CI explained 4.3% and nearly 25% of the change in MPI, respectively (p < 0.001). This suggests the possible role of dietary calcium intake in enhancing health-related fitness components and highlights the need to explore its involvement in muscle-bone crosstalk.

Collaboration with peers both inside and outside the classroom can be an invaluable tool for helping students learn physics. We investigated the impact of peer collaboration on learning physics by examining the characteristics of women and men who typically worked alone versus those who typically collaborated with peers in their algebra-based introductory physics course when they took the course before and during the COVID-19 pandemic when the classes were on Zoom. Our findings indicate that, on average, students who worked with peers had higher grades and reported greater peer influence on their physics self-efficacy during the pandemic compared to those who worked alone. We also observed that, for both women and men, a larger percentage of students typically worked in groups before the pandemic, while a greater percentage typically worked alone during the pandemic. We discuss these results in relation to students’ prior academic preparation, physics grades, self-efficacy and their perception of the effectiveness of peer collaboration on their physics self-efficacy.

Rhizoctonia large patch (Rhizoctonia solani AG2-2 LP) significantly reduces turfgrass quality, aesthetics, and playability. Synthetic fungicides are commonly used for managing this disease, but they present high costs, potential for fungicide resistance, and environmental concerns. We conducted in vitro assays to test the effectiveness of three biofungicides, seven synthetic fungicides, and ten combinations against R. solani. We then assessed seven spray programs that included Bacillus subtilis QST713 and propiconazole, either alone or tank-mixed, on zoysiagrass ‘El Toro’ in a growth chamber and in field trials. Biofungicide B. subtilis QST713 reduced pathogen growth by up to 100% in vitro. B. subtilis QST713 alone or combined with synthetic fungicides and/or in rotation was as effective as the standalone synthetic fungicide, reducing disease severity and AUDPC by 81 and 77% (growth chamber) and by 71 and 52% (field), respectively, while maintaining acceptable turfgrass quality. Additionally, we screened zoysiagrass genotypes and advanced breeding lines against three R. solani isolates in growth chamber studies. Five genotypes and two breeding lines demonstrated resistance to Rhizoctonia large patch across isolates, highlighting their potential for developing disease-resistant cultivars. Our findings suggest that integrating biofungicides, resistant cultivars with chemical controls offer sustainable and effective strategies for managing Rhizoctonia large patch

Anthrax is a notorious disease of public health importance caused by Bacillus anthracis. The causative agent can also be used as a biological weapon. Spores of these bacteria can sustain extreme environmental conditions and remain viable in soil for decades. Domestic and wild ruminants are highly susceptible to this pathogen, which usually presents as a peracute to acute disease. In humans, cutaneous anthrax is frequent but pulmonary and enteric anthrax are more serious. Humans, animals, and the environment are all involved, making anthrax a perfect target for a One Health approach. The environment plays a key role in disease transmission. At a time when the One Health concept is not mere slogans, collaborative efforts of medical professionals, veterinarians, and environmental scientists will be valuable for the prevention and control of this disease. In this review, we discussed the transmission dynamics of anthrax in the environment, animals, and humans, as well as One Health strategies to control and prevent anthrax.

Endophytic bacteria are known to influence vital activities of host plants. Endophytes can promote plant growth and provide a defense response against pathogens. The use of endophytes in crop production has the potential to reduce the application of fertilizer and pesticide input and thus improve the sustainability of crop production. In this study, we investigated the effects of seed inoculation with non-native endophytic bacteria, harvested from Brassica carinata, on oat (Avena sativa L.) growth with root vigor assays and greenhouse experiments. For root vigor assay experiments, seeds of two different oat cultivars were treated with 16 endophytic bacteria previously shown to promote growth benefits on multiple crop species. For the greenhouse experiments, the effect of seed inoculation with bacterial isolates was evaluated on ten oat cultivars at two fertilization levels. The root vigor assay showed that multiple isolates, including Bacillus licheniformis, Enterobacter kobei, B. halotolerans, B. cereus, B. aryabhattai, and Lysinibacillus fusiformis, had a positive effect on seedling root growth in one of the two oat cultivars. In the other cultivar, the bacterial isolates had either no effect or a negative effect on root growth. Greenhouse studies showed that the magnitude and direction of the effect of bacterial inoculation on oat growth varied with fertilization levels, bacterial strain, and oat cultivar. However, we identified two cultivars that were more responsive to bacterial inoculation than the others and for which bacterial inoculation of seed resulted in enhanced growth in several traits under both reduced and full nitrogen levels, and this response was observed for the two isolates tested. Our results show that inoculating oat seeds with non-native bacterial endophytes can promote root and shoot growth in oats. Developing biofertilizers that are effective across crop species, crop cultivars, and environmental conditions may be possible if cultivars are selected for their responsiveness across multiple bacterial isolates and in multiple growing environments. Overall, this study indicates that non-native endophytes could be considered for the development of biofertilizers with effectiveness across crop species.

Pulmonary actinomycosis is an uncommon clinical entity that can be challenging to diagnose due to its non-specific symptomatology. Misdiagnosis and delayed treatment may result in invasive procedures and extended antimicrobial treatment courses. We report a case involving a 65-year-old female with poor oral dentition admitted for acute respiratory failure subsequently found to have a left-sided pleural effusion and perihepatic abscess formation. Cytopathology examination and microbiology studies confirmed the diagnosis of pulmonary actinomycosis.

A subsurface investigation was conducted to assess the suitability of a site for potential tunnel construction, focusing on the determination of shear wave velocities (Vs) in subsurface materials. This study employed three distinct methods to analyze Vs in weathered soft rock: drilling mechanism, multichannel analysis of surface waves (MASW), and microtremor array measurement (MAM). Through the utilization of MASW and MAM, empirical relationships were established, enabling the determination of Vs based solely on soil type and depth, offering a practical alternative to the limitations of SPT N-Value, particularly when exceeding 50 blows. The comparison of Vs values obtained from these methods revealed a close alignment between empirical techniques and MASW/MAM, which proved to be cost-effective and an efficient alternative to drilling for comprehensive underground structure assessments. The reliability of MASW was further underscored through its comparison with existing empirical methods. Moreover, the empirical approach demonstrated its efficacy in predicting velocities in weathered soft rock within the Central Himalayan region of Nepal, thus enhancing the feasibility study of underground structures. Lastly, this study proposed a Vs-Depth correlation specifically tailored for highly weathered meta-sandstone bedrock resulting in clay and sandy soils.

Soybean, a major legume crop, has seen a decline in its production owing to challenges in seed germination and the development of seedlings. Thus, in this study, we systematically investigated the influence of various chitosan–S-nitrosoglutathione (chitosan-GSNO) nanoparticle (0, 25, 50, and 100 µM) and Si (0, 0.5, and 1 mM) priming concentrations on soybean seed germination and seedling growth over five different priming durations (range: 1–5 h at each concentration). Significant differences were observed in all parameters, except seedling diameter, with both treatments. Seed germination was significantly enhanced after 3 h of priming in both treatments. The final germination percentage (FGP), peak germination percentage (PGP), vigor index (VI), seedling biomass (SB), hypocotyl length (HL), and radical length (RL) of 100 μM chitosan-GSNO-nanoparticle-primed seeds increased by 20.3%, 41.3%, 78.9%, 25.2%, 15.7%, and 65.9%, respectively, compared with those of the control; however, the mean germination time (MGT) decreased by 18.43%. Si priming at 0.5 mM increased the FGP, PGP, VI, SB, HL, and RL by 13.9%, 55.17%, 39.2%, 6.5%, 22.5%, and 25.1%, respectively, but reduced the MGT by 12.29% compared with the control treatment. Chitosan-GSNO and Si treatment up-regulated the relative expression of gibberellic acid (GA)-related genes (GmGA3ox3 and GmGA2ox1) and down-regulated that of abscisic acid (ABA)-related genes (GmABA2, GmAAO3, and GmNCED5). Chitosan-GSNO and Si application increased bioactive GA4 levels and simultaneously reduced ABA content. Hence, the use of exogenous chitosan-GSNO nanoparticles and Si as priming agents had a beneficial effect on seed germination and seedling growth because of the up-regulation in the expression of GA and down-regulation in the expression of ABA. Additional research is needed to understand the combined impact of Si and chitosan-GSNO nanoparticles, including their effects on the expression levels of other hormones and genes even in the later growth stage of the crop.

The design of a readily useable technology for routine paddock-scale soil porosity estimation is described. The method is non-contact (proximal) and typically from “on-the-go” sensors mounted on a small farm vehicle around 1 m above the soil surface. This ultrasonic sensing method is unique in providing estimates of porosity by a non-invasive, cost-effective, and relatively simple method. Challenges arise from the need to have a compact low-power rigid structure and to allow for pasture cover and surface roughness. The high-frequency regime for acoustic reflections from a porous material is a function of the porosity ϕ, the tortuosity α∞, and the angle of incidence θ. There is no dependence on frequency, so measurements must be conducted at two or more angles of incidence θ to obtain two or more equations in the unknown soil properties ϕ and α∞. Sensing and correcting for scattering of ultrasound from a rough soil surface requires measurements at three or more angles of incidence. A system requiring a single transmitter/receiver pair to be moved from one angle to another is not viable for rapid sampling. Therefore, the design includes at least three transmitter/reflector pairs placed at identical distances from the ground so that they would respond identically to power reflected from a perfectly reflecting surface. A single 25 kHz frequency is a compromise which allows for the frequency-dependent signal loss from a natural rough agricultural soil surface. Multiple-transmitter and multiple-microphone arrays are described which give a good signal-to-noise ratio while maintaining a compact system design. The resulting arrays have a diameter of 100 mm. Pulsed ultrasound is used so that the reflected sound can be separated from sound travelling directly through the air horizontally from transmitter to receiver. The average porosity estimated for soil samples in the laboratory and in the field is found to be within around 0.04 of the porosity measured independently. This level of variation is consistent with uncertainties in setting the angle of incidence, although assumptions made in modelling the interaction of ultrasound with the rough surface no doubt also contribute. Although the method is applicable to all soil types, the current design has only been tested on dry, vegetation-free soils for which the sampled area does not contain large animal footprints or rocks.

Background: Popular movement-based injury risk screens were shown to lack predictive precision, leading to interest in multifactorial models. Furthermore, there is a lack of research regarding injury risk assessment for those currently or planning to be recreationally active. This study aims to provide injury risk insights by analyzing multifactorial injury risk models and associated clinical measures in the U.S. population. Methods: Data related to injury, inflammatory markers, physical functioning, body composition, physical activity, and other variables from 21,033 respondents were extracted from NHANES. Odds ratios for self-reported injury were calculated for single predictors and risk models. Case–control and principal component analyses (PCA) were conducted to elucidate confounders and identify risk factor clusters, respectively. Receiver operating characteristic analysis was used to test the precision of a risk factor cluster to identify pain points and functional difficulties. Results: Sociodemographic, individual, and lifestyle factors were strongly associated with higher odds of injury. Increases in fibrinogen and C-reactive protein were significantly associated with all risk groups. Membership to the high-risk group (age over 40, obesity, no muscle-strengthening activities, sedentary lifestyle, and low back pain) predicted at least one functional difficulty with 67.4% sensitivity and 87.2% specificity. In the injury group, bone turnover markers were higher, yet confounded by age, and there was a significantly higher prevalence of self-reported osteoporosis compared to the control. In males, low testosterone was associated with injury, and high estradiol was associated with pain and functional difficulties. In females, high follicle-stimulating hormone was associated with functional difficulties. PCA revealed four high-risk profiles, with markers and activities showing distinct loadings. Conclusions: A comprehensive approach to injury risk assessment should consider the nexus of aging, lifestyle, and chronic disease to enhance tailored injury prevention strategies, fostering safe and effective physical activity participation and reducing the burden of musculoskeletal disorders.

The surface spike (S) glycoprotein mediates cell entry of SARS-CoV-2 into the host through fusion at the plasma membrane or endocytosis. Omicron lineages/sublineages have acquired extensive mutations in S to gain transmissibility advantages and altered antigenicity. The fusogenicity, antigenicity, and evasion of Omicron subvariants have been extensively investigated at unprecedented speed to align with the mutation rate of S. Cells that overexpress receptors/cofactors are mostly used as hosts to amplify infection sensitivity to tested variants. However, systematic cell entry comparisons of most prior dominant Omicron subvariants using human lung epithelium cells are yet to be well-studied. Here, with human bronchial epithelium BEAS-2B cells as the host, we compared single-round virus-to-cell entry and cell-to-cell fusion of Omicron BA.1, BA.5, BQ.1.1, CH.1.1, XBB.1.5, and XBB.1.16 based upon split NanoLuc fusion readout assays and the S-pseudotyped lentivirus system. Virus-to-cell entry of tested S variants exhibited cell-type dependence. The parental Omicron BA.1 required more time to develop full entry to HEK293T-ACE2-TMPRSS2 than BEAS-2B cells. Compared to unchanged P681, S-cleavage constructs of P681H/R did not have any noticeable advantages in cell entry. Omicron BA.1 and its descendants entered BEAS-2B cells more efficiently than D614G, and it was slightly less or comparable to that of Delta. Serine protease-pretreated Omicron subvariants enhanced virus-to-cell entry in a dose-dependent manner, suggesting fusion at the plasma membrane persists as a productive cell entry route. Spike-mediated cell-to-cell fusion and total S1/S2 processing of Omicron descendants were similar. Our results indicate no obvious entry or fusion advantages of recent Omicron descendants over preceding variants since Delta, thus supporting immune evasion conferred by antigenicity shifts due to altered S sequences as probably the primary viral fitness driver.

Estimating lung volume capacity is crucial in clinical medicine, especially in disease diagnostics. However, the existing estimation methods are complex and expensive, which require experts to handle and consequently are more error-prone and time-consuming. Thus, developing an automatic measurement system without a human operator that is less prone to human error and, thus, more accurate has always been a prerequisite. The limitation of radiation dose and various medical conditions in technologies like computed tomography was also the primary concern in the past. Although qualitative prediction of lung volume may be a trivial task, designing clinically relevant and automated methods that effectively incorporate imaging data is a challenging problem. This paper proposes a novel multi-tasking-based automatic lung volume estimation method using deep learning that jointly learns segmentation and regression of volume estimation. The two networks, namely, segmentation and regression networks, are sequentially operated with some shared layers. The segmentation network segments the X-ray images, whose output is regressed by the regression network to determine the final lung volume. Besides, the dataset used in the proposed method is collected from three different secondary sources. The experimental results show that the proposed multi-tasking approach performs better than the individual networks. Further analysis of the multi-tasking approach with two different networks, namely, UNet and HRNet, shows that the network with HRNet performs better than the network with UNet with less volume estimation mean square error of 0.0010.

Animals inhabiting colder climates have a larger body size (Bergmann’s rule) and smaller body extremities (Allen’s rule), which help homeothermic animals to retain heat. Such ecogeographical phenomena have frequently been observed in animals along the latitudinal gradient and have occasionally been tested along the elevational gradient. This study tested whether these ecogeographic rules hold true for the morphology of sparrows (Passer spp.) along the elevational gradient offered by the Himalaya in central Nepal. Seventy house sparrows and twenty-eight tree sparrows were captured from 22 different localities of central Nepal between 100 and 3400 m asl, and morphological traits such as body size (body mass, tarsus length, wing length and tail length) and body extremities (bill length and bill width) were measured. Linear regression analysis was used to test the association of morphological measurements with elevation and climatic variables. House sparrows (Passer domesticus) had a wider elevational distribution range and exhibited significantly larger body sizes than the Eurasian tree sparrows (P. montanus). House sparrows had larger body sizes and smaller bills at higher elevations in adherence to Bergmann’s rule and Allen’s rule. Bill length in house sparrows showed a positive association with the temperature following the proposition of Allen’s rule. However, the morphological measurements in Eurasian tree sparrows did not show a distinct pattern with elevation and climatic variables. Therefore, this study concludes that ecogeographical phenomena such as Bergmann’s rule and Allen’s rule could be species-specific based on their biological and ecological characteristics.

The study compares how acetyl donor molecules tetraacetylethylenediamine (TAED) and pentaacetate glucose (PAG) improve the antibacterial efficacy of solutions activated with a low-temperature atmospheric-pressure argon plasma jet. Plasma activation of solubilised TAED and PAG produce solutions with different chemical compositions and oxidative potentials. Both acetyl donor molecules enhance the hydrogen peroxide (H2O2) concentration in solution with TAED being more effective compared to PAG. However, PAG is more effective at forming peracetic acid (PAA) from reaction of its acetyl donor groups with plasma generated H2O2. The enhanced oxidative potential of plasma activated TAED and PAG solutions were shown to significantly improve bactericidal activity against common wound pathogens Gram-negative Pseudomonas aeruginosa and Gram-positive Staphylococcus aureus compared to plasma activated water produced without acetyl donors. Furthermore, the oxidative capacity of plasma activated PAG was least affected by the bacterial oxidative defence enzyme catalase, attributed to the high concentration of PAA produced in this formulation. Overall, the above data show that acetyl donors may help improve next generation of antimicrobial formulations produced by plasma, which might help combat increasing problems of antimicrobial resistance.

Warm autoimmune hemolytic anemia (AIHA) is a rare complication of COVID-19 infection. We report a case of warm AIHA in a patient with COVID-19 pneumonia treated with methylprednisolone and several red blood cell transfusions. Despite treatment of the warm AIHA, the patient’s reticulocyte count remained low, and his biochemical markers were suggestive of pure red cell aplasia, which was later attributed to a concurrent parvovirus B19 infection. This case highlights an unusual situation of two separate hematological processes caused by two separate and simultaneous viral infections.

Land use and land cover (LULC) change is one of the primary contributors to hydrological change in urban watersheds and can potentially influence stream flow and flood volume. Understanding the impacts of LULC change on urban hydrological processes is critical to effective urban water management and minimizing flood risks. In this context, this study aims to determine the impacts of LULC change on hydrological response in a fast transitioning watershed for the predicted years of 2050 and 2080. This research employs the hybrid land use classification technique, Cellular Automata–Markov (CA–Markov) model to predict land use changes, utilizing land use data from 2001, 2013, and 2021. Additionally, it incorporates a calibrated, event-specific hydrologic model known as the Personal Computer Storm Water Management Model (PCSWMM) to assess alterations in hydrological responses for storm events of various magnitudes. The findings indicate a transition of the watershed into an urbanized landscape, replacing the previous dominance of agriculture and forested areas. The initial urban area, constituting 11.6% of the total area in 2021, expands to cover 34.1% and 44.2% of the total area by 2050 and 2080, respectively. Due to the LULC changes, there are increases in peak discharge of 5% and 6.8% and in runoff volume of 8% and 13.3% for the years 2050 and 2080 for a 100-year return period storm event. Yet, the extent of these changes intensifies notably during storm events with lower return periods. This heightened impact is directly attributed to the swift urbanization of the watershed. These results underscore the pressing necessity to regulate LULC change to preserve the hydrological equilibrium.

The ongoing COVID-19 pandemic caused by SARS-CoV-2 is associated with acute respiratory distress syndrome (ARDS) and fatal pneumonia. Excessive inflammation caused by SARS-CoV-2 is the key driver of ARDS and lethal disease. Several FDA-approved drugs that suppress virus replication are in clinical use. However, despite strong evidence for the role of virus-induced inflammation in severe COVID-19, no effective anti-inflammatory drug is available to control fatal inflammation as well as efficiently clear the virus. Therefore, there is an urgent need to identify biologically derived immunomodulators that suppress inflammation and promote antiviral immunity. In this study, we evaluated acellular human amniotic fluid (acAF) containing extracellular vesicles (hAF-EVs) as a potential non-toxic and safe biologic for immunomodulation during COVID-19. Our in vitro results showed that acAF significantly reduced inflammatory cytokine production in TLR2/4/7 and SARS-CoV-2 structural protein-stimulated mouse macrophages. Importantly, an intraperitoneal administration of acAF reduced morbidity and mortality in SARS-CoV-2-infected mice. A detailed examination of SARS-CoV-2-infected lungs revealed that the increased protection in acAF-treated mice was associated with reduced viral titers and levels of inflammatory myeloid cell infiltration. Collectively, our results identify a novel biologic that has potential to suppress excessive inflammation and enhance survival following SARS-CoV-2 infection, highlighting the translational potential of acAF against COVID-19.

The effectiveness of regional storm surge reduction strategies and tank-level structural mitigation measures in reducing the failure probability of aboveground storage tanks (ASTs) were studied. Given past failures during flood and hurricane events, several studies have developed fragility models for ASTs. However, the suitability of these fragility models for different hurricane hazard scenarios is unknown. Furthermore, to combat climate change and sea level rise, several regional storm surge reduction strategies are being proposed. However, the effectiveness of these strategies in improving the safety of ASTs is also unknown. So, herein, a framework was proposed that facilitates assessing the suitability of fragility models and the quantification of AST failures and their consequences while propagating uncertainties using Monte Carlo simulations. The application of the proposed framework to Cameron, Louisiana, provided several key insights: (1) fragility models that do not model wave loads and dislocation failure are not suitable for the region; (2) a regional risk mitigation strategy was insufficient for lowering future spill volume, repair, and cleanup costs; and (3) considering bottom-plate failure of anchored tanks—a structural risk mitigation measure—would lead to a 47–72% reduction in the consequences of tank failure.

The increase in intensity and frequency of drought due to global climate change has increased the urgency of developing crop cultivars suitable for dry environments. Drought tolerance is a complex trait that involves numerous physiological, biochemical, and morphological responses. A better understanding of those mechanisms is critical to develop drought tolerant cultivars. In this study, we aimed to understand the morphophysiological changes at the shoot and root levels in response to drought stress of ten oat genotypes with diverse root morphological characteristics. Twenty-one-day old plants were subjected to drought stress in a greenhouse by withholding water for two weeks. Several characteristics including chlorophyll content, relative water content (RWC), stomatal conductance, stomata number, shoot dry weight (SDW), root dry weight (RDW), root-to-shoot biomass ratio (RSR), root length, root area, and root volume were measured on well-watered, and drought-stressed plants. Grain yield was evaluated by continuing the drought treatment with a drying and rewatering cycle every 15 days until physiological maturity. The water regime had a significant impact on all traits evaluated. A significant interaction between genotype and water treatment was observed for RWC, chlorophyll content, stomatal conductance, stomata number, and grain yield but not for root traits, suggesting that the root system of all genotypes responded similarly to drought stress. Hayden, the cultivar with the lowest reduction in grain yield from the drought treatment, was among the genotypes with the lowest reduction in RWC and chlorophyll content but with a sharp decrease in stomata number, thus indicating that regulating the plant water status and maintaining the photosynthesis level are important for oat plants to maintain grain yield under drought stress. The size of the root system was not correlated with grain yield under drought, but the RWC and grain yield were significantly correlated under drought, thus suggesting that maintaining the RWC is an important characteristic for oat plants to maintain yield under drought stress.

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI’s GPT, Google’s PaLM, and Meta’s Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI’s early stage adoption within the construction sector. Given GenAI’s unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors’ opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains.

Multifunctional composites provide more than one function from the same part. The anisotropy, material, and process characterization challenges and the lack of standardization on the 3D-printed multifunctional carbon composites make it difficult for application into aerospace. The current solutions for additive manufacturing (AM) technologies and additively manufactured monofunctional and multifunctional composites are not mature enough for safety-critical applications. A new approach is proposed to explore the use of machine learning (ML) in the design, development, AM, testing, and certification of multifunctional composites for aircraft, unmanned aircraft systems (UAS), and spacecraft. In this work, an artificial neural network (ANN) architecture is proposed. An AM-embedded building block approach integrates the complete lifecycle of aircraft, UAS, and spacecraft using ANN to support the continued operational safety (COS) of aircraft, spacecraft, and UAS. The proposed method exploits the power of ANN on the metadata for the characterization of multifunctional material properties and processes and the mapping of the failure modes compared with the predicted models and history. This paper provides an in-depth analysis and explanation of the new methods needed to overcome the existing barriers, problems, and situations.

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

Tetanus is extremely rare in developed countries. We report the first documented case of tetanus in the province of Alberta since 2016: a farmer that developed trismus, shoulder stiffness, and fevers eight days following orthopedic surgery. Tetanus immunoglobulin elicited rapid recovery. We highlight risk factors, pathogenesis, epidemiology, and diagnostic challenges.

The objective of the paper is to examine whether female participation in the labor force (FPLF) is influenced by a country’s participation in international markets through foreign direct investment (FDI)—a proxy for globalization. We obtained a panel dataset from the World Development Indicators database for 99 countries from 2001 to 2018, and used the system generalized method of moments (system GMM) to estimate a dynamic panel model with appropriate specification tests. The results show that FDI encourages FPLF to some extent, but the positive effects are more robust for low- and middle-income countries than high-income countries. We also found that results are sensitive to extreme outlier observations in the explanatory factor FDI. These results have important policy implications for low- and middle-income countries; they recommend a focus on sectors that generate higher FDI, as these sectors stand to yield the greatest benefits with regards to female labor force participation.

The study of the importance of physicochemical parameters of water for algal growth and development in Jagdishpur Reservoir was carried out in the present research. Occurrence of algal species and values of water quality parameters also indicate the ecological nature and the present status of the reservoir. Water samples were collected from Jagdishpur Reservoir in Kapilvastu District (Nepal), in two seasons (wet and dry). The following physicochemical parameters of water were analyzed: temperature, pH, dissolved oxygen, alkalinity, concentration of nitrates and phosphates, free CO2, hardness, conductivity, total dissolved solids. The values of free CO2, hardness, phosphate, temperature, and dissolved oxygen were high in wet season while alkalinity, nitrate, conductivity and total dissolved solid were high in dry season. Altogether 81 algal species belonging to 53 genera and 6 classes were recorded. Higher number of algal species was recorded in the dry season rather than in the wet season. The highest number of species was noted among Chlorophyceae, followed by Cyanobacteria > Bacillariophyceae > Euglenophyceae > Crysophyceae and Dinophyceae. Cosmarium had the highest number of species (13) among all genera. During the wet season, Cosmarium, Calothrix and Phormidium were the genera with the highest number of species while Bulbochaete, Trachelomonas and Gomphonema were genera with the lowest number of species. During the dry season, Cymbella, Cosmarium and Rhopalodia were genera with highest number of species while Euglena, Gloeotrichia and Trachelomonas were genera with lowest number of species. Algal diversity was positively correlated with the increase of alkalinity, conductivity and TDS, while negatively correlated with free CO2, hardness, concerntration of phosphates and nitrates, DO, pH and temperature. Shannon–Weiner diversity index value was higher during the dry season than in the wet season.

Electric vehicles (EVs) offer a viable technological solution for mitigating greenhouse gas emissions in the transportation industry, addressing pressing societal concerns regarding climate change, air pollution, and sustainable energy consumption. To effectively promote widespread adoption of EVs, it is crucial to understand consumer preferences and evaluate market dynamics. In Nepal, where proven fossil fuel reserves are absent, the government is actively working towards accelerating EV adoption, leveraging the nation’s significant hydroelectric power generation potential to fulfill EVs’ charging demands. To gain insight into consumer preferences and evaluate market dynamics regarding EVs in Nepal, this study employs a comprehensive approach. Stated preference data are collected through a meticulously designed survey, and sophisticated analytical techniques, namely, the mixed logit model and latent class model, are applied for estimation purposes. The results of this study show that potential EV consumers with small family sizes, lower monthly travel distances, heightened environmental awareness, and substantial knowledge about electric vehicles are more inclined to embrace EV technology. Notably, the study highlights that a reduction in the purchase price exerts the most significant influence on increasing consumers’ likelihood of adopting battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Market simulation results suggest that a policy mix scenario, encompassing a combination of supportive measures, proves more effective in promoting EV adoption compared to relying on single policy initiatives. Furthermore, through latent class estimation, the study identifies three distinct classes of consumers within Nepal, each exhibiting significant variations in preferences. Recognizing and addressing these variations within policy frameworks is crucial for the successful promotion and widespread acceptance of EVs in Nepal.

Recently, the localized oscillatory radial basis functions collocation method (L-ORBFs) has been introduced to solve elliptic partial differential equations in 2D with a large number of computational nodes. The research clearly shows that the L-ORBFs is very convenient and useful for solving large-scale problems, but this method is numerically less accurate. In this paper, we propose a numerical scheme to improve the accuracy of the L-ORBFs by adding low-degree polynomials in the localized collocation process. The numerical results validate that the proposed numerical scheme is highly accurate and clearly outperforms the results of the L-ORBFs.

The importance of various markers such as Sclerostin, Dickkopf-1 (DKK-1), Irisin, receptor activator of NF-kB ligand (RANKL), and Vitamin D have been well studied in bone metabolism. Additionally, inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and Interleukin 6 (IL-6) have been shown to hinder muscle protein synthesis, leading to the loss of muscle and strength. However, a research gap exists in understanding their role in muscle function and physical activity. Therefore, this study aims to explore the serum levels of Sclerostin, DKK-1, Irisin, IL-6, RANKL, Vitamin D, and TNF-α and assess their relationships with upper- and lower-body strength in young adults. In this study, 38 college-aged students (18–23 years), males and females, participated and completed the protocols. The participants’ lower and upper body strength were assessed by the vertical jump test (Just Jump, Probotic, AL) with a Tendo FitroDyne (Tendo Sports Machines, Trencin, Slovak Republic) and handgrip (HG) dynamometry (Takei Scientific Instruments, Yashiroda, Japan), respectively. Fasting morning blood samples were analyzed for serum levels of biomarkers by ELISA. The results indicate significant sex differences in Sclerostin, DKK-1, Irisin, and Vitamin D levels (p < 0.05). Furthermore, a positive association was observed between Sclerostin, DKK-1, and Vitamin D, with lower body muscle performance variables (p < 0.05). Conversely, a significant negative correlation was observed between TNF-α and lower-body muscle performance variables (p < 0.05). The results suggest that these markers may have a distinct effect on muscle performance, underscoring the need for further investigation to elucidate the concept of muscle–bone crosstalk.

Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. An algorithm referred to as Quantum Approximate Optimization Algorithm (QAOA) has been recently proposed to approximately solve hard problems using a protocol know as bang–bang. The technique is based on unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. The QAOA was explored in the context of several optimization problems such as the Max-Cut problem and the Traveling Salesman Problem (TSP). Due to the relatively small node-size solution capability of the available quantum computers and simulators, we developed a hybrid approach where sub-graphs of a TSP tour can be executed on a quantum computer, and the results from the quantum optimization are combined for a further optimization of the whole tour. Since the local optimization of a sub-graph is prone to becoming trapped in a local minimum, we overcame this problem by using a parallel Ant Colony Optimization (ACO) algorithm with periodic pheromone exchange between colonies. Our method exceeds existing approaches which have attempted partitioning a graph for small problems (less than 48 nodes) with sub-optimal results. We obtained optimum results for benchmarks with less than 150 nodes and results usually within 1% of the known optimal solution for benchmarks with around 2000 nodes.

Root morphological traits (RMTs) profoundly influence plant growth, resilience to abiotic stresses, and yield in soybean (Glycine max). In a comprehensive study spanning two consecutive years (2021–2022), the RMTs were assessed in 216 soybean accessions from 34 diverse origins. The investigation involved randomized batches with plants cultivated in PVC pipes filled with horticultural soil and harvested at the V2 growth stage. All the germplasms exhibited significant differences (p < 0.001) in all measured traits, i.e., total root length (TRL), root volume (RV), average diameter (AD), number of tips (NT), number of forks (NF), and tertiary total length (TTL). Among the top 5% performers in TRL, which, interestingly, were exclusively of Korean origin, germplasm IT115491 displayed an impressive average TRL value of 1426.24 cm. Notably, germplasms from Serbia and Korea predominantly occupied the upper AD quantile, with IT156262 exhibiting the highest AD value of 0.57 mm. A correlation analysis showed strong positive associations of TRL with RV (r = 0.85), NT (r = 0.84), NF (r = 0.96), and TTL (r = 0.88), whereas it had a negative association with AD (r = −0.25). A principal component analysis (PCA) showed a cumulative 95% of the total variance in the data in the first three principal components (PCs). PC1 (eigenvalue = 4.64) accounted for a 77.00% variance, with TRL, RV, NF, NT, and TTL exhibiting the highest associated eigenvectors. K-means clustering was performed with three clusters. Cluster 2 contained accessions with higher AD values, whereas Cluster 3 comprised accessions with increased TRL, NT, NF, and TTL, which mostly originated from Korea. Our findings offer targeted insights for plant breeders to optimize specific root traits and enhance crop performance across diverse environmental conditions by strategically targeting these clusters. Additionally, the influence of cultivar origin on root traits warrants further investigation, with implications for future breeding programs.

Dietary isoflavones, a type of phytoestrogens, have gained importance owing to their health-promoting benefits. However, the beneficial effects of isoflavones are mediated by smaller metabolites produced with the help of gut bacteria that are known to metabolize these phytoestrogenic compounds into Daidzein and Genistein and biologically active molecules such as S-Equol. Identifying and measuring these phytoestrogens and their metabolites is an important step towards understanding the significance of diet and gut microbiota in human health and diseases. We have overcome the reported difficulties in quantitation of these isoflavones and developed a simplified, sensitive, non-enzymatic, and sulfatases-free extraction methodology. We have subsequently used this method to quantify these metabolites in the urine of mice using UPLC-MS/MS. The extraction and quantitation method was validated for precision, linearity, accuracy, recoveries, limit of detection (LOD), and limit of quantification (LOQ). Linear calibration curves for Daidzein, Genistein, and S-Equol were set up by performing linear regression analysis and checked using the correlation coefficient (r2 > 0.995). LOQs for Daidzein, Genistein, and S-Equol were 2, 4, and 2 ng/mL, respectively. This UPLC-MS/MS swift method is suitable for quantifying isoflavones and the microbial-derived metabolite S-Equol in mice urine and is particularly useful for large numbers of samples.

Klebsiella spp. are associated with several nosocomial and opportunistic infections. Increasing antimicrobial resistance of Klebsiella species is aggravated by a number of intrinsic and extrinsic factors. The main aim of this study is to determine antimicrobial resistance due to production of β-lactamase enzymes, extended spectrum beta-lactamase (ESBL), metallo-beta-lactamase (MBL) and AmpC and Klebsiella pneumoniae carbapenemase (KPC) and biofilm formation in Klebsiella isolates. A total of 2197 non-duplicate specimens of urine, sputum and pus were obtained from the National Public Health Laboratory (NPHL), Kathmandu, Nepal, between February and August 2019. Klebsiella species were isolated, identified and screened for antimicrobial susceptibility testing with the disk diffusion method. Phenotypic detection of ESBL, MBL, KPC and AmpC production was observed and biofilm production was detected by the microtiter plate method. Out of a total of 2197 clinical specimens, bacterial growth was detected in 8% (175/2197) of the specimens. Of the total isolates, 86.3% (151/175) were Gram-negative bacteria and 37.7% (57/151) were Klebsiella spp. Of the total Klebsiella spp., 56% (32/57) were multi drug resistant (MDR), 16% (9/57) were ESBL, 26% (15/57) were MBL, 4% (2/57) were KPC (class A carbapenemase), 16% (9/57) were AmpC producers and 95% (54/57) were biofilm producers. Gentamicin was the most effective antibiotic, followed by cotrimoxazole, as 68% (39/57) and 47% (27/57) of the Klebsiella isolates were susceptible towards these drugs, respectively. The study results show evidence of β-lactamase production, high prevalence of MDR and biofilm producing Klebsiella species. Integrating the test parameters for phenotypic confirmation of ESBL, MBL, AmpC β lactamase and KPC in routine diagnostic procedures can help in the early detection and management of these resistant strains.

Lake evolution and its changes over time are an evident and easily measurable signal of human activities and climate change impacts in mountain regions. This study presents bathymetric modeling of permanent lakes (Begnas and Rara Lakes) located in two different geographic settings of Nepal. Moreover, temporal changes in land cover and soil erosion of the lake watersheds, as well as climatic trends around these lakes, are assessed. This study supports establishing reference sites for exploring scientific evidence on the impacts of anthropogenic and climate change on lake hydrological systems. Second-order polynomial models best represent the relationship between lake depth and volume. Rara Lake had a maximum depth of 169 m with an area of 10.52 km2 and a volume of 1013.305 million cubic meters (Mm3), whereas Begnas Lake had a maximum depth of 12.5 m with an area of 2.98 ± 0.10 km2 and a water volume of 13.539 Mm3 in the year 2019. Both lake regions are experiencing changes in temperature and rainfall. The area and volume of Rara Lake and its watershed have been relatively stable even with minimal land-cover change during the recent decades. Begnas Lake and its watershed have experienced significant changes in the last few decades. This study concludes that human activities in the Begnas Lake watersheds are the primary source of lake area variation rather than climate change.

Habitat suitability is crucial to ensure the long-term persistence of species and can be identified based on relationships between species occurrences and underlying abiotic and biotic factors. We identified potential nesting habitat for the Egyptian vulture (Neophron percnopterus) in Nepal using ecological niche modeling with climatic variables. We estimated the currently suitable nesting habitat for Egyptian vulture in Nepal at 38,204 km2. We found a high probability of suitable nesting habitat on east-facing aspects, and the probability of a suitable nesting habitat was greater in more mountainous areas, particularly in central and western regions of Nepal. Precipitation was a major factor for predicting probability of the presence of nest sites for Egyptian vultures. After identifying potentially suitable habitat, we identified environmental factors affecting landscape-level suitable nesting habitat for Egyptian vultures using generalized linear models. For Egyptian vultures, sites near forests and human settlements were most suitable for nesting, roosting, and foraging, especially in central and western Nepal. Based on potentially suitable nesting habitat and previous work on Egyptian vulture foraging and roosting habitat, we recommend protecting forests near water sources and open areas for their long-term conservation.

The development of the tertiary industry is of great significance for promoting industrial structure, optimizing and upgrading it, and achieving regional energy conservation and emission reduction goals. This study adopts a quantitative method to analyze the spatio-temporal pattern of carbon emissions from China’s tertiary industry from 2004 to 2019. In order to analyze emissions from aspects such as energy structure, energy intensity, energy carrying capacity, industrial structure, level of industrial development, income level, consumption capacity, energy consumption intensity, and population size, this study establishes a hybrid factor decomposition model called the “energy-industry-consumption” research framework. The study shows that carbon emissions from China’s tertiary industry have been increasing year by year from 2004 to 2019, with a growth rate of 353.10%. Transportation is the largest contributor to the increase in carbon emissions from China’s tertiary industry. The carbon emissions from the tertiary industry in each province show four types: high-speed growth, low-speed growth, fluctuating growth, and stable growth. During the study period, carbon emissions produce a spatial heterogeneity with the highest emissions in the south and lowest in the northwestern part of China. The spatial pattern of per capita carbon emissions is not significant. Guangdong has the highest carbon emissions, and Shanghai and Beijing have higher per capita carbon emissions. Industrial factors and consumption factors have a positive effect on carbon emissions in China’s tertiary industry, while energy factors have a negative effect. The leading factor of carbon emissions in China’s tertiary industry has gradually shifted from energy to industry.

Traditional encoder–decoder networks like U-Net have been extensively used for polyp segmentation. However, such networks have demonstrated limitations in explicitly modeling long-range dependencies. In such networks, local patterns are emphasized over the global context, as each convolutional kernel focuses on only a local subset of pixels in the entire image. Several recent transformer-based networks have been shown to overcome such limitations. Such networks encode long-range dependencies using self-attention methods and thus learn highly expressive representations. However, due to the computational complexity of modeling the whole image, self-attention is expensive to compute, as there is a quadratic increment in cost with the increase in pixels in the image. Thus, patch embedding has been utilized, which groups small regions of the image into single input features. Nevertheless, these transformers still lack inductive bias, even with the image as a 1D sequence of visual tokens. This results in the inability to generalize to local contexts due to limited low-level features. We introduce a hybrid transformer combined with a convolutional mixing network to overcome computational and long-range dependency issues. A pretrained transformer network is introduced as a feature-extracting encoder, and a mixing module network (MMNet) is introduced to capture the long-range dependencies with a reduced computational cost. Precisely, in the mixing module network, we use depth-wise and 1 × 1 convolution to model long-range dependencies to establish spatial and cross-channel correlation, respectively. The proposed approach is evaluated qualitatively and quantitatively on five challenging polyp datasets across six metrics. Our MMNet outperforms the previous best polyp segmentation methods.

Soybean (Glycine max) is a crucial legume crop known for its nutritional value, as its seeds provide large amounts of plant protein and oil. To ensure maximum productivity in soybean farming, it is essential to carefully choose high-quality seeds that possess desirable characteristics, such as the appropriate size, shape, color, and absence of any damage. By studying the relationship between seed shape and other traits, we can effectively identify different genotypes and improve breeding strategies to develop high-yielding soybean seeds. This study focused on the analysis of seed traits using a Python algorithm. The seed length, width, projected area, and aspect ratio were measured, and the total number of seeds was calculated. The OpenCV library along with the contour detection function were used to measure the seed traits. The seed traits obtained through the algorithm were compared with the values obtained manually and from two software applications (SmartGrain and WinDIAS). The algorithm-derived measurements for the seed length, width, and projected area showed a strong correlation with the measurements obtained using various methods, with R-square values greater than 0.95 (p < 0.0001). Similarly, the error metrics, including the residual standard error, root mean square error, and mean absolute error, were all below 0.5% when comparing the seed length, width, and aspect ratio across different measurement methods. For the projected area, the error was less than 4% when compared with different measurement methods. Furthermore, the algorithm used to count the number of seeds present in the acquired images was highly accurate, and only a few errors were observed. This was a preliminary study that investigated only some morphological traits, and further research is needed to explore more seed attributes.

The nitrogen-vacancy (NV) centers in diamond have the ability to sense alternating-current (AC) magnetic fields with high spatial resolution. However, the frequency range of AC sensing protocols based on dynamical decoupling (DD) sequences has not been thoroughly explored experimentally. In this work, we aimed to determine the sensitivity of the ac magnetic field as a function of frequency using the sequential readout method. The upper limit at high frequency is clearly determined by Rabi frequency, in line with the expected effect of finite DD-pulse width. In contrast, the lower frequency limit is primarily governed by the duration of optical repolarization rather than the decoherence time (T2) of NV spins. This becomes particularly crucial when the repetition (dwell) time of the sequential readout is fixed to maintain the acquisition bandwidth. The equation we provide successfully describes the tendency in the frequency dependence. In addition, at the near-optimal frequency of 1 MHz, we reached a maximum sensitivity of 229 pT/Hz by employing the XY4-(4) DD sequence.

Maintaining soil health and sustainable crop production has been challenged by climate variability and wind erosion in semi-arid regions. To understand the initial effects of the transition of tilled cotton systems to no-tillage with winter wheat as a cover crop, we sampled 18 commercial grower sites from 2019 to 2022 in the Southern High Plains (SHP). We evaluated the soil biological component, which often responds rapidly to changes in residue additions or minimized soil disturbance providing an early indication of changes in soil health, especially in the low organic matter soils in this region. After two years, compared to tilled systems, no-till systems had significant increases in ester-linked fatty acid methyl ester (EL-FAME) bacterial and saprophytic and AMF fungal markers, enzyme activities of nutrient cycling, and various SOM pools, under both center-pivot irrigation and dryland. Similar increases were also observed in two dryland sites sampled before and up to two years after transition to no-till. Our study demonstrates the potential of no-tillage and cover crops to improve soil health in cotton production in semiarid regions, and a framework for a soil health assessment that links different soil health indicators with functions related to soil organic matter, soil water, and biogeochemical cycling.

Through the ACE2, a main enzyme of the renin–angiotensin system (RAS), SARS-CoV-2 gains access into the cell, resulting in different complications which may extend beyond the RAS and impact the Arachidonic Acid (ArA) pathway. The contribution of the RAS through ArA pathways metabolites in the pathogenesis of COVID-19 is unknown. We investigated whether RAS components and ArA metabolites can be considered biomarkers of COVID-19. We measured the plasma levels of RAS and ArA metabolites using an LC-MS/MS. Results indicate that Ang 1–7 levels were significantly lower, whereas Ang II levels were higher in the COVID-19 patients than in healthy control individuals. The ratio of Ang 1–7/Ang II as an indicator of the RAS classical and protective arms balance was dramatically lower in COVID-19 patients. There was no significant increase in inflammatory 19-HETE and 20-HETE levels. The concentration of EETs was significantly increased in COVID-19 patients, whereas the DHETs concentration was repressed. Their plasma levels were correlated with Ang II concentration in COVID-19 patients. In conclusion, evaluating the RAS and ArA pathway biomarkers could provide helpful information for the early detection of high-risk groups, avoid delayed medical attention, facilitate resource allocation, and improve patient clinical outcomes to prevent long COVID incidence.

Introduction: Enteric fever, a systemic infection caused by Salmonella enterica Typhi and S. enterica Paratyphi is one of the most common infections in developing countries such as Nepal. Aside from irrational practices of antibiotic use, mutations in chromosomal genes encoding DNA gyrase and Topoisomerase IV and by plasmid mediated quinolone resistant (PMQR) genes are suggested mechanisms for the development of resistance to nalidixic acid and reduced susceptibility to ciprofloxacin. Regardless of high endemicity of enteric fever in Nepal, there is paucity of studies on prevalence and drug-resistance of the pathogen. Therefore, this study aimed to assess the antibiotic susceptibility pattern of Salmonella isolates and determine the minimum inhibitory concentration of ciprofloxacin. Methods: A total of 1298 blood samples were obtained from patients with suspected enteric fever, attending Sukraraj Tropical and Infectious Disease Hospital (STIDH) during March–August, 2019. Blood samples were inoculated immediately into BACTEC culture bottles and further processed for isolation and identification of Salmonella Typhi and S. Paratyphi. Axenic cultures of the isolates were further subjected to antimicrobial susceptibility testing (AST) by using the modified Kirby–Bauer disc diffusion method based on the guidelines by CLSI. The minimum inhibitory concentration (MIC) of ciprofloxacin was determined by agar-dilution method. Results: Out of 1298 blood cultures, 40 (3.1%) were positive for Salmonella spp. among which 29 (72.5%) isolates were S. Typhi and 11 (27.5%) isolates were S. Paratyphi A. In AST, 12.5% (5/40), 15% (6/40) and 20% (8/40) of the Salmonella isolates were susceptible to nalidixic acid, ofloxacin and levofloxacin, respectively, whereas none of the isolates were susceptible to ciprofloxacin. The MIC value for ciprofloxacin ranged from 0.06-16 µg/mL in which, respectively, 5% (2/40) and 52.5% (21/40) of the isolates were susceptible and resistant to ciprofloxacin. None of the isolates showed multidrug-resistance (MDR) in this study. Conclusion: This study showed high prevalence of quinolone-resistant Salmonella spp., while there was marked re-emergence of susceptibilities to traditional first option drugs. Hence, conventional first-line-drugs and third-generation cephalosporins may find potential usage as the empirical drugs for enteric fever. Although our reporting was free of MDR strains, extensive surveillance, augmentation of diagnostic facilities and treatment protocol aided by AST report are recommended for addressing the escalating drug-resistance in the country.

The rapid identification of bacteria causing meningitis is crucial as delays in the treatment increase mortality rate. Though considered as the gold standard for the laboratory diagnosis of bacterial meningitis, culture might give false negative results in a case of patients under antibiotics prior to lumbar puncture. This study aimed to detect Streptococcus pneumoniae, Neisseria meningitidis and Haemophilus influenzae by a multiplex polymerase chain reaction (PCR) in culture-negative cerebrospinal fluid samples collected from clinically suspected meningitis cases attending different hospitals in Kathmandu, Nepal from January 2017 to December 2019. S. pneumoniae, N. meningitidis and H. influenzae were detected in 8.59% (33/384) of the specimens by PCR and 7.55% (29/384) of the specimens by culture. Correlation between culture and PCR of the same sample was good (Spearman’s rho correlation coefficient = 0.932). However, the difference in positivity between culture and PCR was statistically not significant (p value > 0.05). In four specimens, culture could not detect any of the targeted bacteria whereas PCR could detect presence of H. influenzae. PCR increases the diagnostic yield for bacterial meningitis. PCR may be considered as an adjunctive test for establishing the cause of infection in culture negative clinically suspected meningitis cases.