Monodispersed mesoscopic star-shaped gold particles via silver-ion-assisted multi-directional growth for highly sensitive SERS-active substrates.

Surface-enhanced Raman scattering (SERS) exploits localized surface plasmon resonances in metallic nanostructures to significantly amplify Raman signals and perform ultrasensitive analyses. A critical factor for SERS-based analysis systems is the formation of numerous electromagnetic hot spots within the nanostructures, which represent regions with highly concentrated fields emerging from excited localized surface plasmons. These intense hotspot fields can amplify the Raman signal by several orders of magnitude, facilitating analyte detection at extremely low concentrations and highly sensitive molecular identification at the single-nanoparticle level. In this study, mesoscopic star-shaped gold particles (gold mesostars) were synthesized using a three-step seed-mediated growth approach coupled with the addition of silver ions. Our study confirms the successful synthesis of gold mesostars with numerous sharp tips via the multi-directional growth effect induced by the underpotential deposition of silver adatoms (AgUPD) onto the gold surfaces. The AgUPD process affects the nanocrystal growth kinetics of the noble metal and its morphological evolution, thereby leading to intricate nanostructures with high-index facets and protruding tips or branches. Mesoscopic gold particles with a distinctive star-like morphology featuring multiple sharp projections from the central core were synthesized by exploiting this phenomenon. Sharp tips of the gold mesostars facilitate intense localized electromagnetic fields, which result in strong SERS enhancements at the single-particle level. Electromagnetic fields can be further enhanced by interparticle hot spots in addition to the intraparticle local field enhancements when arranged in multilayered arrays on substrates, rendering these arrays as highly efficient SERS-active substrates with improved sensitivity. Evaluation using Raman-tagged analytes revealed a higher SERS signal intensity compared to that of individual mesostars because of interparticle hot spots enhancements. These substrates enabled analyte detection at a concentration of 10- 9 M, demonstrating their remarkable sensitivity for trace analysis applications.

Unveiling the global nexus: Pandemic fear, government responses, and climate change-an empirical study.

This study examined the relationships between pandemic fear, government responses, and climate change using a time-series dataset from January 1, 2020, to December 31, 2020. By employing an auto-regressive distributed lag (ARDL) approach, the results revealed that pandemic fear significantly impacts climate change, while government responses to COVID-19 negatively influence climate change in the long run. Climate change and government responses significantly positively affect pandemic fear in the long run. Moreover, we found a bidirectional causality between government responses and climate change, unidirectional causality from government responses to pandemic fear, and no Granger causality between pandemic fear and climate change. Our findings have some important policy implications. Governments must encourage coordination, enhance crisis responses, and consider revising economic metrics to maintain environmental sustainability. The COVID-19 experience can inform strategies for reducing CO2 emissions and investing in green economies and healthcare to prepare for future challenges.

Assessment of knowledge, attitude, and practices regarding the relationship of obesity with diabetes among the general community of Pakistan.

This study sought to evaluate the influence of knowledge, attitude, and practices assessment on diabetes related to obesity in Pakistani society. Data was collected both through door to door and online survey approaches from 518 participants by using a pre-validated questionnaire. A total of 15.6% were underweight, 61.2% were normal weight, 17.2% were overweight and 5.8% were in obesity class I and 2.9% were classified as obesity class II. The co-occurrence of obesity and diabetes was 29% (n = 22) among diabetic individuals (n = 84). A majority of the residents (59.1%) were from cities. While 94% of the participants responded to what obesity was, 83.8% knew what diabetes was. Fast food, soft drinks, and mayonnaise were deemed to be healthy by 75.1% of the respondents. Obesity was viewed as a disease by 94.8%, a major health issue by 78.2%, and a weight-loss necessity by 44.6% of participants. Only 24.9% exercised every day, and 23.9% engaged in any physical activity daily. The majority of respondents (50.6%) never tried to lose weight and 23.2% ate junk food daily. The sociodemographic variables showed that the age ranges of 25-34 years (P < 0.001; OR 0.531), 45-54 years (P < 0.05; OR 0.527), and urban residency (P < 0.001; OR 0.128) had a significant association with knowledge. The factors of urban residency (P < 0.001; OR 3.996), being unmarried (P < 0.001; OR 1.95), and having an income of 51,000-70,000 (P < 0.001; OR 11.29) showed a very highly significant association with a good attitude regarding the relationship of obesity with diabetes (P < 0.05). Similarly, practices of the participants showed significant association with BMI range of 18.5-24.9 and 25-29.9 (P < 0.001). Our study revealed significant knowledge and understanding of the relationship between obesity with diabetes. However, it was observed that majority of respondents exhibited fundamental knowledge regarding obesity and diabetes, there was a notable absence of understanding regarding crucial elements, such as the significance of maintaining a healthy body weight, participating in physical activity, and implementing appropriate dietary strategies for weight control. We recognize the necessity for education initiatives and strongly encourage them to assist individuals in managing diabetes resulting from obesity.

Cross-cultural ethnobotany of the Baltis and Shinas in the Kharmang district, Trans-Himalaya India-Pakistan border.

Human communities that inhabit the political borders live a transitional life, which is due to several socio-political and geo-climatic factors. The current cross-cultural ethnomedicinal study was conducted at the highly elevated Pakistan-India borders of the Western Himalayas in order to address the medicinal flora and folk knowledge of Balti and Shina communities. Ethnobotanical field surveys were conducted from May 2021 to September 2022. We used semi-structured interviews in order to collect the ethnomedicinal data. The collected data was analyzed using the relative frequency of the citations, use value, and Venn diagrams. A total of 140 interviews were conducted, which comprised of 90 (64.28%) Baltis and 50 (35.71%) Shinas. The interviews recorded 60 medicinal plant species that belong to 56 genera and 35 families. Asteraceae (5 spp.), Lamiaceae (5 spp.), and Apiaceae (4 spp.) were the families that were the most represented. These medicinally valued plants were being used for 55 health issues that are related to different body systems. Delphinium brunonianum, Thymus linearis, Hymenolaena candollei, Corydalis adiantifolia, and Seriphidium brevifolium were medicinal taxa with maximum RFCs and UV. The Baltis have comparatively more ethnomedicinal knowledge than the Shinas. The Baltis commonly used 22 (36.66%) species, which 34 (56.66%) were exclusive to the Baltis and 4 (6.66%) to the Shinas. Both ethnic groups collectively hold significant ethnobotanical knowledge that demands the preservation of risked folk knowledge, which is due to uncertain border situations, outmigration, and permeating allopathic drugs.

Does employee engagement promote innovation? The Facilitators of innovative workplace behavior via mediation and moderation.

The demonstration of innovative behaviour by employees plays a crucial role in enabling organisations to effectively respond and adapt to the rapidly evolving business landscape. There has been an increase in research aimed at identifying the factors that contribute to the progressive development of innovative capacity, as there has been a substantial rise in interest in the comprehension of innovation mechanisms. Drawing on self-determination theory, this study aims to investigate the effect of employee engagement on innovative work behavior. The mediating role of employee voice behavior was also explored. Additionally, this study examines the potential moderating impact of perceived distributive justice on the relationship between employee engagement and innovative work behavior. A quantitative study was undertaken using a sample size of 180 participants who were employed in the manufacturing sector of Pakistan. Partial least squares structural equation modelling (PLS-SEM) was used to analyze the results of the study. The results revealed that engaged employees are more likely to exhibit innovative behavior. Furthermore, employee voice was discovered to fully mediate the relationship between employee engagement and innovative behavior, as well as to have a significant influence on both employee engagement and innovative behavior. However, the findings did not support the hypothesis that perceived distributive justice moderates the association between employee engagement and innovative behavior. Future research directions and managerial implications were also mentioned.

Green road transportation management and environmental sustainability: The impact of population density.

The objective of the study is to examine the moderating role of population density in the relation between road transportation and environment sustainability of South Asian countries from 1990 to 2014. The study uses environment sustainability (population density) as the outcome (moderator), whereas road infrastructure, road density, energy intensity and transportation energy consumption are explanatory variables. The selection of these variables is motivated by their significance in understanding the relationship between road transportation and environmental sustainability. Road infrastructure and road density capture the physical aspects of transportation systems, while energy intensity and road transportation energy consumption provide insights into the energy efficiency and environmental impact of road transport, respectively. The findings show that a positive impact of road infrastructure and road density exists on environmental sustainability. There is contrarily a negative effect of road transportation energy consumption and energy intensity on environmental sustainability. Population density also harms environmental sustainability. When population density is used as a moderator between road transportation energy consumption, energy intensity and environment sustainability, it increases the coefficients of both energy intensity and road transportation energy consumption, which shows that population density plays an enhancing role between road transportation energy consumption, energy intensity and environment sustainability. The coefficients of road density and road infrastructure changed into a negative from a positive in the presence of population density as a moderator, which states that population density plays an antagonistic role between road density and environmental sustainability. We recommend prioritizing sustainable transportation solutions and policies in densely populated areas. Implementing measures such as promoting public transportation and electric vehicles, and investing in infrastructure that supports active transportation modes like cycling and walking can help mitigate the negative environmental effects of transportation while addressing the challenges posed by population density.

Individual and interactive effect of ultrasound pre-treatment on drying kinetics and biochemical qualities of food: A critical review.

One of the earliest and most prevalent processing methods to increase the shelf-life of foods is drying. In recent years, there has been an increased demand to improve product quality while lowering processing times, expenses, and energy usage in the drying process. Pre-treatments are therefore effectively used before drying to enhance heat and mass transfer, increase drying efficiency, and lessen degradation of final product quality. When food is dried, changes are expected in its taste, color, texture, and physical, chemical, and microbial properties. This has led to the need for research and development into the creation of new and effective pre-treatment technologies including high-pressure processing, pulsed electric field, ultraviolet irradiation, and ultrasound. Sound waves that have a frequency >20 kHz, which is above the upper limit of the audible frequency range, are referred to as "ultrasound". Ultrasonication (US) is a non-thermal technology, that has mechanical, cavitational, and sponge effects on food materials. Ultrasound pre-treatment enhances the drying characteristics by producing microchannels in the food tissue, facilitating internal moisture diffusion in the finished product, and lowering the barrier to water migration. The goal of ultrasound pre-treatment is to save processing time, conserve energy, and enhance the quality, safety, and shelf-life of food products. This study presents a comprehensive overview of the fundamentals of ultrasound, its mechanism, and how the individual effects of ultrasonic pre-treatment and the interactive effects of ultrasound-assisted technologies affect the drying kinetics, bioactive components, color, textural, and sensory qualities of food. The difficulties that can arise when using ultrasound technology as a drying pretreatment approach, such as inadequate management of heat, the employment of ultrasound at a limited frequency, and the generation of free radicals, have also been explained.

Evaluation of the antiedematogenic and anti-inflammatory properties of Ximenia americana L. (Olacaceae) bark extract in experimental models of inflammation.

Edema is one of the obvious indicators of inflammation and a crucial factor to take into account when assessing a substance's capacity to reduce inflammation. We aimed to evaluate the antiedematogenic and anti-inflammatory profile of the hydroethanolic barks extract of Ximenia americana (HEXA). The possible antiedematogenic and anti-inflammatory effect of EHXA (50, 100 mg/kg and 250 mg/kg v.o) was evaluated using the paw edema induced by carrageenan, zymosan, dextran, CFA and by different agents inflammatory (serotonin, histamine, arachidonic acid and PGE2), and pleurisy model induced by carrageenan and its action on IL-1ß and TNF-α levels was also evaluated. HEXA demonstrated a significant antiedematogenic effect at concentrations of 50, 100 and 250 mg/kg on paw edema induced by carrageenan, zymosan and dextran. However, the concentration of 50 mg/kg as standard, demonstrating the effect in the subchronic model, induced CFA with inhibition of 59.06 %. In models of histamine-induced paw edema, HEXA showed inhibition of - 30 min: 40.49 %, 60 min: 44.70 % and 90 min: 48.98 %; serotonin inhibition - 30 min: 57.09 %, 60 min: 66.04 % and 90 min: 61.79 %; arachidonic acid inhibition - 15 min: 36.54 %, 30 min: 51.10 %, 45 min: 50.32 % and 60 min: 76.17 %; and PGE2 inhibition - 15 min: 67.78 %, 30 min: 62.30 %, 45 min: 54.25 % and 60 min: 47.92 %. HEXA significantly reduced (p < 0.01) leukocyte migration in the pleurisy model and reduced TNF-α and IL-1ß levels in pleural lavage (p < 0.0001). The results showed that HEXA has the potential to have an antiedematogenic impact in both acute and chronic inflammation processes, with a putative mode of action including the suppression or regulation of inflammatory mediators.

Effect of reducing agents on the synthesis of anisotropic gold nanoparticles.

The seed-mediated method is a general procedure for the synthesis of gold nanorods (Au NRs), and reducing agents such as ascorbic acid (AA) and hydroquinone (HQ) are widely used for the growth process. Further, they are mild reducing agents; however, when AA is used, controlling the size of Au NRs with a higher aspect ratio (localized surface plasmon resonance (LSPR) peak, λLmax > 900 nm) is challenging because it results in a faster growth rate of Au NRs. In contrast, when HQ is used, Au NRs with a higher aspect ratio can be synthesized as it slows down the growth rate of the Au NRs and greatly enhanced the λLmax. However, the increase in λLmax is still needs not satisfactory due to the limited enhancement in the aspect ratio of Au NRs due to utilization of single reducing agent. The growth kinetics of the Au NRs can be modulated by controlling the reducing power of the reducing agents. In such scenario, judicious use of two reducing agents such as AA and HQ simultaneously can help us to design Au NRs of higher aspect ratio in a controlled manner due to the optimum growth rate resulting from the combined effect of both the reducing agents. In this study, we investigated the effect of the two reducing agents by controlling the volume ratios. When the growth solution contains both the reducing agents, the growth of Au NRs is first initiated by the fast reduction of Au3+ to Au+ due to stronger reducing power of the AA and when the AA in the growth solution is completely utilized, further growth of the Au NRs continues as a result of the HQ thereby resulting to high aspect ratio Au NRs. Consequently, the LSPR peak (λLmax > 1275 nm) can be tuned by controlling the volume ratios of the reducing agents.

Carbon sequestration potential of different forest types in Pakistan and its role in regulating services for public health.

A high amount of CO2 causes numerous health effects, including headaches, restlessness, difficulty in breathing, increased heart rate, high blood pressure, asphyxia, and dizziness. This issue of increasing atmospheric CO2 can only be solved via above-ground and below-ground carbon sequestration (CS). This study was designed to determine the relationship between CS with the crown area (CA), diameter at breast height (DBH), height (H), species richness (SR), and elevation in different forest types of Pakistan with the following specific objectives: (1) to quantify the direct and indirect relationship of carbon sequestration with CA, DBH, H, and SR in various natural forest types and (2) to evaluate the effect of elevation on the trees functional traits and resultant CS. We used the linear structural equation model (SEM) for each conceptual model. Our results confirmed that the highest CS potential was recorded for dry temperate conifer forests (DTCF) i.e., 52.67%, followed by moist temperate mix forests (MTMF) and sub-tropical broad-leaved forests (STBLF). The SEM further described the carbon sequestration variation, i.e., 57, 32, 19, and 16% under the influence of CA (ß = 0.90 and P-value < 0.001), H (ß = 0.13 and p-value = 0.05), DBH (ß = 0.07 and p-value = 0.005), and SR (ß = -0.55 and p-value = 0.001), respectively. The individual direct effect of SR on carbon sequestration has been negative and significant. At the same time, the separate effect of CA, DBH, and H had a positive and significant effect on carbon sequestration. The remaining 20% of CS variations are indirectly influenced by elevation. This means that elevation affects carbon sequestration indirectly through CA, DBH, H, and SR, i.e., ß = 0.133 and P-value < 0.166, followed by ß = 0.531 and P-value < 0.001, ß = 0.007 and P-value < 0.399, and ß = -0.32 and P-value < 0.001, respectively. It is concluded that abiotic factors mainly determined carbon sequestration in forest ecosystems along with the elevation gradients in Pakistan. Quantifying the role of various forest types in carbon dioxide (CO2) reduction leads to improved air quality, which positively impacts human health. This is an imperative and novel study that links the dynamics of the biosphere and atmosphere.

High-Alkaline Water-Splitting Activity of Mesoporous 3D Heterostructures: An Amorphous-Shell@Crystalline-Core Nano-Assembly of Co-Ni-Phosphate Ultrathin-Nanosheets and V- Doped Cobalt-Nitride Nanowires.

Introducing amorphous and ultrathin nanosheets of transition bimetal phosphate arrays that are highly active in the oxygen evolution reaction (OER) as shells over an electronically modulated crystalline core with low hydrogen absorption energy for an excellent hydrogen evolution reaction (HER) can boost the sluggish kinetics of the OER and HER in alkaline electrolytes. Therefore, in this study, ultrathin and amorphous cobalt-nickel-phosphate (CoNiPOx ) nanosheet arrays are deposited over vanadium (V)-doped cobalt-nitride (V3% -Co4 N) crystalline core nanowires to obtain amorphous-shell@crystalline-core mesoporous 3D-heterostructures (CoNiPOx @V-Co4 N/NF) as bifunctional electrocatalysts. The optimized electrocatalyst shows extremely low HER and OER overpotentials of 53 and 270 mV at 10 mA cm-2 , respectively. The CoNiPOx @V3% -Co4 N/NF (+/-) electrolyzer utilizing the electrocatalyst as both anode and cathode demonstrates remarkable overall water-splitting activity, requiring a cell potential of only 1.52 V at 10 mA cm-2 , 30 mV lower than that of the RuO2 /NF (+)/20%-Pt/C/NF (-) electrolyzer. Such impressive bifunctional activities can be attributed to abundant active sites, adjusted electronic structure, lower charge-transfer resistance, enhanced electrochemically active surface area (ECSA), and surface- and volume-confined electrocatalysis resulting from the synergistic effects of the crystalline V3% -Co4 N core and amorphous CoNiPOx shells boosting water splitting in alkaline media.

Photocatalytic hydrogen evolution from biomass conversion.

Biomass has incredible potential as an alternative to fossil fuels for energy production that is sustainable for the future of humanity. Hydrogen evolution from photocatalytic biomass conversion not only produces valuable carbon-free energy in the form of molecular hydrogen but also provides an avenue of production for industrially relevant biomass products. This photocatalytic conversion can be realized with efficient, sustainable reaction materials (biomass) and inexhaustible sunlight as the only energy inputs. Reported herein is a general strategy and mechanism for photocatalytic hydrogen evolution from biomass and biomass-derived substrates (including ethanol, glycerol, formic acid, glucose, and polysaccharides). Recent advancements in the synthesis and fundamental physical/mechanistic studies of novel photocatalysts for hydrogen evolution from biomass conversion are summarized. Also summarized are recent advancements in hydrogen evolution efficiency regarding biomass and biomass-derived substrates. Special emphasis is given to methods that utilize unprocessed biomass as a substrate or synthetic photocatalyst material, as the development of such will incur greater benefits towards a sustainable route for the evolution of hydrogen and production of chemical feedstocks.

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate.