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Contaminated air quality, in lieu of massive industrial pollution, is severely attributing to health anomalies in the proximity of industrial units. Cardiovascular Disease (CAD) is rising around industrial units in the planned capital city of Pakistan, Pakistan. To study self-reported CAD in the proximity of Industrial Estate Islamabad (IEI) by equating two distinct study groups as ‘Band-I’: the residence 0–650 meters and ‘Band-II’ 650–1300 meters radius around the perimeter of IEI. The perimeters were digitized using Google Earth and GIS. Field survey was conducted on deploying 388 (194 in each Band) close-ended (self-administered) questionnaires at the household level, after adjusting the potential confounding variables. The research calculated odds ratios (ORs) of the CAD at 95% CI. The study’s findings of the multiple logistic regression for ORs confirmed a significant increase in CAD problems due to industrial affluents in Band-I than in Band-II which were less severe and less life-threatening. Study confirmed high incidences of high blood pressure and breathing issues (up to 67%), due to accumulation of unhealthy affluents thus leading to heart stroke (Band I = 56.20% and Band II = 60.30%). It is aided by smoking that has increased CAD in Band-I. Societal attributes of knowledge, beliefs, attitudes, and preferences fail to safeguard the local residents amid high concentration of harmful pollutants. As a counter measure the affected respondents engaged in highlighting the issue to the concerned public offices, yet there is a high need on part of the capital government to take mitigative measures to immediately halt the disastrous industrial air emissions to save precious lives.

Accurate forecasting of energy demand for electric vehicles (EVs) is crucial for maintaining the stability and reliability of power systems. By predicting demand over various periods, charging station owners can ensure a continuous energy supply. Medium-term and long-term demand predictions, which extend from a few weeks to several days, help analyze charging demand across different periods based on historical trends. This study proposes a Transformer model that utilizes an LSTM-based encoder-decoder for forecasting the demand at electric vehicle charging stations (EVCS). The proposed model is compared with traditional deep learning-based LSTM and Transformer models. The research employs open datasets from ACN, including charging data from Caltech and JPL. Both datasets are used to train and test the models. Predictions are made for 30 days, 120 days, and 240 days ahead, with results compared to actual demand. Performance is evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE). Compared to baseline models, the proposed LSTM-Transformer model for Caltech data shows a significant improvement at the 30-day horizon, lowering MAE by up to 17.27% and MSE by 19.79%. The accuracy improvement are minor but consistent in longer horizons (120 and 240 days), with an MAE and MSE improvement of up to 5.71% and 4.85%, respectively. The LSTM-Transformer model also shows better accuracy across all horizons for JPL data by reducing MAE and MSE by up to 24.91% and 23.17% at 30 days, 5.00% and 5.17% at 120 days, and 3.90% and 4.86% at 240 days. The results indicate that the Hybrid Transformer model outperforms the baseline models for both datasets in medium-term and long-term predictions. The 30, 120, and 240-day predictions demonstrate lower error rates with the proposed model when utilizing Caltech and JPL charging data for these time frames.

Plant growth promoting rhizobacteria (PGPR) are diverse, naturally occurring bacteria that establish a close association with plant roots and promote the growth and immunity of plants. Established mechanisms involved in PGPR-mediated plant growth promotion include regulation of phytohormones, improved nutrient availability, and antagonistic effects on plant pathogens. In this study, we isolated a bacterium from the rhizospheric soil of a soybean field in Chungcheong buk-do, South Korea. Using 16S rRNA sequencing, the bacterium was identified as Bacillus aryabhattai strain SRB02. Here we show that this strain significantly promotes the growth of soybean. Gas chromatography-mass spectrometry analysis showed that SRB02 produced significant amounts of abscisic acid, indole acetic acid, cytokinin and different gibberellic acids in culture. SRB02-treated soybean plants showed significantly better heat stress tolerance than did untreated plants. These plants also produced consistent levels of ABA under heat stress and exhibited ABA-mediated stomatal closure. High levels of IAA, JA, GA12, GA4, and GA7, were recorded in SRB02-treated plants. These plants produced longer roots and shoots than those of control plants. B. aryabhattai SRB02 was found to be highly tolerant to oxidative stress induced by H2O2 and MV potentiated by high catalase (CAT) and superoxide dismutase (SOD) activities. SRB02 also tolerated high nitrosative stress induced by the nitric oxide donors GSNO and CysNO. Because of these attributes, B. aryabhattai SRB02 may prove to be a valuable resource for incorporation in biofertilizers and other soil amendments that seek to improve crop productivity.

Fusarium species are the most destructive phytopathogenic and toxin-producing fungi, causing serious diseases in almost all economically important plants. Sporulation is an essential part of the life cycle of Fusarium. Fusarium most frequently produces three different types of asexual spores, i.e., macroconidia, chlamydospores, and microconidia. It also produces meiotic spores, but fewer than 20% of Fusaria have a known sexual cycle. Therefore, the asexual spores of the Fusarium species play an important role in their propagation and infection. This review places special emphasis on current developments in artificial anti-sporulation techniques as well as features of Fusarium’s asexual sporulation regulation, such as temperature, light, pH, host tissue, and nutrients. This description of sporulation regulation aspects and artificial anti-sporulation strategies will help to shed light on the ways to effectively control Fusarium diseases by inhibiting the production of spores, which eventually improves the production of food plants.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific 90∘ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method.

Plants, due to their sessile nature, face several environmental adversities. Abiotic stresses such as heat, cold, drought, heavy metals, and salinity are serious threats to plant production and yield. To cope with these stresses, plants have developed sophisticated mechanisms to avoid or resist stress conditions. A proper response to abiotic stress depends primarily on how plants perceive the stress signal, which in turn leads to initiation of signaling cascades and induction of resistance genes. New biotechnological tools such as RNA-seq and CRISPR-cas9 are quite useful in identifying target genes on a global scale, manipulating these genes to achieve tolerance, and helping breeders to develop stress-tolerant cultivars. In this review, we will briefly discuss the adverse effects of key abiotic stresses such as cold, heat, drought, and salinity. We will also discuss how plants sense various stresses and the importance of biotechnological tools in the development of stress-tolerant cultivars.

In plants senescence is the final stage of plant growth and development that ultimately leads to death. Plants experience age-related as well as stress-induced developmental ageing. Senescence involves significant changes at the transcriptional, post-translational and metabolomic levels. Furthermore, phytohormones also play a critical role in the programmed senescence of plants. Nitric oxide (NO) is a gaseous signalling molecule that regulates a plethora of physiological processes in plants. Its role in the control of ageing and senescence has just started to be elucidated. Here, we review the role of NO in the regulation of programmed cell death, seed ageing, fruit ripening and senescence. We also discuss the role of NO in the modulation of phytohormones during senescence and the significance of NO-ROS cross-talk during programmed cell death and senescence.

Nitric oxide is a gaseous signalling molecule produced by plants. Slight changes in endogenous NO levels have significant biochemical and physiological consequences. We investigated the structural and functional properties of NO-responsive antiporter genes in Arabidopsis thaliana. Phylogenetic analysis of 50 antiporter genes classified them into four subgroups based on the presence of NHX and CPA domains and the evolutionary similarity of the protein sequences. Antiporters were found scattered across the five chromosomes with unique physico-chemical properties and subcellular localisation in the plasma membrane, nucleus, chloroplasts, and vacuole. Furthermore, we performed QPCR analysis of eight different antiporter genes after infiltrating the plants with 1 mM CySNO (S-nitroso-L-cysteine), a nitric oxide donor, in WT and the loss-of-function atgsnor1-3 (disruptive S-nitrosoglutathione reductase 1 activity) plants. The AT1G79400 (CHX2), AT2G38170 (RCI4), and AT5G17400 (ER-ANT1) showed a significant increase in their expression in response to CySNO infiltration. However, their expression in atgsnor1-3 plants was found to be lower than in the WT plants, indicating a significant redundancy in the response of these genes to 1 mM levels of CySNO and physiological levels of SNOs in atgsnor1-3. On the other hand, a significant reduction in the expression of AT1G16380 (CHX1), AT2G47600 (MHX1), AT3G13320 (CAX2), and AT5G11800 (KEA6) was observed in WT plants after CySNO infiltration as well as in the leaves of atgsnor1-3 plants. Our study identified three NO-responsive antiporter genes in Arabidopsis, indicating their roles in stress responsiveness and ion homeostasis that could be used for further validation of their roles in NO signalling in plants.

IntroductionChildhood mortality in the emergency setting is disproportionately high in low-income and middle-income countries (LMIC), with limited research dedicated to improving timely interventions, especially for critically ill children during transport. To perform essential prehospital paediatric research, there is a need for a tailored consent process, which reflects the specific needs and concerns of participants in this challenging research context.ObjectiveThe objective is to prospectively investigate stakeholder perceptions and preferences regarding consent processes for a specific paediatric ambulance-based telemedicine trial.MethodsExploratory qualitative study design using face-to-face semistructured interviews and focus group discussions. Data were analysed using thematic analysis. Participants included healthcare providers (paediatric telemedicine physicians and emergency medical technicians) and parents of children who required emergency transportation in Karachi, Pakistan.Results47 participants, ranging from 19 to 47 years old, were involved in in-depth interviews or focus group discussions. The participants comprised 29 healthcare workers and 18 parents. Among them, 9 were women and 38 were men. Expressing diverse attitudes towards different consent methods, the majority recommended a prospective written informed consent approach to build trust and provide legal protection. Participants understood the situational incapacity that occurs in emergency settings, emphasised the importance of keeping the consent brief and recommended a subsequent contact in 2–3 days after the emergency transport to reconfirm consent and answer any questions.ConclusionOur interpretation of the findings revealed that participants preferred a staged consent process for telemedicine trials in LMIC paediatric emergency settings.

The growing number of electric vehicles (EVs) on the road poses great challenges to the power supply and causes outages. Most existing research works focus on individual or aggregated charging station data at the city level. However, charging behaviors at different city locations might demonstrate different patterns and characteristics. This study proposed a Context-Aware Temporal Transformer (CAT-Former) model using Temporal and Contextual features for short-term EV charging demand forecasting of one hour and one day ahead using the public EV data from Boulder City, Colorado. The temporal and contextual features are important features, which help the model to understand the charging patterns of different periods over different locations. The charging data with different trends is crucial to train and test the proposed model performance. Therefore, this study chose the three locations with the highest number of sessions from the data. The performance of the proposed model, as well as the baseline models, including LSTM, BiLSTM, and hybrid models such as CNN-LSTM and CNN-BiLSTM, is assessed and compared using Mean Square Error (MSE) and Mean Absolute Error (MAE) on three locations. The proposed model is compared to the Simple and Hybrid Transformer models utilizing the LSTM-based Encoder-Decoder. The proposed model performed better than the baseline models for one hour and one day ahead of forecasting for the selected locations by achieving the lowest MSE and MAE values. The results show that the proposed CAT-Former model using temporal and contextual features can effectively forecast the charging demand using charging data from different locations for short-term periods, including one-hour and one-day ahead predictions.