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As the demand for high-speed data transmission grows with the expected emergence of 6G networks and the proliferation of wireless devices, more than traditional wireless infrastructure may be required. Small cell networks (ScNs) integrated with reconfigurable intelligent surfaces (RISs) and multiple-inputmultiple-output (MIMO) have emerged as promising solutions to address this issue. However, ScNs have resource allocation limitations, and traditional RISs can only reflect signals in a limited propagation space of 1800 with fixed reflection properties. This paper proposes a novel approach to overcome these challenges by introducing actively simultaneously transmitting and reflecting (ASTAR)-RISs. Unlike conventional RIS, ASTAR-RISs actively amplify and transmit signals, effectively mitigating the limited propagation challenge and improving signal strength, especially in dense ScNs. This approach enhances the quality of service in complex channel environments by amplifying, on top of reflection, from the macro base station (mBS), improving the overall signal strength, and providing 3600 flexible propagation space. Furthermore, ASTAR-RIS enables dynamic beam management, significantly improving signal coverage and interference management, which are crucial in dense deployments. In this work, we propose a network architecture where distributed ASTAR-RIS units are deployed to assist small cell mBSs by optimizing signal coverage and enhancing communication performance. ASTAR-RISs dynamically control signal reflection and amplification, complementing the functionality of traditional small-cell BSs in dense network environments. Using the MIMO technique, we design phase shifts for ASTAR elements and develop optimal hybrid beamforming for users at the mBS. We dynamically control the ON/OFF status of the ASTAR-RIS based on active or idle status. We propose an efficient model that ensures fairness of signal-to-noise ratio (SNR) for all users and minimizes overall power consumption while meeting user SNR and phase shift constraints. To this end, we integrate robust beamforming and power allocation strategies, ensuring the system maintains reliable performance even under imperfect channel state information (CSI). We formulate a max-min optimization problem that optimizes the SNR and power consumption, subject to the ON/OFF status, phase shift, and power budget of the ASTAR-RIS. Our proposed method uses an alternating optimization algorithm to optimize the phase shift matrix at the ASTAR-RIS and the hybrid beamforming at the mBS. The approach includes two transmission schemes, and the phase optimization problem is solved using a successive convex approximation method that offers a closed-form solution at each step. Additionally, we use the dual method to determine the optimal ON/OFF status of the ASTAR-RIS. Comprehensive simulations validate the robustness and scalability of our proposed solution, particularly under varying network densities and CSI uncertainties. provides significant performance improvements over 170% compared to traditional RIS schemes.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms.

Introduction We report on the stillbirth rate (SBR) and associated risk factors for births during the COVID-19 pandemic, and change in SBR between prepandemic (2016) and pandemic periods in the Indian state of Bihar.Methods Births between July 2020 and June 2021 (91.5% participation) representative of Bihar were listed. Stillbirth was defined as fetal death with gestation period of ≥7 months where the fetus did not show any sign of life. Detailed interviews were conducted for all stillbirths and neonatal deaths, and for 25% random sample of surviving live births. We estimated overall SBR, and during COVID-19 peak and non-peak periods per 1000 births. Multiple logistic regression models were run to assess risk factors for stillbirth. The change in SBR for Bihar from 2016 to 2020–2021 was estimated.Results We identified 582 stillbirths in 30 412 births with an estimated SBR of 19.1 per 1000 births (95% CI 17.7 to 20.7); SBR was significantly higher in private facility (38.4; 95% CI 34.3 to 43.0) than in public facility (8.6; 95% CI 7.3 to 10.1) births, and for COVID-19 peak (21.2; 95% CI 19.2 to 23.4) than non-peak period (16.3; 95% CI 14.2 to 18.6) births. Pregnancies with the last pregnancy trimester during the COVID-19 peak period had 40.4% (95% CI 10.3% to 70.4%) higher SBR than those who did not. Risk factor associations for stillbirths were similar between the COVID-19 peak and non-peak periods, with gestation age of

The recent outbreak of the COVID-19 pandemic forced education institutes to shift to an internet-based online delivery mode. This unique situation accelerates a long-standing issue of digital inequality among the students in education and warrants a concentrated study to investigate students’ readiness for learning in online environment. This study developed an instrument to meticulously measure the students’ readiness for online learning in a pandemic situation. The proposed model consists of (a) motivation, (b) self-efficacy, and (c) situational factors. The proposed model was validated with the engineering students (for pilot study N = 68 and main study N = 988) from several universities in Bangladesh. To validate the underlying relationships between the latent constructs, an exploratory factor analysis (EFA) was performed followed by structural equation modelling (SEM) for the construct validity of the measurement model and to assess the model fit. The findings showed that besides motivation and self-efficacy, the situational factors describing the contextual dynamics emerging from the COVID-19 significantly influenced the student’s online readiness. We argue that digital inequality is an important factor influencing student readiness for online learning.

Road accidents are increasing worldwide and are causing millions of deaths each year. They impose significant financial and economic expenses on society. Existing research has mostly studied road accident prediction as a classification problem, which aims to predict whether a traffic accident may happen in the future or not without exploring the underneath relationships between the complicated factors contributing to road accidents. A number of research have been done to date to explore the importance of road accident contributing factors in relation to road accidents and their severity, however, only a few of those research have explored a subset of ensemble ML models and the New Zealand (NZ) road accident dataset. Therefore, in this paper, we have evaluated a set of machine learning (ML) models to predict road accident severity based on the most recent NZ road accident dataset. We have also analysed the predicted results and applied an explainable ML (XML) technique to evaluate the importance of road accident contributing factors. To predict road accidents with different injury severity, this work has considered different ensembles of ML models, like Random Forest (RF), Decision Jungle (DJ), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (L-GBM), and Categorical Boosting (CatBoost). New Zealand road accident data from 2016 through 2020 obtained from the New Zealand Ministry of Transport is used to perform this study. The comparison results show that RF is the best classifier with 81.45% accuracy, 81.68% precision, 81.42% recall, and 81.04% of F1-Score. Next, we have employed the Shapley value analysis as an XML technique to interpret the RF model performance at global and local levels. While the global level explanation provides the rank of the features’ contribution to severity classification, the local one is for exploring the use of features in the model. Furthermore, the Shapley Additive exPlanation (SHAP) dependence plot is used to investigate the relationship and interaction of the features towards the target variable prediction. Based on the findings, it can be said that the road category and number of vehicles involved in an accident significantly impact injury severity. The identified high-ranked features through SHAP analysis are used to retrain the ML models and measure their performance. The result shows 6%, 5%, and 8%, increase, respectively, in the performances of DJ, AdaBoost, and CatBoost models.

For its beneficial effects on health, Garcinia lanceifolia Roxb. Has long been used as a vegetable and in juice. In this study, as part of the process of finding and developing new drugs, we examine the cytotoxic and other pharmacological characteristics of isolated molecules. Various chromatographic methods were used to isolate the bioactive compounds identified by spectroscopic techniques. The brine shrimp lethality test was used to evaluate the cytotoxic properties of G. lanceifolia. In addition, the compounds' ADME screening, in-silico cell-line toxicity prediction, and molecular docking investigations were also looked into. The chemicals identified were 3-methoxyxanthen-9-one, Furo [3, 2-g] chromen-7-one, and 7-hydroxy-2-chromenone after a thorough spectrum analysis. According to an in-silico cell-line toxicity investigation, chemicals 1, 2, and 3 are likely to be active against different cancer cell lines. After a docking analysis against the cancer target protein epidermal growth factor receptor (EGFR), compound 1 (3-methoxyxanthen-9-one) showed a significant interaction with the active site of EGFR, with crucial amino acid residues including LEU694, ALA719, THR766, LEU820, and LEU831. The current study provided evidence that compound 1 (3-methoxyxanthen-9-one) may be a potent inhibitor against the cancer target protein EGFR, and this study will also be beneficial for the process of developing anticancer drugs.

Objectives We undertook assessment of quality of antenatal care (ANC) services in public sector facilities in the Indian state of Bihar state delivered under the national ANC programme (Pradhan Mantri Surakshit Matritva Abhiyan, PMSMA).Setting Three community health centres and one subdistrict hospital each in two randomly selected districts of Bihar.Participants Pregnant women who sought ANC services under PMSMA irrespective of the pregnancy trimester.Primary and secondary measures Quality ANC services were considered if a woman received all of these services in that visit—weight, blood pressure and abdomen check, urine and blood sample taken, and were given iron and folic acid and calcium tablets. The process of ANC service provision was documented.Results Eight hundred and fourteen (94.5% participation) women participated. Coverage of quality ANC services was 30.4% (95% CI 27.3% to 33.7%) irrespective of pregnancy trimester, and was similar in both districts and ranged 3%–83.1% across the facilities. Quality ANC service coverage was significantly lower for women in the first trimester of pregnancy (6.8%, 95% CI 3.3% to 13.6%) as compared with those in the second (34.4%, 95% CI 29.9% to 39.1%) and third (32.9%, 95% CI 27.9% to 38.3%) trimester of pregnancy. Individually, the coverage of weight and blood pressure check-up, receipt of iron folic acid (IFA) and calcium tablets, and blood sample collection was >85%. The coverage of urine sample collection was 46.3% (95% CI 42.9% to 49.7%) and of abdomen check-up was 62% (95% CI 58.6% to 65.3%). Poor information sharing post check-up was done with the pregnant women. Varied implementation of ANC service provision was seen in the facilities as compared with the PMSMA guidelines, in particular with laboratory diagnostics and doctor consultation. Task shifting from doctors to ANMs was observed in all facilities.Conclusions Grossly inadequate quality ANC services under the PMSMA needs urgent attention to improve maternal and neonatal health outcomes.

Objective A large proportion of neonatal deaths in India are attributable to low birth weight (LBW). We report population-based distribution and determinants of birth weight in Bihar state, and on the perceptions about birth weight among carers.Design A cross-sectional household survey in a state representative sample of 6007 live births born in 2018–2019. Mothers provided detailed interviews on sociodemographic characteristics and birth weight, and their perceptions on LBW (birth weight

Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market.

BACKGROUND:Animal bites and stings contribute significantly to mortality in certain parts of the world. India accounts for the highest number of snakebites and related mortality globally. We report on mortality due to bite or sting of a venomous animal from a population-based study in the Indian state of Bihar which estimated the causes of death using verbal autopsy. METHODOLOGY/PRINCIPAL FINDINGS:Interviews were conducted for all deaths that occurred from January 2012 to March 2014 in 109,689 households (87.1% participation) covering 627,658 population in 1,017 clusters representative of the state using the Population Health Metrics Research Consortium shortened verbal autopsy questionnaire. Cause of death was assigned using the SmartVA automated algorithm. The annualized mortality rate per 100,000 population due to snakebite, scorpion sting and other animals adjusted for age, sex and urban-rural population distribution of the state; and detailed contextual information on snakebites are reported. Deaths due to bite/sting of a venomous animal accounted for 10.7% of all deaths due to unintentional injuries, with an adjusted mortality rate of 6.2 (95% CI 6.0-6.3) per 100,000 population. The adjusted snakebite mortality rate was 4.4 (95% CI 4.3-4.6) which was significantly higher in the rural areas (4.8, 95% CI 4.7-5.0) and in females (5.5, 95% CI 5.3-5.7). Snakebites accounted for 7.6% of all unintentional injury deaths across all ages but for 33.3% of the deaths in 10-14 years age group. A similar proportion of snakebite deaths occurred while sleeping (30.2%), playing (30.2%) and during field/outdoor activities (27.9%). In these cases, 8.2% people were already dead when found, 34.7% had died before treatment could be provided, and 28 (57.1%) had died post treatment among whom 46.4% had sought treatment at a health facility, 25% with a traditional healer, and the rest from both. Death before reaching a health provider, non-availability of medicines or doctor, referral patterns, and sex-differentials in the context of snakebite deaths are reported. None of the verbatim specifically mentioned anti-venom being used for treatment. The adjusted mortality rate for scorpion sting was 0.9 (95% CI 0.8-0.9). CONCLUSIONS:The findings from this large representative sample documents the magnitude of snakebite mortality in Bihar and highlight the circumstances surrounding the snakebite events that could facilitate prevention and intervention opportunities.

Dieulafoy lesion is characterized by exteriorization of a large pulsatile arterial vessel through a minimal mucosal tear surrounded by normal mucosa, causing massive and recurrent upper digestive bleeding in previously healthy patients. More frequently presented than diagnosed, with the increase of its knowledge among endoscopists, a large number of cases are expected in the literature. Dieulafoy lesion is a distinct nosologic entity that must be suspected in patients with massive digestive bleeding. Endoscopy became the procedure of choice for diagnosis and treatment of this disease.

The recent outbreak of the COVID-19 pandemic forced education institutes to shift to an internet-based online delivery mode. This unique situation accelerates a long-standing issue of digital inequality among the students in education and warrants a concentrated study to investigate students' readiness for learning in online environment. This study developed an instrument to meticulously measure the students' readiness for online learning in a pandemic situation. The proposed model consists of (a) motivation, (b) self-efficacy, and (c) situational factors. The proposed model was validated with the engineering students (for pilot study N = 68 and main study N = 988) from several universities in Bangladesh. To validate the underlying relationships between the latent constructs, an exploratory factor analysis (EFA) was performed followed by structural equation modelling (SEM) for the construct validity of the measurement model and to assess the model fit. The findings showed that besides motivation and self-efficacy, the situational factors describing the contextual dynamics emerging from the COVID-19 significantly influenced the student's online readiness. We argue that digital inequality is an important factor influencing student readiness for online learning.

Previous research from our lab demonstrated that hypergravity that can be simulated using tabletop centrifuges, offering significant benefits to crop plants. Hypergravity enhances seedling vigor and growth parameters in bread wheat (Triticum aestivum) variety UAS 375. This enhanced root growth phenotype is believed to boost abiotic stress tolerance by facilitating deeper access to water and nutrients from the soil. This study investigated whether hypergravity-induced root growth enhancements could offer resilience to induced drought and salt stress, and whether such benefits would extend across other wheat genotypes. Hypergravity (10 g for 12 h) conferred significant tolerance to simulated drought and salt stress, evidenced by improved seedling growth parameters as well as increased chlorophyll content and proline accumulation in response to hypergravity followed by stress challenge, compared to stress challenge alone. Liquid chromatography with tandem mass spectrometry indicated dynamic phytohormone modulation, and quantitative reverse transcription polymerase chain reaction data revealed significant alterations in the expression of genes associated with antioxidant enzymes and abiotic stresses. Thus, this study further supports the view that hypergravity boosts abiotic stress resilience through genetic and hormonal dynamics. Notably, these effects were consistent across genotypes. In conclusion, this study provides evidence that hypergravity can effectively improve resilience against seedling abiotic stresses in wheat. Previous research from our lab showed that hypergravity, simulated with centrifuges, enhances wheat seedling vigor and root phenotype. Present study revealed that hypergravity (10 g for 12 h) significantly boosts wheat's resilience to induced drought and salt stress. The benefits, seen in increased growth and abiotic stress tolerance, were consistent across different wheat varieties. The study further links these improvements to changes in plant hormones and stress-related genes, providing new evidence that hypergravity can effectively enhance stress resilience in wheat. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]