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Increasing temperature and consequent changes in climate adversely affect plant growth and development, resulting in catastrophic loss of wheat productivity. For each degree rise in temperature, wheat production is estimated to reduce by 6%. A detailed overview of morpho-physiological responses of wheat to heat stress may help formulating appropriate strategies for heat-stressed wheat yield improvement. Additionally, searching for possible management strategies may increase productivity and sustainability of growing wheat. The major findings from this review are as follows: (1) heat stress significantly reduces seed germination and seedling growth, cell turgidity, and plant water-use efficiency; (2) at a cellular level, heat stress disturbs cellular functions through generating excessive reactive oxygen species, leading to oxidative stress; (3) the major responses of wheat to heat stress include the enhancement of leaf senescence, reduction of photosynthesis, deactivation of photosynthetic enzymes, and generation of oxidative damages to the chloroplasts; (4) heat stress also reduces grain number and size by affecting grain setting, assimilate translocation and duration and growth rate of grains; (5) effective approaches for managing heat stress in wheat include screening available germplasm under field trials and/or employing marker-assisted selection, application of exogenous protectants to seeds or plants, mapping quantitative trait locus conferring heat resistance and breeding; (6) a well-integrated genetic and agronomic management option may enhance wheat tolerance to heat. However, the success of applying various techniques of heat stress management requires greater understanding of heat tolerance features, molecular cloning, and characterization of genes. The overall success of the complex plant heat stress management depends on the concerted efforts of crop modelers, molecular biologists, and plant physiologists.

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

Glioblastoma is an aggressive primary brain tumor that poses many therapeutic difficulties because of the high rate of proliferation, genetic variability, and its immunosuppressive microenvironment. The theory of cancer immunoediting, which includes the phases of elimination, equilibrium, and escape, offers a paradigm for comprehending interactions between the immune system and glioblastoma. Immunoediting indicates the process by which immune cells initially suppress tumor development, but thereafter select for immune-resistant versions leading to tumor escape and progression. The tumor microenvironment (TME) in glioblastoma is particularly immunosuppressive, with regulatory T cells and myeloid-derived suppressor cells being involved in immune escape. To achieve an efficient immunotherapy for glioblastoma, it is crucial to understand these mechanisms within the TME. Existing immunotherapeutic modalities such as chimeric antigen receptor T cells and immune checkpoint inhibitors have been met with some level of resistance because of the heterogeneous nature of the immune response to glioblastoma. Solving these issues is critical to develop novel strategies capable of modulating the TME and re-establishing normal immune monitoring. Further studies should be conducted to identify the molecular and cellular events that underlie the immunosuppressive tumor microenvironment in glioblastoma. Comprehending and modifying the stages of immunoediting in glioblastoma could facilitate the development of more potent and long-lasting therapies.

Polypharmacy, defined as the concurrent use of multiple medications, is a growing concern globally. This study aimed to identify the significant factors that predict the perceived burden of medication and health-related quality of life. Adults, aged 18 years and above who have used at least two regular medicines, were invited to complete the study questionnaires between June and October 2019. Multiple linear regression analysis was conducted to identify significant predictors for perceived burden of medication and health-related quality of life. A total of 119 participants completed this study. The average age of the participants was 63 years (SD±16 years). Factors significantly predicting perceived burden of medication were participants' current health condition (p = 0.001), overall burden of treatment (p<0.001) and being hypertensive (p = 0.037). Similarly, participants' current health condition (p<0.001) and overall burden of treatment (p = 0.086) were significant predictors for perceived health-related quality of life. This study revealed that hypertensive participants in poor health tended to experience higher perceived burden of medication, which in turn was found to be correlated with lower perceived health-related quality of life.

PurposeThis study explores young consumers' motivations for purchasing Virtual Luxury Non-Fungible Token Wearables (VL-NFTs) from luxury brands, which are virtually crafted luxury wearables minted as blockchain-based NFTs. Specifically, it investigates the relationships among consumers' perceived value of VL-NFTs, engagement with NFTs and purchase intention and the mediating effect of consumer engagement with NFTs.Design/methodology/approachData were collected via an online survey of 504 young US consumers who had previously considered purchasing luxury fashion products and NFTs. Structural equation modelling was adopted for analysis.FindingsPerceived economic, functional (uniqueness) and experiential (self-directed pleasure and affiliation) values of VL-NFTs directly influenced consumers' purchase intention. While symbolic value (self-presentation and conspicuousness) did not significantly influence purchase intention, it facilitated consumer engagement with NFTs. Moreover, consumer engagement mediated the relationship between economic and functional values and purchase intention.Research limitations/implicationsThe sample was only comprised of young consumers, limiting the generalizability. Additionally, consumers may perceive VL-NFTs differently because of differences in past experiences and the varying VL-NFT types, necessitating further investigation on consumers' motivations across different types of VL-NFTs.Originality/valueThis study contributes to the existing literature by examining the importance of multifaceted perceived-value dimensions and engagement with NFTs in consumers' motivation for purchasing VL-NFTs through the lens of the customer value framework.

Expert’s knowledge base systems are not effective as a decision-making aid for physicians in providing accurate diagnosis and treatment of heart diseases due to vagueness in information and impreciseness and uncertainty in decision making. For this reason, automatic diagnostic fuzzy systems are very time demanding to improve the diagnostic accuracy. In this paper, we have developed an automatic fuzzy diagnostic system based on genetic algorithm (GA) and a modified dynamic multi-swarm particle swarm optimization (MDMS-PSO) for prognosticating the risk level of heart disease. Our proposed fuzzy diagnostic system (FS) works as follows: i) Preprocess the data sets ii) Effective attributes are selected through statistical methods such as Correlation coefficient, R-Squared and Weighted Least Squared (WLS) method, iii) Weighted fuzzy rules are formed on the basis of selected attributes using GA, iv) MDMS-PSO is employed for the optimization of membership functions (MFs) of FS, v) Build the ensemble FS from the generated fuzzy knowledge base by fusing the different local FSs. Finally, to ascertain the efficiency of the adaptive FS, the applicability of the FS is appraised with quantitative, qualitative and comparative analysis on the publicly available different real-life data sets. From the empirical analysis, we see that this hybrid model can manage the knowledge vagueness and decision-making uncertainty precisely and it has yielded better accuracy on the different publicly available heart disease data sets than other existing methods so that it justifies its adaptability with different data sets.

Malware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to target diverse platforms such as PCs, mobile devices, IoT, and cloud platforms. While previous studies have explored single platform-based malware detection, no existing research has comprehensively reviewed malware detection across diverse platforms using machine learning (ML) techniques. With the rise of malware on PC or laptop devices, mobile devices and IoT systems are now being targeted, posing a significant threat to cloud environments. Therefore, a platform-based understanding of malware detection and defense mechanisms is essential for countering this evolving threat. To fill this gap and motivate further research, we present an extensive review of malware detection using ML techniques with respect to PCs, mobile devices, IoT, and cloud platforms. This paper begins with an overview of malware, including its definition, prominent types, analysis, and features. It presents a comprehensive review of machine learning-based malware detection from the recent literature, including journal articles, conference proceedings, and online resources published since 2017. This study also offers insights into the current challenges and outlines future directions for developing adaptable cross-platform malware detection techniques. This study is crucial for understanding the evolving threat landscape and for developing robust detection strategies.

The objective of the max-cut problem is to cut any graph in such a way that the total weight of the edges that are cut off is maximum in both subsets of vertices that are divided due to the cut of the edges. Although it is an elementary graph partitioning problem, it is one of the most challenging combinatorial optimization-based problems, and tons of application areas make this problem highly admissible. Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO). Though HHO effectively solved some engineering optimization problems, is sensitive to parameter settings and may converge slowly, potentially getting trapped in local optima. Thus, HHO and some additional operators are used to solve the max-cut problem. Crossover and refinement operators are used to modify the fitness of the hawk in such a way that they can provide precise results. A mutation mechanism along with an adjustment operator has improvised the outcome obtained from the updated hawk. To accept the potential result, the acceptance criterion has been used, and then the repair operator is applied in the proposed approach. The proposed system provided comparatively better outcomes on the G-set dataset than other state-of-the-art algorithms. It obtained 533 cuts more than the discrete cuckoo search algorithm in 9 instances, 1036 cuts more than PSO-EDA in 14 instances, and 1021 cuts more than TSHEA in 9 instances. But for four instances, the cuts are lower than PSO-EDA and TSHEA. Besides, the statistical significance has also been tested using the Wilcoxon signed rank test to provide proof of the superior performance of the proposed method. In terms of solution quality, MC-HHO can produce outcomes that are quite competitive when compared to other related state-of-the-art algorithms.

Background Household air pollution (HAP) from cooking with solid fuels has become a leading cause of death and disability in many developing countries including Bangladesh. We assess the association between HAP and risk of selected adverse birth and maternal health outcomes. Methods Data for this study were extracted from Bangladesh Demographic and Health Survey conducted during 2007–2014. Selected adverse birth outcomes were acute respiratory infection (ARI) among children, stillbirth, low birth weight (LBW), under-five mortality, neonatal mortality and infant mortality. Maternal pregnancy complications and cesarean delivery were considered as the adverse maternal health outcomes. Place of cooking, use of solid fuel within the house boundary and in living room were the exposure variables. To examine the association between exposure and outcome variables, we used a series of multiple logistic regression models accounted for complex survey design. Results Around 90% of the respondents used solid fuel within the house boundary, 11% of them used solid fuel within the living room. Results of multiple regression indicated that cooking inside the house increased the risk of neonatal mortality (aOR,1.25; 95% CI, 1.02–1.52), infant mortality (aOR, 1.18; 95% CI, 1.00–1.40), ARI (aOR, 1.18; 95% CI, 1.08–1.33), LBW (aOR, 1.25; 95% CI, 1.10–1.43), and cesarean delivery (aOR,1.18; 95% CI, 1.01–1.29). Use of solid fuel, irrespective of cooking places, increased the risk of pregnancy complications (aOR, 1.36; 95% CI, 1.19–1.55). Compared to participants who reported cooking outside the house, the risk of ARI, LBW were significantly high among those who performed cooking within the house, irrespective of type of cooking fuel. Conclusion Indoor cooking and use of solid fuel in household increase the risk of ARI, LBW, cesarean delivery, and pregnancy complication. These relationships need further investigation using more direct measures of smoke exposure and clinical measures of health outcomes. The use of clean fuels and structural improvement in household design such as provision of stove ventilation should be encouraged to reduce such adverse health consequences. Trail registration Data related to health were collected by following the guidelines of ICF international and Bangladesh Medical Research Council. The registration number of data collection was 132,989.0.000, and the data-request was registered on March 11, 2015.

This research paper presents a study on designing and implementing a robust key management scheme for lightweight block ciphers in Internet of Things (IoT) networks. Key management is a critical concern for IoT devices due to their limited resources and susceptibility to security threats. The proposed scheme utilises partial key pre-distribution to achieve lightweight and secure key management. The protocol’s security has been analysed against various attacks, demonstrating its resistance. Performance evaluation results indicate that the proposed key management technique is suitable for resource-constraint IoT networks, as it reduces communication overhead, power consumption, and storage space requirements. The methodology employed in this research includes designing and implementing the proposed key management scheme and conducting scenario-based analyses of its functionality. The results affirm that the proposed solution effectively ensures secure communication in IoT networks. Overall, this research contributes to developing a secure and efficient key management scheme for lightweight block ciphers in IoT networks.