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This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer’s disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.

This article demonstrates that the complex value of S11 of an antenna, acquired in a multi-monostatic configuration, can be used for localization of a dielectric anomaly hidden inside a dielectric background medium when the antenna is placed close (~5 mm) to the geometry. It uses an Inverse Synthetic Aperture Radar (ISAR) imaging framework where data is acquired at multiple frequencies and look-angles. Initially, near-field scattering data are used for simulation to validate this methodology since the basic derivation of the Multiple Signal Classification (MUSIC) algorithm is based on the plain wave assumption. Later on, from an applications perspective, data acquisition is performed using an antipodal Vivaldi antenna that has eight constant-width slots on each arm. This antenna operates in a frequency range of 1 to 8.5 GHz and its S11 is fed to the 2D MUSIC algorithm with spatial smoothing whereas the antenna artifact and background effect are removed by subtracting the average S11 at each antenna location. Measurements reveal that this methodology gives accurate results with both homogeneous and inhomogeneous backgrounds because the size of data sub-arrays trades between the image noise and resolution, hence reducing the effect of inhomogeneity in the background. In addition to near-field ISAR imaging, this study can be used in the ongoing research on breast tumors and brain stroke detection, among others.

The previous studies have found an association between Big Five personality traits and postpartum depression in women. The present study aimed to find out an association between Big Five personality traits and postpartum depression in a sample of Pakistani fathers. A total of 400 Pakistani fathers who had birth of a child in the past 1 month to 1 year period and had been living with their married partners were recruited purposively by using Google Form based survey from the major cities of Pakistan. The Urdu translated versions of Big Five Personality Inventory (BFI) and Edinburgh Postnatal Depression Scale (EPDS) were used as the main outcome measures to assess the relationship between personality traits and postpartum depression. The results found a significant negative and moderate association between Big Five personality traits and paternal postpartum depression except openness which had a weak association and neuroticism which had a positive and moderate association with PPPD ( r (398) = .45). The multiple linear regression analysis found that Big Five personality traits significantly predicted paternal postpartum depression ( F (5, 394) = 53.33, p = .001) except openness (B = .007, p = .98). The analysis of variance (ANOVA) found significant differences in paternal postpartum depression for age of father ( F (2, 397) = 6.65, p = .001, ηp 2 = .03), spouse age ( F (2, 393) = 5.97, p = .003, ηp 2 = .02), employment type ( F (2, 395) = 9.69, p = .001, ηp 2 = .04) and time spent at home ( F (2, 397) = 6.23, p = .002, ηp 2 = .03) while there were found no significant differences for education ( F (2, 397) = 1.29, p = .27, ηp 2 = .006), marital duration ( F (2, 397) = 2.17, p = .11, ηp 2 = .01), and birth number of recent child ( F (2, 397) = 1.48, p = .22, ηp 2 = .007). The study concluded that Big Five personality traits are significantly correlated with and predict paternal postpartum depression except openness which did not predict paternal postpartum depression. The occurrence of paternal postpartum depression varied significantly for age of father, age of spouse, type of employment and time spent at home.

The energy sector is a crucial pillar of the economic development of every nation. In developing countries, renewable energy deployment is scarce; consequently, the government and private sectors are exploring efficient energy resources. This research aims to scrutinize the linkages among value orientation, utilitarian benefits, collectivism, the reason for adoption, attitude toward renewable energy (RE), and adoption intention in the renewable energy context. The study analyzes survey data from 359 Pakistani consumers using solar panels for households. An approach called structural equation modeling is applied to evaluate hypotheses. Empirical findings suggest that value orientation positively and significantly influences the reason for the adoption of RE and attitude toward RE. Similarly, the utilitarian benefit positively and substantially affects attitude toward RE. Moreover, collectivism and reason for adoption are substantially and favorably related to attitude toward RE. The study’s findings also show that customer intentions to use renewable energy are favorably and substantially influenced by RE attitudes. The research has contributed to the enhancement of future avenues for scholars and professionals are provided by the literature on renewable practice.

Quercetin, a well-known naturally occurring polyphenol, has recently been shown by molecular docking, in vitro and in vivo studies to be a possible anti-COVID-19 candidate. Quercetin has strong antioxidant, anti-inflammatory, immunomodulatory, and antiviral properties, and it is characterized by a very high safety profile, exerted in animals and in humans. Like most other polyphenols, quercetin shows a very low rate of oral absorption and its clinical use is considered by most of modest utility. Quercetin in a delivery-food grade system with sunflower phospholipids (Quercetin Phytosome , QP) increases its oral absorption up to 20-fold. In the present prospective, randomized, controlled, and open-label study, a daily dose of 1000 mg of QP was investigated for 30 days in 152 COVID-19 outpatients to disclose its adjuvant effect in treating the early symptoms and in preventing the severe outcomes of the disease. The results revealed a reduction in frequency and length of hospitalization, in need of non-invasive oxygen therapy, in progression to intensive care units and in number of deaths. The results also confirmed the very high safety profile of quercetin and suggested possible anti-fatigue and pro-appetite properties. QP is a safe agent and in combination with standard care, when used in early stage of viral infection, could aid in improving the early symptoms and help in preventing the severity of COVID-19 disease. It is suggested that a double-blind, placebo-controlled study should be urgently carried out to confirm the results of our study.

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Purpose The purpose of this study is to demonstrate how alertness enable small and medium scale enterprise (SME) owners to leverage their individual entrepreneurial orientation (IEO) such as risk-taking, pro-activity, innovation, passion and perseverance in a better way to recognize opportunities for financial resources as compared to their counterparts who are not alert. Moreover, it elaborates on the mediating role of opportunity recognition of financial resources between IEO and SMEs’ access to finance (AF). Design/methodology/approach A three-wave time-lagged survey from a stratified sample of 271 small and medium scale business owners in Pakistan was conducted and the data were analysed using PROCESS models 1 and 4. Findings The findings grounded in the theory of Action Regulation, signify that the IEO of small and medium scale business owners helps them attain financial resources through opportunity recognition capacity which is an action characteristic. Moreover, the IEO of SME owners, coupled with entrepreneurial alertness (EA; a cognitive pre-action state), amplifies their ability to recognize opportunities for financial resource availability. Originality/value This is one of the initial studies to test the IEO scale, including passion and perseverance. Moreover, it has added to the individual-level antecedents of AF in small and medium scale businesses through the role of EA and opportunity recognition.

PurposeThe purpose of this paper is to empirically investigate the relationships between career competency, career resilience and career success. The study further examines the mediating role of career resilience on the relationship between career competency and career success.Design/methodology/approachData were collected from 284 Islamic bank employees across Pakistan through a cross-sectional, self-reporting, online questionnaire. Partial least squares structural equation modeling was used to test the proposed hypotheses using Smart PLS version 3.0.FindingsThe study’s results indicate that career competency is a significant predictor of career resilience, and that career resilience is subsequently a significant predictor of career success. Further, the results of the structural equation model analyses supported the proposition that career resilience mediates the relationship between career competency and career success.Practical implicationsHuman resource practitioners and managers can increase the likelihood of their employees’ career resilience by focusing on developing career-related competencies – an antecedent of career success.Originality/valueThe study clarifies prevailing misconceptions that assume a direct linear relationship between career competency and career success by establishing, through empirical evidence, that success is not an ultimate outcome of competence. In addition, it proposes an oversimplified model of the competence–resilience–success relationship.

Folic acid plays a crucial role in diverse biological processes, notably cell maturation and proliferation. Here, we performed a literature review using articles listed in electronic databases, such as PubMed, Scopus, MEDLINE, and Google Scholar. In this review article, we describe contradictory data regarding the role of folic acid in cancer development and progression. While some studies have confirmed its beneficial effects in diminishing the risk of various cancers, others have reported a potential carcinogenic effect. The current narrative review elucidates these conflicting data by highlighting the possible molecular mechanisms explaining each point of view. Further multicenter molecular and genetic studies, in addition to human randomized clinical trials, are necessary to provide a more comprehensive understanding of the relationship between folic acid and cancer.