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In the present work, a class of distributions, called new extended family of heavy-tailed distributions is introduced. The special sub-models of the introduced family provide unimodal, bimodal, symmetric, and asymmetric density shapes. A special sub-model of the new family, called the new extended heavy-tailed Weibull (NEHTW) distribution, is studied in more detail. The NEHTW parameters have been estimated via eight classical estimation procedures. The performance of these methods have been explored using detailed simulation results which have been ordered, using partial and overall ranks, to determine the best estimation method. Two important risk measures are derived for the NEHTW distribution. To prove the usefulness of the two actuarial measures in financial sciences, a simulation study is conducted. Finally, the flexibility and importance of the NEHTW model are illustrated empirically using two real-life insurance data sets. Based on our study, we observe that the NEHTW distribution may be a good candidate for modeling financial and actuarial sciences data.

Conventional computers operate deterministically using strings of zeros and ones called bits to represent information in binary code. Despite the evolution of conventional computers into sophisticated machines, there are many classes of problems that they cannot efficiently address, including inference, invertible logic, sampling and optimization, leading to considerable interest in alternative computing schemes. Quantum computing, which uses qubits to represent a superposition of 0 and 1, is expected to perform these tasks efficiently 1 – 3 . However, decoherence and the current requirement for cryogenic operation 4 , as well as the limited many-body interactions that can be implemented, pose considerable challenges. Probabilistic computing 1 , 5 – 7 is another unconventional computation scheme that shares similar concepts with quantum computing but is not limited by the above challenges. The key role is played by a probabilistic bit (a p-bit)—a robust, classical entity fluctuating in time between 0 and 1, which interacts with other p-bits in the same system using principles inspired by neural networks 8 . Here we present a proof-of-concept experiment for probabilistic computing using spintronics technology, and demonstrate integer factorization, an illustrative example of the optimization class of problems addressed by adiabatic 9 and gated 2 quantum computing. Nanoscale magnetic tunnel junctions showing stochastic behaviour are developed by modifying market-ready magnetoresistive random-access memory technology 10 , 11 and are used to implement three-terminal p-bits that operate at room temperature. The p-bits are electrically connected to form a functional asynchronous network, to which a modified adiabatic quantum computing algorithm that implements three- and four-body interactions is applied. Factorization of integers up to 945 is demonstrated with this rudimentary asynchronous probabilistic computer using eight correlated p-bits, and the results show good agreement with theoretical predictions, thus providing a potentially scalable hardware approach to the difficult problems of optimization and sampling. A probabilistic computer utilizing probabilistic bits, or p-bits, is implemented with stochastic nanomagnetic devices in a neural-network-inspired electrical circuit operating at room temperature and demonstrates integer factorization up to 945.

Given their political incentives, governments in more autocratic polities can strategically channel unearned government and household income in the form of foreign aid and remittances to finance patronage, which extends their tenure in political office. I substantiate this claim with duration models of government turnover for a sample of 97 countries between 1975 and 2004. Unearned foreign income received in more autocratic countries reduces the likelihood of government turnover, regime collapse, and outbreaks of major political discontent. To allay potential concerns with endogeneity, I harness a natural experiment of oil price–driven aid and remittance flows to poor, non–oil producing Muslim autocracies. The instrumental variables results confirm the baseline finding that authoritarian governments can harness unearned foreign income to prolong their rule. Finally, I provide evidence of the underlying causal mechanisms that governments in autocracies use aid and remittances inflows to reduce their expenditures on welfare goods to fund patronage.

Aurora B kinase (AURKB) is the catalytic subunit of the chromosomal passenger complex (CPC), an essential regulator of chromosome segregation. In mitosis, the CPC is required to regulate kinetochore microtubule (K-MT) attachments, the spindle assembly checkpoint, and cytokinesis. Germ cells express an AURKB homolog, AURKC, which can also function in the CPC. Separation of AURKB and AURKC function during meiosis in oocytes by conventional approaches has not been successful. Therefore, the meiotic function of AURKC is still not fully understood. Here, we describe an ATP-binding-pocket-AURKC mutant, that when expressed in mouse oocytes specifically perturbs AURKC-CPC and not AURKB-CPC function. Using this mutant we show for the first time that AURKC has functions that do not overlap with AURKB. These functions include regulating localized CPC activity and regulating chromosome alignment and K-MT attachments at metaphase of meiosis I (Met I). We find that AURKC-CPC is not the sole CPC complex that regulates the spindle assembly checkpoint in meiosis, and as a result most AURKC-perturbed oocytes arrest at Met I. A small subset of oocytes do proceed through cytokinesis normally, suggesting that AURKC-CPC is not the sole CPC complex during telophase I. But, the resulting eggs are aneuploid, indicating that AURKC is a critical regulator of meiotic chromosome segregation in female gametes. Taken together, these data suggest that mammalian oocytes contain AURKC to efficiently execute meiosis I and ensure high-quality eggs necessary for sexual reproduction.

The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.

We sought to determine the significance of myeloid clonal hematopoiesis (CH) in the UK Biobank cohort (n = 502,524, median age = 58 years). Utilizing SNP array (n = 486,941) and whole exome sequencing data (n = 49,956), we identified 1166 participants with myeloid CH, defined by myeloid-associated mosaic chromosome abnormalities (mCA) and/or likely somatic driver mutations in DNMT3A, TET2, ASXL1, JAK2, SRSF2, or PPM1D. Myeloid CH increased by 1.1-fold per annum (myeloid mCA, P = 1.57 × 10−38; driver mutations, P = 5.89 × 10−47). Genome-wide association analysis identified two distinct signals within TERT that predisposed to myeloid CH, plus a weaker signal corresponding to the JAK2 46/1 haplotype. Specific subtypes of myeloid CH were associated with several blood features and clinical phenotypes, including TET2 mutations and chronic obstructive pulmonary disease. Smoking history was significantly associated with myeloid CH: 53% of myeloid CH cases were smokers compared to 44% of controls (P = 3.38 × 10−6), a difference principally due to current (OR = 1.10; P = 6.14 × 10−6) rather than past smoking (P = 0.08). Breakdown of CH by specific mutation type revealed that ASXL1 loss of function mutations were most strongly associated with current smoking status (OR = 1.07; P = 1.92 × 10−5), and the only abnormality associated with past smoking (OR = 1.04; P = 0.0026). We suggest that the inflammatory environment induced by smoking may promote the outgrowth of ASXL1-mutant clones.

The high price of petroleum, overconsumption of plastic products, recent climate change regulations, the lack of landfill spaces in addition to the ever-growing population are considered the driving forces for introducing sustainable biodegradable solutions for greener environment. Due to the harmful impact of petroleum waste plastics on human health, environment and ecosystems, societies have been moving towards the adoption of biodegradable natural based polymers whose conversion and consumption are environmentally friendly. Therefore, biodegradable biobased polymers such as poly(lactic acid) (PLA) and polyhydroxyalkanoates (PHAs) have gained a significant amount of attention in recent years. Nonetheless, some of the vital limitations to the broader use of these biopolymers are that they are less flexible and have less impact resistance when compared to petroleum-based plastics (e.g., polypropylene (PP), high-density polyethylene (HDPE) and polystyrene (PS)). Recent advances have shown that with appropriate modification methods—plasticizers and fillers, polymer blends and nanocomposites, such limitations of both polymers can be overcome. This work is meant to widen the applicability of both polymers by reviewing the available materials on these methods and their impacts with a focus on the mechanical properties. This literature investigation leads to the conclusion that both PLA and PHAs show strong candidacy in expanding their utilizations to potentially substitute petroleum-based plastics in various applications, including but not limited to, food, active packaging, surgical implants, dental, drug delivery, biomedical as well as antistatic and flame retardants applications.

The increasing integration of new technologies is driving a fundamental revolution in the healthcare sector. Developments in artificial intelligence (AI), machine learning, and big data analytics have completely transformed the diagnosis, treatment, and care of patients. AI‐powered solutions are enhancing the efficiency and accuracy of healthcare delivery by demonstrating exceptional skills in personalized medicine, early disease detection, and predictive analytics. Furthermore, telemedicine and remote patient monitoring systems have overcome geographical constraints, offering easy and accessible healthcare services, particularly in underserved areas. Wearable technology, the Internet of Medical Things, and sensor technologies have empowered individuals to take an active role in tracking and managing their health. These devices facilitate real‐time data collection, enabling preventive and personalized care. Additionally, the development of 3D printing technology has revolutionized the medical field by enabling the production of customized prosthetics, implants, and anatomical models, significantly impacting surgical planning and treatment strategies. Accepting these advancements holds the potential to create a more patient‐centered, efficient healthcare system that emphasizes individualized care, preventive care, and better overall health outcomes. This review's novelty lies in exploring how these technologies are radically transforming the healthcare industry, paving the way for a more personalized and effective healthcare for all. It highlights the capacity of modern technology to revolutionize healthcare delivery by addressing long‐standing challenges and improving health outcomes. Although the approval and use of digital technology and advanced data analysis face scientific and regulatory obstacles, they have the potential for transforming translational research. as these technologies continue to evolve, they are poised to significantly alter the healthcare environment, offering a more sustainable, efficient, and accessible healthcare ecosystem for future generations. Innovation across multiple fronts will shape the future of advanced healthcare technology, revolutionizing the provision of healthcare, enhancing patient outcomes, and equipping both patients and healthcare professionals with the tools to make better decisions and receive personalized treatment. As these technologies continue to develop and become integrated into standard healthcare practices, the future of healthcare will probably be more accessible, effective, and efficient than ever before.

We sought to determine the relationship between age-related clonal hematopoiesis (CH) and chronic kidney disease (CKD). CH, defined as mosaic chromosome abnormalities (mCA) and/or driver mutations was identified in 5449 (2.9%) eligible UK Biobank participants (n = 190,487 median age = 58 years). CH was negatively associated with glomerular filtration rate estimated from cystatin-C (eGFR.cys; β = −0.75, P = 2.37 × 10–4), but not with eGFR estimated from creatinine, and was specifically associated with CKD defined by eGFR.cys < 60 (OR = 1.02, P = 8.44 × 10–8). In participants without prevalent myeloid neoplasms, eGFR.cys was associated with myeloid mCA (n = 148, β = −3.36, P = 0.01) and somatic driver mutations (n = 3241, β = −1.08, P = 6.25 × 10–5) associated with myeloid neoplasia (myeloid CH), specifically mutations in CBL, TET2, JAK2, PPM1D and GNB1 but not DNMT3A or ASXL1. In participants with no history of cardiovascular disease or myeloid neoplasms, myeloid CH increased the risk of adverse outcomes in CKD (HR = 1.6, P = 0.002) compared to those without myeloid CH. Mendelian randomisation analysis provided suggestive evidence for a causal relationship between CH and CKD (P = 0.03). We conclude that CH, and specifically myeloid CH, is associated with CKD defined by eGFR.cys. Myeloid CH promotes adverse outcomes in CKD, highlighting the importance of the interaction between intrinsic and extrinsic factors to define the health risk associated with CH.

Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing T max , T min , RH mean , R s , and U 2 , with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R 2 ), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R 2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.