This study investigated the risk of lung and bladder cancers in people residing in proximity of a coal-oil-fired thermal power plant in an area of north-eastern Italy, covered by a population-based cancer registry. Incidence rate ratios (IRR) by sex, age, and histology were computed according to tertiles of residential exposure to benzene, nitrogen dioxide (NO2), particular matter, and sulfur dioxide (SO2) among 1076 incident cases of lung and 650 cases of bladder cancers. In men of all ages and in women under 75 years of age, no significant associations were observed. Conversely, in women aged >75 years significantly increased risks of lung and bladder cancers were related to high exposure to benzene (IRR for highest vs. lowest tertile: 2.00 for lung cancer and 1.94 for bladder cancer) and NO2 (IRR: 1.72 for lung cancer; and 1.94 for bladder cancer). In these women, a 1.71-fold higher risk of lung cancer was also related to a high exposure to SO2. The findings of this descriptive study indicated that air pollution may have a role with regard to the risk of lung and bladder cancers, limited to women aged ≥ 75 years. Such increased risk warrants further analytical investigations.

Background/Objectives: The management of early breast cancer (BC) includes surgery, followed by adjuvant radiotherapy, chemotherapy, hormone therapy, or immunotherapy. However, the influence of these interventions in metabolic reprogramming remains unknown. This study explored alterations in the plasma metabolome of BC patients following distinct treatments to deepen our understanding of BC pathophysiology, outcomes, and the identification of potential biomarkers. Methods: We included 52 women diagnosed with BC and candidates for surgery as primary oncological treatment. Blood samples were collected at diagnosis, two weeks post-surgery, and one month post-radiotherapy. Plasma samples from 49 healthy women served as controls. Targeted metabolomics assessed 74 metabolites spanning carbohydrates, amino acids, lipids, nucleotide pathways, energy metabolism, and xenobiotic biodegradation. Results: Before treatment, BC patients exhibited notable changes in carbohydrate, nucleotide, lipid, and amino acid metabolism. We noticed a gradual restoration of specific metabolite levels (hypoxanthine, 3-phosphoglyceric acid, xylonic acid, and maltose) throughout different treatments, suggesting a normalization of nucleotide and carbohydrate metabolic pathways. Moreover, we observed increased dodecanoic acid concentrations, a metabolite associated with cancer protection. These variations distinguished patients from controls with high specificity and sensitivity. Conclusion: Oncological treatments modified the metabolism of patients towards a favorable profile with a decrease in the pathways that favor cell proliferation and an increase in the levels of anticancer molecules. These findings emphasize the pivotal role of metabolomics in recognizing the biological pathways influenced by each cancer treatment and the resulting metabolic consequences. Furthermore, it aids in identifying potential biomarkers for disease onset and progression.

Cultivation is advocated as a solution for sustainable exploitation of medicinal plants. Understanding environmental factors influencing plant species distribution will eliminate the introduction of medicinal plants indiscriminately to inappropriate cultivation regions. The study investigated environmental conditions for the distribution of Artemisia afra and mapped out potential areas for its cultivation in South Africa. Soil samples were collected in the Free State Province in South Africa for analysis. To identify suitable environmental conditions for the natural distribution of A. afra, the South African National Botanical Institute database and physically collected Global Positioning System points were used in maximum entropy model. Monthly long-term average interpolated weather surfaces were used to estimate the effect of climate change on future climate suitability for A. afra distribution. Sixty-one percent of soil samples from different A. afra populations were clay loam soils with slightly acidic to neutral pH. The carbon source utilization, Shannon Weaver Index, and species richness were positively correlated to one group of fourteen soil samples, and species evenness was positively correlated to the second group, consisting of four samples. Climate change will only affect the distribution of A. afra in the very long term. The current study provides critical information for identifying suitable cultivation areas for A. afra, while supporting conservation efforts from an ecological point.

Physical properties such as shape, volume, and size influence the dynamics of biological systems. In this context, we focus on the geometric properties of limb movements and their physiological and biomechanical effects. Using hand grasping as a paradigmatic example, we describe how dynamic changes in geometric configuration can affect the pathophysiology of grasping. We focus on precision pinch, the simplest and most basic form of grasping, where one finger remains stable while another moves against it (Brand and Hollister, 1999). The thumb and index finger come together to grasp small objects with great precision, such as a pen. During the precision pinch, the geometric configuration of the hand changes, modifying the distribution of forces and the functional connectivity between the fingers. These changes may offer a new approach to the biomechanics of artificial arms. While current approaches consider grasps as homogeneous structures, we argue that local variations in hand geometry can lead to new biomedical effects with intriguing operational implications.

Thoracic ultrasonography (TUS) has emerged as a critical tool in the diagnosis and management of respiratory diseases in cattle, particularly bovine respiratory disease (BRD), which is one of the most economically significant health issues in feedyard operations. The objective of this review is to explore TUS in veterinary medicine including the historical development, methodologies, and clinical applications for diagnosing and prognosing respiratory diseases. The review also emphasizes the importance of operator training, noting that even novice operators can achieve diagnostic consistency with proper instruction. Ultrasound was introduced in the mid-20th century for back-fat thickness measurements, TUS has evolved to offer a non-invasive, real-time imaging modality that allows for the detection of lung and pleural abnormalities such as consolidations, pleural effusions, and B-lines. These features are vital indicators of respiratory disease, and their early identification through TUS can significantly improve clinical outcomes. Compared to traditional diagnostic methods like auscultation or radiography, TUS provides superior accuracy in detecting both subclinical and advanced respiratory conditions, particularly in high-risk populations. Furthermore, TUS has demonstrated strong prognostic value, with studies showing that the extent of lung consolidation correlates with higher relapse risk, reduced growth performance, and increased mortality.

Fish is essential to feeding the world's population and provides hundreds of millions of people with a substantial amount of protein in their diets. Fish is one of the most affordable and readily available forms of animal protein, making up roughly 20% of total animal protein intake. It is simple to obtain these fish through cultivated or captured fisheries. However, the aquaculture industry is viewed as the main means of supplying the world's growing fish demand given the ongoing indiscriminate harvests in capture fisheries. Global aquaculture systems are under serious threats from climate change, which will have an impact on disease prevalence, species mix, water quality, and overall production. The possible impacts of climate change on freshwater aquaculture production and its consequences for the sustainability of the sector are reviewed in this paper. Rising temperatures, El Niño Southern Oscillation events, lowered dissolved oxygen, toxic algal blooms, altered rainfall patterns, water pH, altered salinity, and extreme weather events are a few of the aspects of a changing climate that have been discussed.

Improving the efficiency of germplasm innovation has always been the aim of rice breeders. Tra-ditional hybrid breeding methods for variety selection rarely meet the practical needs of rice pro-duction. The emergence of genome editing technologies, such as CRISPR/Cas9, provides a new approach to the genetic improvement of crops such as rice. The number of published scientific papers related to "gene editing" and "CRISPR/Cas9" retrievable in literature websites of China and other countries exhibited an increasing trend year by year from 2014 to 2023. The literature related to gene editing in rice accounts for 33.4% and 12.3% of all literature on crop gene editing published in China and other countries, respectively, much higher than those in maize and wheat. This article reviews the recent research on CRISPR/Cas9 gene editing technology in rice, especially the germplasm innovation and genetic improvement of commercially promoted varieties with im-proved traits such as disease and insect resistance, herbicide resistance, salt tolerance, quality, nu-trition, and safety. The aim is to provide a reference for the precise and efficient development of new rice cultivars that meet market demand.

The hydroacoustic in-water stations of the International Monitoring System (IMS) network consist of three hydrophones placed at Sound Fixing and Ranging (SOFAR) channel depths and about 2 km away from each other. Whale signals recorded in the vicinity of a hydrophone triplet offer the opportunity to verify simple models of propagation in the immediate neighborhood of the triplet, by comparing arrival times and amplitudes between the three hydrophones. Two examples of whales passing by hydrophone triplets are presented and conclusions drawn about waveform coherency and amplitude of the signals recorded at the three hydrophones in the 0-50 Hz frequency band. On the examples recorded at the HA11 IMS hydroacoustic station, the amplitude decay law of one over the distance is verified, and analysis of hundreds of calls located almost above the hydrophone triplet lead to the possibility that the H11S3 hydrophone may be located further from H11S1 and H11S2, and the H11S1 hydrophone’s depth may be deeper than expected from the published locations.

In the field of machine learning, the rapid development of data volume and variety requires ethical data utilization and strict privacy protection standards. Fair Federated Learning (FFL) has emerged as a key solution that aims to ensure fairness and privacy protection in a distributed learning environment. FFL enhances privacy protection and solves the inherent limitations of existing federated learning (FL) by promoting fair model training in diverse participant groups, preventing the exclusion of individual users or minorities, and improving overall model fairness. In this study, FFL discusses the causes of bias and fairness of existing FL, and separates solutions based on data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies to overcome heterogeneity. In order to improve the causes of bias, fairness, and privacy protection of FL, fairness evaluation indicators and applications and challenges of FFL are discussed. Since it addresses bias, fairness, and privacy issues in FL of all mechanisms, it can be an important resource for practitioners who want to implement efficient FL solutions.

The application of wool as mulch in strawberry cultivation was analysed to find a so-lution for the rational use of wool from mountain sheep. In the plantation, the experimental plots mulched with wool, straw and bark were appointed. The plots were monitored during the ex-periment, while the soil temperature and moisture content were measured. The data collected in two-hour intervals were analysed, taking into account air temperature and falls registered in the local meteorological station. Additionally, the progress of mulch biodegradation was tracked. The changes in the wool morphology that occurred by biodegradation were observed during micro-scopic examinations using the Scanning Electron Microscope (SEM). It was stated that wool mulch plays an essential role in thermoregulation of the soil surface, prevents the overheating of the soil during the summer heat and protects it against excessive cooling during cold nights. The wool mulch minimises the fluctuations between the soil’s day and night temperature. The fluctuations do not exceed 2 - 3 degrees on hot summer days, which are five times smaller than for the control plot. The wool retains large amounts of rainwater several times its weight. The water is then slowly released, providing the growing plants a moist environment during a longer rainless pe-riod. Moreover, wool is difficult to biodegrade and maintain its properties for a long time, lasting longer than one vegetation season. Compared to straw and bark, the temperature fluctuations recorded for wool are two times smaller, and its effectiveness in water management is consid-erably better. The beneficial impact of the wool mulch ensuring favourable conditions for strawberry growth was explained by the specific wool structure and its unique properties.

Background/Objectives: To understand the lived experiences of the informal caregivers of people with chronic noncommunicable diseases and their interpretations of the significance of that care. The perspectives of men and women interacting with people in a situation of chronicity are examined. Methods: This qualitative phenomenological-hermeneutic study was based on Van Manen’s comprehensive and interpretative vision. Criterion-oriented non-probability random sampling was used. In-depth interviews were used to collect the information. Twenty caregivers participated in the study. Results: The following five themes emerged: transformation of the caregiver’s world; uncertainty of care; tireless guardianship and unwavering devotion; isolation and emotional exhaustion; and imbalance between caring for others and the self. Conclusions: The experience of informal caregivers reveals that both men and women face significant challenges, albeit from gender-differentiated perspectives and experiences. Men and women elaborate different meanings of caregiving, reflecting their gender roles and expectations. Therefore, an inclusive and equitable approach that recognizes and addresses these gender differences is essential in order to improve the support and quality of life for all informal caregivers, respecting and valuing their unique voices and experiences. An inclusive and equitable approach that recognizes gender intersections is required to improve support and the quality of life for caregivers.

We evaluated the inhibitory effects of our developed Chol-PEG2000 micelles and Chol-PEG500 vesicles on amyloid-β (Aβ) aggregation, a key factor in Alzheimer's disease. ThT assay proved that Chol-PEG2000 delayed Aβ fibril elongation by 17 hours and Chol-PEG500 by 40 hours at 10-times molar ratio against Aβ40 peptide. At 50-times molar ratio, both Chol-PEG2000 and Chol-PEG500 significantly inhibited Aβ aggregation, as shown by minimal fluorescence intensity increase over 48 hours.

Einstein equation approximated to second order in Newton constant G is quantized and applied to a large region of the universe. The two-point correlations of the vacuum energy density and pressure are included and the resulting metric agrees with the one derived from the $\Lambda CDM$ cosmological model plus an additional contribution of the quantum vacuum. The conclusion is that at least a part of the acceleration in the expansion of the universe is due to the vacuum fluctuations.

The novel phosphor Eu3+ doped Na5Zn2Gd(MoO4)6 triple molybdate were prepared by the sol-gel method. The structure, morphology, and luminescent properties have been characterized by X-ray diffraction, thermogravimetry-differential thermal analysis, scanning electron microscopy, FTIR spectroscopy and Luminescence spectroscopy. The results indicated that the synthesized Na5Zn2Gd1-x(MoO4)6: xEu3+phosphor consisted of a pure phase with monoclinic structure. Under excitation at 465 nm, the Na5Zn2Gd1-x(MoO4)6: xEu3+ phosphor exhibits intensive red emission band around 610 nm corresponding to the transition of 5D0→7F2 is much higher than that 5D0→7F1 at 594 nm, which was appropriate for blue LED. According to the influence of the synthesis conditions, the phosphors show the highest emission intensity as the doping concentration of Eu3+ was 25 mol.% and the molar ratio of citric acid to metal ions was 2:1. Na5Zn2Gd0.75(MoO4)6: 0.25 Eu3+ with the color coordinates (x= 0.658, y= 0.341) is a stable red phosphor for blue-based white LEDs due to it is closer to the NTSC standard values (0.670, 0.330) comparing with the color coordinates (0.48, 0.50) of the commercial Y2O2S: Eu3+ red phosphor.

The aim of the study was to analyze the association between single nucleotide polymorphism (SNP) rs80860411A>C located in the intergenic region of chromosome 13 (near the GALNT15 gene encoding Polypeptide N-Acetylgalactosaminyltransferase15, GALNTL2) and fat-tening and slaughter traits of pigs. The study was conducted on 426 individuals belonging to three breeds: Polish Landrace (n = 192), Polish Large White (n = 187) and Pulawska (n = 48). The rs80860411 genotypes were determined using the PCR-RFLP method. Association analysis was performed for each breed separately, as well as for the entire study group. The study showed that rs80860411A>C had a significant effect on fattening performance traits, as well as on several slaughter performance traits, including: width of the loin eye, loin and ham weights without skin and backfat and carcass meat content (P ≤ 0.01, P ≤ 0.05). The obtained results indicate that the studied SNP has the potential to be included in pig selection programs, thus potentially influencing the improvement of fat-tening and slaughter traits of different pig breeds.

Cone-beam computed tomography (CBCT) has become a vital tool in implantology, playing a key role in both pre- and post-surgical phases. Preoperatively, CBCT provides detailed three-dimensional evaluation of anatomical structures, offering precise information on bone quality and quantity, as well as the location of critical structure. This accuracy enhances surgical planning, reducing intra- and postoperative risks and ensuring more precise implant placement. Post-surgically, CBCT is equally important for monitoring osseointegration and detecting potential complications, such as failed integration, infections, or implant misalignment. Its high-resolution imaging capabilities enable clinicians to make accurate therapeutic adjustments, improving treatment outcomes. Overall, CBCT improves decision-making in implantology, offering greater predictability and safety compared to traditional two-dimensional radiography, significantly enhancing the success rates of implant-based oral rehabilitation procedures.

Environmental pollution indicated by the presence of PM2.5 particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and PM2.5 particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between PM2.5 and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Data sets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between PM2.5 and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with PM2.5. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show opposite behavior. PM2.5 exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between PM2.5 levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance of the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCA analyses with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic. Multiple regression analysis indicates a significant and consistent dependence of PM2.5 on temperature and solar radiation across both analyzed periods. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts.

Digitized heritage is regarded as an emerging field, with existing studies primarily focusing on one case study or technological aspect. Thus, there remains a notable gap in the literature regarding the understanding of how these technologies can be utilized in smart heritage management. Approaches that harness technological advancements to enhance smart management and aid the decision-making process regarding the use of appropriate technology at various heritage sites remain insufficiently explored. To address this gap, this study conducted a cross-case analysis to survey the use of heritage technologies across selected cases to find underlying trends and correlations related to the scale and technology used. The findings of this study provide insights into the nexus of technology integration into heritage management, highlighting the role of tangible heritage documentation technologies. The present study concludes that smart management framework necessitates an ongoing process involving the integration of cultural heritage data, documentation, and the Internet of Things (IoT). IoT facilitates the connectivity of physical items to the internet, thereby supporting the safeguarding and interpretation of cultural heritage knowledge. Such framework operates through a collaboration platform that engages experts, local communities, and tourists to ensure a meaningful and interactive interconnection.

The human brain, a highly complex dynamical system, exhibits various states of consciousness—such as wakefulness, sleep, and altered states—each characterized by distinct patterns of neural activity. To capture the dynamical properties of these states, a range of complexity measures is utilized, with a primary focus on Statistical Complexity (SC) and Lempel-Ziv complexity (LZc), and supplemented by Approximate Entropy (ApEn) and Kolmogorov Complexity (KC). These measures are applied to both simulated data, generated through logistic maps and Multivariate Autoregressive (MVAR) models, and intracranial depth electrode recordings from patients. The results demonstrate that these complexity measures effectively capture intricate dynamics of the brain across different states. Specifically, SC captures the structural complexity and information processing within the system, reflecting organized and predictive neural behavior by accounting for temporal correlations in the data. In contrast, LZc is more sensitive to randomness, measuring the diversity and unpredictability of patterns within the data. This distinction allows SC to serve as a more reliable indicator of organized information processing, while LZc highlights the variability in neural signals. Notably, the study reveals that states of higher consciousness are associated with greater complexity, supporting the entropic brain hypothesis. This research contributes to the ongoing efforts to quantify consciousness through mathematical frameworks and offers insights into the neural correlates of different states of awareness.

Diagnostics often require specialized equipment and trained personnel in laboratory settings, creating a growing need for point-of-care tests (POCT). Among genetic testing methods, Loop-mediated Isothermal Amplification (LAMP) offers a viable solution for developing genetic POCT due to its compatibility with simplified devices. This study aimed to create a genetic test that integrates all steps from sample processing to results while minimizing the complexity, handling, equipment, and time required. Several challenges were addressed to achieve this goal: 1) Developing a buffer for bacterial DNA extraction that is compatible with both LAMP and immunochromatographic tests; 2) Adapting the LAMP protocol for its use with the device; and 3) Optimizing the detection protocol for specific test conditions, with a lateral flow immunoassay format selected for its POCT compatibility. Following these developments, the test was validated using Escherichia coli and non-E. coli strains. A portable heating station was also developed to enable amplification without costly equipment. The resulting genetic POCT achieved 100% sensitivity and 85% specificity, with results available in 60 to 75 minutes. This study demonstrated that our closed-method POCT efficiently performs DNA extraction, amplification, and detection for bacterial identification. The test’s simplicity and cost-effectiveness will support its implementation in various settings.

Animal venoms are complex mixtures of bioactive peptides and enzymes that have garnered significant interest for their therapeutic potential, particularly in drug delivery systems. Among these peptides, cell-penetrating peptides (CPPs) have emerged as promising candidates due to their ability to traverse cellular membranes and deliver a variety of molecular cargos, including drugs, imaging agents, and nanoparticles. Recent advancements in omics technologies have enhanced our understanding of the structural and functional properties of CPPs, leading to their classification based on conformation, origin, and physicochemical characteristics. This review discusses the diverse types of CPPs, including three-finger toxins, disintegrins, and bradykinin-potentiating peptides, highlighting their roles in targeted drug delivery and therapeutic applications. The potential of venom-derived peptides in developing novel strategies for drug delivery and disease treatment is explored, emphasizing their impact on biotechnological and biomedical research.

This paper explores the burgeoning field of mindfulness in organisational contexts, examining its evolution from Buddhist practices to contemporary corporate applications. It critically analyses the implementation of mindfulness interventions in Fortune 500 companies, elucidating both the potential benefits and challenges. The research highlights the role of mindful leadership in fostering positive organisational cultures and addresses the complexities of integrating these practices within diverse corporate settings. Drawing on case studies, the paper illuminates the variability in mindfulness approaches and their impacts on employee well-being, productivity, and organisational resilience. Whilst acknowledging the promising outcomes, the study also considers critiques regarding the commodification of mindfulness and its potential to blur work-life boundaries. The concept of Microdosed Mindfulness is introduced as a nuanced strategy for overcoming resistance and facilitating gradual adoption. This comprehensive analysis provides valuable insights for organisations navigating the implementation of mindfulness practices in the contemporary world.

Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as Random Forest, for categorizing defoliation levels in R7-stage soybean plants. The research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The Random Forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97 and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the RedEdge and NIR bands, and relative vegetation cover, with the highest Gini importance index.

The primary goal of Theory of Everything (ToE) is to unify General Relativity and Quantum Mechanics, establishing a theoretical framework that explains all natural phenomena and fundamental laws of the universe. The rapid development of artificial intelligence in the 21st century presents new opportunities for the establishment of TOE. Research has revealed that the observer and the agent, as key concepts in physics and intelligence science respectively, share intrinsic consistency. Using this as a breakthrough point, we first proposed Standard Agent Model, which, from the perspective of information processing, establishes a unified structure for agents. This model posits that any agent possesses five essential capabilities: input, output, storage, creation, and control of these four capabilities.By setting the values of these five essential capabilities in Standard Agent Model to either zero or infinity, we identify two extreme states of agents: Absolute Zero Agent (α Point) and Omniscient and Omnipotent Agent (Ω Point). Based on the evolution of agents towards these two extreme states, we derive two driving forces for agent evolution: α Gravity and Ω Gravity. These elements collectively form General Model of Agent Evolution.Building on the first two models, we further derive Intelligent Universe Evolution Model, proposing that Universe, as an agent, evolves dynamically between α Point and Ω Point under the influence of α Field and Ω Field. These three models together constitute "Theory of Universal Intelligent Evolution," abbreviated as Ω Theory. According to Ω Theory, we demonstrate that the observer in physics and a wide range of research objects can be unified within the framework of Standard Agent Model. Additionally, we argue that the four fundamental forces have the potential to be unified into α Gravity and Ω Gravity.Within the framework of Ω Theory, through theoretical analysis and experimental validation, it has been demonstrated that the differences between classical mechanics, relativity, and quantum mechanics stem from the varying intelligence levels assigned to observers. Furthermore, it has been discovered that the differences in intelligent characteristics between the theoretical Universe proposed by these three major physical theories and Universe in which humanity currently resides are the root cause of the varying precision and limitations of these theories.

The advent of Large Language Models (LLMs) has significantly advanced natural language processing (NLP), yet their application to historical texts remains challenging due to archaic language, distinct terminologies, and varied contextual backgrounds. This study introduces Historical Domain Large Language Models, designed to bridge this gap by adapting LLMs for better comprehension and processing of historical data. Our approach leverages context-aware and task-specific prompts to enhance model performance in tasks such as named entity recognition (NER), sentiment analysis, and information extraction within historical contexts. We propose an iterative refinement process to improve prompt quality and model outputs continuously. Instruction tuning on newly collected evaluation data ensures our methods' efficacy, avoiding biases from previously used datasets. Evaluations using GPT-4 demonstrate significant improvements in handling historical texts, underscoring the potential of our approach to unlock profound insights from historical data. This work highlights the importance of tailored LLM adaptations for specialized domains, offering a robust framework for future research in historical NLP.

Wireless mesh networks (WMNs) are gaining popularity due to their versatility and cost-effectiveness. However, selecting appropriate gateways to connect these networks to external networks remains a key challenge. This paper presents a comprehensive review of gateway selection methods in WMNs, classifying them into network-centric, user-centric, and hybrid approaches. Key factors such as network topology, traffic volume, quality of service, and available resources are considered in evaluating these methods. The effectiveness of different strategies is assessed using metrics like throughput, latency, energy usage, and fairness. The impact of mobility, node heterogeneity, and security concerns on gateway selection is also explored. In addition to reviewing existing methods, the paper identifies research gaps and proposes future directions. Advanced methods that can adapt to changing network environments and support large-scale deployments are highlighted. This survey offers valuable insights for researchers and network designers to improve the efficiency and reliability of WMNs in real-world applications.

Anterior cruciate ligament (ACL) injuries are common, particularly among athletes, leading to significant functional impairment, prolonged rehabilitation, and an elevated risk of secondary complications. The ACL plays a critical role in knee stabilization, and its rupture initiates a cascade of molecular and neuromuscular changes that disrupt joint stability and motor control. One of the primary consequences of an ACL injury is the onset of Arthrogenic Muscle Inhibition (AMI), a neurological phenomenon characterized by reduced voluntary muscle activation, particularly in the quadriceps, resulting from altered afferent feedback, nociceptive inputs, and local inflammatory responses. The acute phase is dominated by pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α, which propagate local inflammation, sensitize nociceptors, and disrupt both spinal and supraspinal motor pathways. This leads to central and peripheral sensitization, exacerbating AMI and impairing rehabilitation. As the injury progresses, an anti-inflammatory shift marked by IL-10 and IL-4 promotes tissue repair and resolution of inflammation. However, dysregulation in this transition can result in chronic inflammation and fibrosis, further complicating recovery. Understanding the temporal dynamics of these molecular mediators is essential for developing targeted interventions. This review explores the complex interplay between cytokine profiles and neuromuscular adaptations post-ACL injury, highlighting potential therapeutic targets and personalized rehabilitation strategies that could optimize functional recovery and mitigate long-term complications.

This review explores the prevalence and implications of muscular power and dynamic balance asymmetry among soccer players, focusing on its impact on performance and injury risk. Asymmetry is common in soccer due to the sport's specific demands, such as the frequent use of one leg for kicking and passing. While some asymmetry may allow for functional specialization and may not impair performance, significant imbalances are often linked to an increased risk of injuries, particularly to the lower extremities. Dynamic balance asymmetry is also associated with a higher risk of non-contact injuries, highlighting the importance of joint stability in preventing such injuries. However, the direct impact of balance asymmetry on performance metrics like sprint speed and agility is less definitive, suggesting that compensatory mechanisms or training adaptations might mitigate potential negative effects. Advancements in assessment technologies, such as motion capture systems and wearable devices, have enhanced the precision of asymmetry evaluations, enabling more targeted interventions. Despite these technological advancements, further research is needed to understand the long-term effects of asymmetry across different player demographics, including youth and female athletes, and to evaluate the effectiveness of specific training interventions. This review recommends a multidisciplinary approach, integrating biomechanics, sports medicine, and coaching insights, to develop comprehensive strategies for managing asymmetry in soccer. Such strategies will be crucial for optimizing player performance, minimizing injury risk, and improving training and rehabilitation programs tailored to the diverse needs of soccer players.

This study examines the impact of foreign direct investment (FDI) on economic growth using panel data from 84 countries between 2010 and 2019. We utilized a two-step system generalized method of moments (GMM) to analyze how FDI affects economic growth through productivity gains and the role of economic freedom in attracting FDI. The findings suggest that FDI has a positive impact on productivity and benefits both OECD and non-OECD countries. Economic freedom plays a significant role in attracting FDI, particularly in OECD countries, and contributes to economic growth in non-OECD countries. However, economic freedom alone does not guarantee strong economic growth in OECD countries, but it significantly enhances growth in non-OECD countries. The results also highlight that only economies with robust economic infrastructure and development levels benefit more from FDI.

As wireless networks have become an integral part of modern communication infrastructure, ensuring their security against a rapidly evolving threat landscape is a critical concern. This research article provides a comprehensive overview of the emerging threats targeting wireless networks, including advanced persistent threats, man-in-the-middle (MitM) attacks, and AI-driven adaptive malware. With the advent of new technologies such as 5G, the Internet of Things (IoT), and artificial intelligence (AI), the attack surface for wireless networks has significantly expanded, demanding more robust and adaptive security protocols.The paper analyzes the efficacy of current wireless security protocols, such as WPA3 and the 802.11i standard, in addressing these emerging vulnerabilities. While these protocols have introduced significant improvements, they are not without limitations. The article further explores innovative solutions such as blockchain-based security frameworks, AI-powered threat detection systems, and the future potential of quantum cryptography in safeguarding wireless communications.Through a critical review of recent case studies and empirical data, the article highlights the key challenges that organizations face in securing wireless networks, particularly in IoT environments where security standards lag behind technological advancements. The research concludes that while existing protocols provide foundational security, they must be continuously updated and augmented with cutting-edge technologies to counter the growing sophistication of cyberattacks.This article aims to provide insights into the state of wireless network security and offer practical recommendations for enhancing security protocols. Future research directions are also discussed, focusing on the integration of AI-driven threat intelligence and the standardization of security protocols across various wireless technologies. The findings underscore the importance of proactive security measures to safeguard wireless networks in an increasingly interconnected world.

The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided.

This article presents a method for detecting P, QRS, T, and U waves in electrocardiogram (ECG) signals through advanced signal processing techniques, including a Butterworth bandpass filter and peak finding algo- rithms. The results are evaluated against the voltage ranges recommended in the literature, underscoring the importance of accurate wave detection for the diagnosis of various cardiac conditions.

In recent years, digital transformation has become a pivotal factor for Small and Medium-sized Enterprises (SMEs), empowering them to adopt emerging technologies for competitive advantage. This systematic literature review examines 60 research papers published between 2014 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to explore the impact of digital technologies such as artificial intelligence, cloud computing, blockchain, and the Internet of Things (IoT) on SME performance. The review focuses on key drivers and barriers, including operational efficiency, customer engagement, and market expansion. Methodologies employed in the analyzed studies include quantitative approaches (60%), such as surveys and structural equation modelling, qualitative methods (25%), including interviews and case studies, and mixed methods approaches (15%) that combine both. A geographic distribution analysis highlights contributions from various countries, revealing regional research gaps. The results show that digital technologies have significantly improved SME operations, but challenges like limited resources and strategic focus persist. Additionally, the dominance of quantitative methods indicates a potential bias, suggesting the need for more diverse methodological approaches to fully understand the transformative potential of digital technologies for SMEs.

Pre-trained language models (PLMs) have significantly advanced natural language processing (NLP), establishing the "pre-training + fine-tuning" paradigm as a cornerstone approach in the field. However, the vast size and computational demands of Transformer-based PLMs present challenges, particularly regarding storage efficiency and processing speed. This paper addresses these limitations by proposing a novel lightweight PLM optimized for accurately understanding domain-specific computer terminology. Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. The model is further fine-tuned using a dataset that mixes source and target languages to enhance its versatility. Comprehensive experimental evaluations demonstrate that the proposed approach successfully achieves a balance between model efficiency and performance, offering a scalable solution for NLP tasks involving specialized terminology.

Aim: The aim in this study was to conduct a prospective survey regarding the socio-demographics and lifestyle risk factors for cancer of a population of Caribbean immigrants living in the U.S. Materials and Methods: The data were processed and analysed using SPSS software 27 and Excel. Crosstabulations were performed. The chi-square test was used to evaluate differ-ent hypotheses. Statistical significance was defined as p < 0.05. Results: Statistically significant differences in the country of birth of the Caribbean immigrants were found for sex (p = 0.038) and the cleanness of their neighbourhoods (p = 0.045). We found differences in occupations between men and women (p = 0.001). Men were less unemployed than women (p = 0.011). Sex also showed statistically significant differences in how easily the immigrants balanced their work and per-sonal life (p = 0.044). Age groups depicted differences in the physical health of the immigrants (p = 0.001). The use of alcohol and tobacco was not an important risk factor among participants (p = 0.529). Conclusions: These findings suggest that the sociodemographics risk factors for cancer among a population of Caribbean immigrants were significantly different between the sexes. However, the use of tobacco and alcohol showed insignificant differences among the immigrants.

Introduction: Variants of COVID-19 are responsible for 700 million infections and 7 million deaths worldwide. Vaccinations have high efficiency in preventing infection and secondary benefits of reducing COVID-19 hospital admissions, attenuating disease severity and duration of illness. Conflicting reports were published regarding COVID-19 among PLWH. Objective: The aim of this study was to evaluate COVID-19 morbidity, hospitalization and the magnitude of immunological response to sequential BNT 162b2 mRNA vaccines in PLWH regarding demographic and clinical factors. Results: Our retrospective study included 784 PLWH who had at least one anti- SARS-CoV-2 antibody test between March 2021 and October 2021. Half of our patients (392) had CD4 cells count above 500 cells/µl, 40.2% (315) had 200<CD4<500 cells/µL and only 9.8% (77) had CD4 <200 cells/µL at their last laboratory workup. The mean age was 50.2±12.2 years. About 90% of our patients were given at least two doses of the BNT 1626b2 Pfizer vaccines, about 60% received three doses of the vaccine. About a quarter of our patients (27.3%) had COVID-19 infection. Only 6 patients required hospital admission. All six patients recovered from COVID-19 infection. Titers of COVID-19 antibodies were lower for patients with CD4 cells count of less than 200 cells/ µL in the first, second and third serological tests with statistical significance. In a multinomial logistic regression the influence of other factors such as age, sex, previous COVID-19 infection on first COVID-19 antibodies titers was not significant. Conclusions: PLWH are responsive to COVID-19 vaccines. As was expected, patients with higher CD4 cells count have higher titers of COVID-19 antibodies and less severe illness. Age, sex and previous COVID-19 infection did not significantly affect antibodies titers according to our study. Larger prospective studies with control groups are needed to further characterize immunologic response to COVID-19 vaccination among PLWH.

In order to study the thermal deformation behavior of uranium-50wt.%zirconium, used Gleeble 3800 thermal simulation testing machine to carry out compression deformation experiments at different temperatures for this material. The effects of deformation temperature and strain rate on the alloy were studied. The results show that the peak stress of U-50wt.%Zr metallic fuel decreases with the increase of deformation temperature after thermal deformation at 500°C~600°C, especially at 600°C compared with that at 500°C and 550°C. At the same temperature, the peak stress increases with the increase of strain rate, especially the peak stress at strain rate 0.1s-1 and 1s-1 is significantly higher than that at strain rate 0.01s^-1, and then the microstructure and diffraction results of U-50wt.%Zr metal-type fuel after thermal deformation are compared and analyzed, and the Arrhenius-type constitutive equation and thermal proces550℃,έ=1s^-1 diagram of U-50wt.%Zr metal-type fuel are established based on the peak stress.

This work aimed to determine the effect of PBZ concentrations and application protocols on the ornamental quality and global preference of customers. PBZ was applied in five concentrations by three protocols (drench at transplanting, drench at 30 days after transplanting and immersion). At commercial maturity, the plants were evaluated for plant growth parameters related to height and canopy characteristics, fruits and leaves, in addition to sensory analysis. PBZ produced plants with darker green leaves and the drench method was more effective in modulating plant growth pa-rameters compared to immersion. Conversely, the sensory analysis showed a greater preference among evaluators for plants treated with PBZ at 2.5 and 5 mg L-1 via drench at 30 DAT or through immersion (IM), even though IM was not associated with any of the ornamental quality parameters evaluated. Likewise, the application of PBZ by drench during transplanting is not recommended as the plants showed a reduced number and size of fruits, which affected the ornamental value of the plants and global preference. Therefore, PBZ applied by drench 30DAT at concentrations of 2.5 and 5 mg L-1 produced ‘Biquinho Vermelha’ pepper plants with attractive ornamental characteristics being an alternative method for producers of potted ornamental plants.

Introduction: microRNA-181a (miR-181a) is a crucial post-transcriptional regulator of many mRNA transcripts and ncRNAs, influencing cell proliferation, cancer cell stemness, apoptosis, and immune responses. Its abnormal expression has been characterized in numerous cancers, making it a significant genomic vulnerability and biomarker in cancer research. Areas Covered: Here, we summarize miR-181a’s correlation with poor patient outcomes across numerous cancers, and the mechanisms governing miR-181a’s activity and processing. We comprehensively describe miR-181a’s involvement in multiple regulatory cancer signaling pathways, cellular processes, and the tumor microenvironment. We also discuss current therapeutic approaches to targeting miR-181a, highlighting their limitations and future potential. Expert Opinion: miR-181a is a clinically relevant pan-cancer biomarker with potential as a therapeutic target in cancer. Its regulatory control of tumorigenic signaling pathways and immune responses positions it as a promising candidate for more personalized treatments. The success of miR-181a as a target relies on the development of specific therapeutics platforms. Future research on miR-181a's role in the tumor microenvironment and the RNA binding proteins that regulate its stability will help uncover new techniques to targeting miR-181a. Further research into miR-181a serum levels in patients undergoing therapy will help to better stratify patients and enhance therapeutic success.

Retinal diseases are an important focus of in vivo gene therapy, for which adeno-associated virus is a preferred vector. Intravitreal injection is a highly desirable delivery route because it is much simpler, safer and inexpensive than the main alternative subretinal injection. However, existing capsids have low efficiency, require high doses and are susceptible to neutralization by antibodies that are present in the vitreous. Using AAV5 as starting material, we applied a combination of directed evolution and rational design to develop PT1, a novel AAV capsid capable of efficiently transducing the retina after intravitreal injection. The most striking property of PT1 is its remarkable ability to evade preexisting neutralizing antibodies, thanks to its multiple mutations spread over large areas of the capsid surface, disrupting several epitopes. Our results suggest that PT1 has a strong potential as a useful candidate capsid for human retinal gene therapy.

Fall are significant cause of mortality and disability among older adults globally, affecting individual health and society. Addressing preventable factors, such as physical health and environmental conditions, is essential. This cross-sectional study aims to understand the factors that influence fall prevention behaviors among older adults in semi-urban community. Methods: A total of 440 older adults were randomly selected from six districts in Samutprakarn Province, Thailand. Well-trained data collectors conducted individual interviews with the participants in community-based setting. Results: Multiple logistic regression analysis was conducted, revealing that 96.3% of older adults demonstrated good fall prevention behaviors. The factors significantly associated with these behaviors included being female (OR = 4.39, 95% CI: 1.42-13.52), having good knowledge of fall prevention (OR = 9.19, 95% CI: 2.78-30.38), and perceiving the severity and susceptibility of falls, along with the benefits and barriers to prevention (OR = 11.25, 95% CI: 3.36-37.69). Additionally, sufficient access to health information (OR = 4.94, 95% CI: 1.61-15.16) and strong social support (OR = 4.06, 95% CI: 1.30-12.71) were also significant factors. In conclusion, a high percentage of older adults practiced good fall prevention behaviors, influenced by factors such as gender, knowledge, perception of fall risks, access to health information, and social support. Policy makers should prioritize enhancing health education on fall prevention, improving access to health information, and strengthening social support systems for older adults to further reduce fall-related risks and disabilities.

The broad adoption of information and communication technology and the shift to a fully digital society are together referred to as the "digital revolution." Similar to how the first industrial revolution affected western nations in the 19th century, the digital revolution has the power to drastically develop and transform Africa. The majority of African nations, if not all of them, are still searching for answers to the political, social, and educational issues that their citizens deal with. The principal objectives of the research were to ascertain the obstacles and possible patterns in African nations concerning the execution of STEM education and 4IR across the region, in addition to offering a plan and approach for doing so. In order to explore the sought papers and find answers to the research questions, the study employed the archival research study process. The results show that certain patterns that were adopted in Africa must be cooperatively adopted throughout the continent.

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Singapore Swimming Club. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots. Key results include: 1. The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. 2. QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. 3. Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). 4. A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players' shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.

Class Incremental Learning (CIL) has emerged as a vital machine learning paradigm, particularly suited for environments with continuous data streams, such as those encountered in the financial industry. CIL enables models to progressively acquire and accumulate new knowledge without retraining the entire dataset, making it highly efficient for dynamic financial markets. In this paper, we explore the application of CIL to financial data streams, where high-frequency, high-dimensional data, such as stock prices and cryptocurrency transactions, evolve rapidly. The challenges inherent in CIL, including catastrophic forgetting—where the model loses previously learned knowledge—and intransigence—where it resists learning new classes—are addressed through both regularization-based techniques and dynamic architecture expansion. We present a comprehensive analysis of the algorithms designed to mitigate these challenges and highlight their relevance in financial applications like algorithmic trading, portfolio management, risk assessment, and fraud detection. By demonstrating how CIL enables models to adapt to new market conditions and asset classes while retaining past knowledge, we illustrate its critical role in advancing real-time financial decision-making. Through a detailed examination of the methodologies and their applications, this paper aims to provide insights into the future of adaptive financial models using Class Incremental Learning. We have also summarized methodologies into a model optimization-based class and a data focused class.

Increases in water temperature caused by climate change will challenge water and species management in the San Francisco Estuary. Our goals were to describe spatial and seasonal patterns in water temperature across the upper Estuary, and evaluate how temperature stress and suitability vary across the aquatic ecosystem. We synthesized 10 years of continuous water temperature data at 75 stations across six regions of the Estuary between 2010 and 2019. We identified stressful temperature thresholds for species of interest using published physiological limits and observed distribution, including ESA-listed native (e.g., osmerids, salmonids), non-ESA-listed native (e.g., cyprinids), non-native (e.g., centrarchids, bivalves), and nuisance species such as invasive aquatic vegetation, and harmful cyanobacteria. We then quantified thermal stress across varying spatial and temporal scales and metrics. Analyses indicated there were detectable regional temperature differences, and that Suisun Bay was the only region to provide regular thermal refuge during the warmer parts of the year, though portions of the Confluence and Suisun Marsh also provided potential thermal refuge during summer/fall and during cooler parts of the day. Meanwhile, the Central and South Delta experienced the warmest temperatures, exceeding thresholds for listed species throughout summer and early fall. We found that listed species such as juvenile salmonids and osmerids experience more thermal stress across a number of metrics. Fishes with higher heat tolerance (including non-natives and select natives), aquatic vegetation, and Microcystis (a harmful cyanobacteria) had the lowest average number of thermal stress days. This study demonstrates that the Estuary is already exhibiting stressful conditions for species of concern, and thermal stress will only increase with climate change. We identify at what times of year and where the Estuary may provide refuge from thermal stress conditions which has important implications for restoration prioritization and design and species management.

In recent years, there has been a significant demand for Instructional Design (ID) professionals due to the rise of remote work, increased reliance on digital learning solutions for professional development, and the integration of digital technologies in formal and informal learning spaces. However, practical and authentic personalized courses are unavailable for professionals who want to transition into the field. This study addresses the gaps in most of the available ID courses and details the design of an Instructional Design Skill-Strength Assessment System (IDSSAS) that evaluates gaps in users' skills and offers them courses and mentors to help them bridge them in preparation for work as an ID in the corporate space. Employing a User-Centered Design (UCD) approach, the system prioritizes personalization, real-world application, and practical skills that better position them to be more employable, a significant improvement over the mostly available traditional, theory-focused ID training programs. IDSSAS, which is integrable with any Learning Management System (LMS) or Learning Experience Platform (LXP), was developed with Storyline 360, and it offers personalized learning and feedback as well as provides users with the agency to choose a mentor that is best aligned with their area of interest. The design was grounded in Self-Determination Theory (SDT) and incorporates design features like a progress bar, feedback loop, challenges, social interaction, and agency to support students' motivation and promote Autonomy, Relatedness, and Competence. IDSSAS, in its manual and present automated form, has been successfully used to mentor some transitioning ID professionals globally who are now gainfully employed.

The objective of the study was to analyze the climate change experienced in Spain between 1971 and 2022 and to estimate the future climate (2050). The main objectives were: 1) to analyze the temporal evolution of temperature from 1971 to the present, to quantify the warming process experienced in the case study and to evaluate the increase in ex-treme heat events (heatwaves); 2) to study the evolution of the precipitation regime to determine whether there is a statistically representative trend towards a drier climate and an increase in extreme precipitation; 3) to investigate the interaction between annual precipitation and the continuous increase in temperature; and 4) to estimate the future climate scenario for mainland Spain and the Balearic Islands towards 2050, analyzing the trends in land aridity and predicting a possible change from a Mediterranean climate to a warm steppe climate, according to the Köppen classi-fication. In this study, we used the E-OBS dataset provided by the European Climate Assessment and Dataset (ECA&D) project. E-OBS is a high-resolution gridded daily climate dataset. Its advantage over other databases is that it is based on direct climate information from the weather stations included in the Copernicus ECA&D. Furthermore, a detailed interpolation and refinement process allows a complete, uniform view of the studied territory. This dataset has pro-vided daily weather information since 1950 and is presented as a complete dataset of the European region. It is availa-ble on a regular grid of 0.1 and 0.25 degrees for the elements of daily average temperature (TG), daily minimum tem-perature (TN), daily maximum temperature (TX) and the sum of daily precipitation (RR), among other climatic varia-bles. In this study, we obtained daily temperature data, including the average, maximum and minimum temperature, and precipitation from 1971 to 2022, using the 27.0e dataset. We used information from E-OBS from 1 January 1971 to 31 December 2022, with a resolution of 0.25 degrees. The main purpose of this study was to analyze whether there is a temporal evolution of the precipitation regime in Spain towards less precipitation, and to study the relationship between annual precipitation and the tendency to-wards progressive warming. Specifically, the aim was to test the hypothesis that the increase in temperature resulting from the global warming process implies a tendency towards progressive drought. The hypothesis is + T  - RR. Given the extreme annual variability of the climate, in addition to the ordinary least squares methodology, the techniques mainly used in this study were the Mann-Kendall test and the Kendall-Theil-Sen regression. The Mann-Kendall test confirmed the statistical significance of the relationship between RR and TX, with a statistical relia-bility close to 99.9%. The KTS regression predicted a reduction of 38.25 mm for each 1°C increase. If the warming trend experienced in recent years (1973– 2022) continues, it is foreseeable that by 2050 there will be a reduction in precipitation in Spain of between 14% and 23% with respect to the precipitation of the reference period (understood as the average between 1971 and 2000). Spain’s climate is likely to vary from Mediterranean to warm steppe in the Köppen classification system (from “C” to “B”).

This research investigates the influence of yoga on social intelligence and academic performance of business students. Employing a randomised controlled experiment with Executive MBA participants, the research explores the mediating role of mindfulness in these relationships. Results indicate that yoga positively influences social intelligence, decision-making skills as reflected in case study scores, and overall academic performance. Mindfulness is found to partially mediate these relationships, accounting for a significant portion of the observed effects. The study reveals that yoga directly contributes 43% to increased social intelligence, with improved mindfulness accounting for 57%. Similarly, yoga directly accounts for 46% of improved academic achievement, while enhanced mindfulness contributes 54%. These findings suggest that incorporating yoga into business education curricula could potentially enhance both the interpersonal capabilities and academic outcomes of students, preparing them more holistically for the challenges of the modern corporate world.

Older people with sleep disturbances also have other chronic pathologies that may interfere with these disturbances. One of the comorbidities that is frequently present is type II diabetes. Objetives: This research aims to find out whether type 2 diabetes mellitus present in elderly people affects the level of severity of obstructive sleep apnoea syndrome (OSAS). Methods: Cross-sectional descriptive study, with a sample of 75 elderly people who attended the Sleep Unit of Doctor Sagaz Hospital in Jaen to be diagnosed with OSAS, and classified according to severity. Results: Significant differences were obtained in the severity of OSAHS between the participants who had type II diabetes mellitus and those who did not, with the former showing higher scores (U=326, p=0.02). Conclusions: Therefore, the presence of type 2 diabetes in the el-derly in our study was associated with more severe OSA. The main discrepancies when compar-ing this research with other studies result from the lack of research addressing these variables in this population group, as well as from the great heterogeneity of methods and techniques used in the different studies (different sample sizes, age range of the population, and diagnostic methods used), causing great variability in the results found. Given the findings obtained in this research, we hope to contribute to future translational research and improve the quality of life of this population.

While the financial technology (FinTech) sector has revolutionized conventional banking services with technology-driven solutions, it also faces considerable cybersecurity hurdles to guard against data protection and sustain the overall financial performance. The main purpose of this paper is to identify the key cybersecurity challenges encountered by FinTech companies, such as data confidentiality concerns, complex cyber-attacks, difficulties with regulatory compliance and third-party risks or technological innovations. This also assesses the regulatory responses to those challenges, such as data privacy regulations, macroprudential regimes, industry-specific guidance and international regulatory cooperation. Through this analysis, the paper attempts to synthesize current state of cybersecurity domain within FinTech and provides suggestions for strengthening data shelter and financial stability by implementing best practices with regulatory strategies.

Graph unlearning has emerged as a crucial technique in privacy-preserving applications, particularly in scenarios where sensitive data must be erased from graph-based systems. This paper explores the fundamental concepts, challenges, and methodologies associated with graph unlearning, including its application to domains such as social networks, financial transaction systems, and healthcare networks. By delving into various algorithms, including local and global unlearning methods, we analyze the trade-offs between accuracy, privacy, and computational efficiency. The paper further investigates the security risks associated with graph unlearning, including adversarial and inference attacks, and proposes mitigation strategies to safeguard unlearning processes. We also examine the unique challenges posed by federated learning systems, where unlearning requires coordination across decentralized clients. Finally, the paper evaluates graph unlearning techniques using performance metrics such as time complexity, accuracy, and privacy guarantees, supported by real-world examples from diverse applications. The findings emphasize the importance of developing robust, scalable, and secure unlearning mechanisms to ensure data privacy and compliance with regulations. Evaluation and metrics for Graph Unlearning will also be discussed in this paper.

Afghanistan is located on the Eurasian tectonic plate's edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush-Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. The reoccurrence and severity of earthquakes have been increasing since 1900. This study maps Afghanistan's tectonic structures, identifying the most active geological faults and regions with heightened seismicity. The increasing trend in recurrence and intensity of the earthquakes are drawn. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property.

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-minute interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNN), across different data splits and feature sets. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features has also been found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.

Education for sustainable development seeks transformative learning in order to empower learners to change the ways they understand the world. In order to achieve its goals, teaching materials are needed, that reflect its principles and goals in their content and pedagogy they promote. The study of the epistemological background on which the teaching material is structured is a research proposal, which is addressed to researchers, authors and practitioners for sustainable development, pointing out that the structuring and development of the materials should be based on a theoretical framework that effectively link the teaching activity with all 17 Sustainable Development Goals with a focus on Quality Education. This scoping review attempts to map the existing literature that assesses the effectiveness of formal and non-formal educational materials that seek to promote education for sustainability. In addition, research is presented that evaluates the effectiveness of teaching materials from the perspective of sociocultural learning theory, considering that education for sustainable development (an ideal to be achieved) is in a dynamic relationship with constructivism (a learning theory) on the basis of knowledge construction, learning and sustainability's sociocultural aspects. It was found that the international literature investigating the contradictions presented by educational materials in relation to the objectives they set and achieve and the way in which the activities they propose are implemented in practice, is limited.

Objective: To evaluate the value of otolith‑associated protein otoconin‑90 (OC90) and otolin-1 in the pathogenesis research and clinical treatment of benign paroxysmal positional vertigo (BPPV). Material and Method: The study included 50 patients with BPPV admitted to neurology and otorhinolaryngology departments and 30 healthy subjects with no history of dizziness as control group. Results: BPPV and controls were similar in terms of gender and age. Otolin-1 level was significantly greater in the BPPV group than controls (710.44 vs 280.45 ; p<0.001). No statistical significance was found although OC90 were higher in the BPPV group than controls. There was a strong positive correlation between otolin-1 and OC90, a moderate negative correlation between otolin-1 and vitamin D, and a strong negative correlation between OC90 and vitamin D in BPPV patient group. Otolin-1 had high specificity and AUC values for BPPV (AUC: 0.933; 95% CI: 0.881-0.986, 79.2% sensitivity, 100% specificity with a cutoff greater than 525). Conclusion: High serum levels of otolin-1 were associated with an increased risk of BPPV, but not CO90. Serum levels of otolin-1 can potentially be used as a biomarker for acute onset of inner ear disorders due to its significant increase in patients with BPPV. Vitamin D has high specificity and sensitivity in the patients with BPPV patients. It also provides evidence that BPPV patients with vitamin D deficiency may improve their complaints with replacement therapy. More large-scale prospective studies is required to confirm these association and clarify the exact mechanism.

The use of digital twin and shadow concepts for industrial material processes have promoted new notions to bridge the gap between physical and cyber manufacturing processes. Hence, many multi-disciplinary areas like advanced sensor technologies, material science, data analytics, and machine learning algorithms are employed to create such hybrid systems. On the other hand, new additive manufacturing (AM) processes for metals and polymers based on novel emerging technologies have already shown a promising horizon for manufacturing of sophisticated parts with complex geometries. These processes are going through a major transformation with the emergence of digital technology, hybrid physical-data driven modelling and fast reduced models. This paper presents a fresh look at the hybrid physical-data driven and reduced order modelling (ROM) techniques for digitalization of AM processes within a digital-twin concept. The main contribution is to show the benefits of ROM and machine learning (ML) technology for process data handling, optimization\controlling and their integration into the real-time assessment of AM processes. Hence, a novel combination of efficient data solver technology and architecturally designed neural network (NN) module has been developed for the transient manufacturing processes with high heating\cooling rates. Furthermore, a real-world case study has been presented where the combination of hybrid modelling along with ROM and ML schemes are used for an industrial wire arc AM (WAAM) process.

In Eucalyptus plantations, coppice rotations often yield less than initial rotations. The TECHS project (Tolerance of Eucalyptus Clones to Hydric, Thermal and Biotic Stresses) studied short rotation coppicing across a 3,000 km gradient. The main objective of this work was to compare the survival, sprouting and initial growth of Eucalyptus clones managed, and to examine factors that might influence the productivity of the coppice rotation: climate, genotypes and stocking. Eight of the TECHS sites spread from latitudes 6o S to 30o S were included in the coppice study, with 17 genotypes at each site. The initial rotation had been planted at a 3 x 3 m spacing, and also in a spacing trial at densities from 450 to 14,000 trees ha-1. Six months after harvesting the initial Eucalyptus rotation, average survival was 88%, with tropical clones showing over twice the sprouting biomass (6.7 vs 2.9 Mg ha-1) and four times the woody biomass compared to subtropical clones (4.7 vs 1.1 Mg ha-1). Greater initial water deficits had stronger sprouting and growth. Clones with higher belowground carbon allocation in the initial rotation performed better in coppicing, and precipitation became more influential after 12 months. Drought and spacing trial significantly affected growth.

This paper presents a simple and streamlined compensation technique for improving the quality of Synthetic Aperture Radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications.

Achieving Urban Flood Resilience (UFR) is essential for modern societies, requiring the implementation of effective practices in different countries to mitigate hydrological events. Green Water Systems (GWS) emerge as a promising alternative to achieve UFR, but they are still poorly explored and present varied definitions. This article aims to define GWS within the framework of sustainable practices and propose a regulation that promotes UFR. Through a systematic review of existing definitions and an analy-sis of international regulations on Sustainable Urban Drainage Systems (SuDS), the study reveals diverse perceptions and applications of GWS and their role in Blue-Green Infrastructure (BGI). The research proposes a standardized definition of GWS and their role in Blue – Green Infrastructure (BGI). The research proposes a standardized definition of GWS and the implementation of SuDS in Peru, addressing the current knowledge gap and contributing to the development of sustainable urban infrastructure.

The recent growth of wearable medical device technology in the form of fitness trackers, smart watches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully “connected healthcare system.” Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified, but even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors, but most are unaware that data from these systems may be transmitted, stored, and even sold without the user’s specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber-villains. It is also important that efforts be taken to make PGHD more secure, since medical data may have great value to telehealth applications and AI-physician assistants. Clinicians should take an active role in informing patients about both risks and benefits of wearables and similar devices.

Canine distemper virus (CDV) poses a substantial threat to diverse carnivores, leading to sys-temic and often fatal diseases. Accurate and prompt diagnosis is paramount for effective man-agement and curbing further transmission. This study evaluates the diagnostic efficacy of drop-let digital PCR (ddPCR) in comparison to conventional reverse transcription (RT-PCR) and quantitative reverse transcription real-time PCR (RT-qPCR). Seventy-six clinical samples were collected from dogs with CDV symptoms diagnosed by specialized veterinarians, and sixteen samples from asymptomatic individuals. Conventional PCR, quantitative real-time PCR, and ddPCR were deployed, and their diagnostic capabilities were meticulously assessed. DdPCR ex-hibited heightened analytical sensitivity, reaching a detection limit of 3 copies/μL, whereas RT-qPCR had a detection limit of 86 copies/μL. The comparative analysis between clinical diagnosis and molecular techniques, including RT-PCR and RT-qPCR, demonstrated low concordance, with Kappa coefficients of 0.268 and 0.324, respectively. In contrast, ddPCR, showed a moderate concordance, with a Kappa coefficient of 0.477. Sensitivity was 42.39% for RT-PCR, 57.89% for RT-qPCR and 72.37% for ddPCR, with 100% of specificity for all methods. This study under-scores ddPCR's superior sensitivity and concordance with clinical CDV diagnosis, even at low viral concentrations, suggesting it as a promising alternative for CDV diagnosis.

Melanoma, a deadly form of skin cancer, has seen improved survival rates due to advances in diagnosis and treatment, yet the need for further improvement remains critical. Tumor-associated antigens, such as PRAME (Preferentially Expressed Antigen in Melanoma), offer promising avenues for enhanced diagnostic precision, prognostic assessment, and targeted immunotherapy. PRAME, a cancer-testis antigen, is selectively expressed in various cancers, including melanoma, and plays a key role in promoting tumorigenesis through inhibition of retinoic acid signaling, epithelial-to-mesenchymal transition, and immune evasion. This review explores the diagnostic utility of PRAME in distinguishing melanoma from benign nevi, its prognostic value in aggressive melanoma subtypes, and its potential as a therapeutic target in cancer vaccines and adoptive T-cell therapies. While PRAME-targeted therapies face challenges such as tumor heterogeneity and immune suppression, ongoing research aims to overcome these barriers, offering hope for more effective melanoma treatments.

This study aims to explore the phenomenon of hadith distortion in hadith literature and analyze its impact on contemporary hadith studies. The intended hadith distortion here refers to the alteration or falsification of hadith narratives that can threaten the integrity and authenticity of sacred texts as sources of Islamic teaching. This article identifies the factors contributing to hadith distortion and its serious consequences in contemporary hadith studies.

DNA - the repository of genetic information harbours sequences rich in Guanine that adopt non-B form four-stranded DNA secondary structures termed as G-quadruplexes (G4s). Such non-canonical structures tend to impede the progression of routine replisome and are implicated to have crucial roles in replication, transcription and epigenetic regulation while also ensuring genomic stability. It is now fully established that G4s are enzymatically unfolded by helicases, thus, preventing transient yet apparent impediments to the vital replisome progression. This unfolding of G-quadruplex ensures genetic and epigenetic stability. Given the importance of G4 biology for multiple human parasites such as, Mycobacterium tuberculosis, research efforts probing parasitic G4 biology is gaining momentum with the hope to enable therapeutic intervention. We herein shed light on G4s, conditions that lead to their formation and the mechanisms employed by cells to cause their unfolding. Towards the end, potential drug discovery avenues that arise from targeting parasitic helicases and G4 biology are summarized.

Observation of the distribution of the velocity of galactic rotation curves differed from their expected centripetal form and lead to the notion of Dark Matter or modifications to Newtonian and General Relativity, such as MOND, TeVeS and the like and even Quantised Inertia. We aim to show that General Relativity with Dark Energy/the Cosmological Constant is all that is needed, with the proviso that the Cosmological Constant can increase in the presence of a gravitational field and become gravitating to account for the hypothesis of Dark Matter Haloes.

p97(also known as Cdc48 or VCP) is a conserved AAA+ ATPase that plays a key role in diverse cellular pathways and processes. p97 segregates damaged or misfolded substrates from cellular organelles or membranes in corporation with its cofactors. These cofactors regulate p97’s function by its directing to different cellular pathways and have diverse effect on p97´s conformation. Recently, several high-resolution structures of substrate-engaged p97 in complex with cofactors have been resolved, providing structural insights into the molecular mechanism of substrate processing. In this review, we present a comprehensive overview of how p97 in complex with substrate recruiting and substrate processing cofactors recognize, extract, translocate, unfold and release ubiquitinated substrates.

Background. Depression, a widespread mental health issue, is often marked by impaired cognitive control, particularly in managing proactive and reactive processes. The Dual Mechanisms of Control (DMC) framework differentiates between these two modes of cognitive control: proactive control involves sustained goal maintenance, while reactive control is more stimulus-driven and transient. Stress, known to exacerbate cognitive dysfunction in depression, may influence the balance between these control processes, though the specific effects remain poorly understood. This study aimed to investigate how acute stress influences proactive and reactive control in individuals with depressive symptoms. Methods. A total of 88 participants were divided into high-stress and control conditions and further categorized based on their depression levels, measured using the Beck Depression Inventory-II (BDI-II). Cognitive control was assessed using the AX-Continuous Performance Task (AX-CPT), both before and after exposure to a stress-inducing anagram task, which was designed to differentiate between high-stress and low-stress conditions. Results. Participants exposed to the high-stress condition reported significantly greater stress and fatigue levels compared to the control group, validating the stress manipulation. Although the balance between reactive and proactive control, as measured by the Proactive Behavioral Index (PBI), did not show significant changes, depressive individuals in the high-stress condition exhibited a significant decline in their ability to retain contextual information, as indicated by a reduction in the d’-context index. This suggests that depressive individuals are particularly vulnerable to stress-induced impairments in proactive control. Discussion. These findings highlight the selective impact of stress on proactive cognitive control in individuals with depressive symptoms, shedding light on a potential cognitive vulnerability in depression. While the balance between reactive and proactive control remained stable, the impaired retention of contextual information post-stress points to a specific deficit in proactive control. This could have implications for targeted cognitive interventions, such as cognitive control training, aimed at enhancing resilience against stress in depressive populations. Future research should explore the long-term effects of stress on cognitive control, particularly in clinically diagnosed individuals.

People with aphantasia exhibit the inability to voluntarily generate or form mental imagery in their minds. Since the term “aphantasia” was proposed by Zeman et al. in 2015 to describe this, it has gained increasing attention from psychiatrists, neuroscientists, and clinicians. Previous studies have mainly focused on definition, prevalence, and measurement, its impacts on individuals’ cognitive and emotional processing, and theoretical frameworks synthesizing existing findings, which have contributed greatly to our understanding of aphantasia. However, there are still some debates regarding the conclusions derived from existing research and the theories that were constructed from various sources of evidence. Building upon existing endeavors, the current systematic review emphasizes that future research is much needed to refine definition and diagnosis of aphantasia, strengthen empirical investigations at behavioral and neural levels, and more importantly, develop or update theories. These multiple lines of efforts could lead to a deeper understanding of aphantasia and further guide researchers in future research directions.

Various measures have been attempted to prevent infectious diseases in calves, such as envi-ronmental improvement and vaccine administration. Probiotics are commonly used to im-prove the body condition of newborn calves and prevent disease. In our previous research, Lactiplantibacillus plantarum RGU-LP1 (LP1) inhibited the expression of inflammatory cyto-kines in PBMC from cows fed LP1. In this study, we evaluated the effect of LP1 on calf growth and disease control. Methods: Twenty-six one-week-old Holstein bull calves were divided into two groups (13 each), LP1 group (LP1 treated) and CN group (no LP1 fed), and tested as follows. The LP1 group was fed lyophilised LP1 (10⁹ CFU/head/day) in milk replacer for 40 days. The CN group was fed the same diet only. Calves were followed for 63 days. During this period, the weight and treatment times of the calves were recorded. Faeces and blood were collected from each calf during this period. Faeces were examined for gut microbiota, and blood for immune assay and cytokine gene expression. The LP1-treated group showed a de-crease in disease incidence and an increase in body mass compared to controls. Results: The average treatment cost during the observation period was significantly reduced compared to the CN group. The expression of TGFβ and IL10, inhibitory cytokines of inflammation, was sig-nificantly increased. The simultaneous expression of this set of inhibitory molecules resulted in low serum IL1β levels during the growth period. Conclusions: The Th1-type cytokine IFNγ was also significantly increased in LP1-treated calves. By reducing the number of disease treatment times and increasing body weight, LP1 is effective in preventing infectious diseases in calves. In addition, the increase in IFNγ by LP1 indicates improved Th1-type immunity in calves. These results show that LP1 has effects on the regulated inflammatory response and growth of calves.

Introduction: Pharmaceutical tablets are solid forms administrated orally and they constitute 80% of the market because of its best stability and ease of large-scale production, adherence and treatment. The Brazilian Legislation establishes that for a drug to be registered as a similar or generic drug is necessary to verify the pharmaceutical equivalence in relation to the reference drug, indicated by ANVISA, through quality control tests, ensuring that the drug is approved with satisfactory quality. Objective: to evaluate the reference, generic and similar glibenclamide tablets through quality control tests, comparing the results with each other and relate clinical impacts on the patient in the face of changes in the quality control of medicines. Methodology: tests of average weight, friability and disintegration were done, according to the Brazilian Pharmacopoeia VI Edition, in uncoated tablets of Glibenclamide 5mg Reference (Daonil), Generic (Glibenclamide) and Similar (Glionil), and the results obtained were compared with the that advocates the scientific literature. Results: Data referring to the average weight: Reference 0.1591g, Generic 0.1002g and Similar 0.0996g; For friability: Reference value 0.54%, Generic value 0.49% and Similar value 0.28%. The disintegration tests showed the following times: Reference 35 seconds, Generic 29 seconds and Similar 33 seconds. Conclusion: the tests to which the glibenclamide tablets were submitted showed results within the parameters established by the Brazilian Pharmacopoeia.

Background: Old age and illnesses can limit physical activity (PA). Objectives: To assess the level of PA and its limitations. Design: Cross-sectional study, January and June 2024. Setting: Diabetes Center. Subjects: Patients (N=207) >64 years with diabetes (DM). Methods: Surveying using: IPAQ, Accompanying Survey (AS). The significance was assessed by: Student’s t-test, Mann-Whitney U test, Pearson’s test. The relationships between the IPAQ and the AS results were analyzed based on a meta-analysis model for variable effects, OR with a 95% CI. ROC curve was used to determine the threshold values for age, BMI. Correlations between selected key variables were evaluated using the PCA. Results: The median age: 72 years (65–87), BMI: 28.70 kg/m2 (18.61–49.69). The median PA level: 1,837 MET-min/week (0–9,891). The individuals who obtained insufficient (n=28), sufficient (n=75), and high levels of PA (n=53) were 17.95%, 48.08%, and 33.97%, respectively. Diseases were the main factor limiting PA (53,54%) in patient’s opinion, followed by the lack of a companion or motivation. The meta-analysis found no diseases linked to insufficient PA, but those with BMI > 33.3 kg/m² and over 75 years old more often had insufficient PA. PCA revealed several characteristics that predispose individuals to insufficient PA. Conclusions: People over 75 years of age tend to avoid PA more than younger seniors, leading to its insufficient level, similarly like higher BMI. The individual with DM who has an insufficient level of PA is typically a single, woman, over 75, BMI>33.

Soil stabilization using Portland cement is a widely adopted technique. Previous research has demonstrated that molasses, which contains sugars, enhances the reaction between cement and aggregates. This study investigates the impact of adding molasses to soil stabilized with Ordinary Portland Cement (OPC) on geotechnical properties. Expansive clay soil samples from Taru Jabba, District Nowshera, Pakistan, were treated with various combinations of molasses and cement. The concentrations of each stabilizer were varied at 0%, 4%, 8%, and 12% by dry weight of the soil. Additionally, the soil was treated with constant molasses contents of 4%, 8%, and 12%, while varying the cement content at 4%, 8%, and 12% by dry weight. Geotechnical tests, including Proctor compaction, Atterberg limits, Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and swelling potential, were conducted to assess the effects of the stabilizers. The results indicated that the addition of molasses improved soil strength, mitigated shrinkage cracks, and reduced brittleness. Specifically, the CBR value increased from 3.2% in the native soil to 12.3% with 12% molasses and 12% cement. The Plasticity Index (PI) decreased from 14.23% to 8.12%, and the CBR swell value reduced from 9.66% to 3.82%. Furthermore, the UCS of the stabilized soil increased by 64.7% compared to the untreated soil after a 7-day curing period.

With the increased global attention to environmental protection, social responsibility and corporate governance (ESG) standards, integrating ESG into supply chain management (SCM) is becoming a strategic requirement for companies to achieve competitiveness and sustainable development. This study reviews the impact factors of ESG practices on supply chain competitiveness, and empirically examines the mechanism of ESG performance on supply chains through a questionnaire survey with a sample of Chinese SMEs. It was found that normative pressure, coercive pressure and imitative pressure in SMEs have a significant positive effect on cognition and attitude and perceived behavioral control; good corporate cognition and attitude, perceived behavioral control and corporate pressure also enhance the level of ESG practices. Further analysis shows that cognition and attitude and perceived behavioral control play a mediating role in the impact of ESG practices, and ESG performance promotes sustainable development of SMEs. The article puts forward the following suggestions: (1) close supply chain partnerships to enhance green innovation; (2) the government should give more resource support to ESG-invested enterprises and support the development of advantageous enterprises; (3) improve the corporate disclosure mechanism and strengthen the supervision; and (4) strengthen the training of professionals in supply chain ESG management.

This study analyzes groups about game performance elements and sport class composition in each country by utilizing official records provided by major international wheelchair basketball games. To achieve the objective of this study, major game factors were explored by analyzing descriptive statistics in each year regarding the latest trend of sport class composition and playing time of each sport class and conducting the difference test between the groups. To analyze performance details of international wheelchair basketball games, this study selected official records of 209 games of 24 teams that participated in major international wheelchair basketball games. A test for difference also was conducted to examine the sport class composition, playing time, and game details of each group. First, scoring factors directly affecting the game results were compared between the groups. The result of this difference test shows that the success rate of 2-point and 3-point shots was significantly different except for the free throw success rate. The numbers of assists, defense rebounds, and steals were significantly different. Second, the medal group was compared with the other group, the average class of the other group's participation in quarters was higher than that of the medal group and the difference was significant. Regarding the difference between groups in terms of game performance, the difference in playing time between the medal group and the other group was in the order of 2.5 points, 4.0 points, 3.0 points, and 1.5 points. Third, given the general trend in 2012 to 2022 international events, the sport class composition and para-athlete played minutes in quarterly participation changes.

In this paper, we propose "Synergized Efficiency Optimization for Large Language Models" (SEO-LLM), a groundbreaking approach that integrates advanced data utilization and model compression techniques to significantly enhance the performance, efficiency, and scalability of large language models (LLMs). Our method synergistically combines Adaptive Data Augmentation (ADA), Transfer- Active Learning (TAL), Adaptive Iterative Pruning (AIP), and Synergistic Quantization and Distillation (SQD). These components work together to reduce the training data requirement by 30%, compress model size by 67.6%, and improve inference speed by up to 50%, while preserving or even enhancing model accuracy across various NLP tasks. ADA dynamically adjusts augmentation strategies to optimize model generalization, while TAL leverages pre-trained models to focus learning on the most informative data samples. AIP intelligently prunes less significant weights, and SQD harmonizes quantization with knowledge distillation to achieve high compression rates without significant performance loss. The synergy between these techniques makes SEO-LLM a robust solution for deploying LLMs in resource-constrained environments, maintaining state-of-the-art performance with a fraction of the computational and data resources.

Advances in diagnostic procedures have led to an increasing rate of diagnosis of autoimmune encephalitis or paraneoplastic neurological syndrome (AE/PNS) among patients with progressive supranuclear palsy (PSP)-like manifestations. The antibodies involved in these conditions include anti-IgLON5, -Ma2, and -Ri antibodies, each of which has a characteristic clinical presentation. The steps in the diagnosis of AE/PNS in patients with PSP-like manifestations include (i) suspicion of AE/PNS based on clinical presentations atypical of PSP and (ii) antibody detection measures. Methods used to identify antibodies include a combination of tissue-based assays and confirmatory tests. The primary confirmatory tests include cell-based assays and immunoblotting (line or western blot). Treatments can be divided into immunotherapy and tumor therapies, the former of which includes acute and maintenance therapies comprising corticosteroids, intravenous immunoglobulins, plasmapheresis, rituximab, and cyclophosphamide. One of the major challenges of diagnosis is that existing reports on PSP-like patients with AE/PNS include only case reports, with the majority discussing antibodies other than anti-IgLON5 antibody. As such, more patients need to be evaluated to establish the relationship between antibodies and PSP-like manifestations.

Privacy protection is a fundamental guarantee for secure data flows and serve as a basic requirement for data security. A reasonable privacy protection system acts as a catalyst for unlocking the financial value of data. The current legislative framework for privacy protection in data flows in China, adhering to the principle of proportionality, establishes the “informed-consent” rule for data collection and processing, data classification and grading management measures, and remedies for data leakage and other risks. In addition, in judicial practice, typical disputes regarding personal information protection and privacy rights have promoted to clarify the scope of collecting user personal information and biometric data. Despite ongoing improvements needed in legislative, judicial and technical approaches to data privacy protection, China’s commitment and practice in privacy protection of data flows are noteworthy. The existing legislation, law enforcement and technical practices are playing an increasingly vital role in realizing the financial value of data and are essential for international cooperation on data privacy protection. Furthermore, it is crucial to actively explore cooperation mechanism for cross-border data flows under the principle of data sovereignty, participate in developing international rules for cross-border data flows, and formulate different management norms for cross-border circulation of data in different industries.

The integration of genetic technologies into neurological diagnostics has significantly enhanced our ability to understand and manage neurological disorders. This article discusses the functional analysis of key genetic tests, including microarray analysis, variant sequencing, exome sequencing, RNA sequencing, and whole genome sequencing. Each of these technologies offers unique advantages and challenges, providing a comprehensive toolkit for diagnosing and understanding the genetic basis of neurological conditions. The functional insights gained from these tests not only detect genetic abnormalities but also elucidate their functional consequences, aiding in the diagnosis, prognosis, and management of neurological disorders. As these technologies continue to evolve, they are expected to play an increasingly important role in the field of neurological diagnostics, paving the way for personalized medicine and improved patient outcomes.

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and free of charge Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) Machine Learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated classification. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living Above-Ground Biomass (AGB) was estimated with an accuracy of -1.0% (± 5.9%). In conclusion, the proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Epigenetics, the study of heritable changes in gene expression that do not involve alterations to the DNA sequence, is revolutionizing our understanding of psychiatric disorders. This article provides a comprehensive overview of the role of epigenetics in psychiatry, discussing its implications for genomics, evolution, inheritance patterns, and the development of biomarkers. Epigenetic mechanisms, including DNA methylation, histone modifications, and non-coding RNAs, are explored in the context of psychiatric conditions such as depression, schizophrenia, and post-traumatic stress disorder (PTSD). The discussion highlights how epigenetic modifications can influence brain dynamics, as measured by electroencephalography (EEG), and how artificial intelligence (AI) can accelerate the development of treatment options. The potential for epigenetic biomarkers to improve diagnosis, prognosis, and treatment is emphasized, along with the role of behavioral interventions in inducing beneficial epigenetic modifications. Future directions in epigenetic research, including the development of epigenetic therapies and the elucidation of molecular mechanisms underlying epigenetic inheritance, are also discussed. This article underscores the transformative potential of epigenetics in advancing our understanding of mental health and disease, paving the way for novel therapeutic interventions.

In the increasingly competitive SVOD market, platforms face high churn rates and substantial revenue losses from SVOD content piracy, all of which limit their ability to invest in acquir-ing/creating content compelling enough to win and retain subscribers. Based on social exchange theory, this study argues that platforms can improve relationships with SVOD content users by offering tiered discounts in exchange for advertising/loyalty and by promoting anti-piracy messages with a prosocial (threatening) approach that emphasizes harm to filmmakers (punishment for pi-rates). We hypothesize that these incentives enhance subscription intention when the incentive specifications (advertising levels, loyalty levels, message approach, and message credibility) match the public’s heterogeneous dispositions (advertising attitude, loyalty attitude, justice sensitivity, and fear of punishment). In a survey on the intention to subscribe to a hypothetical new platform, we confirmed the hypothesized interactions for advertising-based discounts, loyalty-based discounts, and prosocial messages, but did not find support for threatening messages. Further exploration showed that the evaluation of platform content was much more influential than any other incentive and that tiered loyalty discounts had a remarkable capacity to enhance subscription intention. This study’s findings may help shape incentives that are more satisfying to users and ultimately more profitable for platforms.

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.

This comprehensive analysis aims to offer valuable insights into the safety profiles of these glucocorticoids, facilitating the identification of compounds with potentially reduced adverse effects. This study investigates the toxicity profiles of various glucocorticoids, utilizing predictions from the pKCSM Server. Among the glucocorticoids examined, Dexamethasone, Triamcinolone, Hydrocortisone hemisuccinate, Paramethasone, Fluprednisolone, and Flumethasone emerge as potentially less toxic, displaying favorable scores across diverse parameters such as Max. Tolerated Dose (human, MRT), Oral Rat Acute Toxicity (LD50), and Oral Rat Chronic Toxicity (LOAEL). Notably, Hydrocortisone hemisuccinate and Triamcinolone stand out for their high tolerability capacity based on the Max. Tolerated Dose (human, MRT), suggesting enhanced safety profiles. These findings contribute insights into selecting glucocorticoids with optimal safety and tolerability for potential therapeutic applications.Further research and clinical investigations are warranted to validate and refine these findings, ensuring the responsible and effective use of glucocorticoids in medical practice.

This paper critically reviews the convergence between Business Ecosystem Theory and Social Network Theory in sustainability studies. While both frameworks view organizations as part of larger, interconnected systems, they can be differentiated by six key dimensions: unit of analysis, focus, decomposability, types of relationships, market segment, and worldview. To better reflect real-world phenomena, this paper argues for a new stream of theoretical convergence that is practical, reliable, generalizable, and reproducible. Specifically, it proposes shifting from interoganizational networks to interfunctional networks, which reduces complexity, enhances stability over time, and provides a clearer theoretical distinction. This is illustrated through a case study of Tesla Inc.’s ecosystem, built from data presented in the company’s 10K form, which also serves as an example of the emergence of a new strategic construct named the Business Ecosystem Footprint. This construct could assist managers in understanding where their organization stands within the network of functions, guiding them in making informed decisions about resource allocation and diversification, specifically aimed at supporting financial goals as well as sustainability and decarbonization objectives. The article concludes by suggesting potential research agendas, such as automating ecosystem mapping, exploring the constraints of the new construct, and testing new hypotheses related to firm performance.

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% ac-curacy in verbal abuse detection. Additionally, a disaster psychological recovery support module was included, which demonstrated a 30% improvement in users' stress reduction rates in simulated disaster scenarios. The results of this study suggest that such digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues and provide effec-tive support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies.

Late-onset Alzheimer’s disease (LOAD) is a dementia category disease that starts after the age of 65. Progressive problems in thinking, behaviour, learning etc. are its characteristics. Due to its progressive nature, early diagnosis is crucial. A complex pathway mechanism with different risk factors is involved in LOAD progression. A comprehensive study is done on factors causing LOAD, biomarkers used for diagnosis in clinical and preclinical stage of the disease, ML (Machine Learning) techniques to identify disease stage. We reviewed research papers on clinical and preclinical diagnosis of LOAD using ML techniques from biomarker data: MRI, PET image, MMSE, Genome / Gene expression, DNA / RNA binding sites with protein, Epigenetic expression /DNA, methylation, Speech and text. We have listed improvement scope after finding the gaps and scope of using Generative AI and SMOTE (Synthetic Minority Oversampling Technique) for data augmentation. At the end, we have elaborated current use of DL (Deep Learning) and CNN (‘Convolution Neural Network’) in Alzheimer’s disease (AD) diagnosis.

In an era where digital technologies are integral to daily life, developing higher-order thinking skills with appropriate information and communication technology (ICT) support is crucial. The purpose of this study was to examine synergies of systems thinking and technology-enhanced learning from the perspective of flow theory. For that purpose, we surveyed more than 65 pre-service preschool teachers engaged in a design, technology, and engineering (DTE) course at the University of Ljubljana. Mapping of systems thinking revealed that pre-service preschool teachers needed support with regard to feedback and understanding the interrelationship dimen-sion of systems thinking. Predictive and mediation analyses yielded some interesting results. Those who participated in the ICT-enhanced DTE course showed higher cognitive and social engage-ment, which is correlated with better systems thinking, while their aesthetic engagement was lower. Experiencing a flow state during ICT activities positively influenced systems thinking, par-ticularly in terms of clear goals and autotelic experiences. Flow theory seems to be a solid and ap-propriate framework to use for studying synergies in technology-enhanced systems thinking for sustainable education. These findings underscore the importance of integrating systems thinking into curricula to enhance learning outcomes and prepare students for future challenges toward the achievement of Sustainable Development Goals.

This study investigates the use of flours derived from the leaves of Pereskia aculeata and Pereskia grandifolia, commonly known as ora-pro-nóbis, in pasta products. The motivation stems from the search for nutritious and affordable dietary alternatives that promote food security. The objective is to produce flours from the leaves of these species through convection drying and to assess their physicochemical quality and potential application in spaghetti-type pasta. The leaves were dehydrated and processed into flour, which was then incorporated into pasta formulations in varying concentrations from 1% to 7%. The resulting samples underwent physicochemical, antioxidant, toxicity, and mineral composition analyses. Significant differences were observed between the flours of P. aculeata and P. grandifolia in terms of protein, lipid, carbohydrate, and bioactive compound content. P. grandifolia was noted for its higher protein and mineral content, while P. aculeata exhibited superior antioxidant activity. The pasta enriched with these flours demonstrated improvements in nutrient content and changes in colorimetric properties. The study concludes that ora-pro-nóbis flours possess high nutritional value and are viable for application in pasta products, contributing to a healthier and more diversified diet without compromising the sensory characteristics of the products.

Porcine metabolomics research has been extensively conducted, primarily focusing on conventional cohorts or animal models. However, the composition and functions of fecal metabolites in pigs at various ages, particularly in the elderly, remain poorly understood. In the present study, an untargeted metabolomics approach was employed to analyze the fecal metabolomes of pigs at three distinct age stages, with the aim of elucidating age-associated changes in metabolite composition and functionality under standardized rearing conditions. Through the untargeted metabolomic analysis of fecal samples from young (one year), middle-aged (four years), and elderly (eight years) pig cohorts, a variety of age-related metabolites were identified. For example, L-methionine sulfoxide was observed to increase with age, while cytidine-5-monophosphate exhibited a concomitant gradual decrease during the aging process. These metabolites were found to undergo alterations across multiple biological pathways, including energy metabolism, pyrimidine metabolism, lipid metabolism, and amino acid metabolism. Furthermore, the age-dependent metabolic changes were associated with known senescence biomarkers, offering new insights into the mechanistic understanding of aging in pigs.

In this paper, we study the asymptotic properties of slowly varying functions of one real variable in the sense of Karamata. We establish analogs of fundamental theorems on uniform convergence and integral representation for slowly varying functions with remainder depending on the types of remainder. We also prove several important theorems on the asymptotic representation of integrals of Karamata functions. Under certain conditions, we observe a “narrowing” of classes of slowly varying functions concerning the types of remainder. At the end of the paper, we discuss the possibilities of application of slowly varying functions in the theory of stochastic branching systems. In particular, under the condition of the finiteness of the moment of the type Exlnx for the particle transformation intensity, it is established that the property of slow variation with remainder is implicitly present in the asymptotic structure of a non-critical Markov branching random system.

Pressmud, a by-product of sugarcane processing, is typically disposed of through incineration or landfilling, though it has considerable potential in organic agriculture. This study explored the com-posting of pressmud through bioaugmentation using specific bacterial strains. Two experimental setups were created: E2 with a cellulolytic and phosphorus-solubilizing strain, Bacillus amyloliquefa-ciens-ASK11, and E3 with a nitrogen-fixing strain, Bacillus megaterium-ASNF3. A control setup (E5) was also maintained without bacterial augmentation. Nitrogen (N) and phosphorus (P) contents were measured using the Kjeldahl and Olsen methods, respectively, while Fourier-Transform Infra-red (FTIR) spectroscopy confirmed compost maturity. Results indicated that the Bacillus-enhanced composts in E2 and E3 significantly increased the nitrogen(49.27% and 91.23%) and phosphorus contents(129% and 83.85%), respectively, compared to the control with only 13.62% N and 65% P contents after 60 days of the pressmud compost maturity.. Additionally, organic matter decomposi-tion improved by 49-50% in the bioaugmented setups after 60 days. FTIR analysis revealed organic phosphate peaks and P-O-C stretching bands at 1025 cm-1 in the E2 compost, while a nitrogen vi-bration band at 3849 cm-1 in E3 indicated significantly higher nitrogen content compared to the control. The Bacillus-enriched pressmud compost not only accelerated the composting process but also enhanced nutrient levels, positioning it as a promising biofertilizer for rehabilitating barren lands.

Background: The lateral antebrachial cutaneous nerve (LACN) is the terminal sensory branch of musculocutaneous nerve and is rarely entrapped or injured. This study describes the electrodiagnostic (EDX) findings and etiologies of LACN neuropathy. Methods: This is a review of 49 patients with pain and/or paresthesia of the forearm who underwent EDX studies. The diagnosis of LACN neuropathy was based on clinical and sensory conduction abnormalities. Results: The most common etiology of LACN neuropathy was iatrogenic injury in 30 (61.2%) patients, primarily due to biceps tendon repair at the elbow (11 [36.7%]) and phlebotomy (5 [16.7%]. Fifteen (30.6%) patients sustained a non-iatrogenic injury at the proximal forearm/elbow, consisting of 6 (60%) laceration injuries and 5 (33.3%) stretch injuries. Four (8.2%) patients comprised the “other” etiology category, including 2 mass lesions causing LACN compression. Pain, paresthesia, and/or numbness in the LACN distribution were reported in 33 (67.3%), 27 (55.1%), and 23 (46.9%) patients, respectively. Hypoesthesia was detected in 45 (91.8%) patients, and dysesthesia in 7 (14.3%). The sensory nerve action potentials (SNAPs) of the LACN on the symptomatic side were absent in 44 (89.8%) patients. Of the 5 patients whose SNAPs of the LACN were detected, all had a decreased amplitude and 2 had increased sensory latency. Conclusion: The most common etiology for LACN neuropathy in this series was iatrogenic injury; repair of biceps tendon at the elbow was the most frequent provoking cause. Protection of the LACN during surgical procedures at the elbow and forearm is vital to prevent iatrogenic injury.

Acquisition traits are those that depend for their evolutionary (fitness) value on the acquisition of something that gradually accumulates during an organism’s life but is not transmitted genetically to descendants. This situation causes these traits including language, immunity, intelligence, and social status to represent a special need for evolvability and programmed aging. Programmed aging refers to the idea that mammals and some other multiparous sexually reproducing organisms evolved complex biological mechanisms that cause gradually increasing fitness deterioration and internally limit individual organism lifetimes. The rationale is that this process enhances the survival (non-extinction) of a species population. Evolvability theories propose that mammals have evolved design characteristics that aid their ability to evolve, that is, reduce the time required for a particular increment of evolutionary adaptation and/or increase the precision with which the adaptation can be performed. Because an increase in evolvability aids a population in escaping extinction, evolution selects the associated design characteristics. Both concepts conflict with traditional (Darwinian) theory regarding details of the nature of evolution. However, more recent genetics discoveries have exposed rich detail regarding the nature of the evolution process including the fact that biological inheritance involves the transmission of organism design information from parent to descendant in digital form. These discoveries have acted to support both programmed aging and evolvability theories.

Cats, including companion cats, inflict extensive harm on wildlife. Using a national survey of 2000 households in New Zealand, we investigated the influence of involvement (an indicator of motivation) on the willingness of cat owners to keep their cats indoors at night. We found that respondents’ intentions to protect wildlife, and the frequency with which respondents with cats kept them indoors at night, was influenced by their involvement with cat welfare and their involvement with protecting wildlife, in addition to their attitudes and subjective norms. We also found that keeping cats indoors at night could be characterised as involving approach-avoidance conflict. Our findings have implications for efforts to increase the adoption by cat owners of keeping cats inside at night regarding the attentiveness of cat owners to promotional activities. Our findings suggest that such activities will not be particularly effective in the absence of cat-friendly, inexpensive, practical, and easily maintained devices that enable cats to be kept inside. Importantly, when the adoption of keeping cats inside at night is appropriately characterised as approach-avoidance conflict, our results suggest that promotional activities seeking to persuade cat owners that pet cats cause much greater harm to wildlife than they might believe are most likely to have a limited and likely temporary effect and may even be counter-productive.

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex — the smallest quantized object in superconductors — can enable drastic miniaturization to the nanometer scale. In this work, we present numerical modeling of such cells using time-dependent Ginzburg-Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultra-fast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, ∼100 nm, and can enable ultrafast switching on the picosecond scale with ultra-low energy per operation, ∼10−19 J.

Tianjin is currently in the phase of urban stock renewal, yet its industrial heritage encounters significant challenges, including constrained spatial development, a misalignment between industrial progress and the reuse of industrial heritage, and an understated cultural representation. This study applies geospatial analysis to delineate the distribution characteristics of Tianjin's industrial heritage across time, space, and typology. Moreover, the geographic detector is employed to assess the impact of various factors on the development of Tianjin's industrial heritage, ultimately elucidating the correlations between existing challenges and objective patterns. The conclusion of this paper presents three strategic approaches for the renewal of industrial heritage, emphasizing historical culture, environmental space, and sustainable development. These strategies are designed to provide a solid research foundation for protecting and revitalizing Tianjin's industrial heritage resources.

Artificial General Intelligence (AGI) represents a pivotal advancement in AI with far- reaching implications across technological, ethical, and societal domains. This paper addresses the following: (1) an in-depth assessment of AGI’s transformative potential across different sectors and its multifaceted implications, including significant financial impacts like workforce disruption, income inequality, productivity gains, and potential systemic risks; (2) an examination of critical ethical considerations, including transparency and accountability, complex ethical dilemmas and societal impact; (3) a detailed analysis of privacy, legal and policy implications, particularly in intellectual property and liability, and (4) a proposed governance framework to ensure responsible AGI development and deployment. Additionally, the paper explores and addresses AGI's political implications, including national security and potential misuse. By analyzing and considering computer science, philosophy, economics, and policy perspectives, we offer a multidisciplinary view of AGI’s challenges and opportunities, advocating for proactive measures to align AGI development with human values and societal interests.

Hepatitis B Virus (HBV) infection carries a significant health burden worldwide. It is a leading cause of acute and chronic liver disease resulting in cirrhosis and hepatocellular carcinoma (HCC). This virus affects millions globally, and despite several medical advancements, research, and efforts to develop curative treatment, the persistence of HBV in infected individuals by cccDNA and integration pose formidable challenges. This review aims to provide a broad-based overview of HBV persistence, integration and the implications for chronic infection and tumorigenesis. By considering these points, we bring forth insights into the advancement of therapies that aim to disrupt the chronicity of HBV infection and improve clinical outcomes.

Background/Objectives: Determining the outcome of epilepsy is crucial for making proactive and timely treatment decisions, as well as for counseling patients. Recent research efforts have focused on using various imaging techniques and EEG for prognostication; however, there is insufficient evidence regarding the role of blood parameters. Our study aimed to investigate the additional prognostic value of routine blood parameters in predicting epilepsy outcomes. Methods: We analyzed data from 1,782 patients who underwent routine blood tests within 90 days of their first visit and had a minimum follow-up duration of three years. The etiological types were structural (35.1%), genetic (14.2%), immune (4.7%), infectious (2.9%), and unknown (42.6%). The outcome was defined as the presence of seizures in the last year. Results: Initially, a multivariate analysis was conducted based on clinical variables, MRI, and EEG data. This analysis revealed that sex, age of onset, epileptiform discharge, structural etiology, and the number of antiseizure medications were related to the outcome, with an area under curve (AUC) of 0.708. Among the blood parameters, fibrinogen, bilirubin, uric acid, and aPTT were significant, with AUCs of 0.602, 0.597, 0.455, and 0.549, respectively. Including these blood parameters in the analysis slightly improved the AUC to 0.713. Conclusions: Some blood parameters were found to be related to the final outcome, potentially paving the way to understanding the mechanisms of epileptogenesis and drug resistance.

Current treatments for medial meniscus lesions in association with CCL ruptures [1–5] have shown disappointing long-term outcomes [6]. Meniscal suturing may improve the outcome, but their clinical application remains limited [7–9]. This study aims to assess the practicability of a minimally invasive DNT to facilitate meniscal suturing in dogs. Ten stifles of eight client-owned dogs with arthroscopically confirmed CCL disease and medial meniscal tears in the abaxial third of the meniscus were included. The described suture technique was applied under joint distraction using the Titan joint distractor.[8] Surgical stabilization of all stifles was then accomplished through a X-Porous TTA procedure [10]. The DNT allowed for the precise placement of meniscal sutures. Minor intraoperative complications included reduced arthroscopic visibility (n=2) and suture breakage during its passage through the meniscus (n=3). No complications related to the meniscal sutures were noted throughout the six-month follow-up. The described DNT proved to be a viable and effective method for suturing lesions of the caudal horn of the medial meniscus, provided there is adequate visualization during the procedure. Appropriate stifle stabilization postoperatively is mandatory for protecting the suture and avoiding concomitant meniscal lesions due to joint instability.