Search Results (2,119)

The increasing global demand for oil and gas, together with the depletion of shallow reservoirs, has driven exploration toward deep and ultra-deep formations characterized by high-temperature and high-pressure (HTHP) conditions. In such environments, conventional drill pipes often experience thermal stress, corrosion, and mechanical degradation, which can reduce drilling efficiency and compromise operational reliability. Thermal insulated drilling pipes (TIDPs) have therefore emerged as an effective solution to minimize heat transfer between drilling fluids and the surrounding formation. This review summarizes recent advances in TIDP materials, structural design strategies, fabrication technologies, and critical performance. Relevant studies were collected from major scientific databases, including Web of Science and Google Scholar, with a focus on insulation materials, coating technologies, and thermal management approaches used in drilling systems. The analysis indicates that advanced insulation systems, including polymer-based coatings, silica aerogels, vacuum-insulated layers, and phase-change materials, can significantly enhance thermal management in drilling operations. These technologies can reduce heat loss by approximately 40–60% (i.e., 400–600 W·m⁻²) and maintain drilling-fluid temperature differentials of 10–18 °C under HTHP conditions. In addition, fabrication techniques such as plasma spraying, composite fabrication, and additive manufacturing enable the development of multifunctional insulation systems with improved thermal, mechanical, and corrosion-resistant properties. Hybrid TIDP systems integrating nanocomposites and advanced polymers show strong potential for improving drilling safety and efficiency. However, challenges related to durability, scalability, and cost remain, highlighting the need for further research on multilayer insulation architectures and sustainable materials. Full article

Deep learning models represent one of the most advanced and effective approaches in predictive modeling. Their hierarchical architectures enable the extraction of complex, non-linear feature relationships and the identification of latent patterns within data, making them highly suitable for tasks involving high-dimensional or unstructured inputs. However, these models are computationally demanding, requiring significant processing resources and time. Furthermore, their predictive performance is largely contingent upon the availability of large-scale datasets. In this study, a Deep Green Framework is employed for the prediction of two computer vision tasks. CIFAR-10 and CIFAR-00 have been taken for image classification. Fifteen convolutional neural network (CNN) variants categorized into light-weight and heavy-weight are trained for the prediction of these two datasets. Based on energy footprint, time, memory usage, Top-1 accuracy, Top-3 accuracy, model size, and model parameters. The study highlights that MobileNetV3-Small produces the best outcomes when compared to other trained models having low task latency and higher efficiency, making it highly suitable for edge environments where resources are scarce. Full article

As a crucial component of multimodal sensing in modern AI agents, remote sensing images have attracted significant attention, for which neural representation is a promising direction. Implicit Neural Representations (INRs) using Multi-Layer Perceptrons (MLPs) have the ability to model images by learning an implicit mapping from pixel coordinates to pixel intensities. This paper revisits the ReLU activation function, a widely adopted non-linearity known for its dead region on the negative axis, within the context of MLP-based INRs. We introduce the Dead-Free Linear Unit (DeLU), a novel activation function that leverages a linearly transformed absolute value to eliminate inactive regions. By combining dead-free non-linearity with adaptive linear scaling, DeLU enhances the expressiveness of INR architectures, particularly those employing periodic activations. Extensive experiments across multiple remote sensing datasets, including LandCover.ai, LoveDA, INRIA, UAVid, and ISPRS Potsdam, validate the efficacy of our proposed method. Full article

Objective: This study aimed to estimate the trends, projections, and determinants of standalone and coexisting forms of malnutrition (CFM) at the global, regional, national, and individual level among children under five in low- and middle-income countries (LMICs). It also assessed the projection trajectory towards the 2030 global nutrition targets (GNTs) for child growth including stunting, wasting, obesity, and CFM. Methods: Data from 48 LMICs were analyzed using the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Children with complete anthropometry were included for national- and individual-level descriptive analyses. Projected prevalence of each form of malnutrition, including CFM, was calculated using the Annual Rate of Change. Inferential analyses employed generalized linear regression models with two-way interaction terms to identify determinants of each malnutrition type. Findings: By 2030, 22 of 48 LMICs are projected to achieve the GNT of stunting, wasting, and obesity, that is up from 10 countries currently, while Yemen and Zimbabwe are expected to remain off-track. Stunting is the most prevalent form, affecting 42 countries, with nine nations projected to have over 50% of children affected by a form of malnutrition. Wasting, obesity, and CFM are rising in several countries. Maternal education and household wealth were the strongest determinants, with children of uneducated mothers and from poorest households at the highest risk. Inequalities are narrowing slowly by 1–2% per year, and marked regional disparities persist. Conclusions: Many LMICs are off-track to meet child-growth targets when CFM is considered alongside standalone indicators. The government and global health partners must strengthen nutrition surveillance systems and equity-focused policies and programs to routinely capture CFM and prevent as well as manage all forms of malnutrition at the national and individual levels. Full article

Background and objective: Adverse drug reactions (ADRs) cause substantial harm, and a considerable proportion may be preventable, but under-reporting persists and weakens pharmacovigilance. Spontaneous reporting depends on clinicians, yet under-reporting persists and weakens pharmacovigilance. To map barriers to adverse drug reaction reporting among physicians, a comparison of nurses and pharmacists in single-country studies was carried out between high-income countries (HICs) and low- and middle-income countries (LMICs). Methods: A scoping review was conducted following PRISMA-ScR guidance. PubMed and Web of Science were searched for studies published from 2016 onward. Eligible studies were single-country primary empirical studies including physicians, nurses, or pharmacists and examining ADR reporting. Only barriers that were measured or explicitly explored and reported as extractable results were included. Barriers were coded into 12 domains and summarised by income group and profession. Results: A total of 44 studies were included, with 18 from HICs and 26 from LMICs. Survey designs were most common. Pharmacists were the most frequently studied cadre. Knowledge and training barriers were reported in all studies in both income groups. Fear of legal or punitive concerns was reported in 13/18 (72.2%) HIC studies and 17/26 (65.4%) LMIC studies. Time and workload barriers were reported in 10/18 (55.6%) HIC studies and 11/26 (42.3%) LMIC studies. Access barriers to tools, forms, and information technology showed the clearest income group difference: these were reported in 5/18 (27.8%) HIC studies versus 16/26 (61.5%) LMIC studies. Lack of feedback or acknowledgement was reported in 8/18 (44.4%) HIC studies and 10/26 (38.5%) LMIC studies. Conclusions: Barriers extend beyond individual knowledge in all settings. The main income group difference was the greater prominence of reporting system access barriers in LMICs compared with workflow and time pressure barriers in HICs. Addressing fear and building a supportive non-punitive reporting culture remains a cross-cutting priority because these were common issues in both income groups and can limit reporting even when infrastructure and training exist. Full article

Background/Objectives: Drowning is a leading preventable cause of unintentional injury death, yet U.S. prevention efforts have largely focused on children. Despite international declines in pediatric drowning mortality, adult trends remain poorly characterized. We examined long-term trends and disparities in unintentional drowning mortality among U.S. adults aged ≥25 years from 1999 to 2024. Methods: Using CDC WONDER Multiple Cause of Death data, drowning deaths were identified using ICD-10 codes W65–W74, V90, and V92. Age-adjusted mortality rates (AAMRs) per 100,000 were computed by direct standardization to the 2000 U.S. standard population. Joinpoint regression estimated the annual percent change (APC) and average annual percent change (AAPC). Three sensitivity analyses assessed transport-related code exclusion, pandemic-era restriction, and multiple cause-of-death coding. Results: During 1999–2024, 101,743 unintentional drowning deaths occurred among U.S. adults aged ≥25 years (76,554 males; 25,201 females), with 58.09% in natural water or outdoor settings. The overall AAMR showed a non-significant increase (AAPC: 0.55%, p = 0.054); however, joinpoint analysis identified stable rates through 2013 followed by a significant sustained increase (APC: 1.32%, 95% CI: 0.32–2.32, p = 0.012). The male-to-female rate ratio narrowed significantly from 4.00 (1999) to 3.32 (2024) (ratio of rate ratios: 0.83, p = 0.0006), driven by a sustained female increase (AAPC: 1.27%, p < 0.001). Adults aged 65–85+ showed the steepest rise (AAPC: 1.15%, p < 0.001). Non-Hispanic AI/AN adults had the highest rates (3.47–5.44 per 100,000), and non-metropolitan areas consistently exceeded metropolitan rates. Conclusions: A significant upward trajectory has persisted since 2013, with marked disparities by age, sex, race/ethnicity, and geography. Adult-focused, equity-driven prevention strategies aligned with USNWSAP implementation are needed to address this underrecognized burden. Full article

As Open Radio Access Network (O-RAN) deployments expand and adversaries adopt “store-now, decrypt-later” strategies, operators need empirical data on the cost of migrating critical control interfaces to post-quantum cryptography (PQC). This paper experimentally evaluates the impact of integrating a NIST-aligned Module-Lattice Key-Encapsulation Mechanism (ML-KEM) into IKEv2/IPsec, protecting the E2 interface between the 5G Node B (gNB) and the Near-Real-Time RAN Intelligent Controller (Near-RT RIC). Using an open-source testbed built from srsRAN, Open5GS, FlexRIC and strongSwan (with liboqs), we compare three configurations: no IPsec, classical Elliptic Curve Diffie–Hellman (ECDH)-based IPsec, and ML-KEM-based IPsec. This study focuses on IPsec tunnel-setup latency and the runtime behaviour of Near-RT RIC xApps under realistic signalling workloads. Results from repeated, automated runs show that ML-KEM integration adds a small overhead to tunnel establishment, which is approximately 2.7~4.7 ms in comparison to classical IPsec, while xApp operation and RIC control loops remain stable in our experiments. These findings, produced from an open, reproducible testbed, indicate that ML-KEM-based IPsec on the E2 interface is practically feasible and inform quantum-safe migration strategies for O-RAN deployments. Full article

Background: Anemia is a major public health issue, particularly among women of reproductive age (WRA) in low- and middle-income countries (LMICs). Pakistan’s National Nutrition Survey (NNS) 2011 showed a high prevalence of vitamin B₁₂ (B₁₂) and folate deficiency among WRA, necessitating further investigation in subsequent surveys. Methods: Blood samples from 31,828 WRA (15–49 years old) were collected using a stratified multi-stage sampling technique in NNS-2018. We conducted a secondary analysis using population-weighted logistic regression to assess the association of potential factors with B₁₂ and folate deficiency. B₁₂ (n = 4442) and folate (n = 12,662) samples were measured using an electrochemiluminescence immunoassay and a Centers for Disease Control and Prevention, USA (CDC)-approved microbiologic assay, respectively. Results: Folate deficiency was present in 44.7% WRA, and 20.2% had B₁₂ deficiency. Provincial distribution was associated with folate deficiency, i.e., Sindh (OR = 1.140, 95% CI 1.018, 1.285), Baluchistan (OR = 1.237, 95% CI 1.052, 1.453), and Islamabad (OR = 1.524, 95% CI 1.109, 2.092), while B₁₂ deficiency was prevalent in Islamabad (OR = 1.673, 95% CI 1.122, 2.497), Gilgit Baltistan (OR = 2.472, 95% CI 1.197, 5.106), and newly merged districts of KPK (OR = 1.584, 95% CI 0.977, 2.570). Rural residence (OR = 1.407, 95% CI 1.125, 1.760), obesity (OR = 1.649, 95% CI 1.282, 2.122), and overweight (OR = 1.560, 95% CI 1.262, 1.928) were associated with B₁₂ deficiency. Conclusions: Our results show regional and demographic differences in the prevalence of folate and B₁₂ deficiencies among WRA. This underscores the need for targeted nutritional interventions and further longitudinal studies to identify potentially associated factors. Full article

Short-term forecasting of solar radiation is essential for the efficient operation of solar energy systems. This study presents a neural network-based approach for short-term solar radiation forecasting using a hybrid framework that integrates temporal characteristics with weather-based features. The proposed model combines a Gated Recurrent Unit (GRU) to capture short-term temporal dynamics, a Transformer Encoder, and a Multilayer Perceptron (MLP) to integrate these representations for final prediction. Key meteorological variables, including temperature, humidity, and wind speed, are incorporated along with engineered time-related features such as lagged values, rolling statistics, and cyclical time-of-day encodings. The results demonstrate that the hybrid model effectively integrates sequential learning and feature interaction, leading to improved forecasting accuracy. The proposed approach achieves a test Mean Absolute Error (MAE) of 0.056, Root Mean Square Error (RMSE) of 0.086, and coefficient of determination (R²) of 0.92, outperforming benchmark models such as AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), GRU, and Extreme Gradient Boosting (XGBoost). The model maintains stable performance across cross-validation folds, multiple forecasting horizons, and varying weather conditions. These findings indicate that the proposed framework provides a reliable and practical solution for accurate short-term solar radiation forecasting, supporting real-time solar energy management and renewable energy system optimization. Full article

Background: Tirzepatide, a dual GIP/GLP-1 receptor agonist, demonstrates substantial glycemic and weight benefits versus GLP-1 receptor agonists in indirect comparisons, but direct comparative safety evidence versus dulaglutide remains limited. We evaluated comparative safety (primary outcome: overall adverse events) and efficacy. Methods: Following PRISMA 2020 (prospectively registered: PROSPERO CRD420251276594), we searched MEDLINE, Embase, Scopus, and CENTRAL (inception–31 December 2025) for randomized controlled trials (≥26 weeks) comparing once-weekly tirzepatide with dulaglutide in adults with type 2 diabetes. Three trials (N = 13,590 participants) were included. Dichotomous outcomes were pooled using random-effects models (risk ratios [RRs], 95% confidence intervals [CIs]). GRADE assessed certainty of evidence. Results: Overall adverse event incidence did not differ significantly (RR 1.04 [0.98–1.10]; I² = 36%; moderate-certainty evidence). Discontinuation due to adverse events was consistently higher with tirzepatide (RR 1.32 [1.20–1.45]; I² = 0%; high-certainty evidence), representing a 32% increased risk across all populations. Categorical HbA1c target achievement was analyzed in two trials; the third trial reported HbA1c as a continuous outcome only. At the primary threshold (HbA1c < 7.0%), tirzepatide was consistently superior with no heterogeneity (RR 1.48 [1.33–1.64]; I² = 0%; p < 0.00001). Across all thresholds combined, heterogeneity was extreme (I² = 92%), limiting confidence in any pooled summary estimate; the greatest instability occurred at the strictest threshold (HbA1c < 5.7%; I² = 98%; p = 0.40). Tirzepatide showed greater HbA1c target attainment in treatment-naive patients receiving dulaglutide 0.75 mg, whereas the glycemic advantage was smaller in patients with established cardiovascular disease receiving dulaglutide 1.5 mg. Categorical weight-loss outcomes were analyzed in two trials; tirzepatide was associated with greater weight-loss threshold achievement (RR 8.80 [4.04–19.17]; very low-certainty evidence), although interpretation is limited by substantial heterogeneity and restricted generalizability. Serious adverse events were not significantly different (RR 0.82 [0.47–1.43]; I² = 42%). Conclusions: Overall adverse events were similar between treatments, but tirzepatide consistently increased discontinuation risk, indicating a clinically important tolerability-persistence trade-off. Glycemic efficacy was highly population-dependent: benefits were consistent at the primary HbA1c target (<7.0%; I² = 0%) in early-stage disease, whereas the advantage was smaller in long-standing disease with established cardiovascular disease. Tirzepatide may be favored when glycemic or weight efficacy is prioritized in earlier-stage disease, provided tolerability is proactively managed. Dulaglutide remains appropriate when persistence is threatened by tolerability concerns or cardiovascular risk reduction is the primary goal. Full article

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that supply chain information sharing and quality management have a significant impact on a firm’s performance. Similarly, the sharing of supply chain information and quality management has a significant impact on firm performance by mediating supply chain agility. The study offers actionable insights for managers in volatile fashion supply chains. Firms can enhance performance by sharing real-time demand and inventory information, strengthening key quality practices, and adopting flexible, data-driven production processes. Integrating information sharing, quality management, and agility enables faster responses to shifting consumer trends, thereby improving overall competitiveness in fast-fashion environments. This study offers valuable guidance for supply chain professionals seeking to enhance practices within their networks. The results underscore the strategic importance of information sharing and quality management in promoting agility, an essential capability for achieving a competitive advantage. Additionally, the insights generated are relevant to practitioners, policymakers, and industry leaders aiming to strengthen supply chain responsiveness and resilience. Full article

Introduction: Diabetic gastroparesis increases the risk of aspiration, pneumonia, and sepsis, yet the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on these outcomes is uncertain because of their gastric-emptying effects. Methods: We performed a retrospective cohort study using the TriNetX Global Research Network. Adults (≥18 years) with diabetes mellitus and gastroparesis were identified and divided into two cohorts based on GLP-1 RA exposure. Propensity score matching (1:1) balanced demographics, comorbidities, and antidiabetic medications, yielding 23,371 patients per cohort. Outcomes, assessed from 180 days after index, included pneumonia, pneumonitis, mechanical ventilation, ventilator-associated pneumonia, sepsis, bacteremia, empyema, lung abscess, acute respiratory distress syndrome (ARDS), and need for enteral feeding. Risk ratios (RRs) and hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated. Results: Compared with GLP-1 users, non-GLP-1 patients had higher incidences of pneumonitis (3.6% vs. 2.5%; HR 1.76, 95% CI 1.58–1.95), pneumonia (13.2% vs. 12.2%; HR 1.34, 95% CI 1.27–1.41), mechanical ventilation (4.4% vs. 3.3%; HR 1.63, 95% CI 1.49–1.79), sepsis (12.8% vs. 11.1%; HR 1.44, 95% CI 1.37–1.52), and bacteremia (5.2% vs. 4.4%; HR 1.46, 95% CI 1.35–1.59) (all p < 0.001). Empyema and ARDS were also numerically lower among GLP-1 users, while ventilator-associated pneumonia and lung abscess were rare and similar between groups. No patients required percutaneous endoscopic gastrostomy or nasal enteral feeding. Conclusions: In patients with diabetes and gastroparesis, GLP-1 RA therapy was associated with significantly fewer pulmonary and systemic infectious complications. These data suggest that the systemic benefits of GLP-1 RAs may outweigh concerns regarding delayed gastric emptying in this high-risk population. Full article

Phase-change materials (PCMs), such as paraffin wax, are widely used in latent heat storage (LHS) because they store substantial thermal energy at nearly constant temperature; however, their low thermal conductivity limits heat transfer and slows melting/solidification. In this work, two flat-plate solar collectors are coupled with a paraffin-based LHS unit for low-temperature solar heating, and the design is optimized by introducing improved fin-geometry combinations on both the heat transfer fluid (HTF) tube and shell side. The M-shaped fins combined with rectangular fins significantly enhanced convective heat transfer by generating localized vortices, while the extended surface area improved conduction within the solid PCM, facilitating efficient heat dissipation and accelerating the phase transition. The LHS unit without fins showed complete melting in 67 min. However, fin introduction remarkably mitigated charging duration to 44 min, 52.3% faster than bare tubes having no fins. The experimental melting process exhibited a 7 min delay by comparing experimental and numerical results, achieving complete melting in 51 and 44 min, respectively. Discharging was completed in 48 min. During PCM charging, sensible heating produces a rapid temperature rise with only a small energy increase, but once the PCM entered into the melting range (320–324 K), the energy changed more steeply. Adding fins boosts stored energy from 2.10 MJ to 3.25 MJ (54.8%) and exergy from 0.15 MJ to 0.27 MJ (80.0%), yet exergy remains far smaller than energy (92.9% lower without fins and 91.7% lower with fins), indicating fins enhance total heat storage more than recoverable work potential. Full article

This paper proposes a new fuzzy logic-based rotor flux model reference adaptive system (FLC-MRAS) for rotor speed estimation in induction motor drives, replacing the constant-gain PI controller used in conventional MRAS schemes. The proposed observer simultaneously incorporates both rotor flux and electromagnetic torque errors to enhance estimation accuracy and robustness against load torque disturbances. A nonlinear Mamdani-type fuzzy logic controller (FLC) with two inputs and one output, employing triangular membership functions and seven fuzzy sets, is adopted. The effectiveness and useful operational performance of the proposed approach is examined through extensive simulation cases under various vehicle speed driving profiles and load torque conditions using an indirect vector-controlled induction motor drive. In order to investigate the effective operational performance of a speed estimator, different cases are prepared according to the vehicle requirements. To examine the robustness of the proposed observer under realistic operating conditions, rotor resistance variation is incorporated into the simulation framework. This approach allows assessment of MRAS performance under practical nonlinearities and parameter uncertainties encountered in real applications. Comparative results demonstrate superior speed regulation and speed tracking, reduced estimation error, and faster convergence of the adaptive tuning signal for better speed estimation compared to the PI-MRAS observer. The proposed scheme provides the suitable choice of traction drive adoption for electric vehicle (EV) applications. Full article

It is necessary to understand the complicated interplay between diseases and medicinal plants to find new curing agents that may be used in natural sources. Nevertheless, the state of interaction between diseases and plants today is not fully developed yet, and the potentially productive plant-based treatment can hardly be identified rationally. In order to elaborate on this challenge, we will offer a heterogeneous network approach to the prediction of novel disease–plant associations by using the Random Walk with Restart (RWR) algorithm. The framework combines three significant relational networks, including (i) a disease–plant association network, which has been built using curated literature and biological databases, (ii) a disease–disease similarity net, which is constructed using shared symptoms and therapeutic profiles, and (iii) a plant–plant similarity net using phytochemical and functional similarities. These elements are integrated into a homogeneous graph that is heterogeneous in nature, and thus, information flows through related nodes. The model begins by finding RWR between known disease or plant nodes and develops the network by exploring the graph further to make estimates of the probability of association between disease and plant networks that were not previously connected. Experimental tests show that the proposed model has an excellent predictive ability, ROC-AUC of 0.9987, PR-AUC equal to 0.915, and Precision = 10 of 1.0, significantly better than the results of the base models, including Random- and Degree-based models. The bootstrap analysis supported the strength of the model as the mean ROC-AUC was 0.9987 with a standard deviation of 0.00051. The suggested structure offers an effective computational methodology to systematically explore disease–plant interactions to aid in finding novel herbal drugs to treat diseases and speed up the drug discovery process by means of inference based on networks. Full article