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In this study, Ailanthus altissima sawdust was chemically activated and characterized by Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), Energy Dispersive X rays (EDX), and surface area analyzer. The sawdust was used as an adsorbent for the removal of azo dye; Acid Yellow 29 (AY 29) from wastewater. Different kinetic and equilibrium models were used to calculate the adsorption parameters. Among the applied models, the more suitable model was Freundlich with maximum adsorption capacities of 9.464, 12.798, and 11.46 mg/g at 20 °C, 30 °C, and 40 °C respectively while R2 values close to 1. Moreover, the kinetic data was best fitted in pseudo second order kinetic model with high R2 values approaching to 1. Furthermore, adsorption thermodynamics parameters such as free energy, enthalpy, and entropy were calculated and the adsorption process was found to be exothermic with a value of ∆H° = −9.981 KJ mol−1, spontaneous that was concluded from ΔG° values which were negative (−0.275, −3.422, and −6.171 KJ mol−1 at 20, 30, and 40 °C respectively). A positive entropy change ∆S° with a value of 0.0363 KJ mol−1 indicated the increase disorder during adsorption process. It was concluded that the activated sawdust could be used as a suitable adsorbent for the removal of waste material, especially dyes from polluted waters.

Edge computing (EC) is a distributed computing approach to processing data at the network edge, either by the device or a local server, instead of centralized data centers or the cloud. EC proximity to the data source can provide faster insights, response time, and bandwidth utilization. However, the distributed architecture of EC makes it vulnerable to data security breaches and diverse attack vectors. The edge paradigm has limited availability of resources like memory and battery power. Also, the heterogeneous nature of the hardware, diverse communication protocols, and difficulty in timely updating security patches exist. A significant number of researchers have presented countermeasures for the detection and mitigation of data security threats in an EC paradigm. However, an approach that differs from traditional data security and privacy-preserving mechanisms already used in cloud computing is required. Artificial Intelligence (AI) greatly improves EC security through advanced threat detection, automated responses, and optimized resource management. When combined with Physical Unclonable Functions (PUFs), AI further strengthens data security by leveraging PUFs’ unique and unclonable attributes alongside AI’s adaptive and efficient management features. This paper investigates various edge security strategies and cutting-edge solutions. It presents a comparison between existing strategies, highlighting their benefits and limitations. Additionally, the paper offers a detailed discussion of EC security threats, including their characteristics and the classification of different attack types. The paper also provides an overview of the security and privacy needs of the EC, detailing the technological methods employed to address threats. Its goal is to assist future researchers in pinpointing potential research opportunities.

Background RBC distribution width is a key variable in complete blood counts, associated with immature RBC release into circulation due to various processes, including systemic inflammation. RDW correlates with elevated acute inflammatory markers like ESR, CRP, and interleukin-6, and is a biomarker in conditions like kidney disease and multiple myelomas. It independently predicts disease severity in critically ill adults and is associated with morbidity, mortality and length of stay in pediatric intensive care unit, though its potential as an early biomarker for detecting pediatric patients with multiple organ dysfunction (MODS) remains unknown. Methods The study retrospectively reviewed PICU patients admitted to Aga Khan University Hospital from September 2018 to December 2022, excluding those admitted for less than 48 h for elective procedures, received recent RBC transfusions, or were anemic. RDW > 14.0% was considered elevated. MODS, defined as dysfunction in two or more organs, was the primary outcome. Data included demographics, PRISM III scores, laboratory values (RDW, BUN, creatinine, CRP, etc.), and clinical outcomes. Patients were stratified into three RDW groups: <13.4%, 13.4–14.3%, and > 14.4%. Analysis focused on associations between RDW levels and MODS within the first 7 days of PICU admission. Results The study included 680 patients. Higher RDW was associated with younger age and higher PRISM III scores, but not with sex. RDW Group III had longer hospital stays, higher mortality, and higher incidence of MODS, but not significant. Hemoglobin and MCHC levels were lower in Group III, whereas BUN and creatinine levels showed no significant differences across groups. The OR for MODS was highest for Group II. Conclusions This retrospective study evaluated the prognostic value of RDW in predicting length of stay, mortality, and early identification of MODS within seven days. Among 680 pediatric patients, higher RDW levels were associated with increased mortality, longer LOS, and higher rates of sepsis and MODS, though these findings lacked statistical significance. Elevated RDW was linked to inflammation and critical illness severity but did not correlate well with pediatric severity scores or MODS trends. Future multicenter studies are recommended to explore RDW’s utility in predicting early organ dysfunction and critical illness outcomes.

The Silica monolith particles (SMP) were prepared from Tetra-Methyl-Ortho-Silicate (TMOS) and characterized by Fourier transforms infrared (FTIR), Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and surface area analyzer. FTIR analysis showed the Si−O stretching confirming SMP formation. SEM analysis provided information about the mean diameter of SMP (1−5 µm). EDX confirmed the presence of silicon and oxygen in the SMP. Moreover, the calculated surface area for SMP was found to be around 367 m2/g, whereas BJH pore size distributed particles were 87.15 along with the total pore volume and pore radius of 0.073 cm3/g and 16.627 Å, respectively. Besides, the removal efficiency was found to be about 96%. Various kinetic equations were used to calculate the adsorption parameters. Overall, the results show that the most appropriate model for the kinetics data was the pseudo-second order kinetics model while the mechanism of adsorption was best explained by the Langmuir isotherm. The highest removal of Basic Red 5 dye after 120 min at 298 K was 576 mg/g. Moreover, the thermodynamics parameters (Enthalpy, Gibb’s energy, and Entropy) were also estimated. The ΔH° (0.995 kJ/mol) value depicted the endothermic nature of the process. The non-spontaneous aspect of the process was evident from the ΔG° values which were 60.431, 328.93, and 339.5 kJ/mol at 293, 303, and 313 K, respectively. From the high removal efficiency value, it can be concluded that the prepared adsorbent can be a potential adsorbent in the reclamation of dyes from wastewater.

In the present study, we report the solvothermal synthesis of a novel trimetallic CuNiZn–PDC Metal Organic Framework (CuNiZn–PDC MOF) engineered for robust and efficient adsorption of Rhodamine B (RhB) dye from aqueous media. Comprehensive characterization, including SEM, XRD, FTIR, BET, TGA, zeta potential, EDX, and elemental mapping, confirmed its crystalline morphology, thermal stability up to 220 °C, and high specific surface area (529 m 2 /g) with pronounced microporosity. The material exhibits an average particle size of about 97 nm and an aggregate size of about 348 nm. Under optimized conditions (0.01 g adsorbent amount, 333 K, pH 8, 60 min contact time, 200 mg/L initial dye concentration), the MOF achieved > 93% RhB removal. Adsorption kinetics conformed to a pseudo-second-order model (R2 > 0.99), indicating chemisorption dominance, while equilibrium data fit the Langmuir isotherm (R2 = 0.99), yielding a maximum monolayer adsorption capacity of 395 mg/g at 333 K. Thermodynamic parameters (ΔH° =  + 11.77 kJ/mol, consistently negative ΔG° across the studied temperature range) denote an endothermic and spontaneous adsorption process. The adsorption mechanism likely arises from synergistic interactions of chemisorption, π–π stacking, pore-filling, electrostatic interactions, and hydrogen bonding. Impressively, the MOF retained 69% of its removal efficiency after seven adsorption–desorption cycles. These findings underscore the MOF’s strong potential as a stable, high-capacity, and recyclable adsorbent for industrial wastewater treatment.

Background Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth. Methods Height-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models. Results Of the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ < − 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models. Conclusion We demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world.

Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security threats include encryption, secure authentication, regular updates, tamper-resistant hardware, and lightweight security protocols. Physical Unclonable Functions (PUFs) are digital fingerprints for device authentication that enhance interconnected devices’ security due to their cryptographic characteristics. PUFs produce output responses against challenge inputs based on the physical structure and intrinsic manufacturing variations of an integrated circuit (IC). These challenge-response pairs (CRPs) enable secure and reliable device authentication. Our work implements the Arbiter PUF (APUF) on Altera Cyclone IV FPGAs installed on the ALINX AX4010 board. The proposed APUF has achieved performance metrics of 49.28% uniqueness, 38.6% uniformity, and 89.19% reliability. The robustness of the proposed APUF against machine learning (ML)-based modeling attacks is tested using supervised Support Vector Machines (SVMs), logistic regression (LR), and an ensemble of gradient boosting (GB) models. These ML models were trained over more than 19K CRPs, achieving prediction accuracies of 61.1%, 63.5%, and 63%, respectively, thus cementing the resiliency of the device against modeling attacks. However, the proposed APUF exhibited its vulnerability to Multi-Layer Perceptron (MLP) and random forest (RF) modeling attacks, with 95.4% and 95.9% prediction accuracies, gaining successful authentication. APUFs are well-suited for device authentication due to their lightweight design and can produce a vast number of challenge-response pairs (CRPs), even in environments with limited resources. Our findings confirm that our approach effectively resists widely recognized attack methods to model PUFs.

In low‐ and middle‐income countries (LMIC), growth impairment is common; however, the trajectory of growth over the course of the first month has not been well characterised. To describe newborn growth trajectory and predictors of growth impairment, we assessed growth frequently over the first 30 days among infants born ≥2000 g in Guinea‐Bissau, Nepal, Pakistan and Uganda. In this cohort of 741 infants, the mean birth weight was 3036 ± 424 g. For 721 (98%) infants, weight loss occurred for a median of 2 days (interquartile range, 1–4) following birth until weight nadir was reached 5.9 ± 4.3% below birth weight. At 30 days of age, the mean weight was 3934 ± 592 g. The prevalence of being underweight at 30 days ranged from 5% in Uganda to 31% in Pakistan. Of those underweight at 30 days of age, 56 (59%) had not been low birth weight (LBW), and 48 (50%) had reached weight nadir subsequent to 4 days of age. Male sex (relative risk [RR] 2.73 [1.58, 3.57]), LBW (RR 6.41 [4.67, 8.81]), maternal primiparity (1.74 [1.20, 2.51]) and reaching weight nadir subsequent to 4 days of age (RR 5.03 [3.46, 7.31]) were highly predictive of being underweight at 30 days of age. In this LMIC cohort, country of birth, male sex, LBW and maternal primiparity increased the risk of impaired growth, as did the modifiable factor of delayed initiation of growth. Interventions tailored to infants with modifiable risk factors could reduce the burden of growth impairment in LMIC. In low‐ and middle‐income countries, growth impairment is common; however, the trajectory of growth over the course of the first month has not been well characterized. To describe newborn growth trajectory and predictors of growth impairment, we assessed growth frequently over the first 30 days among infants born ≥2000 grams (g) in Guinea‐Bissau, Nepal, Pakistan, and Uganda. In this cohort, country of birth, male sex, LBW and maternal primiparity increased the risk of impaired growth, as did the modifiable factor of delayed initiation of growth. Key messages In our study, 98% of infants initially lost weight after birth before beginning weight gain. Although most infants began weight gain by 2 days of age, those with delayed initiation of weight gain were more likely to be underweight at 30 days of age. Male sex, country of birth, low birth weight, maternal primiparity and delayed initiation of weight gain were predictors of being underweight at 30 days of age and of wasting at 30 days of age.

Pd–Ni nanoparticles supported on activated carbon (Pd–Ni/AC) were prepared using a phase transfer method. The purpose of synthesizing ternary composites was to enhance the surface area of synthesized Pd–Ni nanoparticles, as they have a low surface area. The resulting composite was characterized by scanning electronic microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDX) for investigating its surface morphology, particle size, percentage of crystallinity and elemental composition, respectively. The XRD data and EDX analysis revealed the presence of Pd–Ni alloys impregnated on the AC. Pd–Ni/AC was used as an adsorbent for the removal of the azo dye basic blue 3 from an aqueous medium. Kinetic and isotherm models were used to calculate the adsorption parameters. The most suitable kinetic model amongst the applied models was the pseudo-second-order model, confirming the chemisorption characteristics of the process, and the most suitable isotherm model was the Langmuir model, with a maximum adsorption capacity of 333 mg/g at 333 K. Different experimental parameters, such as the adsorbent dosage, pH, temperature and contact time, were optimized. The optimum parameters reached were: a pH of 12, temperature of 333 K, adsorbent dosage of 0.01 g and optimum contact time of 30 min. Moreover, the thermodynamics parameters of adsorption, such as Gibbs free energy (ΔG°), enthalpy (ΔH°) and entropy (ΔS°), showed the adsorption processes being exothermic with values of ΔH° equal to −6.206 kJ/mol and being spontaneous with ΔG° values of −13.297, −13.780 and −14.264 kJ/mol, respectively at 293, 313 and 333 K. An increase in entropy change (ΔS°) with a value of 0.0242 kJ/mol K, indicated the enhanced disorder at a solid–solution interface during the adsorption process. Recycling the adsorbent for six cycles with sodium hydroxide and ethanol showed a decline in the efficiency of the selected azo dye basic blue 3 up to 79%. The prepared ternary composite was found effective in the removal of the selected dye. The removal of other pollutants represents one of the possible future uses of the prepared adsorbent, but further experiments are required.