Noakhali Science and Technology University

Kinetic Alfvén waves (KAWs) are fundamental low-frequency electromagnetic modes in magnetized plasmas, playing a crucial role in energy transfer and wave–particle interactions. Their dynamics are significantly influenced by the adiabatic pressure, which arises from the thermal motion of plasma particles and is further modified by the presence of charged dust grains. This study investigates the nonlinear behavior of KAWs in an electron-depleted (which describes that dust particles capture nearly all free electrons, dramatically reducing electron density and amplifying the influence of ions and dust grains) magnetized plasma featuring nonextensive two-temperature ion distributions and the impact of negatively charged dust-induced adiabatic pressure. Using the reductive perturbation method, we derive the Korteweg–de Vries (KDV) and modified KDV (MKDV) equations to describe the propagation of KAWs. Our analysis reveals that the nonextensive distribution significantly alters the wave’s nonlinear properties, while the two-temperature ion model introduces additional complexities to wave behavior. Moreover, the presence of dust enhances the thermal pressure, affecting both the propagation and stability of the waves and leading to the emergence of dust KAWs. We demonstrate that the KDV solitary amplitude is not affected by adiabatic pressure; however, its influence is apparent in the amplitude when higher-order nonlinearities (i.e., the MKDV equation) are taken into account. These findings provide deeper insights into nonlinear phenomena in space plasmas, with significant implications for astrophysical research.

Evaluating climate change, particularly rainfall fluctuations, is critical for regions prone to extreme weather, like Cox's Bazar, Bangladesh. This study employs both traditional time-series models and deep learning techniques to forecast rainfall patterns. An Exploratory Data Analysis (EDA) was performed on the monthly average rainfall data (1981-2022) from the Bangladesh Meteorological Department, revealing significant trends and seasonal patterns that guided model selection. ARIMA (0,0,1)(2,0,1)12, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were developed to predict future rainfall. Among them, the GRU model achieved the lowest training errors (RMSE: 5.61, MAE: 3.48), while LSTM excelled in test data performance, producing lower errors (RMSE: 7.79, MAE: 4.68). Additionally, LSTM demonstrated the smallest percentage difference between training and testing errors, highlighting its superior generalization capabilities compared to SARIMA and GRU. These findings underscore the effectiveness of LSTM in capturing complex rainfall patterns, contributing to improved flood risk assessment and water resource management strategies.

Purpose This study aimed to investigate the presence, morpho-polymer characteristics, and potential ecological impacts of microplastics (MPs) in sediment samples from two popular tourist beaches along the northern Bay of Bengal. MP contamination is a rising issue in Bangladesh coastal waters, given its dense population, extensive plastic manufacturing, and a lack of waste processing facilities. Materials and methods Sediment samples were collected from two tourist beaches. Microplastics were identified, quantified, and categorized by type, size, shape, and color. Statistical analyses were performed to compare microplastic abundance between the two sites. Polymer composition was analyzed using Fourier-transform infrared spectroscopy (FTIR). Multivariate analyses were used to identify potential sources of contamination. Results and discussion Microplastic abundance ranged from 55.83 ± 10.10 to 116.67 ± 11.27 items kg⁻¹ at Patenga beach and 60.00 ± 6.61 to 135.83 ± 12.58 items kg⁻¹ at Parki beach. Fibers dominated Patenga beach (68.71%), while foam was prevalent at Parki beach (48.97%). Most microplastics were < 0.5 mm, filamentous, and transparent (Patenga) or white (Parki). Polyethylene (33.33%) was the most common polymer. Morpho-polymer characteristics and statistical analyses indicated the sources of MPs were linked to tourism, industrial waste, river discharge, and fishing activities. Conclusions This study revealed moderate microplastic pollution at the study beaches, offering baseline data for the northeastern Bay of Bengal. The findings highlighted that targeted waste management policies are urgently needed to reduce coastal microplastic contamination.

The study presents the successful one-step synthesis of a polyacrylamide/NiO nanocomposite (PAM/NiO NC) through the simultaneous formation of NiO nanoparticles (NiO NPs) and the polymerization of acrylamide (AM), which was carried out using the respective metal salt and AM monomer in ethylene glycol (EG) under microwave-assisted heating. The formation of the PAM/NiO NC was confirmed through UV-vis absorption spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM) analyses. XRD data were used to calculate various crystallographic parameters, including crystallite size (via the Scherrer equation and other models), dislocation density, crystallinity, residual stress, and microstrain, and the analysis showed that the generated PAM/NiO NC has crystallite sizes ranging from 1 to 100 nm, within the accepted range. The crystallinity analysis revealed that the synthesized NC is semi-crystalline, with an average particle size of 26.33 nm, as determined by the Scherrer equation. As synthesized, NC exhibited excellent antibacterial activity against both Gram-negative (Klebsiella spp.SK4, E. coli RN89) and Gram-positive (Streptococcus aureus-8a) bacterial strains with the highest bactericidal capability against Klebsiella spp.SK4 irrespective of concentration. In vitro cytotoxicity study revealed the strongest toxicity against HELA cell lines by PAM/NiO NC and no toxicity towards BHK-21 cell lines indicating PAM/NiO NC potential for biomedical applications.

The continuous increase in ammonia (NH3) and methane (CH4) concentrations in pig barns is primarily driven by the expansion of pig farming, which significantly contributes to the increase in greenhouse gases (GHG) in the atmosphere. Therefore, this experiment aimed to investigate the NH3 and CH4 concentrations based on daily activities, pig physiological parameters such as body mass (BM) and feed intake (FI), and various growing phases of pigs. Two independent experiments were carried out in two pig barns across 2022 and 2023. Both barns were equipped with biological and environmental management sensors (BEMS) and livestock environment management sensors (LEMS) to monitor the pigs’ daily activities, indoor air temperature (IT), relative humidity (IRH), and NH3 and CH4 concentrations. The results of the study revealed that BM and FI had a strong positive correlation with NH3 (r > 0.84 with BM and r ≥ 0.85 with FI) and CH4 (r > 0.83 with BM and r ≥ 0.81 with FI) concentrations during both study periods. Moreover, it was observed that NH3 and CH4 concentrations were lowest in growing phase 1 (G1) and highest in growing phase 4 (G4). Additionally, it was found that the NH3 and CH4 concentrations were highest during the morning defecation (7 AM − 8 AM) and lowest during sleeping at night (9 PM– 7 AM) activity time. In conclusion, these findings provide crucial insights into the patterns of NH3 and CH4 concentrations in pig barns, which are valuable for improving pig production practices and environmental management strategies.

Traditionally regarded as a vestigial organ, the appendix is now being reevaluated for its significant function in health and nutrition of humans. Serving as a “safe house” for beneficial, desired gut bacteria, the appendix is protected by resilient biofilms that create a secure environment. This makes the appendix a”basin” for gut microbiota (GM), replenishing the microbial population following disruptions from infections, antibiotic use, or inflammatory bowel disease (IBD). Beyond simply hosting bacteria, the appendix has an active role in functions of the immune system. Disruption of the Appendix Microbiome (AM), such as through appendectomy, was found to result in lowered diversity of gut microorganisms and an increased risk of various diseases. The potential therapeutic applications of the AM are a particularly promising area of research. The appendix’s unique microbial environment and its impact on immunity open new avenues for treatments. These include modulating GM to improve cancer treatment outcomes, mitigating IBD, regulating metabolic pathways in obesity and diabetes, influencing neurotransmitter production in neurological disorders, and addressing cardiovascular and autoimmune diseases. This review highlights the appendix’s transformation from a misunderstood organ to a critical component of gut health and immunity. It explores the function of the human appendix as a resilient reservoir for desired microorganisms, and its role in disease progression. Furthermore, it examines the potential therapeutic applications of AM, presenting exciting opportunities for future research and treatment innovations.

COVID-19 has been associated with significant alterations in lipid metabolism, which may influence disease severity, especially in patients with comorbidities such as diabetes. This meta-analysis aimed to assess the relationship between serum lipid profiles and COVID-19 severity in patients with and without diabetes. A total of 41 studies were included, comprising 11,372 COVID-19 patients—7,785 classified as mild to moderate and 3,587 as severe to critical cases. Data from 974 diabetic and 1,117 non-diabetic patients across 9 studies were also analyzed using RevMan V5.4 and STATA 14. The meta-analysis revealed that, compared to mild-moderate COVID-19 patients, the severe-critical group exhibited significantly lower levels of HDL-C [SMD: 0.38 mmol/L; 95% CI = (0.24, 0.51); p-value < 0.00001; I² = 83%], TC [SMD: 0.18 mmol/L; 95% Cl = (0.01- 0.35); p-value 0.03; I² = 90%], and ApoA [SMD: 0.77 mmol/L; 95% CI = (0.31,1.23); p-value 0.001; I² = 85%]. Conversely, the severe-critical group showed higher concentrations of TG [SMD: -0.20 mmol/L; 95% CI = (-0.38, -0.02); p value = 0.03; I² = 92%] and Lp(a) [SMD: -0.07 mmol/L; 95% CI = (-0.37, 0.22); p value = 0.31; I² = 4%], while LDL-C and ApoB did not show significant differences. When comparing diabetic to non-diabetic patients, TG levels were significantly higher [SMD: 1.16 mmol/L; 95% Cl = (0.15, 2.16); p value = 0.2; I² = 99%] in diabetic patients, while no significant differences were observed for TC, HDL-C and LDL-C. These findings suggest that altered lipid profiles, particularly decreased HDL-C, TC, and ApoA levels, may serve as potential biomarkers of COVID-19 severity, especially in diabetic populations, and support the need for lipid monitoring in clinical assessments of high-risk patients.

Kinetic Alfvén solitary waves (KASWs), which propagate in the presence of magnetic field lines, play a significant role in the dynamics of astrophysical plasmas, influencing energy transfer where nonlinear interactions are predominant. The adiabatic pressure, a reflection of the plasma's response to compression expansion, significantly influences the wave dynamics. In this paper, we employ a comprehensive model that includes nonextensive mixed (hot and cold) electrons with dynamical heavy ions, the adiabatic index, and thermal pressure to analyze how these factors modify the properties of heavy ion‐acoustic KASWs (HIAKASWs) in a magnetized plasma system. We derive the Korteweg‐de Vries (K‐DV) and modified Korteweg‐de Vries (MK‐DV) equations using the reductive perturbation approach. Subsequently, we analyze the solutions of these equations to investigate the amplitude and width of HIAKASWs. We examine the influence of different factors (e.g., the heavy ion pressure to the magnetic pressure ratio (), the propagation angle (), plasma particle concentration ratios ( and ), nonextensive parameters (), cold and hot electron temperature ratios (), and the adiabatic pressure) on the dynamics of HIAKASWs within the context of a magnetized complex plasma system. These parameters significantly alter the characteristics of HIAKASWs, and we also found that the presence or absence of adiabatic pressure greatly affects the nonlinear properties of HIAKASWs. This study provides valuable insights into the complex interplay of thermal pressure, heavy ion mass, and electron temperature in assessing the behavior of KASWs in magnetized astrophysical environments.

Chittagong, the port city of Bangladesh, has become a major hub for the world’s shipbreaking industry, ideally suited for developing shipbreaking yards and recycled steel industries. This study aimed to evaluate the air quality of recycled steel industries in Chittagong. Formaldehyde (HCHO), total volatile organic compounds (TVOC), and Particulate matter (PM2.5, PM10) were investigated from three shipwrecking yards and five sites in the city near the recycled steel industries. The mean concentrations of HCHO ranged from 204.50 to 463.67 µg m-3, TVOC 326.67–2391.33 µg m-3, PM2.5 ranged from 125.17 to 226.67 µg m-3, and PM10 ranged from 162.33 to 276.60 µg m-3 in the air near the recycled steel industries. All these values exceeded the recommended chronic exposure limits. Calculated hazard quotient (HQ) and hazard index (HI) values consistently exceeded threshold limit 1, indicating a high risk of adverse health effects for children and adults due to air pollutant exposure. These findings highlight the urgent need for stricter environmental regulations and enforcement to promote sustainable shipbreaking and recycled steel industries that balance economic benefits with environmental and public health protection in Bangladesh. Graphical Abstract

Deepfake technology, which enables the creation and manipulation of images in videos has attracted significant attention in the field of image forgery detection. The challenge of detecting altered content intensifies as these tools evolve and become more widely accessible. Many studies indicate that integrating frequency and spatial features from images generated by Deepfake Algorithms yields superior detection results compared to using these features in isolation, particularly when implemented within vision transformer architectures. However, a robust method for effectively combining these features remains an unresolved issue, often resulting in limited performance across varied datasets. This paper presents a framework that utilizes multi-scale cross-attention along with the Discrete Cosine Transform (DCT) and the Xception network to address this challenge. Our approach seeks to address these problems by merging spatial and frequency-based features through a multi-scale cross-attention module to incorporate diverse feature representation which enables more effective deepfake identification. We introduce a multi-scale frequency filter fusion technique with convolution based on DCT for extracting frequency details, which are then integrated with spatial information obtained from the modified Xception network. Extensive evaluations on the FaceForensics++ (FF++) and Celeb-DF datasets demonstrate that our method outperforms previous state-of-the art approaches utilizing vision transformers and CNN models, achieving an impressive F1 score of 98.91%,99.89% across two datasets.

Background Breastfeeding is a crucial practice offering significant nutritional and health benefits to infants and mothers. Despite global recommendations, colostrum avoidance remains a prevalent issue in various regions, including Bangladesh. Aim: The objective of this study was to investigate the prevalence of colostrum avoidance among mothers and its associated factors in Noakhali, Bangladesh. Methods: A total of 397 mothers of infants less than six months seeking care at Noakhali General Hospital were included in this cross-sectional study. A face-to-face interview was taken using a structured questionnaire. Results: About 37% of the mothers were observed to practice colostrum avoidance. Employment status was a significant factor, with employed mothers having 5.422 higher odds of colostrum avoidance than unemployed mothers ( p < 0.001). Additionally, mothers having > 1 child were less likely to avoid colostrum (adjusted odds ratio: 0.412, 95% confidence interval: 0.188–0.901, p = 0.026). Conclusion: Colostrum avoidance was evident among a high proportion of mothers. Hence, the findings recommend more targeted interventions to promote colostrum feeding, address cultural beliefs, and enhance quality breastfeeding counseling during antenatal and postnatal care.

Background: Emerging evidence suggests a potential link between heavy metals and Alzheimer’s disease and related dementias (AD/ADRD). This study compiled epidemiological evidence from research published over the past 11 years on the impact of metals on AD/ADRD in women. Women have unique risk factors for late onset of AD/ADRD, in addition to genetic factors, apolipoprotein E allele (APOE4), and longer life expectancy. Furthermore, women are twice likely as men to experience depression, which increases their risk of developing AD/ADRD. Our narrative review underscored the necessity of a sex-specific approach to address women’s vulnerability to AD/ADRD. Methods: Electronic databases, including PubMed, Google Scholar, NIOSH Toxline, and Scopus, were thoroughly searched to identify primary epidemiological studies on older women exposed to metals and published between 2014 to 2024. Results: We identified 34 epidemiological studies that met the inclusion criteria. The findings revealed a complex interplay between environmental metals such as lead (Pb), cadmium (Cd), arsenic (As), manganese (Mn), selenium (Se), iron (Fe), zinc (Zn), copper (Cu), magnesium (Mg), and calcium (Ca) and the risk of AD/ADRD in women. Significant adverse effects were reported for Cu, Cd, As, Pb, and Mn while significant protective effects were found between Se, Fe, and Zn in blood and AD/ADRD among older women. However, some studies also reported no correlations. Conclusions: Overall, our review identified contrasting results regarding the effects of metals on AD/ADRD in women. Future studies should collect additional evidence to understanding the effects of heavy metals on AD/ADRD in women for developing preventive measures.

The environmental challenges presented by plastic waste, particularly polyethylene terephthalate (PET), necessitates innovative biodegradation strategies. The cutinase from Thermobifida cellulosilytica, Thc_Cut1 (Cut), was site-specifically conjugated with alkyl tethers of varying lengths (C3, C6, C9) through 1H-1,2,3-triazole-4-carbaldehyde (TA4C) derivatives. These conjugations were provided to enhance affinity for PET by adjusting the enzyme's hydrophobicity. The enzyme activity and kinetic parameters of both conjugated and unconjugated cutinases revealed that the modifications have minimal impact on catalytic activity. However, a significant improvement in PET hydrolysis efficiency was observed. Specifically, hexyl and nonyl TA4C-containing cutinase display notable increases in terephthalic acid (TPA) release, exceeding the performance of unconjugated cutinase by 65% and 69%, respectively. Scanning electron microscopy and water contact angle measurements confirmed the enhanced erosion and hydrophilicity of the PET surface following enzyme treatment. Increased enzyme adsorption on the PET surface for C6-Cut and C9-Cut was validated by X-ray photoelectron spectroscopy. Moreover, high-speed atomic force microscopy demonstrated faster and more stable adsorption of C6-Cut and C9-Cut on PET surfaces compared to the slower adsorption of unconjugated cutinase. Additionally, molecular dynamics simulations indicate a higher affinity of conjugated cutinase for PET film. These results suggest that conjugating an alkyl tether to the N-terminus strengthens the interaction between cutinase and PET, improving hydrolysis.

Background Early initiation of breastfeeding, defined as breastfeeding within one hour of birth, halves the risk of neonatal mortality, establishing it as a crucial outcome component in various interventions implemented across South Asian countries. However, the overall effect of these interventions remain unexamined. Therefore, this study seeks to address this knowledge gap by evaluating the overall effect of these interventions on maternal early initiation of breastfeeding practice. Methods A systematic literature search was performed to identify randomised controlled trials conducted in South Asia focusing on early initiation of breastfeeding as an outcome variable. The interventions identified were categorized into behavioral, mobile health (mHealth), health system strengthening, and nutritional interventions. Random effects meta-analysis was conducted to estimate the pooled effect of interventions and effectiveness by intervention categories. Heterogeneity was explored by sub-group and meta-regression analyses. The risk of bias and strength of evidence were assessed by Cochrane’s RoB2 assessment tool and GRADE criteria, respectively. Results We included 22 articles published, representing 19 unique interventions, from a pool of 2,524 screened records for review and narrative synthesis. Among these, 19 articles were eligible for meta-analysis. The pooled relative risk (RR) of early initiation of breastfeeding among mothers in the intervention groups, as compared to their counterparts, was 1.55 (95% CI: 1.24, 1.95; I² = 99.56; p < 0.001). Interventions targeted health system strengthening represented stronger effect than other types of interventions. The overall strength of evidence was moderate. Conclusion The overall intervention effect appeared efficacious in improving maternal early initiation of breastfeeding practice in South Asia, providing valuable insights for policymakers to develop contextually feasible strategies.

Type 2 diabetes mellitus (T2DM) is a global health concern, particularly prevalent in low to middle‐income countries like Bangladesh. This case‐control study aims to explore the correlation between the ADIPOQ rs1501299 polymorphism and susceptibility to T2DM among the population of Noakhali region of Bangladesh. The study, involving 152 T2DM patients and 118 healthy controls, explores the genetic underpinnings of T2DM, considering the rising prevalence in Bangladesh. The ADIPOQ gene, implicated in diabetes development, is examined for the rs1501299 polymorphism, known for its associations with insulin resistance and T2DM in various populations. Genotyping, conducted through PCR and RFLP analysis, reveals significant deviations from Hardy–Weinberg equilibrium for the TT genotype, suggesting potential demographic influences. Clinical and biochemical characteristics, including blood pressure and lipid levels, highlight the complex interplay between genetics, metabolic outcomes and cardiovascular health in T2DM patients. This study identifies a significant association between the ADIPOQ rs1501299 T allele and increased T2DM risk, emphasizing the need for personalized risk assessment. However, ADIPOQ rs1501299 did not show any substantial association with CVD in the studied population. Despite limitations in sample size and regional focus, this study provides valuable insights into the genetic landscape of T2DM in the Noakhali population, paving the way for future research and personalized therapeutic interventions in addressing the global T2DM epidemic.

Utilizing crystallographic engineering, dual-functional zinc oxide nanoparticles (ZnO-NPs) are revealed to have superior antimicrobial and photocatalytic properties. They surpass traditional ZnO (20–40% degradation, <25 mm zones) by achieving 34 mm inhibition zones against Staphylococcus aureus and 50% Congo red degradation under visible light. Four distinct synthesis methods were used to create the facet-tuned NPs: PEG-assisted co-precipitation (Z1), oleic acid-modified hydrothermal (Z2), conventional hydrothermal (Z3), and Canna indica-mediated green synthesis (Z4). XRD analysis revealed that Z1/Z3 (crystallite size: 34.40–36.69 nm, microstrain: 0.1334–0.1394) and Z2/Z4 (84.51–97.20 nm, microstrain: 0.0611–0.0816) grew preferentially in the (101)/(103) and (112)/(110) planes, respectively. FESEM showed that the performance of nanodiscs (Z1), cubic rods (Z2), plate-needle hybrids (Z3), and nanorods (Z4) depended on their morphology. While FTIR found residual C 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 O/C–O groups impacting surface contacts, EDX verified high Zn purity (>86%). Defect-rich E2 (high) phonon modes (439 cm⁻¹) were validated by Raman spectroscopy, confirming defect-mediated charge separation, which is essential for photocatalytic effectiveness. The effectiveness of Z2/Z4 was demonstrated by antibacterial tests (34–26 mm against S. aureus and 18–10 mm against E. coli), using nanorod-driven membrane penetration and Zn²⁺ release. The dual function of the (112) plane, which connects the production of ROS and the inhibition of bacteria, creates a model for ZnO-NPs of the future in the fight against healthcare infections and water pollution.

Coastal ecosystems are increasingly threatened by polycyclic aromatic hydrocarbons (PAHs), which are persistent organic pollutants known for their carcinogenic and mutagenic effects. Bangladesh’s coastal regions are particularly vulnerable due to rapid industrialization, transportation, and tourism. However, limited research exists on PAH contamination in the sediments of these coastal regions. The motivation for this research arises from the potential health risks and ecological impacts associated with PAH accumulation, prompting an urgent need for effective pollution management strategies. This study aims to assess the levels, distribution, and sources of PAHs in the coastal sediment of three districts in Bangladesh using gas chromatography–mass spectrometry (GC–MS) and multivariate receptor models. Sixteen priority PAHs were analyzed, revealing a predominance of three to five ring structures. Source apportionment using Principal Component Analysis with Multiple Linear Regression (PCA-MLR), Positive Matrix Factorization (PMF), and the Unmix model identified coal combustion, traffic emissions, and biomass/wood burning as the primary contributors. PCA-MLR attributed 44.28%, 42.66%, and 13.07% to these sources, while PMF estimated 27.98%, 21.08%, and 38.85%, and the Unmix model assigned 24.91%, 25.23%, and 29.88%, respectively. Additionally, PMF and Unmix identified a mixed source contributing 12.09% and 24.9%, respectively. Our findings offer a comprehensive understanding of PAH contamination patterns in Bangladesh’s coastal sediments, identifying the critical sources of pollution and their relative contributions. The study underscores the urgent need for effective pollution control strategies to mitigate PAH accumulation and protect the ecological and socio-economic integrity of coastal regions.

Timely monitoring and precise estimation of physicochemical properties, such as pH, total soluble solids (TSS), and firmness, are crucial for assessing the quality and ripeness of strawberries. Therefore, this study examined the application of convolutional neural network (CNN)-regression models for predicting pH, TSS, and firmness of strawberries based on image data captured by RGB camera. Three CNN architectures, namely a typical single branch convolutional neural network (CNNtl), a parallel convolutional neural network (CNNpl), and a series convolutional neural network (CNNsl) architectures were developed, and their performance were compared. To develop these models, 600 fruits in six different ripening stages were collected and indexed for enabling the measurement of pH, TSS, and firmness levels, as well as the acquisition of images. Through statistical analysis, significant correlations were obtained among pH, TSS, and firmness in strawberries, suggesting valuable insights into the physicochemical changes that occurred during the ripening process. The pH and TSS levels exhibited a continuous increase from the early to late ripening stages, while fruit firmness significantly decreased throughout the ripening process. Among the tested models, CNNsl outperformed CNNtl and CNNpl in predicting the physicochemical properties of strawberries, which precisely explained the relationship between the image data and the targeted properties. For pH prediction, CNNsl achieved an R2 greater than 0.74 and an RMSE below 0.20. The CNNsl model demonstrated better performance in predicting TSS, with a 9.65% increase in R2 and reductions of 14.34% and 14.51% in RMSE and MAE, respectively, compared to the CNNtl model. Furthermore, the CNNsl architecture achieved the best results for firmness prediction, with an increase inss R2 of 2.74% and 6.92%, and reductions of 9.13% and 16.38% in RMSE, and 8.34% and 15.33% in MAE, compared to the CNNpl and CNNtl models, respectively. The consistency assessment of these models indicated that CNNsl exhibited the highest consistency among the tested models with minimal decreases in R2 and small increases in RMSE and MAE, followed by CNNpl and CNNtl. However, in terms of detection speeds, CNNtl required the shortest prediction time compared to CNNpl and CNNsl. Overall, this study demonstrated the potential of CNN-regression models in precisely predicting the physicochemical properties of strawberries based on image data. The findings may contribute valuable insights in determining physicochemical characteristics of strawberries, emphasizing the importance of advanced deep learning techniques in agricultural applications.

Pharmaceuticals and microplastics are persistent emerging contaminants that pose significant risks to aquatic ecosystems and ecological health. Although extensively reviewed individually, a comprehensive, integrated assessment of their environmental pathways, bioaccumulation dynamics, and toxicological impacts remains limited. This review synthesizes current research on the environmental fate and impact of pharmaceuticals and microplastics, emphasizing their combined influence on aquatic organisms and ecosystems. This review provides a thorough and comprehensive examination of their predominant pathways, sources, and distribution, highlighting wastewater disposal, agricultural runoff, and atmospheric deposition. Studies indicate that pharmaceuticals, such as antibiotics and painkillers, are detected in concentrations ranging from ng/L to μg/L in surface waters, while MPs are found in densities up to 106 particles/m³ in some marine and freshwater systems. The toxicological effects of these pollutants on aquatic organisms, particularly fish, are discussed, with emphasis on bioaccumulation and biomagnification in the food chain, physiological effects including effects on growth, reproduction, immune system performance, and behavioral changes. The ecological consequences, including disruptions to trophic dynamics and ecosystem stability, are also addressed. Although valuable efforts, mitigation and remediation strategies remain inadequate, and further research is needed because they do not capture the scale and complexity of these hazards. This review highlights the urgent need to advance treatment technologies, establish comprehensive regulatory frameworks, and organize intensive research on long-term ecological impacts to address the environmental threats posed by pharmaceuticals and microplastics.