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The fractional order SEIQRD compartmental model of COVID-19 is explored in this manuscript with six different categories in the Caputo approach. A few findings for the new model’s existence and uniqueness criterion, as well as non-negativity and boundedness of the solution, have been established. When R Covid 19 <1 at infection-free equilibrium, we prove that the system is locally asymptotically stable. We also observed that R Covid 19 <1, the system is globally asymptotically stable in the absence of disease. The main objective of this study is to investigate the COVID-19 transmission dynamics in Italy, in which the first case of Coronavirus infection 2019 (COVID-19) was identified on January 31 st in 2020. We used the fractional order SEIQRD compartmental model in a fractional order framework to account for the uncertainty caused by the lack of information regarding the Coronavirus (COVID-19). The Routh-Hurwitz consistency criteria and La-Salle invariant principle are used to analyze the dynamics of the equilibrium. In addition, the fractional-order Taylor’s approach is utilized to approximate the solution to the proposed model. The model’s validity is demonstrated by comparing real-world data with simulation outcomes. This study considered the consequences of wearing face masks, and it was discovered that consistent use of face masks can help reduce the propagation of the COVID-19 disease.

Upazila Health Complexes (UzHC) serve as the backbone of primary healthcare (PHC) at the sub-district level in Bangladesh, delivering comprehensive healthcare services including both inpatient and outpatient services to the grassroots levels. However, not all the prescribed medicines and diagnostics services are always available at these facilities for outpatient care. This results in out-of-pocket expenditure (OOPE) to the patients for getting prescribed medicines and diagnostics services which has not been properly explored. Thus, we aimed to estimate the overall provider and user costs for outpatient care services at selected UzHCs in Bangladesh. An ingredient-based costing approach was applied to estimate the costs for the most commonly reported illnesses at outpatient of UzHCs from a societal perspective. We conducted a health facility survey at four purposively selected UzHCs to estimate provider costs and a patient exit survey among 452 patients of selected illnesses to estimate the user costs. Commonly reported illnesses were identified in consultation with healthcare providers of these facilities. The difference between costs of prescribed and provided medicines at UzHCs was estimated using the market prices. Data was collected between February to March 2021. The societal costs of the common outpatient illness or symptoms varied significantly, ranging from BDT 642 to BDT 1,384 per episode. Antenatal care had the highest cost burden at BDT 1,384, followed by respiratory illness at BDT 783 and urinary tract infection at BDT 670. On average, the provider spent BDT 289 for treating an outpatient, while a patient incurred BDT 446 as OOPE. Further, a patient was expected to spend an average of BDT 341 for purchasing medicines not provided from UzHCs. Our study found significant gaps between prescribed and provided medicines at UzHCs, leading to higher OOPE for patients. The current healthcare resource allocation strategy does not consider the outpatient load and healthcare demand at PHC facilities, which further exacerbates this gap. Addressing this gap requires a fundamental shift towards a demand-driven resource allocation model within the healthcare financing strategy to improve healthcare access and achieve health for all.

Clarkson disease, or monoclonal gammopathy-associated idiopathic systemic capillary leak syndrome (ISCLS), is a rare, relapsing-remitting disorder featuring the abrupt extravasation of fluids and proteins into peripheral tissues, which in turn leads to hypotensive shock, severe hemoconcentration, and hypoalbuminemia. The specific leakage factor(s) and pathways in ISCLS are unknown, and there is no effective treatment for acute flares. Here, we characterize an autonomous vascular endothelial defect in ISCLS that was recapitulated in patient-derived endothelial cells (ECs) in culture and in a mouse model of disease. ISCLS-derived ECs were functionally hyperresponsive to permeability-inducing factors like VEGF and histamine, in part due to increased endothelial nitric oxide synthase (eNOS) activity. eNOS blockade by administration of N(γ)-nitro-l-arginine methyl ester (l-NAME) ameliorated vascular leakage in an SJL/J mouse model of ISCLS induced by histamine or VEGF challenge. eNOS mislocalization and decreased protein phosphatase 2A (PP2A) expression may contribute to eNOS hyperactivation in ISCLS-derived ECs. Our findings provide mechanistic insights into microvascular barrier dysfunction in ISCLS and highlight a potential therapeutic approach.

Dengue, a vector-borne disease, is a major public health problem in many tropical and subtropical countries including Bangladesh. The objective of this study is to estimate the societal cost of illness of dengue infections among the urban population in Dhaka, Bangladesh. A cost-of-illness study was conducted using a prevalence-based approach from a societal perspective. Costs attributable to dengue were estimated from a bottom-up strategy using the guideline proposed by the World Health Organization for estimating the economic burden of infectious diseases. A total of 302 hospitalized confirmed dengue patients were enrolled in this study. The average cost to society for a person with a dengue episode was US$ 479.02. This amount was ranged between US$ 341.67 and US$ 567.12 for those patients who were treated at public and private hospitals, respectively. The households out-of-pocket cost contributed to a larger portion of the total costs of illness (66%) while the cost burden was significantly higher for the poorest households than the richest quintile. Dengue disease imposes a substantial financial burden on households and society. Therefore, decision-makers should consider the treatment cost of dengue infections, particularly among the poor in the population while balancing the benefits of introducing potentially effective dengue preventive programs in Bangladesh.

This paper puts forward a lead compensator- based PI controller for Demand Response (DR) loop which is included in the conventional Automatic Load Frequency Control (ALFC) model to improve the frequency control process of power system. Though DR is a good solution for ALFC but the vital problem in DR is the existence of communication delay between control centre and appliances. The proposed lead compensator can generate phase lead at the output of the DR loop to eliminate the adverse effects of the delay on the system performance. To verify the effectiveness of the proposed controller for ALFC problem, two different two- area transfer function models of power system are tested. At first the approach is analysed for a wind integrated two- area thermal power system, later the same is extended for a two- area hydrothermal system. The system dynamic performances in presence of proposed compensator are obtained with all the controllers tuned. The Particle Swarm Optimization (PSO) technique is used to tune all the controller parameters of the DR loop as well as the ALFC loop. The results demonstrate the usefulness of the proposed lead compensator in the event of communication delay and step load variation. Finally, the performance of lead compensator in DR exhibits robust performance even with the variation in disturbance parameters and operating conditions in the system

The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep learning techniques integrated with IoT. The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions (MBConv) blocks, followed by Convolutional Long Short Term Memory (ConvLSTM) to capture spatio-temporal patterns. A classification module with global average pooling, a fully connected layer, and a dropout layer generates the final predictions. The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets, focusing on MRHA such as sneezing, falling, walking, sitting, etc. It achieves 94.85% accuracy for cross-subject evaluations and 96.45% for cross-view evaluations on NTU RGB+D 120, along with 89.22% accuracy on HMDB51. Additionally, the system integrates IoT capabilities using a Raspberry Pi and GSM module, delivering real-time alerts via Twilios SMS service to caregivers and patients. This scalable and efficient solution bridges the gap between HMR and IoT, advancing patient monitoring, improving healthcare outcomes, and reducing costs.

The rising challenges posed by electronic waste (e‐waste) to environmental and human health necessitate the advancement of smarter, faster, and more accurate e‐waste management solutions. Despite significant advancements in object detection technologies, current models often struggle with the real‐time classification of diverse e‐waste categories, limiting their practical application in large‐scale recycling operations. Addressing this gap, this paper introduces YOLO11, a next‐generation real‐time object detection framework specifically optimized for the detection and classification of diverse e‐waste categories. Leveraging the power of deep learning, our model was trained on two distinct custom datasets, achieving remarkable classification accuracies of 99% and 98%, respectively. This study is among the first to demonstrate the real‐world applicability of the newly released YOLO11 architecture in the domain of e‐waste, showcasing its robustness and speed in diverse and cluttered environments. The system is capable of accurately detecting and classifying various categories of e‐waste in real‐time, offering a practical solution for automated sorting and collection processes. Through extensive experiments, the YOLO11 model achieved exceptional performance, achieving a recall of 0.968, mAP@ (Mean Average Precision) 0.5 of 0.992, and mAP@0.5–0.95 of 0.884 in all classes. Strong generalization and precise object recognition are shown by high class‐wise mAP@0.5–0.95 values, such as 0.991 for phone, 0.943 for keyboard, and 0.912 for laptop. The model is ideal for real‐time applications since it can identify a variety of e‐waste objects with a fast GPU‐based inference time (4.9 ms). The experimental findings show that YOLO11 is not only a significant advancement in the field of real‐time object detection but also a promising step toward smarter, more automated e‐waste management systems. By bridging AI innovation with environmental sustainability, the proposed work contributes to the urgent global effort to foster greener, more circular economies. The proposed system introduced YOLO11, an advanced real‐time object detection framework specifically developed to address the complex challenges of detecting and classifying diverse categories of electronic waste. This study is among the first to demonstrate the real‐world applicability of the newly released YOLO11 architecture in the domain of e‐waste, showcasing its robustness and speed in diverse and cluttered environments.

Reasonable use of agricultural machinery has an extraordinary potential for poverty alleviation by increasing land and labor productivity in Thailand, Vietnam, and even in Bangladesh. This study was conducted under a program entitled “Agriculture Mechanization, Agro-Processing, Value addition and Export Market Development in Thailand and Vietnam from 1–14 November, 20I9” from the Ministry of Agriculture, Bangladesh. In all three distinct nations, farming activities represent a significant area of activity and remains the biggest wellspring of agricultural business. About 10.5% of Thailand’s, 21.5% of Vietnam’s, and 14.23% of Bangladesh’s GDP come from agriculture. For sustainable development, it is essential to modernize agriculture through the mechanization of its operations, which is therefore inevitable in the studied countries. Thailand’s government started mechanization in 1891 with the import of steam-powered tractor and rotary hoes. Since then the country has witnessed several milestones in the course of mechanization development. The focal plain agro-ecological zone of the state is the maximum and almost fully modernized area. As of now, there are two methods of practicing farming apparatus use: as a proprietor and/or through custom renting provision which coincides with Vietnam and Bangladesh. Historically, mechanization patterns in Vietnam can been described by tillage machinery with associated implement equipment use preceding 1975. This was non-linear, followed by a decreasing trend during the 80s prior to recovery during the 90s, with significant disparities in implementation status across the areas. In 2018, the number of tillage implements and harvesters was boosted about 1.6 and 25.6 times, respectively compared with 2006. The percentage of machinery use in soil tillage operation is 80% of the whole territory of cultivable land in Vietnam, compared to about 90% in Bangladesh and 100% in Thailand. Mechanization in Bangladesh started before independence with the importation of 2-wheel tractors and irrigation pumps in the last part of the 1960s as part of ‘Green Revolution’ activities. To continue this momentum, the Bangladesh Government permitted the continuation of agricultural machinery importation after later autonomy. Machinery use in different agricultural activities has increased in recent years in the areas of irrigation, land preparation, intercultural operation, and threshing. Though its degree of advancement is by and large still quite low contrasted with other South Asian nations, it is noticeable that the most recent two decades, the pace of mechanization has increased rapidly with the increase of mechanical power use in farm activities. The use of farm machinery in rice cultivation has been the most amazing when contrasted with different crops in these three nations. A clear comparison has been given in the paper, which aims to help researchers and policymakers take necessary measures.

Background Patients with systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) have been under-represented in studies about safety of the COVID-19 immunization. We aimed to inquire about the safety and tolerability of COVID-19 immunization in this population. Methods An anonymous online survey on closed Facebook groups for patients and parents with self-reported sJIA/AOSD was posted from June 27th until August 30th, 2022. Continuous variables were analyzed using t-tests or the Mann-Whitney U test if non-normally distributed. Fisher`s tests were used for categorical variables. Results Of a total of 167 responses, 17 were excluded. Ninety-nine patients received the COVID-19 immunization, and 51 patients did not. Patients in both immunized and unimmunized groups had a similar history of disease complications such as macrophage activation syndrome (50% vs. 49%), lung disease (17% vs. 29%), arthritis (51% vs. 50%), and pericarditis/myocarditis (10% vs. 8%). Unimmunized patients were younger (median age 8 yo vs. 12 yo, p < 0.001) and had a higher incidence of a history of disease flare or severe side effects with other immunizations (24% vs. 4%, p < 0.001). Thirty-nine patients reported mostly mild immunization side effects. Severe side effects included 6 reports of disease flare and 2 reports of cardiac side effects (pericarditis and atrial fibrillation). Seven patients reported side effects lasting ≥ 8 days. Three patients developed AOSD following COVID-19 immunization, and 2 of them had the only hospital admissions for immunization side effects. Regarding COVID-19 infection, 46 patients were infected without full immunization, and 33 were infected after 2 doses of immunization. There was one hospitalization in the immunized group, compared to one ICU admission leading to death in the non-immunized group. There was a trend (p > 0.05) toward a higher risk of disease flare after COVID-19 infection among non-immunized patients (43%), compared to immunized patients (24%). Conclusions The COVID-19 immunization was well tolerated by sJIA/AOSD patients even in this group of patients with severe disease. There was a low incidence of disease flare with immunization. Most immunization side effects were mild and lasted < 7 days. The only ICU admission and death from COVID-19 infection occurred in unimmunized subjects.

ObjectivesOur current understanding of the genetic architecture of childhood-onset SLE (cSLE) is limited by a dearth of comprehensive genomic studies in cSLE. We have quantified the number of known rare and common SLE risk variants in a diverse cSLE cohort. We characterised type I interferon (IFN) gene expression scores along with genomic data.MethodsWe performed whole genome sequencing on 83 patients with cSLE and 109 unaffected parents and analysed sequences for known common and rare SLE-associated risk variants. Type I IFN gene expression was quantified on a subset of patients. We investigated the relationship between clinical phenotype, genomic profile and type I IFN signatures in this cohort.ResultsPatients with cSLE were enriched for common SLE risk variants compared with unaffected parents and controls. We identified rare SLE risk variants in 11% of individuals with cSLE; those with rare variants had earlier disease onset (<12 years) than those without variants. Patients with cSLE had elevated type I IFN gene expression compared with unaffected parents and controls, even though most patients were treated with immunosuppressive therapy.ConclusionsPatients with cSLE from this ancestrally and geographically diverse cohort are enriched for common cSLE risk variants compared with controls, and 11% carry a rare variant in known monogenic SLE risk genes. The relationship between rare and common risk variant burden is more complex than previously hypothesised. Our findings indicate that studying patients with cSLE is important for understanding genetic contributions to SLE pathogenesis.