Enhancing agricultural automation through weather invariant soil parameter prediction using machine learning.

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.

Early Outcome of On-pump Versus Off-pump Elective Surgical Revascularization in Patients with Prior ST-Segment Elevation Myocardial Infarction.

Coronary artery bypass graft surgery (CABG) is a proven treatment for coronary artery disease. History of a ST-elevation myocardial infarction (STEMI) is considered an independent risk factor for CABG irrespective of timing for an emergency or elective surgery. Patients with STEMI are candidates for both On-pump and Off-pump CABG procedures. This paper discusses the possible best option for elective surgical revascularization in patients with prior STEMI. This prospective clinical trial of 60 eligible patients with prior STEMI was conducted in a Tertiary Care Hospital from April 2018 to March 2019. Among them, 30 patients underwent off-pump (Group A) and 30 patients underwent on-pump (Group B) CABG procedures. Outcomes between both groups were observed from surgery to 1 month postoperatively. Data was analysed by the software statistical program for social science (SPSS 25.0 Inc). The surgery was successful in both groups of patients. Differences were observed by mean number of grafts per patient (2.77±0.43 vs. 3.10±0.71) and duration of operation (4.41±0.35 hours vs. 5.71±0.48 hours). An improvement in Left Ventricular Ejection Fraction (LVEF %) was observed in both groups postoperatively (17.98% vs. 10.98%) and the postoperative LVEF% at different time points were found statistically significant (p<0.05) over preoperative LVEF%. Multivariable stepwise logistic regression analysis correlated on-pump CABG with prolonged need for ionotropic support, need for blood transfusion, longer hospital stay and less improvement in LVEF%. The study supports the Off-pump CABG as a better surgical option over on-pump CABG in patients with prior STEMI.

Economic well-being induced Women's empowerment: Evidence from coastal fishing communities of Bangladesh.

Women's empowerment is an important policy agenda that is critical for developing countries like Bangladesh to achieve sustainable development goals (SDGs). The prime objective of this paper was to examine whether community savings groups can truly improve the economic conditions of women which turns into women's empowerment in fishing communities or not. The propensity score matching (PSM) and logistic regression technique were incorporated, and required data were collected from Community Savings Groups (CSG) interventions and non-CSG villages of coastal Bangladesh. Quantitative data were collected from 615 women comprising 306 CSG participants (treatment group) and 309 non-participants (control group). The results affirm CSG group members were economically more solvent and less dependent on borrowed money than non-CSG group members. Improved economic indicators (savings, income and expenditure) of CSG households make the foundation of attaining women's empowerment for the intervened group. The findings revealed that CSG women performed better in various dimensions of leadership capacity than non-CSG women. Econometric analysis confirmed positive impacts of CSG interventions on savings, gross household income, earning from catching fish, alternative income-generating activities (AIGAs), expenditure, and women's empowerment. The initiatives of CSG not only generate economic well-being but also contribute to women's empowerment. Financial access, improved literacy and an enabling environment for the productive engagement of women reduce gender inequality in fishing communities. To sustain the benefits of CSG, establishing institutional linkages (advisory and financial), legality/registration of CSGs from the government authority, and facilitation of alternative IGAs are crucial.

Assessing sustainable waste management practices in Rajshahi City Corporation: an analysis for local government enhancement using IoT, AI, and Android technology.

This study addresses the pressing challenge of inefficient waste management practices within the Rajshahi City Corporation (RCC), Bangladesh. Despite rapid urbanization and escalating waste generation rates, RCC struggles with diverse waste disposal practices, limited supervision, irregular waste collection schedules, and inadequate disposal infrastructure. In this context, this study examines the possible improvements that could be made by combining the Internet of Things (IoT), artificial intelligence (AI), and Android application to improve waste management methods in the RCC. The study's foundation is a vast amount of information gathered from residents, with particular attention paid to waste disposal methods, the role of the local government, the frequency of waste collection, and public attitudes toward waste management. The results point to a complicated waste management environment with a range of waste disposal practices, little supervision, irregular waste collection, and insufficient disposal methods. The importance of RCC in waste management is emphasized, highlighting the need for proactive measures including effective monitoring, constant waste collection, and routine drain cleaning. Additionally, it is suggested that combining IoT, AI, and Android technology is a possible way to improve waste management procedures. These technologies have the potential to increase productivity, lessen their negative effects on the environment, and produce cleaner, more sustainable urban environments.

Detection of Campylobacter spp. in farmed sheep in Mymensingh division of Bangladesh: Prevalence, risk factors, and antimicrobial susceptibility status.

Background and Aim: Campylobacter infections in sheep may be asymptomatic or cause enteritis, ileitis, infertility, and abortion. Thus, this study aimed to estimate the prevalence of Campylobacter spp. in farming sheep and to detect risk factors, molecular patterns, and antimicrobial susceptibility status of these pathogens. Materials and Methods: Four hundred and eight fecal samples were collected from 12 flocks in the Mymensingh and Sherpur districts. Samples were tested by both basic (culture and biochemical tests) and molecular (initially 16S rRNA and later hipO gene-based polymerase chain reaction). Furthermore, the antimicrobial susceptibility status of Campylobacter jejuni was confirmed using disk diffusion. Flock- and animal-level data were captured using semi-structured interviews with farm owners under bivariate and multivariate logistic regression analyses to confirm the risk factors for Campylobacter-positive status. Results: The prevalence of C. jejuni staining at the animal and flock levels was 8.82% (36/408) and 66.70% (8/12), respectively. The age of sheep was identified as an important risk factor. Up to 1 year of age, sheep were 3.78 times more likely to be infected with C. jejuni (95% confidence interval: 1.0736-13.3146, p = 0.038). Of the 36 isolates of C. jejuni, all were found to be fully susceptible (100%) to gentamicin and ciprofloxacin. In this study, three antimicrobial agents, oxytetracycline, azithromycin, and ceftriaxone, were fully resistant (100%). The majority of isolates were resistant to a combination of 4-6 antimicrobial agents. Conclusion: The present study highlights the predominant maintenance of zoonotic Campylobacter species in sheep, and their burden on human health is enormous. Therefore, environmental, animal, and human health needs to be focused under a One Health lens to mitigate the occurrence of Campylobacter in farm settings and to prevent further introduction to animals and humans.

OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data.

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.

Development of DynamicMC for PHITS Monte Carlo package.

Previously, we have developed DynamicMC for modeling relative movement of Oak Ridge National Laboratory phantom in a radiation field for the Monte Carlo N-Particle package (Health Physics. 2023,124(4):301-309). Using this software, three-dimensional dose distributions in a phantom irradiated by a certain mono-energetic (Mono E) source can be deduced through its graphical user interface. In this study, we extended DynamicMC to be used in combination with the Particle and Heavy Ion Transport code System (PHITS) by providing it with a higher flexibility for dynamic movement for an anthropomorphic phantom. For this purpose, we implemented four new functions into the software, which are (1) to generate not only Mono E sources but also those having an energy spectrum of an arbitrary radioisotope (2) to calculate the absorbed doses for several radiologically important organs (3) to automatically average the calculated absorbed doses along the path of the phantom and (4) to generate user-defined slab shielding materials. The first and third items utilize the PHITS-specific modalities named radioisotope-source and sumtally functions, respectively. The computational cost and complexity can be dramatically reduced with these features. We anticipate that the present work and the developed open-source tools will be in the interest of nuclear radiation physics community for research and teaching purposes.

Toll-like receptor 9-positive plasmacytoid dendritic cells promote Th17 immune responses in oral lichen planus stimulated by epithelium-derived cathepsin K.

Oral lichen planus (OLP) is a chronic inflammatory disease associated with T cell infiltration. The crosstalk between oral epithelium and mucosal T cells was considered to be crucial in the pathogenesis of OLP. Here, we selectively extracted the normal epithelium (NE) and lesional epithelium (LE) of buccal mucosa specimens from three patients with OLP by laser capture microdissection due to identify the pathogenic factors. Cathepsin K (CTSK) was identified as one of common upregulated genes in the LE by DNA microarray. Immunohistochemically, CTSK was distinctly detected in and around the LE, while it was rarely seen in the NE. Recent studies showed that CTSK enhanced Toll-like receptor 9 (TLR9) signaling in antigen-presenting cells, leading to Th17 cell differentiation. TLR9 expression mainly co-localized with CD123+ plasmacytoid dendritic cells (pDCs). The number of RORγt-positive cells correlated with that of CTSK-positive cells in OLP tissues. CD123+ pDCs induced the production of Th17-related cytokines (IL-6, IL-23, and TGF-ß) upon stimulation with TLR9 agonist CpG DNA. Moreover, single cell RNA-sequencing analysis revealed that TLR9-positive pDCs enhanced in genes associated with Th17 cell differentiation in comparison with TLR9-negative pDCs. CTSK could induce Th17-related production of CD123+ pDCs via TLR9 signaling to promote the pathogenesis of OLP.

Hypoalbuminaemia as a Marker of Severity of Patients of Community Acquired Pneumonia.

Community-acquired pneumonia (CAP) is a common presentation with an acute infection of the pulmonary parenchyma occurring in the community level. Despite the availability of potent antibiotics, it remains as a serious illness with significant morbidity and mortality in both developed and developing countries. This study was undertaken to determine the relation between serum Albumin and severity of CAP. This was a cross sectional descriptive study which was carried out in the Department of Medicine of Mymensingh Medical College Hospital (MMCH), Bangladesh from July 2019 to December 2019. The sample size was 67. Purposive sampling technique was employed. Patients of community acquired pneumonia (CAP), aged ≥14 years of both sex with recently developed radiological pulmonary shadowing with compatible clinical symptoms and signs were included. Patients who were chronically immunosuppressed, with chronic starvation, advanced liver disease or chronic kidney disease with or without receiving haemodialysis were excluded. Data analysis was done by SPSS software for Windows (version 23.0). The mean age 65.7±15.3 years, majority 13(19.4%) patients had chronic lung disease, 12(17.9%) had diabetes mellitus, 9(13.4%) had heart failure, 6(9.0%) had cerebrovascular disease, 6(9.0%) had neoplastic disease and 5(7.5%) had chronic renal failure. Majority 22(32.8%) patients had CURB-65 score 3, out of which 12(54.5%) had albumin level <20g/l, 9(40.9%) had albumin level 20.0-24.9g/l and 1(4.5%) had albumin level 25-29g/l. 17(25.4%) had score 4-5 out of which 10(58.8%) had albumin level <20g/l and 7(41.2%) had albumin level 20.0-24.9g/l, 15(22.4%) had score 2 and 13(19.4%) had score 0-1. Negative significant correction (r=-0.782; p=0.001) was found between CURB-65 score and albumin level. Significant number of patients with severe CAP show low serum albumin level at admission which is statistically significant when compared with CURB-65 score. Thus hypoalbuminaemia may be a good marker of severity of patients with CAP.

Human exposures to multiple water sources in the southwestern coastal region of Bangladesh: water quality, pollution sources, and preliminary health risks appraisals.

The coastal areas of Bangladesh have poor accessibility to fresh drinking water and the groundwater is not suitable for drinking, cooking, and other domestic uses due to high levels of salinity and potentially toxic elements. The current study focuses on understanding of the distribution of some physicochemical parameters (temperature, pH, EC, TDS, and salinity) and chemical elements (Fe, Mn, Zn, Ca, Mg, Na, K, Cu, Co, Pb, As, Cr, Cd, and Ni) with health perspective in drinking water from the southwestern coastal area of Bangladesh. The physicochemical properties of the water samples were examined with a multiparameter meter, while the elemental concentrations were analyzed using atomic absorption spectrometer. Water quality index (WQI) and irrigation indices were utilized to determine the drinking water quality and irrigation feasibility, respectively, whereas hazard quotients (HQs) and hazard index (HI) were used to assess the probable pathways and the associated potential risks to human health. The concentrations of some toxic elements in measured samples were relatively higher compared to drinking water guidelines, indicating that ground and surface water are not apt for drinking and/or domestic uses. The multivariate statistical approaches linked the source of the pollutants in the studied water body mostly to the geogenic origin including saline water intrusion. WQI values ranged from 18 to 430, reflecting excellent to unsuitable categories of water quality. The assessment of human health risks due to exposure to contaminated water demonstrated both carcinogenic and non-carcinogenic health risks in the exposed residents of the study area. Therefore, appropriate long-term coastal area management strategies should be adopted in the study region for environmental sustainability. The findings of this research will be supportive in understanding the actual situation of fresh drinking water in the area for policymakers, planners, and environmentalists to take effective necessary measures to ensure safe drinking water in the study area.

LDSSNV: A Linkage Disequilibrium-Based Method for the Detection of Somatic Single-Nucleotide Variants.

Single nucleotide variants (SNVs) are very common in human genome and pose a significant effect on cellular proliferation and tumorigenesis in various cancers. Somatic variant and germline variant are the two forms of SNVs. They are the major drivers of inherited diseases and acquired tumors respectively. A reasonable analysis of the next generation sequencing data profiles from cancer genomes could provide crucial information for cancer diagnosis and treatment. Accurate detection of SNVs and distinguishing the two forms are still considered challenging tasks in cancer analysis. Herein, we propose a new approach, LDSSNV, to detect somatic SNVs without matched normal samples. LDSSNV predicts SNVs by training the XGboost classifier on a concise combination of features and distinguishes the two forms based on linkage disequilibrium which is a trait between germline mutations. LDSSNV provides two modes to distinguish the somatic variants from germline variants, the single-mode and multiple-mode by respectively using a single tumor sample and multiple tumor samples. The performance of the proposed method is assessed on both simulation data and real sequencing datasets. The analysis shows that the LDSSNV method outperforms competing methods and can become a robust and reliable tool for analyzing tumor genome variation.

Role of Vitamin B12 Deficiency and Hyperhomocysteinemia in Diabetic Retinopathy.

Diabetic retinopathy (DR) is one of the most important causes of preventable visual impairment among patients of working age and leading cause of blindness. Deficiency of vitamin B12 and folate has been associated with increased serum homocysteine (Hcy) levels. This study was done to find out the role of vitamin B12 and Hyperhomocysteine (HHcy) in Diabetic retinopathy. The present study is a hospital-based case-control study conducted during over a period of 12 months from January 2019 to December 2019 study conducted in the Department of Ophthalmology at BIRDEM General Hospital, Dhaka, Bangladesh consisting of 100 Type 2 DM patients either with or without retinopathy (DR, n=50 and DNR, n=50, respectively). Subjects with Type 2 DM with and without retinopathy were recruited from patients attending in the department of Ophthalmology at BIRDEM General Hospital, Dhaka and were matched for duration of diabetes. Diabetes subjects on nutritional supplements for the last 6 months and those with a history of nephropathy (based standard renal function tests) and complications other than DR were excluded. Homocysteine (Hcy) levels were inversely related (p<0.05) with Diabetes patients with retinopathy. Vitamin B12 also significant correlated with Diabetes patients with retinopathy. A statistically significant negative linear relationship was found between serum homocysteine and vitamin B12 levels (Pearson r = -0.918, p=0.001) Diabetes patients with retinopathy. Vitamin B12 significantly correlated with diabetes retinopathy and homocysteine levels were inversely related with diabetes patients with retinopathy.

Scattering of e± by C2H6 Molecule over a Wide Range of Energy: A Theoretical Investigation.

The present work reports the theoretical investigation of the scattering of electrons and positrons by the ethane (C2H6) molecule over the energy range 1 eV-1 MeV. The investigation was carried out by taking into account the screening correction arising from a semiclassical analysis of the atomic geometrical overlapping of the scattering observables calculated in the independent atom approximation. The study is presented through the calculations of a broad spectrum of observable quantities, namely differential, integrated elastic, momentum transfer, viscosity, inelastic, grand total, and total ionization cross-sections and the Sherman functions. A comparative study was carried out between scattering observables for electron impact with those for positron impact to exhibit the similarity and dissimilarity arising out of the difference of the collisions of impinging projectiles with the target. Partial-wave decomposition of the scattering states within the Dirac relativistic framework employing a free-atom complex optical model potential was used to calculate the corresponding observable quantities of the constituent atoms. The results, calculated using our recipe, were compared with the experimental and theoretical works available in the literature. The Sherman function for a e±-C2H6 scattering system is presented for the first time in the literature. The addition of the screening correction to the independent atom approximation method was found to substantially reduce the scattering cross-sections, particularly at forward angles for lower incident energies.

Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach.

In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.

DynamicMC: An Open-source GUI Program Coupled with MCNP for Modeling Relative Dynamic Movement of Radioactive Source and ORNL Phantom in a 3-dimensional Radiation Field.

ABSTRACT: The present work introduces an open-source graphical user interface (GUI) computer program called DynamicMC. The present program has the ability to generate ORNL phantom input script for the Monte Carlo N-Particle (MCNP) package. The relative dynamic movement of the radiation source with respect to the ORNL phantom can be modeled, which essentially resembles the dynamic movement of source-to-target (i.e., human phantom) distance in a 3-dimensional radiation field. The present program makes the organ-based dosimetry of the human body much easier, as users are not required to write lengthy scripts or deal with any programming that many may find tedious, time consuming, and error prone. In this paper, we have demonstrated that the present program can successfully model simple and complex relative dynamic movements (i.e., those involving rotation of source and human phantom in a 3-dimensional field). The present program would be useful for organ-based dosimetry and could also be used as a tool for teaching nuclear radiation physics and its interaction with the human body.

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review.

Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapist-like assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues.

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems' progress.

The impact of mobile money on long-term poverty: evidence from Bangladesh.

Mobile money has become a lifeline for millions of poor people who have limited access to a formal banking system. It encompasses a wide range of benefits such as women's empowerment, risk sharing, improved labor market outcomes and reductions in poverty. In this paper, we ask whether mobile money can help lift people out of poverty. Previous studies have addressed this question by using microanalyses of field experiments or longitudinal data on rural households, whereas we use district-level data to reevaluate the mobile money-poverty nexus. In particular, we study the impact of mobile money on district-level poverty in Bangladesh over the period 2010-2016. Our study finds that every 1 billion Taka (approximately US$ 11.76 million) increase in mobile money transactions via the bKash system in 2015 is associated with 0.71% point reduction in the poverty rate in Bangladesh. The marginal impact ranges from 0.12 to 1.15% points across the districts categorized in five groups as per 2010 poverty rates. The findings suggest that mobile money has been successful in fostering various poverty reduction initiatives and that targeted policy prescriptions can be devised to lift up poorer societies that are still outside the purview of mobile financial services. To further increase mobile money use, the government could use its own infrastructure to enhance mobile agent density in the poorest sectors of society.

Octopus-inspired adhesive skins for intelligent and rapidly switchable underwater adhesion.

The octopus couples controllable adhesives with intricately embedded sensing, processing, and control to manipulate underwater objects. Current synthetic adhesive-based manipulators are typically manually operated without sensing or control and can be slow to activate and release adhesion, which limits system-level manipulation. Here, we couple switchable, octopus-inspired adhesives with embedded sensing, processing, and control for robust underwater manipulation. Adhesion strength is switched over 450× from the ON to OFF state in <50 ms over many cycles with an actively controlled membrane. Systematic design of adhesive geometry enables adherence to nonideal surfaces with low preload and independent control of adhesive strength and adhesive toughness for strong and reliable attachment and easy release. Our bio-inspired nervous system detects objects and autonomously triggers the switchable adhesives. This is implemented into a wearable glove where an array of adhesives and sensors creates a biomimetic adhesive skin to manipulate diverse underwater objects.