Bias and Scale Effects of Decoupled Farm Payments

Agricultural and Food Policy, International Relations/Trade, Production Economics,

Interactive IIoT-Based 5DOF Robotic Arm for Upper Limb Telerehabilitation

Significant advancements in contemporary telemedicine applications enforce the demand for effective and intuitive telerehabilitation tools. Telerehabilitation can minimize the distance, travel burden, and costs between rehabilitative patients and therapists. This research introduces an interactive novel telerehabilitation system that integrates the Industrial Internet of Things (IIoT) platform with a robotic manipulator named xARm-5, aiming to deliver rehabilitation therapies to individuals with upper limb dysfunctions. With the proposed system, a therapist can provide upper limb rehab exercises remotely using an augmented reality (AR) user interface (UI) developed using Vuforia Studio, which transmits bidirectional data through the IIoT platform. The proposed system has a stable communication architecture and low teleoperation latency. Experimental results revealed that with the developed telerehabilitation framework, the xArm-5 could be teleoperated from the developed AR platform and/or use a joystick to provide standard upper limb rehab exercises. Besides, with the designed AR-based UI, a therapist can monitor rehab/robot trajectories along with the AR digital twin of the robot, ensuring that the robot is providing passive therapy for shoulder and elbow movements

Graphene-Flakes Printed Wideband Elliptical Dipole Antenna for Low Cost Wireless Communications Applications

This letter presents the design, manufacturing and operational performance of a graphene-flakes based screenprinted wideband elliptical dipole antenna operating from 2 GHz up to 5 GHz for low cost wireless communications applications. To investigate radio frequency (RF) conductivity of the printed graphene, a coplanar waveguide (CPW) test structure was designed, fabricated and tested in the frequency range from 1 GHz to 20 GHz. Antenna and CPW were screen-printed on Kapton substrates using a graphene paste formulated with a graphene to binder ratio of 1:2. A combination of thermal treatment and subsequent compression rolling is utilized to further decrease the sheet resistance for printed graphene structures, ultimately reaching 4 Ohm/sq. at 10 {\mu}m thicknesses. For the graphene-flakes printed antenna an antenna efficiency of 60% is obtained. The measured maximum antenna gain is 2.3 dBi at 4.8 GHz. Thus the graphene-flakes printed antenna adds a total loss of only 3.1 dB to an RF link when compared to the same structure screen-printed for reference with a commercial silver ink. This shows that the electrical performance of screen-printed graphene flakes, which also does not degrade after repeated bending, is suitable for realizing low-cost wearable RF wireless communication devices.Comment: Accepted, in press (online preview available

Assessment of Hand Hygiene Knowledge and Practices among Private Dental Clinics in Munshiganj, Bangladesh: A Cross-Sectional Study

Introduction: The study was performed to assess knowledge about infection control through hand-washing practices among dentists and paramedical professionals working at private dental clinics and provide help to identify and overcome obstruction towards proper hand hygiene pattern. Method: A questionnaire-based study was supervised at 15 different private dental clinics situated in the Munshiganj district of Bangladesh. Data on handwashing practices and other factors were collected from 60 samples during the months of November and December 2020. Site inspections of and washing facilities were also supervised. The data was analyzed on SPSS. Result: Majority (78.3%, 50.0% &amp; 35.0%) of the respondents strongly agreed that hand washing helps to prevent spread of infection to the patients. Less than half (48.3%) of the respondents washed their hands before wearing gloves. 31 doctors claimed that hand washing is done after termination of duty while 10 claims that they wash hands after each examination (p< 0.001). And most of the doctors use antibacterial soap (p=0.004). Conclusion: The study wraps up that most of the health care providers had the education about the advantage of handwashing, but proper technique was not followed. The reasons given for not adopting handwashing practices were shortage of time, being busy with a greater number of patients, a skin reaction, and an unsuitable atmosphere. Update Dent. Coll. j: 2023; 13(1): 23-2

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective: This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results: A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance

Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts

Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining

Type-2 Diabetes Mellitus Individuals Carry Different Periodontal Bacteria

Objective: To identify etiologic microbiota associated periodontal diseases among diabetes patients and the factors related to the most commonly identified bacteria species. Material and Methods: Periodontal plaque samples from 11 diabetic participants and 13 non-diabetic controls were collected to assess their aerobic and anaerobic bacterial growth. Different distinct colonies were identified by microscopic and 16srDNA sequencing. Pearson's chi-square tests were conducted to examine any association between categorical variables. Results: The diabetic subjects revealed a more intense plaque formation with a mean plaque index of 2.4 compared to 1.8 in non-diabetics. A total of 86 bacteria were isolated from 24 plaque samples, 44 were aerobic, and 42 were anaerobic. Only aerobic isolates, 22 from diabetic patients and 22 from non-diabetic patients, were evaluated in these analyses. Bacillus spp. (B. cereus mainly) and Klebsiella spp. (K. pneumoniae, K. aerogenes, K. oxytoca) were detected markedly higher in non-diabetic individuals than in diabetic subjects (p=0.026 and p=0.021, respectively). Some bacteria were only identified in the dental plaque of diabetic individuals, namely, Bacillus mojavensis, Enterobacter cloacae, Proteus mirabilis, Staphylococcus epidermidis, Staphylococcus hominis, Staphylococcus pasteuri, Streptococcus mutans, and Streptococcus pasteurianus. The presence of acid reflux and jaundice were significantly associated with the most common bacterial isolate, namely Bacillus spp., with the p-values of 0.007 and 0.001, respectively. Conclusion: Type-2 diabetes mellitus is associated with a higher amount of dental plaques. Periodontal plaque samples from diabetic and non-diabetic subjects possess differential microbial communities. Diabetic plaques contain more versatile microbes predominated by gram-positive streptococci and staphylococci

An adaptive medical cyber-physical system for post diagnosis patient care using cloud computing and machine learning approach

Medical care is one of the most basic human needs. Due to the global shortage of doctors, nurses, and other healthcare personnel, medical cyber-physical systems are quickly becoming a viable option. Post-diagnosis surveillance is an essential application of these systems, which can be performed more successfully using various monitoring devices rather than active observation by nurses in their physical presence. However, most existing solutions for this application are rigid and do not consider current difficulties. Intelligent and adaptive systems can overcome the challenges because of the advances in relevant technology, especially healthcare 4.0. Therefore, this work presents an adaptive system based on cloud and edge computing architecture and machine learning approaches to perform post-diagnosis medical tasks on patients, thus reducing the need for nurses, especially in the post-diagnosis phase

Availability and price changes of potential medicines and equipment for the prevention and treatment of COVID-19 among pharmacy and drug stores in Bangladesh; findings and implications

Objective: There are concerns with increased prices and drug shortages for pertinent medicines and personal protective equipment (PPE) to prevent and treat COVID-19 enhanced by misinformation. Community pharmacists and drug stores play a significant role in disease management in Bangladesh due to high co-payments. Consequently, a need to review prices and availability in the pandemic. Materials and Methods: Multiple approach involving a review and questionnaire among pharmacies and stores early March to end May 2020. Results and Discussion: 170 pharmacies and drug stores took part, giving a response rate of 63.9%. Encouragingly, no change in utilization of antimalarial medicines in 51.2% of stores despite global endorsements. However, increased utilisation of antibiotics (70.6%), analgesics (97.6%), vitamins (90.6%) and PPE (over 95%). Encouragingly, increases in purchasing of PPE. No increase in prices among 50% of the stores for antimalarials, with a similar situation for antibiotics (65.3%), analgesics (54.7%), and vitamins (51.8%). However, price increases typically for PPE (over 90% of stores). Shortages also seen for medicines and PPE, again greater for PPE. Conclusions: The pandemic has impacted on the supply and prices of medicines and PPE in Bangladesh. Key stakeholder groups can play a role addressing misinformation, with enhanced local production helping address future shortages and prices