Comparative effects of mulligan’s mobilization and proprioceptive neuromuscular facilitation technique on pain and disability in patients with sacroiliac joint dysfunction

Purpose: To evaluate the efficacy of Mulligan’s Movement of Mobilization and contract- relax technique on pain and disability in patients suffering from sacroiliac joint Dysfunction. Method: A randomized clinical trial was done at DHQ hospital Jhang. 38 persons including both genders old enough 20-35 years were associated with this review who meet the inclusion criteria were recruited by consecutive sampling technique and allocated to the groups by simple random sampling process and by sealed opaque enveloped labeled as 0 for group A and 1 for group B and indiscriminately allocated into two sets. One set A was specified to mulligan mobilization technique and the second set B was specified to contract-relax technique for 6 weeks as three sessions per week. Baseline treatment of hot pack and ultrasound was given to both groups. All the patients were assessed for pain with NPRS and for disability with MOPDQ before and after treatment. Data was analyzed using SPSS 22. Results: After treatment, both groups significantly improved in terms of pain and disability. Mean value of NPRS was reduced from 6.89±1.15 to 1.68±.58 in MWM Group while in Contract-Relax from 6.78±1.18 to 2.57±.90. Mean Value of MOPDQ improved from 31.00±6.24 to 2.95±.911 and 32.26±7.14 to 4.31±1.20 in MWM and Contract-Relax group. However, group that received mulligan technique had significantly better improved NPRS and MOPDQ values than contract-relax group in patients with Sacro-iliac dysfunction (p<0.05). Conclusion: In the management of sacroiliac joint dysfunction, Mulligan mobilization is more efficient than contract-relax approach

Exploiting visual cues for safe and flexible cyber-physical production systems

Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions

Closed-loop elastic demand control under dynamic pricing program in smart microgrid using super twisting sliding mode controller

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid

Power Transformer Fire and Explosion: Causes and Control

An increasing number of failures of power transformers over the world has led to greater interest in building up much needed expertise in electric power transformers, from its design to both preventive and prescribed maintenance. Winding failure is a frequent cause of transformer failure, bushing failure leads of fire and explosion, but it is still uncertain whether the increasing failure of transformers may be related to increasing lightning activity or increasing electric energy of the transient, surge voltages generated by lightning, especially long continuing currents and rate of rise of currents. But there are other important causes as well which need close attention, including wearing out of the contact points of tap changers in power generating and substation transformers, and poor maintenance of transformer oil. This paper seeks to review some of the well-known causes that lead to transformer fire and explosion, and highlights the important parts of the power transformer that need careful selection, installation, maintenance and condition monitoring. Moreover the containment of fires and measures that help to prevent transformer explosions in case of transformer fires are also discussed. Keywords: Embedded systems, Smart Antenna; Adaptive Array; Artificial Neural Network

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Design analysis and synthesis of a non-conventional parallel manipulator with innovative joints

This thesis concerns a research project in which parallel kinematic manipulators consisting of different types of joints and varying number of limbs are assessed in terms of their output characteristics. To increase the orientation capability of a parallel kinematic machine, a small number of limbs is necessary lo reduce limb interference. An innovative algorithm is implemented based on physical limits of the joints and condition number calculation of inverse Jacobians. Larger workspaces are found in the cases where spherical joints are used on the lower end of the limb as compared to other joints. A three legged SPS system is selected for further ysis due to its minimum number of limbs and the use of spherical joints in order to gain maximum degree of freedom.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies

The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs

Recurrent solitary fibrous tumor of eyelid: A rare entity

Orbital and adnexal solitary fibrous tumors (SFT) are rare entities. The clinico-radiological and histologic features overlap with those of other spindle cell variants, and hence the use of immunohistochemical stains helps in making an accurate diagnosis. Furthermore, a thorough surgical resection is imperative to prevent tumor recurrences. We report a rare case of SFT arising primarily from the eyelid with multiple recurrences

Improved Accuracy for Subject-Dependent and Subject-Independent Deep Learning-Based SSVEP BCI Classification: A User-Friendly Approach

In the brain-computer interface, the SSVEP (steady-state visual evoked potential) method serves to foster collaboration between humans and robots. SSVEP-based detection methods require complex multichannel data acquisition, making them difficult to deploy due to discomfort during extended use and the complexity of the algorithms involved. On the other hand, single-channel setup offers simplicity and ease of use. However, in a single channel, achieving encouraging performance in the SD (subject-dependent) scenario is challenging, and accuracy drops further in the SI (subject-independent) scenario. This requires the development of a generalized approach to improve performance in both scenarios. This study proposes (VMD-DNN) to detect SSVEP in single-channel setups for SD and SI scenarios. The novelty of the proposed method lies in utilizing VMD (Variational Mode Decomposition) as a preprocessor, leveraging harmonic information and Kurtosis of the cross-correlation function to select harmonics from VMD decomposed signal. The preprocessed reconstructed signal uses complex spectrum features as input to the DNN for classification. The results show an average accuracy of 93%, 95.3% in SD and 79%, 92.33% in SI scenarios tested on two publicly available datasets, respectively. The ITR (Information transfer rate) was 67.50 bit/min, 92.31 bit/min for SD, and 46.13 bit/min, 85.94 bit/min for SI for both datasets, respectively. In SD, accuracy is improved by 3.34% and 5%, and ITR by 8.87% and 12.91% over baseline methods for both datasets respectively. The proposed VMD-DNN model is effective, with improved performance and lower computational complexity. The robust single-channel approach makes it user-friendly for human-robot collaboration. © 2013 IEEE