Operative treatment of bilateral hip dislocation in children with arthrogryposis multiplex congenita

Abstract PURPOSE: Arthrogryposis multiplex congenita (AMC) is a rare syndrome with multiple joint contractures. It is commonly believed that bilaterally dislocated hips associated with joint contractures should not be reduced, because movement is satisfactory, while open reduction leads to poor results. This report presents our experience with surgical management of bilateral dislocation of hips in children with AMC. METHODS: During the period 1990 to 2000, we performed open reduction on 8 hips of 4 children with AMC. The mean age at surgery was 23 months (range, 5-48 months). Open reduction and capsular plication without any bony procedure were performed in 4 hips (2 patients). De-rotation and varus osteotomy of the femur was performed in 4 hips, and Salter osteotomy of the innominate bone in 2 hips. The average acetabular index was 44 degrees, and the mean centreedge angle was -41 degrees preoperatively. RESULTS: The average follow-up period was 4 years (range, 2-9 years). The average acetabular index and centre-edge angle were 19 and 18 degrees, respectively at the time of last follow-up. All children could walk without support. One child required re-opening for redislocation of hip joint. The clinical results were good in 6 hips and fair in 2 hips, according to Severin\u27s and McKay\u27s classifications. CONCLUSION: Our experience shows that open reduction for bilateral dislocation of hips in children with AMC is a suitable option with generally good results. Surgery performed at earlier age gives the best functional outcome

OBSERVATIONS ON LERNAEID PARASITES OF CATLA CATLA FROM A FISH HATCHERY IN MUZAFFARGARH, PAKISTAN

During the present study, 120 fishes (Catla catla) maintained at a fish hatchery in Muzafargarh, Pakistan were examined for lernaeid parasites over a 12 months period from February 2000 to January 2001. Out of 120 C. catla fishes, 96 were infested, showing an overall prevalence of 80%. Six species of Lernaea recovered were: L. cyprinacea, L. polymorpha, L. ctenopharyngodonis, L. arcuata, L. lophiara and L. oryzophila. L. cyprinacea showed the highest parasitic burden (3.61 parasites per fish), while L. lophiara had the lowest parasitic burden (1.00 parasite per fish). The infestation was lowest in fishes with body length of 23.00-25.75 cm and maximum in 25.76-31.25 cm long fishes. Similarly, the parasitic infestation increased with body weight range of 160-258 gm to 456-553 gm, while almost no parasites were seen in heavier fishes (>553 gm)

Association of Physical Activity with Co-morbid Conditions in Geriatric Population

To find out association of physical activity with co-morbid conditions in geriatric population, a cross-sectional study was conducted in different cties of Pakistan in 2015. A total of 114 participants were inducted by non-probability convenience sampling technique. Data was collected after informed verbal consent by a validated questionnaire that is Rapid Assessment of Physical Activity (RAPA). Participants were categorized into two groups i.e. physically active and physically inactive. Data was entered and analyzed in SPSS version 20. There were 66 (57.9%) males and 48 (42.1%) females with mean age of 57.04±7.348 years. Among hypertensive individuals (n=43, 37.7%) there were 39 (90.7%) physically inactive, among individuals having angina (n=17, 14.9%) there were 15 (88.2%) physically inactive. Out of 37 (32.5%) diabetics, 35 (94.6%) were physically inactive. Among individuals suffering from arthritis (n=40, 35.1%), there were 38 (95%) physically inactive. A significant association was found between physical activity and diabetes and arthritis with p-value of 0.048 and 0.029 respectively. Physical activity is significantly associated with diabetes and arthritis in geriatric population. Adequate physical activity should be performed to reduce the risk of co-morbid conditions and improve the quality of life in geriatric population

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author’s research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher’s scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author’s profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The selfcitation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect selfcitation must be included

Ordering of droplets and light scattering in polymer dispersed liquid crystal films

We study the effects of droplet ordering in initial optical transmittance through polymer dispersed liquid crystal (PDLC) films prepared in the presence of an electrical field. The experimental data are interpreted by using a theoretical approach to light scattering in PDLC films that explicitly relates optical transmittance and the order parameters characterizing both the orientational structures inside bipolar droplets and orientational distribution of the droplets. The theory relies on the Rayleigh-Gans approximation and uses the Percus-Yevick approximation to take into account the effects due to droplet positional correlations.Comment: revtex4, 18 pages, 8 figure

Sizing HESS as inertial and primary frequency reserve in low inertia power system

Energy storage systems are recognised as the potential solution to alleviate the impacts of reduced inertia and intermittency in power systems due to the integration of renewable energy sources. Several energy storage technologies are available in the market with diverse power and energy characteristics, operational limitations, and costs. Besides, frequency regulations in power systems have different requirements, for example, inertial response requires high power for a short period while primary frequency regulation requires steady power for a longer time. Thus, it is crucial to find out the optimum sizes and types of storage technologies for these services. In this paper, a methodology for sizing fast responsive energy storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is developed. The sizing of storage systems for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is done separately. The sizing of storage for inertial response is done in two steps. A region reduction iterative algorithm is proposed to estimate the storage size for inertial response. The sizing of the storage system for primary frequency regulation is done analytically. The sizing methodology incorporates the frequency dynamics of storage, converters, and other associated controls that affect the frequency response. Moreover, an economic analysis is carried out to find the optimum combination of storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation services. The accuracy of the proposed sizing method has been compared with the metaheuristic algorithm based technique. The effectiveness of the proposed method is also compared with those in the literature. Simulation results show that the proposed method outperforms the existing methods in the literature. Finally, the non‐linear simulations revealed the validity of the optimal solutions

Ankle arthrodesis using Ilizarov ring fixator: A primary or salvage procedure?: An analysis of twenty cases

Introduction: Ankle arthrodesis using the Ilizarov technique provides high union rate with the added benefits of early weight-bearing, and the unique advantage of its ability to promote regeneration of soft tissue around the bone, including skin, muscle and neuro-vascular structures, and its versatility to allow correction of the position of the foot by adjusting the frame post-operatively as needed. We describe our experience with this technique and the functional outcomes in our patients. Materials and Methods: This retrospective study was conducted in 20 ankle fusion cases using the Ilizarov method between the years 2007 and 2017. We defined success in treatment by loss of preoperative symptoms and radiological union on plain radiographs of the ankle. Results: Fusion was achieved in all patients (100%). Immediate post-operative ambulation was with full weight bearing (FWB) in 16 (83%) of the participants and non-weight bearing (NWB) in 3 patients (17%). Post-procedure 11 patients (67%) of the participants who were full weight bearing required some form of support for walking for 2-3 weeks. Post-operatively three patients had pin tract infection requiring intravenous antibiotics. Radiological union took range of 6-12 weeks, mean union time was 8 weeks. Only one patient required bone grafting due to bone loss. Average follow-up period was 10-45 months. Conclusion:The Ilizarov technique has a high union rate and leads to general favourable clinical outcome and may be considered for any ankle arthrodesis but is especially useful in complex cases such as for revisions, soft-tissue compromise, infection and in patients with risk for non-union. Early weight bearing is an extra benefit

A framework for dynamic heterogeneous information networks change discovery based on knowledge engineering and data mining methods

Information Networks are collections of data structures that are used to model interactions in social and living phenomena. They can be either homogeneous or heterogeneous and static or dynamic depending upon the type and nature of relations between the network entities. Static, homogeneous and heterogenous networks have been widely studied in data mining but recently, there has been renewed interest in dynamic heterogeneous information networks (DHIN) analysis because the rich temporal, structural and semantic information is hidden in this kind of network. The heterogeneity and dynamicity of the real-time networks offer plenty of prospects as well as a lot of challenges for data mining. There has been substantial research undertaken on the exploration of entities and their link identification in heterogeneous networks. However, the work on the formal construction and change mining of heterogeneous information networks is still infant due to its complex structure and rich semantics. Researchers have used clusters-based methods and frequent pattern-mining techniques in the past for change discovery in dynamic heterogeneous networks. These methods only work on small datasets, only provide the structural change discovery and fail to consider the quick and parallel process on big data. The problem with these methods is also that cluster-based approaches provide the structural changes while the pattern-mining provide semantic characteristics of changes in a dynamic network. Another interesting but challenging problem that has not been considered by past studies is to extract knowledge from these semantically richer networks based on the user-specific constraint.This study aims to develop a new change mining system ChaMining to investigate dynamic heterogeneous network data, using knowledge engineering with semantic web technologies and data mining to overcome the problems of previous techniques, this system and approach are important in academia as well as real-life applications to support decision-making based on temporal network data patterns. This research has designed a novel framework “ChaMining” (i) to find relational patterns in dynamic networks locally and globally by employing domain ontologies (ii) extract knowledge from these semantically richer networks based on the user-specific (meta-paths) constraints (iii) Cluster the relational data patterns based on structural properties of nodes in the dynamic network (iv) Develop a hybrid approach using knowledge engineering, temporal rule mining and clustering to detect changes in the dynamic heterogeneous networks.The evidence is presented in this research shows that the proposed framework and methods work very efficiently on the benchmark big dynamic heterogeneous datasets. The empirical results can contribute to a better understanding of the rich semantics of DHIN and how to mine them using the proposed hybrid approach. The proposed framework has been evaluated with the previous six dynamic change detection algorithms or frameworks and it performs very well to detect microscopic as well as macroscopic human-understandable changes. The number of change patterns extracted in this approach was higher than the previous approaches which help to reduce the information loss

Cross-layer MAC/routing protocol for reliable communication in Internet of Health Things

Internet of Health Things (IoHT) involves intelligent, low-powered, and miniaturized sensors nodes that measure physiological signals and report them to sink nodes over wireless links. IoHTs have a myriad of applications in e-health and personal health monitoring. Because of the data’s sensitivity measured by the nodes and power-constraints of the sensor nodes, reliability and energy-efficiency play a critical role in communication in IoHT. Reliability is degraded by the increase in packets’ loss due to inefficient MAC, routing protocols, environmental interference, and body shadowing. Simultaneously, inefficient node selection for routing may cause the depletion of critical nodes’ energy resources. Recent advancements in cross-layer protocol optimizations have proven their efficiency for packet-based Internet. In this article, we propose a MAC/Routing-based Cross-layer protocol for reliable communication while preserving the sensor nodes’ energy resource in IoHT. The proposed mechanism employs a timer-based strategy for relay node selection. The timer-based approach incorporates the metrics for residual energy and received signal strength indicator to preserve the vital underlying resources of critical sensors in IoHT. The proposed approach is also extended for multiple sensor networks, where sensor in vicinity are coordinating and cooperating for data forwarding. The performance of the proposed technique is evaluated for metrics like Packet Loss Probability, End-To-End delay, and energy used per data packet. Extensive simulation results show that the proposed technique improves the reliability and energy-efficiency compared to the Simple Opportunistic Routing protocol

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems’ impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively