Cosine Similarity Measure According to a Convex Cost Function

In this paper, we describe a new vector similarity measure associated with a convex cost function. Given two vectors, we determine the surface normals of the convex function at the vectors. The angle between the two surface normals is the similarity measure. Convex cost function can be the negative entropy function, total variation (TV) function and filtered variation function. The convex cost function need not be differentiable everywhere. In general, we need to compute the gradient of the cost function to compute the surface normals. If the gradient does not exist at a given vector, it is possible to use the subgradients and the normal producing the smallest angle between the two vectors is used to compute the similarity measure

Projections Onto Convex Sets (POCS) Based Optimization by Lifting

Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in R^N the corresponding set is a convex set in R^(N+1). The iterative optimization approach starts with an arbitrary initial estimate in R^(N+1) and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp, p<1 can be handled by using the supporting hyperplane concept

Compressive sensing using the modified entropy functional

Cataloged from PDF version of article.In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. All rights reserved

A case control study to compare the effect of dynamisation of tibia nail in union of tibia shaft fracture versus non-dynamisation

Background: Tibial shaft fractures are commonly treated with intramedullary nails (IMN), with union rates of 90-100%, but complications such as delayed union occur in up to 40%. The rise of technology and urbanization has led to an increase in road traffic injuries and deaths. The treatment of distal tibia fractures has undergone various modifications over the years, with emphasis on preserving local biology and soft tissue handling. Methods: A retrospective case-control study involving 132 patients with closed or open grade 1 tibia shaft fractures was conducted from September 2021 to May 2022. Patients received tibia IMN with either dynamic locking (group A) or static (group B). Patients were evaluated for fracture healing and clinical condition, with variables including presence or absence of union and time to union. Follow-up clinical evaluations were conducted monthly for six months. Results: The association between union of bones seen at 1.5 and 3 months between group A and group B was extremely statistically significant (p&lt;0.0001). Conclusions: Intramedullary nailing with dynamic nailing assemblies is safe and effective for closed or type I open tibial fractures with limited comminution. This approach may reduce complications and re-operations and allow for early weight-bearing. Proper management of tibial fractures requires an interprofessional team.

Entropy-Functional-Based Online Adaptive Decision Fusion Framework with Application to Wildfire Detection in Video

Cataloged from PDF version of article.In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented

Data-driven short-term load forecasting for heating and cooling demand in office buildings

Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings

Plasma disposition, concentration in the hair, and anthelmintic efficacy of eprinomectin after topical administration in donkeys

Objective-To investigate plasma disposition, concentration in the hair, and anthelmintic efficacy of eprinomectin after topical administration in donkeys. Animals-12 donkeys naturally infected with strongyle nematodes. Procedures-The pour-on formulation of eprinomectin approved for use in cattle was administered topically to donkeys at a dosage of 0.5 mg/kg. Heparinized blood samples and hair samples were collected at various times between 1 hour and 40 days after administration. Samples were analyzed via high-performance liquid chromatography with fluorescence detection. Fecal strongyle egg counts were performed by use of a modified McMaster technique before and at weekly intervals for 8 weeks after treatment. Results-Plasma concentration and systemic availability of eprinomectin were relatively higher in donkeys, compared with values reported for other animal species. Concerning the anthelmintic efficacy against strongyle nematodes, eprinomectin was completely effective (100%) on days 7 and 14 and highly effective (> 99%) until the end of the study at 56 days after treatment. No abnormal clinical signs or adverse reactions were observed for any donkeys after treatment. Conclusions and Clinical Relevance-Eprinomectin had excellent safety. The relatively high plasma concentration after topical administration could result in use of eprinomectin for the control and treatment of parasitic diseases in donkeys

Use of Hartshill rectangle with sublaminar wiring for posterior stabilization of D7-D9 tubercular spondylodiscitis with paraplegia: a cost effective treatment

Tuberculosis presents a significant health challenge, with extrapulmonary cases comprising 15-20%. Spinal tuberculosis often leads to neurological deficits, requiring surgical intervention such as Hartshill system fixation. Various posterior instrumentation methods are employed, with sublaminar wiring pioneered by Luque and enhanced by Dove's Hartshill system for superior biomechanical performance. This case underscores Hartshill system's efficacy in stabilizing the spine post-tubercular destruction, offering a cost-effective alternative to pedicle screws. An 18-year-old presented with 6-month upper back pain, progressing weakness in lower limbs, weight loss, and fever. Radiographs revealed D7-D9 vertebral destruction, leading to kyphosis. MRI showed paradiscal bony destruction and abscess, suggestive of tubercular spondylodiscitis. Surgery with Hartshill rectangle and sublaminar wiring provided kyphosis correction. Post-operative Gene-Xpert confirmed tuberculosis. Mobilization and chemotherapy led to limb power restoration within 3 months, with ongoing rehabilitation and consolidation of affected segments with complete recovery by eight months. Instrumented stabilization in spinal TB prevents kyphosis and graft complications; Hartshill loop rectangle and sublaminar wire fixation, cost-effective and suitable for resource-poor settings, offer comparable outcomes to pedicle screws, enabling hybrid fixation, especially in low-income countries