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The integration of Artificial Intelligence techniques, particularly Artificial Neural Networks (ANNs), has transformed predictive modeling in structural and durability engineering. This study investigates the use of ANN-based approaches to predict the corrosion rates of mild steel reinforcement embedded in cementitious composites subjected to clay-dominated soil environments. Key environmental parameters, sodium chloride (NaCl) content (0-4%), inhibitor dosage (DOI) (0-5%), and exposure duration (30-180 days), were selected as input variables. Two ANN architectures, Feedforward Backpropagation (FFBP) and Cascadeforward Backpropagation (CFBP), were developed and trained using 72 experimental data points extracted from the literature. The FFBP model outperformed CFBP in terms of predictive accuracy, achieving a correlation coefficient (R) of 0.998, a mean absolute percentage error (MAPE) of 30.43%, and a root mean square error (RMSE) of 0.071 during testing. Sensitivity analysis revealed that inhibitor dosage had the most significant influence on corrosion behavior, followed by NaCl concentration and exposure duration. The findings confirm that ANN models can effectively capture the nonlinear interactions governing corrosion progression, even under complex environmental conditions associated with clayey soils. This research provides a reliable and practical AI-driven framework for assessing corrosion risk, guiding material design, and enhancing long-term infrastructure durability in aggressive subsurface conditions. The study underscores the growing relevance of machine learning in simulating time-dependent deterioration processes in geotechnical and structural materials.

Laplace transform is one of the powerful tools for solving differential equations in engineering and other science subjects. Using the Laplace transform for solving differential equations, however, sometimes leads to solutions in the Laplace domain that are not readily invertible to the real domain by analytical means. Thus, we need numerical inversion methods to convert the obtained solution from Laplace domain to a real domain. In this paper, we propose a numerical scheme based on Laplace transform and numerical inverse Laplace transform for the approximate solution of fractal-fractional differential equations with order . Our proposed numerical scheme is based on three main steps. First, we convert the given fractal-fractional differential equation to fractional-differential equation in Riemann-Liouville sense, and then into Caputo sense. Secondly, we transform the fractional differential equation in Caputo sense to an equivalent equation in Laplace space. Then the solution of the transformed equation is obtained in Laplace domain. Finally, the solution is converted into the real domain using numerical inversion of Laplace transform. Three inversion methods are evaluated in this paper, and their convergence is also discussed. Three test problems are used to validate the inversion methods. We demonstrate our results with the help of tables and figures. The obtained results show that Euler’s and Talbot’s methods performed better than Stehfest’s method.

Piriformis syndrome is also synonymous with sciatica or buttock pain. This is a condition where the muscle irritates the sciatic nerve. This nerve passes above, below, or in between the piriformis muscle piercing it. The muscle tightens or shortens, thus compressing the nerve and disturbing the impulses passing from it. The sciatic nerve is a combination of nerve roots from L4 to S3. Piriformis works as a lateral rotator and is a synergistic muscle of the flexor and abductor group. Females most commonly present with piriformis syndrome than males. Many causative factors are responsible for the compression or impingement of the sciatic nerve, one of which is piriformis syndrome. Tingling, numbness, and pain are most often felt by patients when they have compression of any of the nerves. Many physiotherapy techniques have been found to be effective in managing this problem. Techniques like nerve mobilization, stretching, myofascial release, deep friction massage, and many more have been studied by authors describing their effects in the treatment of piriformis syndrome. Neural mobilization consists of two techniques, nerve gliding and nerve tensioning. Studies have found that the gliding technique produces less strain on the nerve than the tensioning technique. Piriformis stretch reduces the tightening, which has caused the impingement. Two techniques have been used for this stretch, stretching with hip flexion over 90 degrees and hip flexion under 90 degrees. This review focuses on the different advances in treating piriformis syndrome.

. The aim of this study is to investigate the numerical treatment of the Painlevé equation-II arising in physical models of nonlinear optics through artificial intelligence procedures by incorporating a single layer structure of neural networks optimized with genetic algorithms, sequential quadratic programming and active set techniques. We constructed a mathematical model for the nonlinear Painlevé equation-II with the help of networks by defining an error-based cost function in mean square sense. The performance of the proposed technique is validated through statistical analyses by means of the one-way ANOVA test conducted on a dataset generated by a large number of independent runs.

In the present work, an advance computational intelligence paradigm based on the artificial neural network is presented for analyzing the complex threshold scenarios of a steady-state thermal explosion, where temperature oscillations arise just below the system’s thermal threshold, leading to either a blow-up or settling down to a steady state. The solution of the governing differential equation is performed by using the artificial neural networks optimized with global search differential evolution enhanced by local refinements of principal axis maximization. The proposed scheme is applied for analyzing two scenarios: steady state with and without the blowing effect. In order to validate the accuracy of the designed methodology, a comparison of the proposed and exact solutions has been made. Moreover, the statistical interpretations are used to prove the worth, convergence, accuracy, stability, and robustness of the proposed algorithm.

Chronic marijuana and alcohol consumption leads to many forms of physical and physiological deterioration in the human body, mainly affecting the neurological system. A ​​​​65-year-old male patient suffered from stroke with the involvement of middle cerebral artery of the left side. Nine years later, patient presented with gangrene due to a traumatic unhealed wound on his left leg. The patient was under the influence of alcohol when he was injured. The infection spread, causing peripheral arterial disease, which eventually led to the development of gangrene till mid-calf of the left leg, for which he was amputated. Above-knee amputation with a fish-mouth incision was performed. Physiotherapy management focused on prehension and grip exercises for right upper limb, strengthening of all the limb musculature, stump management, transfer training, gait training with walker/crutches and prosthesis, and home exercise program, retraining activities of daily living. This case study embodies a rehabilitation program for this patient who suffered from stroke followed by amputation of the left leg. It focuses on bringing the patient back to his near-normal life. Regular physiotherapy helped the patient build up confidence, helped in resolving his addictions, and provided individual structure management. We present a rare case of above-knee amputation secondary to complications of alcohol abuse where an extensive post-op care and elaborate physiotherapy program resulted in a successful recovery.

A Hill-Sachs lesion is a bony defect in the head of the humerus due to recurrent dislocation, which results in friction between the humeral head and the glenoid fossa. This recurrent incident of dislocation that occurs in the anterior direction eventually leads to a Bankart lesion (a defect in the glenoid rim). A 21-year-old male, a recreational football player, reported recurrent shoulder dislocation, complaining of pain and difficulty doing certain activities. He had hypermobility of the shoulder joint during joint play assessment. Proprioception is the sense of the position and movement of one's own body. Exercises that improve proprioception can help improve shoulder stability and reduce the risk of shoulder injuries. Proprioception has shown significant positive results in shoulder dislocations. A physiotherapy protocol was designed that included strengthening of shoulder and scapular musculatures, proprioceptive exercises, and plyometric exercises for developing agility. All these exercises were given with taping for the shoulder joint. Taping helps stabilize the shoulder and normalizes muscle function. With the help of physiotherapy, patients can avoid invasive procedures for restoring stability in non-traumatic recurrent shoulder dislocations.

A woman in early 70s with retinitis pigmentosa (RP) had sudden decrease in vision in her right eye. She previously had cataract surgery in left eye. Her best corrected visual acuity (BCVA) was 20/80 in the right eye and 20/200 in the left eye. Examination showed corneal decompensation, advanced pigmentary retinopathy and macular oedema in both eyes.Optical coherence tomography (OCT) confirmed bilateral cystoid macular oedema, initially thought to be from pigmentary retinopathy. However, fundus angiography found a vasoproliferative tumour (VPT), measuring 3.187 mm × 3.477 mm, in the temporal periphery of the right eye, identified as VPT. Multiple Intravitreal ranibizumab injections resolved the macular oedema over few years. During VPT treatment, corneal decompensation worsened in right eye, causing severe corneal oedema and Descemet’s folds, reducing vision to counting fingers.She underwent iris claw lens implantation and penetrating keratoplasty (PKP) in the right eye. After surgery, a VPT was clearly visible in the peripheral fundus.

. A nature-inspired, integrated computational heuristic paradigm is developed for the piecewise solution of the nonlinear Bratu problem arising in fuel ignition model, electrically conducting solids and related fields, by exploiting the strength of Cascade Artificial Neural Networks (CANN) modeling, optimized with the memetic computing procedure based on global search efficacy of genetic algorithms (GAs), aided with the efficient local search of teaching learning based optimization (TLBO). The proposed technique incorporates the log-sigmoid activation function in the CANN model, trained by GAs hybridized with TLBO, i.e. , CANN-GA-TLBO. As a first application of CANN-GA-TLBO, 1D nonlinear Bratu's system represented with a boundary value problem of the second-order ordinary different equation has been solved, which is a benchmark for testing new algorithms. Comparison of the results with exact solution and previously reported solutions, including Adomian decomposition method, Laplace transformed decomposition method, B-Spline method and artificial neural network solutions, confirms the superiority of the designed stochastic solver CANN-GA-TLBO in terms of accuracy and convergence measures.

Understanding the influence of environmental variables on the spatial distribution of ecological communities is essential to predict the response of vegetation to various environmental drivers. Ecological theory suggests that multiple environmental factors shape local species assemblages and should influence the various components of community structure and composition in different ways. This study aimed to classify Pinus wallichiana dominated forests in the Swat Hindukush range mountains to understand the relative influence of multiple environmental filters on its composition and structure. These forests represent the most typical of the species distribution in northern Pakistan and were not subjected to any phytosociological study. For this purpose, thirty forest stands, spanning a wide range of physical habitats were sampled using 10 × 10 m plots and the importance value index was calculated. The floristic and environmental data were subjected to Ward’s agglomerative cluster analysis for objective classification and ordinated with NMS ordination for pattern description and testing the vegetation–environmental relationships. Three floristically and ecologically distinct communities were recognized along the topographic gradient (elevation, r  = 0.377; slope, r  = 0.5548) coupled with soil physical (clay, r  = 0.2782; silt, r  = 0.3225) and chemical properties (pH, r  = 0.4975; lime, r  = 3982). An elevation gradient of 100 m separated the low ( Pinus wallichiana – Quercus dilatata community) and middle elevations forest stands ( P. wallichiana pure population) from the highland population type ( Pinus wallichiana – Cedrus deodara community). The floristics and structure of these forest types respond directly or indirectly to topographic and soil variables which were evidenced from the floristic composition, species richness, and community physiognomy. These characteristics of the communities changed from heterogenous, dense stands to sparsely dispersed conifers, broadleaved-evergreen, and deciduous vegetation types along the environmental gradients. We concluded that several factors explained the spatial pattern. Thus, we predict that elevation coupled with soil physical and chemical properties could be considered if the ultimate goal is the restoration and conservation of these forests. We also recommend broad-scale phytosociological study on P . wallichiana forests including multiple sampling from diverse locations to define the floristic diversity and to better understand its ecological requirements.