Vehicle-to-grid is usually justified by energy arbitrage, but the tariff an Indian charging station faces leaves no room for it. This study asks what it is worth at a twenty-bay campus station in Jaipur with the network modelled and owners paid enough to join. The station, with 150 kWp of solar, 100 kW of modelled wind and a 200-kWh battery, is scheduled by a mixed-integer linear program carrying per-vehicle state of charge, battery wear, a linearized model of the 415 V feeder and an outage stage in which the fleet replaces diesel. The surplus is divided between the operator and six owner groups by the Shapley value, the nucleolus, a proportional rule and an equal split, enrolment responds to the payout through a logit model. Vehicle-to-grid eliminates diesel from every outage that is evaluated, reducing the anticipated daily cost of diesel from Rs 41.60 to zero. However, it contributes nothing during regular operation and maintains hosting capacity at 4.10 times current demand. All four rules settle at 9% enrollment, the excess of Rs 37.79 per day is seventy-seven times too tiny. Only the nucleolus is steady. The magnitude of the excess is the binding restriction rather than fairness.
Background: Chronic low back pain is a leading cause of disability, affecting 619 million people in 2020 (1). Most patients report poor sleep, with over half reporting clinical insomnia (2), yet evidence on whether delivery mode affects PSQI-measured sleep remains limited. Objective: To compare PSQI outcomes after conventional physiotherapy, tele-rehabilitation and virtual reality therapy for chronic low back pain. Methods: In this three-arm randomised trial, 78 adults with chronic low back pain were allocated (26 per group) to six weeks of CPT, TRT or VRT; the primary outcome was the PSQI Global Score. Results: Sixty-six participants completed; PSQI Global Scores fell by 3.91, 3.73 and 4.55 points with CPT, TRT and VRT (all p < 0.001), exceeding the 3-point MCID, but the between-group difference was not significant (p = 0.184). Conclusion: All three delivery modes improved sleep, pain and disability; VRT showed the most favourable sleep outcomes, though between-mode differences were small and non-significant.
A neurodevelopmental learning disability called dysgraphia makes it difficult for a person to write legibly, consistently, and correctly. It typically shows itself as poor letter formation, uneven spacing, inconsistent sizing, slow writing speed, and misalignment. Teachers, psychologists, or clinicians must manually observe students in order to make a conventional diagnosis. This approach is laborious, subjective, and challenging to scale across large student populations. This study offers a thorough analysis of a suggested web-based dysgraphia detection system that uses machine learning and image processing to automate this evaluation. Five learning algorithms—Convolutional Neural Network (CNN), ResNet-18, Support Vector Machine (SVM), Decision Tree, and Random Forest—are used by the system to classify handwriting samples as Normal or Dysgraphic after preprocessing operations (grayscale conversion, resizing, normalization, and noise removal) are performed using OpenCV. The system’s foundational machine learning algorithms, deep learning-based dyslexia and learning-disability screening research, and dysgraphia-specific detection studies are all included in the paper’s structured literature review of thirty peer-reviewed and archival sources. The system architecture, technology stack, modular design, testing methodology, and evaluation approach are described after a comparison of the suggested system with conventional manual assessment. The discussion concludes with the system’s limitations and future research directions, such as multilingual support, larger and clinically validated datasets, and mobile deployment. It highlights the research gap addressed by comparing multiple classical and deep-learning models on a common handwriting dataset within a single accessible web application.
Electrifying the powertrain changed something basic about vehicle dynamics: it changed which physical quantities an engineer can command at each tyre contact patch, how fast, and how independently. This review works through the recent literature on electric-vehicle (EV) dynamics and stability control with one question in mind, what has powertrain electrification actually changed about the fundamental capabilities, architectures, and control strategies available for managing how a vehicle moves? Rather than repeating the structure of earlier surveys, the paper follows an evolutionary thread: from classical vehicle dynamics, through the actuators that became newly controllable, through stability objectives, control architecture, control allocation, predictive and learning-based control, integrated vehicle motion control, and finally toward software-defined vehicle platforms. Direct yaw moment control and torque vectoring get a critical look, with particular attention to where they outperform conventional steering and where they don’t. State estimation, the various control-theoretic taxonomies (LPV, sliding-mode, fuzzy, Lyapunov-based, model predictive, adaptive, robust, control allocation, and learning-based methods), and the trade-off between stability and energy consumption that is specific to EVs are all discussed against the primary literature. The review also contributes two original comparative tables, four original figures, and a taxonomy of propulsion architectures, and it closes by naming fifteen open research gaps and sketching where integrated, energy-aware, fault-tolerant motion control for software-defined electric vehicles seems to be headed.
Diabetes mellitus is a fast-growing metabolic disease marked by progressive β-cell dysfunction, insulin resistance, and persistent hyperglycemia. Inflammation, oxidative stress, the production of advanced glycation end products, and vascular problems are all encouraged by persistent hyperglycemia, especially postprandial hyperglycemia. Two important enzymes involved in the breakdown of carbohydrates and the release of glucose are α-amylase and α-glucosidase. Dietary starch is hydrolyzed by α-amylase into oligosaccharides and dextrins, which are then transformed into absorbable glucose by intestinal α-glucosidase. As a result, blocking these enzymes is a crucial tactic for postprandial glucose excursions and postponing the digestion of carbohydrates. For this aim, conventional inhibitors such acarbose, miglitol, and voglibose are used clinically; nevertheless, their use is often linked to gastrointestinal side effects, such as diarrhea, bloating, flatulence, and abdominal discomfort. These restrictions have prompted researchers to look into naturally occurring substances that have better tolerance and multitarget action. The α-amylase and α-glucosidase inhibitory, antioxidant, anti-inflammatory, and insulin-sensitizing characteristics of flavonoids, a significant class of dietary polyphenols, have garnered significant interest. The naturally occurring flavonoids apigenin and quercetin, which have different flavone and flavonol structures, respectively, are the main subjects of this review. There includes discussion of their pharmacological actions, chemical properties, natural sources, and possible methods against α-amylase and α-glucosidase. In addition to influencing oxidative stress, inflammatory signaling, insulin sensitivity, glucose transport, and pancreatic β-cell protection, both substances may decrease the hydrolysis of carbohydrates and the absorption of glucose. The information that is now available suggests that apigenin and quercetin could be useful natural lead molecules for a multitarget strategy to control diabetes. To determine their effectiveness, safety, bioavailability, and therapeutic potential, more mechanistic, pharmacokinetic, preclinical, and clinical research is necessary.
