Our invention presents the design and development of a novel laboratory-scale sky dome test setup that simulates urban street canyon microclimates under controlled, repeatable indoor conditions. The system replicates key thermodynamic processes such as surface radiative heat exchange, heat storage and canyon air temperature, by integrating solar geometry, spectral radiation characteristics, and longwave thermal exchange. Unlike conventional wind tunnels or scaled physical models which focus primarily on airflow and pollutant dispersion, our system uniquely integrates direct and diffuse solar simulation (using a traversing xenon lamp and overhead halogen luminaires) with a temperature regulated hemispherical dome (fabric emissivity =0.8) to enable realistic shortwave and longwave radiation exchange. This setup is particularly significant given the existing indoor scaled experimental setups lack integrated radiative-thermal geometric scaling and outdoor experiments are affected from uncontrollable environmental variability. The setup developed and presented in this manuscript enables systematic parametric testing of surface albedo, material properties, street canyon orientations, and radiation intensity. It proposed to use a comprehensive scaling basis for geometric, thermal, radiative, and aerodynamic similarity, to investigate surface temperature, convective heat transfer, canyon air temperature, and dimensionless parameters such as the Nusselt number, under replicable laboratory conditions. The sky dome thus provides a resource-efficient platform for evaluating urban heat mitigation strategies, such as cool surfaces, with higher fidelity and control than existing solutions.
This invention disclosure presents a novel design of 700 kW rating power wind turbine to operate in Indian weather. The apparatus is a gearless, variable-speed Wind Energy Converter (WEC) optimized for operation in low-wind regimes (IEC Class IIIb) found in Indian subtropical locations, such as Delhi. The core invention is an integrated structural architecture comprising a fabricated hexagonal hub, blade extender interface, and optimized tapered hollow axle. The turbine utilizes a 35-meter rotor diameter. An extender component is integrated into the design with a tip chord length of 1735 mm to prevent physical interference between the blade and the generator stator. Structural analysis of the fixed tapered hollow axle assembly confirms it withstands maximum induced stresses of 93 MPa, which is safely below the design limit of 100 MPa. The maximum free-end deflection of the axle is determined to be 1.84 mm. The WEC system is designed for a rated wind speed of 12 m/s.
KINMET is an open-source, integrated graphical user interface (GUI) software tool designed to accelerate the computation of head injury indicators from six-degree-of-freedom kinematic data. The software provides a comprehensive workflow for processing raw six-degree-of-freedom measurements, enabling computation of widely used biomechanical injury metrics, including Head Injury Criterion (HIC15, HIC36), Brain Injury Criterion (BrIC), Gadd Severity Index (SI), peak resultant linear acceleration and angular velocity, estimated neck forces and moments, and Neck Injury Criterion (Nij). The interface supports selecting data start rows, unit conversion, and configurable signal-filtering options (CFC, Butterworth, Savitzky-Golay, moving average). KINMET automatically generates multipage PDF reports containing summary tables, time-series plots, HIC windows, BrIC and probability curves, neck-load histories, and methodology documentation in addition to a JSON metadata file that is exported for reproducibility. Additional capabilities include live signal preview during the data import stage, automated calculation of sampling frequency, detection of impact event windows, and recommendations for filter parameters based on signal characteristics. This integrated solution addresses the need for accessible, standardized head injury assessment in biomechanics research, automotive safety testing, and sports biomechanics and injury research applications.
The growing popularity of automated processes, empowered by Artificial Intelligence (AI), is changing how companies offer and fill their jobs. The growth of AI has created the need for a more sophisticated way to find a job, through the use of CRM-based applications. Most job search sites offer simple keyword matching (keyword searches) with little or no customization, resulting in a low accuracy rate. This paper presents a new type of Job Recommendation Model, based on the use of Hybrid AI models. The purpose of this new hybrid model is to improve job matching through three separate processes: skill-based filtering, sentiment analysis, and predictive modeling. The proposed hybrid model will take user calculated skill sets into account for the job description and will use AI adoption levels, automation risk, expected salary, and anticipated growth opportunity for the job type. The hybrid model will produce two scores for each job type; the first score represents the potential to match with the specific job type based on the applicant’s skills and the second score represent the applicants’ overall profile. Once combined to create a matching score, the job recommendation system will recommend jobs based upon applicant's (1) Skills, (2) Ability to do the job, (3) Salary Range, and (4) Job Growth Potential (area of interest on the applicant).
In recent years, the adoption of cloud computing and distributed systems has increased exponentially, and the adoption of the Internet of Things (IoT) has also increased rapidly. The requirement to protect user data suggests the need for cryptographic solutions to secure data storage, communication, and authentication. In a typical cryptographic device, key storage is usually centralised, which creates vulnerabilities to attacks and failures by introducing a single point of failure and limited resistance to compromise and fault attacks. Therefore, this work proposes a healing cryptographic device that provides a distributed storage system and an automated healing and recovery mechanism to address the challenges and shortcomings identified above. The proposed device offers fault tolerance and sufficient resistance relative to the conventional centralised system and applies to cloud computing and critical Internet of Things (IoT) industries.
A new chemistry of carbon capture is presented, encompassing the transition metal nucleated molten carbonate splitting of CO2. The present series of inventions relates to a decarbonization system, process, and apparatus for producing a portfolio of different CNTs and nanomaterials and their production using CO2 as the reactant, which is transformed by electrolytic splitting in molten carbonate electrolytes, such as Li2CO3, or in a more advanced forms by less expensive non-lithium carbonates. CO2 is added to a molten carbonate, such as Li2CO3, and the molten carbonate is subjected to electrolysis by passing current from an anode to a cathode. A transition metal nucleation agent results in nucleation sites that grow carbon nanomaterials at the cathode. This decarbonization process splits CO2, which separates into oxygen at the anode, and carbon nano-materials at the cathode. US patent 10730751i is for the system and process, while US patent 11,402,130 is for the apparatus, including driving the process by solar and other energy forms. Other patents apply this process to the formation of specialized carbon nanotubes, such as helical, magnetic, thin, long (wool), doped, open core knotted (nano-bamboo), closed core beaded (nano-pearl), macro-assembly (macroscopic organized assemblies) CNTs, and lithium-free electrolytes for Graphene-Based Nanocarbon synthesis.
The invention relates to a software-based digital ecosystem designed to enhance medical diagnostics and healthcare delivery in underserved rural regions. The system integrates data acquisition from wearables and smart medical devices (e.g., cardiovascular or motion sensors) to enable data-driven medical decision-making. It consists of six core software components: (1) a patient application for data management, visualization, and communication; (2) a central data and interoperability platform that securely transmits medical data between patients and physicians; (3) a physician dashboard providing analytical and diagnostic functionalities based on real-time patient data; (4) a regional health marketplace that connects users to nearby healthcare and diagnostic services, (5) an analysis service that allows external systems to integrate laboratory result, and (6) a measurement service to integrate data from point-of-care smart medical devices. The technical foundation of the ecosystem lies in its FHIR-compliant data exchange and the implementation of a metadata-based data taxonomy that ensures semantic interoperability, standardized data exchange, and integration with existing health information systems. This design enables scalable, interoperable, and automated diagnostic workflows supporting efficient healthcare in rural environments.
Sugarcane (Saccharum officinarum) diseases including smut, yellow leaf disease, pokkah boeng, mosaic, brown spot, grassy shoot, sett rot, brown rust, and banded chlorosis collectively cause yield losses of 20-50% per annum across India, disproportionately affecting the 5.5 million smallholder farmers who cultivate over 5.5 million hectares annually. This paper describes a mobile AI application designed to enable real-time, non-expert identification of all eleven sugarcane leaf categories (nine diseases, healthy, and dried) directly from smartphone images. The application uses an EfficientNetB4 convolutional neural network pre-trained on ImageNet-21k and fine-tuned on a purpose-built dataset of 6748 field-collected images from Maharashtra, India. The model achieves a macro-average F1-score of 96.2% and an overall accuracy of 97.5% on a held-out test set, with per-class accuracy ranging from 95.4% (Banded Chlorosis) to 99.3% (Healthy). The application operates fully offline through INT8-quantised TensorFlow Lite inference, achieving average latency of 2.1–3.8 seconds on mid-range and low-end Android smartphones, with a disk footprint of only 18.4 MB. A pilot field trial with 18 farmers yielded 95.8% real-world classification accuracy and a task completion rate of 94.4% among first-time users, with an average time-to-diagnosis of 8.4 seconds compared with 3.2 days under the prior extension-officer model. Upon disease confirmation, the system retrieves localised remedial recommendations from an integrated agronomy database in Marathi and English. A user feedback loop enables weekly model retraining for continual improvement. The system is designed for Maharashtra’s cooperative agricultural system. Current constraints includes agricultural sector with single climatic zone.
This invention discloses a drip irrigation system with a distributed control architecture designed to improve operational resilience in automated irrigation. Rather than relying on a single centralised controller, the system employs a network of autonomous local controllers, each responsible for a discrete group of solenoid valves and environmental sensors covering a subset of the cultivation plots. A hydraulic subsystem comprising an elevated water storage tank, an electric pump, and a pressure-regulation assembly supplies water to the driplines, and each dripline incorporates a solenoid valve together with an in-line manual override valve. Each local controller combines a microcontroller-based processing unit, a relay module, and a wireless transceiver, and draws real-time agronomic data from a sensing module that includes soil-moisture, pH, temperature, and rain sensors. The local controllers communicate with a central server, which acts as a gateway to a mobile application through which the user can monitor valve and pump status, view sensor data, and issue remote commands. Because control is decentralised, the failure of any single controller affects only the plots connected to it, while the remainder of the field continues to irrigate normally. Power is supplied by an integrated solar generation assembly with battery storage and Maximum Power Point Tracking charge control, enabling off-grid operation. The architecture is particularly suited to tropical deployments, including Malaysian horticulture and plantation nurseries, where rainfall variability and intermittent grid supply reward resilient, self-powered systems. The inventor is seeking collaborative and licensing partners for commercialisation in agricultural and horticultural applications.
We have identified an antibiotic drug lead, named MGC-10, which kills Gram positive bacteria, including methicillin-resistant Staphylococcus aureus (MRSA), with a MIC (minimum inhibitory concentration) of 6 μM, while being harmless to mammalian cells in vitro in that concentration range. The antibacterial activity of MGC-10 was broad against over 50 strains of antibiotic-resistant samples obtained from hospital infections, where MGC-10 inhibited all tested strains of MRSA.The target for the MGC-10 activity is the ternary complex comprised of elongation factor Tu (EF-Tu), an aminoacylated tRNA (aa-tRNA), and guanosine triphosphate (GTP). Ternary complex delivers amino acids to the growing protein chain, bringing aa-tRNA into the codon-programmed A-site of the ribosome.MGC-10 appears to have potential as a topical treatment for difficult-to-treat wounds or skin infections by gram-positive pathogens such as MRSA. In a mouse skin-infection model with MRSA, MGC-10 performed as well or better than the present topical drug of choice, mupirocin. The drug showed little if any accumulation in the livers of topically treated mice.
MyKidney is an accessible, web-based software platform designed to assist in the automated detection of kidney stones from axial computed tomography (CT) images. By leveraging a custom convolutional neural network (CNN), the system classifies CT slices and visually highlights potential stone regions, aiding in preliminary diagnosis. The model achieved a classification accuracy of 98.80 %, demonstrating a superior balance between sensitivity and specificity when compared to established architectures like ResNet-18 and VGG19. The platform provides a cost-effective, real-time solution for clinics, particularly in under-resourced or rural areas where access to radiology expertise may be limited. Users can upload images directly via the web interface, and the system processes the data using a trained deep-learning model hosted on a secure backend. This software provides a potential entry point for further Artificial Intelligence (AI) assisted diagnostic tools and can be integrated into telehealth systems. We welcome potential collaborators interested in further validating or extending this tool for broader medical imaging applications.•Provides a low-cost, accessible tool for automated kidney stone detection using CT imaging, supporting early identification and reducing diagnostic delays in clinical settings.•Healthcare professionals in rural or resource-limited environments, academic researchers in medical imaging, and developers of telehealth platforms can benefit from its deployment.•The modular architecture and open-source nature allow the software to be reused, extended, or adapted for additional diagnostic tasks or integrated into broader AI-driven radiology systems.•Offers real-time visual feedback with interactive elements, making it suitable for training, demonstration, and patient education in medical institutions.•Bridges the gap between technical deep learning models and practical clinical tools through a browser-based interface that requires no installation or specialist hardware.•Supports the ongoing development of explainable AI tools by providing interpretable predictions and confidence indicators, contributing to transparency in clinical AI adoption.
This work presents a laminated solid-electrolyte architecture that improves damage tolerance, performance, and safety in lithium‑metal batteries by controlling lithium‑filament (dendrite) propagation. The design embeds a thin mixed ionic–electronic conducting (MIEC) interlayer, exemplified by reduced graphene oxide (rGO), between two dense ceramic electrolyte layers to bias filaments to grow laterally at the interlayer rather than through‑thickness. In symmetric Li‖Li tests, the interlayered electrolyte sustains higher current density before catastrophic failure (e.g., ∼3.8 mA·cm⁻² vs ∼0.6 mA·cm⁻² without the interlayer) and exhibits self‑recovery behavior consistent with ohmic cycling. Although demonstrated with a garnet‑oxide platform (LLZTO), the architectural principle is compatible with multiple solid‑electrolyte families (oxide garnets, sulfide argyrodites/glasses, polymers/composites).
The invention discloses a method for the Centralized Vehicle Information and Document Repository System, which comprises a mobile, tablet, and computer device and a server connected to a network. The system is mainly intended to provide transportation companies with a system that will protect the vehicles’ important documents to avoid operational disruption with quick access, notification of registration’s validity, and quick search of specific or volume information. The server computer will verify all incoming requests from the query parsing application, and the query parsing application should be registered by connecting the device to the network and keying in the registration details submitted to the server through its network. The admin either approves or rejects the registration. If approved, the admin can later manage the user (activate/deactivate). The approved user logs in with credentials, and a 2FA code is sent to the registered mobile number for confirmation. If the 2FA check succeeds, the user gains access to the system dashboard; if it fails, access is denied. The user can perform CRUD (Create, Read, Update, Delete) operations on vehicle records, such as uploading files or entering vehicle data. The invention is best for micro and macro wherein companies indulged to invested in technological infrastructure and focused mainly on operational functions in the transportation industry. Moreover, the potential when you try to understand the idea of the invention is that it can minimize, if not eliminate, the duplication of data, overspending in document registration and renewal, and mainly avoid excess penalties for smooth everyday transport operation.
Alkaline electrolyzer technology is widely considered the most mature and cost-effective way of producing green hydrogen, provided the electricity to operate the electrolyzer is generated via renewable energies. These electrolyzers typically consist of two electrodes, anode and cathode, separated by a diaphragm which is often Zirfon™, a porous composite separator material composed of a polysulfone matrix and ZrO in powder form. This porous membrane allows for the ion transport in the electrolyte and separates the product gases hydrogen and oxygen from each other. However, there are dissolved product gases in the liquid electrolyte that cross the porous membrane via diffusion. The main reason for this crossover rate is the fact that the aqueous liquid electrolyte phase is supersaturated with hydrogen on one side and oxygen on the other. We are proposing to add a layer of coating to the outside faces of the Zirfon membrane to promote the formation of hydrogen/oxygen bubbles outside of the membrane and thus reduce the hydrogen and oxygen crossover.
This invention presents an innovative anaerobic bioreactor prototype, called the electrostimulated anaerobic bioreactor (EAB), consisting of a tubular container with conductive and non-conductive supports, arranged radially and alternately around the circular cross-section and aligned with the longitudinal axis. Electrical stimulation applied to the conductive supports facilitates direct microbial electron transfer, enhancing reaction kinetics. In combination with non-conductive supports, which provide micro-niches that promote diverse microbial attachment and metabolic activity, the system fosters syntrophic interactions, accelerates substrate conversion, ensures rapid process stabilization, and maintains sustained long-term performance, thereby increasing the overall efficiency of anaerobic digestion. The EAB provides an adaptable platform for treating organic-based substrates and generating renewable energy. It enables the production of methane, hydrogen, and recoverable chemicals. It can be integrated with other bioprocesses for process optimization, testing of alternative substrates, or inclusion in biorefineries for the development of new bio-based products.
Accurate measurements of body composition (Fat and Fat-Free Mass) are critically important in the clinical and sport-performance settings. In clinical settings, it is important that allied-health professionals can measure body composition to determine potential risk of obesity-related health complications, as well as to track progress during pharmacological, dietary, or physical activity programs aimed at fat loss. In sport-performance settings, athletes must measure and track improvements in Fat-Free Mass as it is related to strength and power. However, traditional body composition assessment techniques are expensive, not portable, and require a great deal of technical expertise to administer. However, recently anthropometric measurements of body size and shape have been used to estimate body composition, overcoming many of the barriers experienced with other techniques. The methods and systems described in this invention measure the anatomical dimensions of an individual from a single two-dimensional (2D) digital image. The digital image is taken from the front/anterior view using a mobile, handheld communication device. The linear measurements are used to estimate the body volume of the individual, from which total body density can be calculated from the estimated body volume and body weight. Body composition (Fat Mass and Fat-Free Mass) of the individual is derived from density using known mathematical conversion formulas.
This invention introduces an effective in situ granulation method that transforms non-granular microbiomes into granular forms suitable for bioprocess applications. The approach has significant potential to advance anaerobic biotechnologies, particularly in systems designed for energy recovery or the production of high-value biochemicals from organic waste streams, while simultaneously reducing carbonaceous contaminants to support carbon neutrality and mitigate global climate change. This capability is attributed to fermentative granular microbiomes, whose dense and compact structures harbor specialized microbial consortia capable of efficiently degrading organic matter. The present invention proposes a methodology that integrates microbial preconditioning for extreme environments, fermentable substrates to promote granulation, and electrical stimulation to accelerate the process. This strategy is readily applicable in standard reactors currently in use. Its prospective applications lay the groundwork for single-platform biorefineries spanning sectors such as wastewater treatment, VFA-to-biochemicals conversion, digestate upcycling, biogas-to-power (biomethane and biohydrogen), and specialty chemicals production, including bioplastics and green solvents.
The invention embodies a method to incorporate nanoparticles in an energy storage device, more specifically batteries and supercapacitors, to enable quantized capacitance energy storage mechanism. It operates on the principle of quantization in nanoscale, also referred to as the solvated Coulomb blockade mechanism, rather than the classical intercalation mechanism, allowing greater electron storage potential within the nanoparticles relative to classical energy storage devices. This enables pseudocapacitive battery to theoretically store as much energy as lithium-ion batteries while all its other properties resemble ultracapacitors, including the theoretical ability to have long cycle life and can be charged and discharged at very high rates, a significant departure from lithium-ion battery technology which are well known to have long charging times. This invention also enables electrochemical storage devices to be designed with sustainable materials (e.g., carbon), transitioning from the heavy reliance on precious metals (e.g., lithium, manganese, cobalt, and nickel).
In this invention, a novel family of benzohomoadamantane-based soluble epoxide hydrolase inhibitors is disclosed. The benzohomoadamantane scaffold is a polycyclic, readily accessible, system that features a homoadamantane unit fused with an aromatic ring. Although it has been scarcely used in medicinal chemistry, it is very versatile and permits several chemical derivatizations in its structure in order to improve the inhibitory activity and/or the DMPK properties of the compounds. This invention encompasses ureas, amides, carbamates and thioureas. The compounds are of interest for the broad range of therapeutic indications related to the inhibition of soluble epoxide hydrolase, including inflammatory diseases, pain and neurodegeneration. This invention has been licensed to a US-based start-up that is actively recruiting investors and strategic partners for advancing a selected candidate to regulatory studies.
Screening and mining efficient polyethylene terephthalate (PET)-degrading enzyme is a promising strategy for plastic waste treatment and recycling. This invention provides a novel transcription factor-based biosensor method for identifying potential PET-degrading enzymes from the microorganisms. When PET is degraded by a specific enzyme to produce terephthalic acid (TPA), the transcription factor TphR recognizes the TPA substrate and then activates the transcription of downstream green fluorescent protein (GFP) reporter, generating measurable fluorescence signals in bacterial cells.
