Technology Gap Research Articles

Development of an Innovative Pupillometer Able to Selectively Stimulate the Eye's Fundus Photoreceptor Cells.

Recent advancements in clinical research have identified the need to combine pupillometry with a selective stimulation of the eye's photoreceptor cell types to broaden retinal and neuroretinal health assessment opportunities. Our thorough analysis of the literature revealed the technological gaps that currently restrict and hinder the effective utilization of a method acknowledged to hold great potential. The available devices do not adequately stimulate the photoreceptor types with enough contrast and do not guarantee seamless device function integration, which would enable advanced data analysis. RetinaWISE is an advanced silencing pupillometry device that addresses these deficiencies. It combines a Maxwellian optical arrangement with advanced retinal stimulation, allowing for calibrated standard measurements to generate advanced and consistent results across multiple sites. The device holds a Class 1 CE marking under EU regulation 2017/745, thus facilitating clinical research progress.

Transforming air pollution management in India with AI and machine learning technologies

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

Yield Gap Analysis and Impact Assessment of Broccoli in Tirap District of Arunachal Pradesh, India

A frontline demonstration (FLD) on broccoli was carried out during 2021-22 and 2022-23 respectively by Krishi Vigyan Kendra (KVK), Tirap, Arunachal Pradesh at farmer’s field. The improved variety- Solan Green head was demonstrated with full package of practices. The FLD plots was recorded 146 & 167 q/ha yield as compared to 97 & 126 q/ha respectively under farmer’s practice. The technology gap was 34 & 13 q/ha, extension gap was 49 & 41 q/ha and technology index was 18 & 7 respectively, during the both the years of study. Similarly, the net income (Rs.34700 and 408000) and B:C ratio (3.81 and 4.38) was also higher under FLD as compared farmer’s practice (Rs.215000 and 294000, 2.82 and 3.50) due to implementation of improved production technologies.

Advanced Optical‐Thermal Integrated Flexible Tactile Sensor for High‐Fine Recognition of Liquid Property in Non‐Contact Mode

AbstractFlexible sensors have been applied to accurately identify the basic features of various solid objects. However, a significant obstacle is the accurate identification of liquids, due to it is impossible to exert force directly on those. Here, an optical‐thermal flexible tactile sensor is designed for liquid recognition, which integrates PEDOT:PSS/SrTiO3 thermoelectric foam and ZnS‐CaZnOS mechanoluminescent (ML) film coated with thermoplastic polyurethane (TPU), enabling the simultaneous detection of thermal and optical changes with low interference, via its exceptional mechanical stimuli‐caused light and superior thermoelectric voltage, respectively. In addition, the TPU coating imparts the device with excellent hydrophobic and oleophobic properties, enhancing its durability in complex conditions. This flexible sensor, possessing high stability under thermal and mechanical cycles, can distinguish different pure solvents and turbid suspensions, achieving a high‐fine recognition accuracy of 95% and 97.5%, respectively, with the aid of the fusion algorithm of multimodal sensory data based on the thermal and optical outputs. This capability of the device can underscore the practical utility of humanoid robots to identify hazardous liquids in some scenarios like safety inspection and kitchen condiments, which addresses the critical gaps in current sensory technologies by providing a robust, sensitive, and multifunctional strategy for accurate liquid recognition.

Full-duplex two-way no-relay cross-media communication method based on acoustic wave conversion

Abstract Due to the difference in acoustic impedance between water and air, the water interface becomes a natural barrier. Underwater sound waves will bounce when they propagate to the water surface. Electromagnetic waves and light waves in the air will rapidly attenuate after passing through the water surface and going underwater, making it difficult to complete air-water cross-media information transmission. In this article, we first analyze the theoretical characteristics and vibration model of the water surface after medium collision, and develop a new full-duplex communication method across the air-ocean medium, which does not rely on relay equipment and only uses three transmission media in the air. Propagate signals in the sea channel physical field to achieve two-way communication, using the mutual conversion between laser, acoustic, and radio frequency signals to fill the gap in cross-media communication technology that can simultaneously achieve two-way, full-duplex and non-relay. It has been verified through experimental installations carried out in laboratory water tanks and large comprehensive anechoic pools that it is able to achieve full-duplex communication links across the air-sea surface, indicating that the combination of laser acousticization and radio frequency-acoustic wave conversion can be used across medium full-duplex wireless communication provides a feasible method for communication that breaks through the water interface.

An analysis of technological gap in fig cultivation in Ballari district of Karnataka

An analysis of technological gap in fig cultivation in Ballari district of Karnataka

Defining and modeling dynamic spatial heterogeneity within tumor microenvironments

Many solid tumors exhibit significant genetic, cellular, and biophysical heterogeneity which dynamically evolves during disease progression and after treatment. This constant flux in cell composition, phenotype, spatial relationships, and tissue properties poses significant challenges in accurately diagnosing and treating patients. Much of the complexity lies in unraveling the molecular changes in different tumor compartments, how they influence one another in space and time and where vulnerabilities exist that might be appropriate to target therapeutically. Recent advances in spatial profiling tools and technologies are enabling new insight into the underlying biology of complex tumors, creating a greater understanding of the intricate relationship between cell types, states, and the microenvironment. Here we reflect on some recent discoveries in this area, where the key knowledge and technology gaps lie, and the advancements in spatial measurements and in vitro models for the study of spatial intratumoral heterogeneity.

Cyclic pitch control for aerodynamic load reductions of floating offshore wind turbines under pitch motions

Floating offshore wind turbines suffer severe aerodynamics under platform motions. Novel control methods are urgently needed to improve power and load performances. A mechanical cyclic pitch control strategy is introduced in this paper, and the load reduction effect on the NREL 5-MW wind turbine is investigated under dominant pitch motions. The simulation is performed by the standalone Aerodyn_Driver code. Results show that the relative airflow under pitch motions can be equated to a linear sheared flow and is only related to wind velocity and pitch angular velocity. Cyclic pitch control hardly affects power and thrust but significantly changes aerodynamic moments. The resultant moment and moment axis azimuth are defined, and they are controlled respectively by pitch amplitude and phase. The optimal pitch parameter equations to minimize pitching and yawing moments are derived from fitting and derivation, and the standard deviations of aerodynamic moments are reduced by over 70 % under the sinusoidal pitch motion and steady inflow below rated operation. This paper provides a new idea for the individual pitch control structure, and the mechanism study for aerodynamic control is important and instructive to fill the technological gap in mature pitch control strategies for floating offshore wind turbines.

Pre-implementation planning for a sepsis intervention in a large learning health system: a qualitative study

BackgroundSepsis survivors experience high morbidity and mortality. Though recommended best practices have been established to address the transition and early post hospital needs and promote recovery for sepsis survivors, few patients receive recommended post-sepsis care. Our team developed the Sepsis Transition and Recovery (STAR) program, a multicomponent transition intervention that leverages virtually-connected nurses to coordinate the application of evidence-based recommendations for post-sepsis care with additional clinical support from hospitalist and primary care physicians. In this paper, we present findings from a qualitative pre-implementation study, guided by the Consolidated Framework for Implementation Research (CFIR), of factors to inform successful STAR implementation at a large learning health system prior to effectiveness testing as part of a Type I Hybrid trial.MethodsWe conducted semi-structured qualitative interviews (n = 16) with 8 administrative leaders and 8 clinicians. Interviews were transcribed and analyzed in ATLAS.ti using a combination deductive/inductive strategy based on CFIR domains and constructs and the Constant Comparison Method.ResultsSix facilitators and five implementation barriers were identified spanning all five CFIR domains (Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of Individuals and Process). Facilitators of STAR included alignment with health system goals, fostering stakeholder engagement, sharing STAR outcomes data, good communication between STAR navigators and patient care teams/PCPs, clinician promotion of STAR with patients, and good rapport and effective communication between STAR navigators and patients, caregivers, and family members. Barriers of STAR included competing demands for staff time and resources, insufficient communication and education of STAR’s value and effectiveness, underlying informational and technology gaps among patients, lack of patient access to community resources, and patient distrust of the program and/or health care.ConclusionsCFIR proved to be a robust framework for examining facilitators and barriers for pre-implementation planning of post-sepsis care programs within diverse hospital and community settings in a large LHS. Conducting a structured pre-implementation evaluation helps researchers design with implementation in mind prior to effectiveness studies and should be considered a key component of Type I hybrid trials when feasible.Trial registrationClinicaltrials.gov, NCT04495946. Registered August 3, 2020.

Advancing Redgram Productivity: Impact of Cluster Front Line Demonstrations in Wanaparthy District, Telangana, India

This study aimed to assess the productivity improvement of redgram under Cluster Front Line Demonstrations (CFLD) in Wanaparthy district in the operational area of YFA-KVK, Mahabubnagar-I. A total of 261 demonstrations were conducted over a seven-year period (2016-17 to 2022-23), covering 104.4 hectares during the kharif season under rainfed situation with protected irrigation. Results showed that with the introduction of improved high yielding short duration varieties -PRG176 and WRGE 97 when combined with seed treatment, recommended fertilizer doses, weed management, and plant protection, achieved an increase in average yield of 10.20 quintals per hectare, compared to 6.23 quintals per hectare with traditional farmers’ practices. Net returns averaged ₹35000 from CFLD demonstrations, significantly higher than the ₹14,230 from conventional methods, though variations were influenced by the Minimum Support Price (MSP) set by the Government of India. The study identified an average extension gap of 3.98 quintals per hectare between the demonstrated technology and traditional practices, with an average technology gap of 14.80 quintals per hectare across the years. This gap varied annually due to the performance of recommended varieties and interventions. The technology index correlated with the technology gaps. The findings suggest that redgram production can be significantly improved through the adoption of improved technologies via CFLD. Therefore, it is crucial to spread these improved technologies among farmers using effective extension methods such as awareness camps, farmers-scientist interactions, group discussions, trainings, demonstrations, field days, and exposure visits through CFLD.

Contribution of Artificial Intelligence (AI) in Education to Support the Achievement of Sustainable Development Goals (SDGs) 2030

This research aims to find out the contributions and threats of AI in education so that it can contribute to technological development in the SDGs 2030 era. The method in this research is qualitative using literature study. The contributions of AI in education include personal virtual tutors, adaptive learning systems, learning Chabot, game-based learning, AI based assessment systems, educational data analysis, automatic evaluation, accessibility and inclusiveness, assistants for learning, and helping to develop curriculum. The contribution of AI in education is very helpful for students to improve their skills and knowledge. It is expected that more will be able to participate in SDGs 4 (Quality Education). Threats of AI in education include data privacy and security, technology gaps, dependence on technology, fraud, and algorithm imperfections. Thus, there should be collaboration with AI experts to develop strategies and data simulations to minimize the threats.

Impact of frontline demonstration on the knowledge and adoption level of farmers during kharif onion production in North Alluvial plains of Bihar

The objective of this study was to demonstrate the technology of kharif onion production with the use of improved kharif onion variety Bhima Super during kharif seasons of the years 2020–2021 and 2022–2023 at farmer’s field of various locations under the guidance of Krishi Vigyan Kendra, Birauli, Samastipur, Bihar. The investigation included measures like yield and economic performance, technology gap, extension gap, frontline demonstration technology index, and respondent farmer’s degree of adoption of the demonstration. Over the course of the study’s two years, it was found that there was a 19.74% enhancement in bulb yield above local check and that the average B:C ratio under the demonstration plot was 3.98 which was higher than that of control plots 2.19. When enhanced quality seed was adopted, the extent of adoption increased to 47.12 percent. The study displays the technology index (22.64%), the average technical gap (81.22 q/ha), and the extension gap (62.98 q/ha). Current research highlights the potentiality of frontline demonstration for dissemination of technology at the grassroots level.

Efficiency evaluation, regional technological heterogeneity and determinant of total factor productivity change in China’s healthcare system

Enhancing efficiency and productivity in countries’ healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China’s healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region’s efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country’s attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal–Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system’s efficiency, TFPC, and TGR.

What tool or method do you wish existed?

We asked researchers from a range of disciplines across biology, engineering, and medicine to describe a current technological need. The goal is to provide a sample of the various technological gaps that exist and inspire future research projects.

MiniXL: An open-source, large field-of-view epifluorescence miniature microscope for mice capable of single-cell resolution and multi-brain region imaging.

Capturing the intricate dynamics of neural activity in freely behaving animals is essential for understanding the neural mechanisms underpinning specific behaviors. Miniaturized microscopy enables investigators to track population activity at cellular level, but the field of view (FOV) of these microscopes have been limited and does not allow multiple-brain region imaging. To fill this technological gap, we have developed the eXtra Large field-of-view Miniscope (MiniXL), a 3.5g lightweight miniaturized microscope with an FOV measuring 3.5 mm in diameter and an electrically adjustable working distance of 1.9 mm ± 200 μm. We demonstrated the capability of MiniXL recording the activity of large neuronal population in both subcortical area (hippocampal dorsal CA1) and deep brain regions (medial prefrontal cortex, mPFC and nucleus accumbens, NAc). The large FOV allows simultaneous imaging of multiple brain regions such as bilateral mPFCs or mPFC and NAc during complex social behavior and tracking cells across multiple sessions. As with all microscopes in the UCLA Miniscope ecosystem, the MiniXL is fully open-source and will be shared with the neuroscience community to lower the barriers for adoption of this technology.

The impact of FDI on R&D investment of small and medium-sized enterprises in Vietnam: The role of institutions

This paper investigates how the quality of the local institutional environment affects the impact of foreign direct investment (FDI) on R&D investment of domestic small and medium-sized enterprises (SMEs) in Vietnam. By analysing data of 2690 manufacturing SMEs in 2011, 2013 and 2015, we find that FDI presence hinders R&D activities of domestic SMEs, probably due to SMEs' low absorptive capacity, large technological gap, lack of economies of scale and low market power. However, institutions positively moderate the relationship, that is better institutions mitigate the negative impacts of FDI on R&D investment of domestic SMEs. When the quality of local institutions improves beyond a certain minimum level, FDI exerts positive spillovers that encourage R&D activities of domestic SMEs. Our hypothesis suggests that institutions can moderate FDI spillovers to SMEs' R&D by promoting the foreign investors' incentive and ability to create technological spillovers, mitigating FDI crowding-out effects, enhancing domestic SMEs' absorptive capacity, and facilitating interaction and knowledge sharing in the local business environment. We also find that the institutional moderating effect is less pronounced for FDI originating from countries with lower institutional quality compared to that from countries with higher institutional quality. This suggests that multinational enterprises (MNEs) in the former group may be more susceptible to institutional impacts due to their unfamiliarity with institutional obstacles in the host economy. Our study highlights the importance of institutions to researchers who explore the relationship between FDI and domestic firms’ R&D investment and to policymakers in emerging economies where FDI inflows and domestic SMEs play a pivotal role.

Plant Growth and Yield Dynamics in Tomato Cultivation: Insights from Front Line Demonstrations

The present study was undertaken to assess the yield gap through Front Line Demonstrations (FLDs) on tomato crops. Krishi Vigyan Kendra, Malkangiri (Odisha), conducted 13 trials on tomatoes in 2015-16 and 2016-17 across different villages. The predominant grower practices were treated as controls, while recommended practices were implemented in demonstration fields. Over the two years, the average yield in the demonstration fields was 622.3 quintals per hectare (q/ha), compared to 305.2 q/ha in the control fields. The average technology gap, representing the difference between potential and actual yields in the demonstration fields, was found to be 377.7 q/ha. The technology index, indicating the percentage deviation from potential yield achieved averaged 37.77%.

Automatic Path-Planning Techniques for Minimally Invasive Stereotactic Neurosurgical Procedures-A Systematic Review.

This review systematically examines the recent research from the past decade on diverse path-planning algorithms tailored for stereotactic neurosurgery applications. Our comprehensive investigation involved a thorough search of scholarly papers from Google Scholar, PubMed, IEEE Xplore, and Scopus, utilizing stringent inclusion and exclusion criteria. The screening and selection process was meticulously conducted by a multidisciplinary team comprising three medical students, robotic experts with specialized knowledge in path-planning techniques and medical robotics, and a board-certified neurosurgeon. Each selected paper was reviewed in detail, and the findings were synthesized and reported in this review. The paper is organized around three different types of intervention tools: straight needles, steerable needles, and concentric tube robots. We provide an in-depth analysis of various path-planning algorithms applicable to both single and multi-target scenarios. Multi-target planning techniques are only discussed for straight tools as there is no published work on multi-target planning for steerable needles and concentric tube robots. Additionally, we discuss the imaging modalities employed, the critical anatomical structures considered during path planning, and the current status of research regarding its translation to clinical human studies. To the best of our knowledge and as a conclusion from this systematic review, this is the first review paper published in the last decade that reports various path-planning techniques for different types of tools for minimally invasive neurosurgical applications. Furthermore, this review outlines future trends and identifies existing technology gaps within the field. By highlighting these aspects, we aim to provide a comprehensive overview that can guide future research and development in path planning for stereotactic neurosurgery, ultimately contributing to the advancement of safer and more effective neurosurgical procedures.

The Impact Analysis on Productivity Enhancement of Blackgram through Front Line Demonstration in Chandel District of Manipur State

Background: Blackgram is the major pulse crop cultivated all over the India. The crop is resistant to unfavourable weather patterns and helps in enhancing soil fertility. It is a significant component of the Indian diet since it provides vegetable protein and balances the diet. About 26 per cent of it is protein, which is nearly three times more than cereal and other vitamins and minerals. In addition, it is utilised as nutrient-rich fodder, particularly for dairy cattle. A significant obstacle to expanding blackgram production in Chandel district, Manipur State is the technology gap. Frontline demonstration (FLD), is one of the most significant and effective methods for technology transfer. By using the newest technologies, Krishi Vigyan Kendra Chandel’s interventions aim to boost productivity and production. Methods: The study on Blackgram var. PU-31 under rainfed farming situations of Chandel district, Manipur was carried out by ICAR-Krishi Vigyan Kendra-Chandel, during Kharif 2017 to 2022 with the objectives to increase production and productivity of pulse in the district conducted in the farmers’ field for 118 hectares of land with all the scientific package of practices. Result: The average grain yield of 776.50 kg/ha was obtained in the demonstration plot with 38.13 per cent increase in yield over farmers practices. The technological and extension gap were 723.50 kg/ha and 214.33 kg/ha respectively. Similarly, technological index was decreased from 50.33 percent to 44.53 percent during the study period, revealing satisfactory performance of technological interventions which can further be improved by bridging the gaps with more efficient research and extension services.

Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining

In the present era dominated by Industry 4.0, the digital transformation and intelligent management of industrial systems is significantly important to enhance efficiency, quality, and the effective use of resources. This underscores the need for a framework that goes beyond merely boosting productivity and work quality, aiming for a net-zero impact from industrial activities. This research introduces a comprehensive and adaptable analytical framework intended to bridge existing gaps in research and technology within the manufacturing sector. It encompasses the essential stages of using artificial intelligence (AI) for modelling and optimizing manufacturing systems. The effectiveness of the proposed AI framework is evaluated through a case study on electric discharge machining (EDM), concentrating on optimizing the electrode wear rate (EWR) and overcut (OC) for aerospace alloy Inconel 617. Utilizing a comprehensive design of experiments, the process modelling through an artificial neural network (ANN) is carried out, accompanied by careful fine-tuning of hyperparameters throughout the training process. The trained models are further assessed using an external validation (Valext) dataset. The results of the sensitivity analysis indicated that the surfactant concentration (Sc) has the highest level of influence, accounting for 52.41% of the observed influence on the EWR, followed by the powder concentration (Cp) with a contribution of 33.14%, and the treatment variable with a contribution of 14.43%. Regarding OC, Sc holds the highest percentage significance at 72.67%, followed by Cp at 21.25%, and treatment at 6.06%. Additionally, parametric optimization (PO) shows that EWR and OC overcome experimental data by 47.05% and 85.00%, respectively, showcasing successful performance optimization with potential applications across diverse manufacturing systems.