Limitations Of Technology Research Articles

Performance Analysis of 8x4 Barrel Shifters in CMOS and FINFET Technology

Shifters are essential components in digital circuits, enabling data manipulation and routing for a wide range of computational tasks. This study presents a comprehensive performance analysis of 8x4 barrel shifters implemented in two distinct semiconductor technologies: Complementary Metal-Oxide-Semiconductor (CMOS) and FinFET. This study compares and evaluates the important parameters for these shifters at both technological nodes, such as power consumption. Additionally, we examine the effects of scaling on CMOS barrel shifters. Conversely, FinFET technology, recognized for overcoming certain limitations of CMOS, is assessed for its potential to enhance performance while preserving energy efficiency. Through detailed simulation and analysis, this study offers valuable insights into the benefits and limitations of CMOS and FinFET implementations for 8x4 barrel shifters. The circuit is implemented using Cadence Virtuoso and optimized to leverage the unique features and address the limitations of 45nm CMOS and 22nm FinFET technologies. Furthermore, the study offers recommendations for selecting the appropriate technology based on specific performance criteria and application needs. The findings aim to guide future design decisions and improvements in shifter technology.

Applications of remote sensing technology in agriculture: A review of contributions to food security challenges and strategies in developing countries

In the agricultural sphere, particularly in developing countries where food security is still a major concern, remote sensing technology has also surfaced as an important tool. In this review, the usages of remote sensing in agricultural areas and its contributions to food security are surveyed; The overall framework is illustrated dynamically by indicating available image platforms that were instrumental for agriculture application paradigms. This research identifies certain areas where some selected case studies and existing literature are reviewed to serve as proof that remote sensing technology has been tremendously impactful. Areas like crop monitoring, yield prediction, or natural resource management have been very well reported by the literature review process adopted. This field analysis of related studies used highly sophisticated RS sensor data. The challenges like data availability, technological limitations, and socio-economic factors have been discussed besides the strategies, for instance, capacity building, policy interventions, and prospects for technological advancements. These results underscore the vitality and adoption of remote sensing technologies in agricultural practices and policies which is the key driver of improved food security in developing countries.

Deep Learning and Human-machine Integration in Smart Prosthetic Control: Process and Perspectives

With the rapid development of science and technology and the maturity of deep learning technology, intelligent prosthetic limb control has made significant progress in human-machine integration. While traditional prosthetic technology has seen improvements in form and function, it still faces challenges in user experience and physiological adaptability. Deep learning, with its powerful pattern recognition and decision-making capabilities, offers new possibilities to address these issues. This paper reviews the current status and future development of the application of deep learning in intelligent prosthetic limb control, emphasizing its close relationship with the concept of human-machine integration. Intelligent prosthetic control involves not only mechanical engineering and bioengineering, but also interdisciplinary integration of the fields of neural engineering, electrical engineering, motion control and computer science. This paper reviews the limitations in traditional prosthetic technology, such as accuracy and user adaptability, introduces the successful cases and latest research results of deep learning in the medical field, and explores the potential of deep reinforcement learning in optimizing complex movements and adapting to different environments. Finally, the future direction of deep learning technology in smart prosthetic limb control is envisioned, and research challenges and recommendations are presented in the hope of providing theoretical support for the innovative development of smart prosthetic limb technology and improving the quality of life of people with disabilities.

Review of Angular-Selective Windows with Guest–Host Liquid Crystals for Static Window Applications

This review focuses on the development and advancements in angular-selective smart windows, with particular emphasis on static windows utilizing guest–host liquid crystal (GHLC) systems. Angular-selective windows are designed to adjust their transmittance based on the angle of incident light, offering enhanced energy efficiency and visual comfort in both architectural and automotive applications. By leveraging the anisotropic absorption properties of dichroic dyes, GHLC-based windows can selectively block oblique sunlight while preserving clear visibility from normal viewing angles. Various liquid crystal (LC) alignment configurations, including vertically aligned, homogeneously aligned, hybrid aligned, uniformly lying helix, and twisted aligned LC cells, have been investigated to optimize light control for different installation angles, such as for automotive windshields and building windows. These advancements have demonstrated significant improvements in energy conservation and occupant comfort by reducing cooling demands and regulating sunlight penetration. This review summarizes key findings from recent studies, addresses the limitations of current technologies, and outlines potential future directions for further advancements in smart window technology.

Application of high-throughput sequencing in research on plant endophytes

Plant endophytes spend at least part of their life cycle in plants without causing diseases in the hosts, being the microbial resources with rich species and diverse functions. With the advancement in sequencing technology, the microbiological study of endophytes has become increasingly intensive. Being praised for the targeted validation and low cost, Sanger sequencing has been preferred by researchers. However, Sanger sequencing is no longer suitable for deeper genomic study of endophytes due to the low throughput. In this paper, we briefly summarize the research history of endophytes, review the applications of next-generation sequencing characterized by high throughput and third-generation sequencing (single-molecule real-time sequencing) in the research on endophytes, and summarize the research results of different sequencing technologies. Furthermore, we summarize the advantages and limitations of different sequencing technologies and discuss how to choose the appropriate sequencing technology according to the research needs. This review provides a reference for researchers to further explore the potential value of plant endophytes.

Nice day to go fishing

This article analyzes the representation of repetition in Viva la Dirt League’s online fantasy television series Epic NPC Man. The show is a parodic depiction of a fictional multiplayer fantasy computer game in which a variety of non-player characters (NPCs) exhibit varying degrees of self-awareness. The very concept of the show entails an underlying formal reliance upon strategies of reproduction, with the series providing live-action reenactment of various conventional situations from video games. But repetition also appears within the show as an explicit theme, with the characters defined by and in tension with the repetitive dialogue and routines of the game. This article argues that the significance of repetition in Epic NPC Man is defined by its relationship to the show’s broader investment in thematizing the escapist properties of fantasy. At one level, repetition emerges as a limit point, the moment at which fantasy breaks down. But the show also asserts repetition’s imaginative potential, situating it in opposition to a ludic, instrumental conception of gaming. The resulting image of fantasy gaming constructs the genre as both liberatory and surprisingly fragile, threatened both by the limitations of technology and by the actions of the players themselves.

Gas dynamic optimization of the work process of a single-stage cooled axial turbine with an inside baffle

The article presents the results of optimization of the work process of a single-stage axial turbine in order to increase its efficiency. During optimization, it was necessary to preserve the construction of the original turbine as much as possible. To solve this problem, a parameterization scheme for turbine blades and path contours was developed, taking into account the design and technological limitations. The turbine nozzle blade had an inside baffle. To control the possibility of placing the baffle, a special program was developed that automatically monitors the spatial position of the sections of the blade of the nozzle set. A post-processing program was developed to control the flow parameters at the turbine outlet in height. The efficiency and the vertical deviation of the angle of the outflow from the turbine from the original one were used as optimization criteria. The limitations were the mass flow rate of the working fluid and the total pressure ratio of the turbine. The problem was solved in several stages with different changing variables. As a result of solving the problem, it was possible to increase the turbine efficiency by 0.9%

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Low‐cost animal tracking using Bluetooth low energy beacons on a crowd‐sourced network

Abstract Animal tracking has opened the door to address many fundamental questions in ecology and conservation. Whilst historically animals have been tracked as a means to understand their large‐scale movements, such as migration, there is now a greater focus on using tracking to study movements over smaller scales, individual variation in movement or how movements shape social network structure. With this shift in focus also comes different tracking needs, including the need to track larger numbers of individuals. Tracking studies all face some technological limitations. For example, GPS and other active tracking solutions can collect fine‐scale movement data, but have a high cost per tag, limiting the number of individuals that can be followed. They also have high low‐energy costs of data acquisition and download, limiting time periods over which data can be collected. Low‐energy passive (e.g. PIT) or active (e.g. reverse GPS) tags can overcome these limitations, but instead require animals to remain within a bounded study area or to come into close proximity to detectors. Here we describe one solution that can overcome many current limitations by employing the massive global network of personal mobile phones as gateways for tracking animals using Bluetooth low‐energy (BLE) beacons. In areas with medium to high density of people, these simple‐to‐make beacons can provide regular updates of position over long time periods (battery life 1–3 years). We describe how to use off‐the‐shelf components to produce BLE beacons that weigh c. 5–6 g and cost <$7USD. Using field‐testing, we then show that beacons are capable of producing high‐frequency tracking data that can be used to build home ranges or to detect spatiotemporal co‐occurrences among individuals. BLE beacons are a low cost, low‐energy solution for studying organisms (e.g. birds, mammals and reptiles) living and moving in urban landscapes. Their low weight and small size makes them particularly well‐suited for tracking smaller species. When combined with fixed gateways, their use can also be extended to non‐urban habitats. Their high accessibility is likely to make them an attractive solution for many research projects.

Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics—sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.

Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

Abstract The inversion of precipitable water vapor (PWV) using the Global Navigation Satellite System (GNSS) has advantages such as all-weather observation, high precision, low cost, and high temporal resolution. Currently, long-term GNSS-PWV data has become an important data source for studying climate change. However, due to factors such as equipment failures, observation technology limitations, and estimation model errors, missing data and outliers often occur in real-time or post-processed PWV time series data. Furthermore, the main sources of GNSS-PWV errors are influenced by the atmospheric weighted mean temperature (Tm) and surface meteorological data (pressure and temperature). The results indicate that the European Centre for Medium⁃Range Weather Forecasts Reanalysis v5 (ERA5) dataset exhibits high accuracy in the Chinese region, making it suitable for GNSS-PWV inversion. By utilizing ERA5 meteorological data to calculate hourly GNSS-PWV and conducting accuracy assessments, it is demonstrated that the PWV inverted based on GNSS and ERA5 meteorological parameters possesses high precision. Based on this, this study selects GNSS stations from the Crustal Movement Observation Network of China (CMONOC) where the proportion of missing measured data is less than 8%. By combining ERA5, random forest (RF), and particle swarm optimization (PSO) algorithms, a new model called PSORF is proposed to fill in missing values in GNSS-PWV time series data. The research findings reveal that the R2 and RMSE of PSORF-PWV are 0.98 and 2.16 mm, respectively. Additionally, GNSS stations with more than 8% missing measured data are utilized to validate the accuracy of the PSORF model. A comparative analysis is conducted between the results obtained through the PSORF model and the ERA5-PWV acquired via traditional interpolation methods. The MAE and RMSE of PSORF-PWV are reduced by 21% and 17%, respectively, indicating that the PSORF model excels in filling missing data and effectively enhances the accuracy and reliability of PWV time series analysis. This study not only presents an effective approach for processing missing PWV data but also evaluates the applicability and accuracy of the ERA5 dataset in PWV inversion. This provides crucial technical support and data security for climate change research, short-term humidity field forecasting, and studies in related fields.

Investigating the factors affecting sustainability of ready mix concrete plants: case study of Pune region

Construction industry plays a significant role in the economic development of the nation. Due to site and other constraints, concreting is preferred by ready mix concrete (RMC) plants. It is very well-known fact that the construction industry immensely contributed for CO2 emission mainly from cement-based materials. One of the way to achieve smart city objective to effectively manage ready mix concrete plants. While opting for RMC over site mixing offers various advantages. In this study, key factors governing the sustainability and energy consumption of RMC plants are identified. This study investigates the efficiency and sustainability of RMC production in Maharashtra, suggesting that its efficiency hinges on various factors. Through a constructivist perspective, the research examines twelve RMC plants in Pune mainly and other parts of Maharashtra, finding that power supply, plant output, and capacity require sustainability. While raw material supply is deemed sustainable, management methods and product control were found lacking. The study identifies challenges affecting demand, such as regulatory issues, management issues and technological limitations. The conclusion emphasizes the need for alternative and sustainable power sources, skilled labor, and improved business processes to enhance RMC production efficiency in Maharashtra, recommending measures like waste reuse and innovative advancements. This study will help the stakeholders in prioritizing the resources and process towards more sustainable way.

A triple-mode strategy combining low-temperature photothermal, photodynamic, and chemodynamic therapies for treating infectious skin wounds.

The skin is the first natural barrier of the human body. Bacterial infections severely hinder the healing process of skin wounds and pose a great threat to human health. Therefore, it is particularly urgent to develop new antimicrobial strategies for bacterial pathogen clearance and wound healing. In this study, a metal-organic framework (MOF), Fe-MIL88B-NH2, was incorporated with the photosensitizer indocyanine green (ICG) to construct composite nanoparticles (MOF@ICG NPs) with multiple antibacterial activities. Under mild near-infrared (NIR) irradiation, the photosensitizer ICG in the MOF@ICG NPs undergoes photothermal conversion (∼45 °C) and photodynamic reactions to generate heat and singlet oxygen (1O2). In addition, the Fenton reaction of the NPs with hydrogen peroxide (H2O2) in the bacterial infection microenvironment resulted in the generation of hydroxyl radicals (˙OH), thus achieving the three-mode combination of low-temperature photothermal therapy (PTT)/photodynamic therapy (PDT)/chemodynamic therapy (CDT). The in vitro experimental results showed that MOF@ICG MPs had excellent antibacterial properties and good cytocompatibility, with some ability to promote the migration of L-929 fibroblasts. Furthermore, under NIR irradiation, MOF@ICG NPs could significantly kill bacteria and promote skin wound healing according to the results of animal experiments. The wound healing rate reached 87.1% after 7 days of treatment. The research results break through the limitations of single-mode antibacterial technology and provide certain theoretical guidance and technical support for the research and development of new antibacterial materials.

Iontophoresis and electroporation-assisted microneedles: advancements and therapeutic potentials in transdermal drug delivery.

Transdermal drug delivery (TDD) using electrically assisted microneedle (MN) systems has emerged as a promising alternative to traditional drug administration routes. This review explores recent advancements in this technology across various therapeutic applications. Integrating iontophoresis (IP) and electroporation (EP) with MN technology has shown significant potential in improving treatment outcomes for various conditions. Studies demonstrate their effectiveness in enhancing vaccine and DNA delivery, improving diabetes management, and increasing efficacy in dermatological applications. The technology has also exhibited promise in delivering nonsteroidal anti-inflammatory drugs (NSAIDs), treating multiple sclerosis, and advancing obesity and cancer therapy. These systems offer improved drug permeation, targeted delivery, and enhanced therapeutic effects. While challenges remain, including safety concerns and technological limitations, ongoing research focuses on optimizing these systems for broader clinical applications. The future of electrically assisted MN technologies in TDD appears promising, with potential advancements in personalized medicine, smart monitoring systems, and expanded therapeutic applications.

5G Network Slicing as a Service Enabler for the Automotive Sector

ABSTRACTNetwork slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range of heterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of network slicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the state of the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such as automotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previous cellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enabler to face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers, and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services.

Appealing, but misleading: a warning against a naive AI realism

AbstractScientists, educators, and instructional designers are facing numerous challenges due to the introduction of generative AI tools that can create appealing realistic imagery based on text prompts. Given that realism contributes to the trustworthiness of images coupled with people’s eagerness to externalize complex tasks to AI systems, the problem of a naive AI realism arises in which image creation and optimization is offloaded without considering the limitations of AI-driven technology. However, scientific visualizations and images used for educational purposes must go beyond an appealing presentation; above all, they should be accurate and factually correct. We argue that the utilization of generative AI tools for these types of visualizations requires human oversight, subject matter knowledge, and knowledge of effective design. In particular, we warn of a naive adoption of technological possibilities to “optimize” visualizations for educational purposes, such as memorability. A reductionist and naive view of AI-based optimization that fails to take into account the complex prerequisites for learning and instruction is likely to have negative consequences.

Skyrmion motion monitoring based on a ferromagnetic nanodot chain

As one of the most promising information carriers, the generation, manipulation, and detection of magnetic skyrmions have emerged as a hot topic in the field of spintronics. However, a major bottleneck to their practical application lies in the existing limitations of detection technology, which fails to accurately locate skyrmions or monitor their real-time motion behavior. In this work, we propose a patterned heterostructure scheme comprising a nanodot chain (NDC) layer and a skyrmion nano-racetrack layer for precise monitoring of skyrmion motion. By exploiting the stray field generated by the moving skyrmion within the racetrack layer, magnetization changes are induced in nanodots within the NDC layer. These changes then translate into high-frequency magnetization oscillation signals that encode valuable information about the dynamic characteristics of driven skyrmions, such as speed and acceleration of the skyrmion, either by spin waves or spin currents. This scheme holds great potential for advancing spintronic devices based on a profound understanding of skyrmion dynamics.

Unravelling the Current Status of Rice Stripe Mosaic Virus: Its Geographical Spread, Biology, Epidemiology, and Management

Rice stripe mosaic virus (RSMV) belongs to the Cytorhabdovirus species in the Rhabdoviridae family. Recently, RSMV was widely spread in East Asia and caused severe yield losses. RSMV is transmitted by the planthopper vectors, Recilia dorsalis, Nephotettix virescens, and Nilaparvata lugens, that mostly affect rice. The adult vectors can hibernate, transmit the virus, lay eggs on rice plants, and, finally, multiply in subsequent generations, resulting in new infection outbreaks. RSMV-infected rice varieties display striped mosaicism, mild dwarfism, stiff and twisted leaves, delayed heading, short panicles with large unfilled grains, and yield reduction. In nature, the infection of multiple pathogens in the same host is widespread, which is defined as co-infection. It can be antagonistic or synergistic. Pathological synergistic effects between RSMV and other viruses can generate strains with new genetic characteristics, leading to unpredictable epidemiological consequences. After the first identification of RSMV in 2015, significant advancements in understanding the disease’s characteristics, symptoms, cycles, geographic distribution, potential vectors, and synergistic interaction, as well as its management strategies, were developed. To reduce the damage due to RSMV infection, many scientists have recommended pest control techniques to target adult vectors. It is also essential to confirm the actual time of monitoring, development of resistant varieties, and changes in cultivation systems. Due to the limitations of the conventional plant disease control technologies, improvements in efficiency and safety are in high demand. Therefore, to find efficient and environmentally safe controls to mitigate these challenges, reviews of research are the foremost step. In this review, we summarize the basic epidemiological information about the origin of RSMV and its infection symptoms in the field, synergistic interaction with viruses during co-transmission, yield losses, formulation of the disease cycle, and control strategies from several case studies. Finally, we recommend the formulation of the disease cycle and management strategies of RSMV infection.

The Role of Endoscopic Ultrasound-Guided Shear Wave Elastography in Pancreatic Diseases.

Elastography is a non-invasive imaging modality that has been developed for the evaluation of the stiffness of various organs. It is categorized into two main types: strain elastography and shear wave elastography. While strain elastography offers valuable information on the mechanical properties of the organ being studied, it is limited by the qualitative nature of its measurements and its reliance on operator skills. On the other hand, shear wave elastography overcomes these limitations as it provides a quantitative assessment of tissue stiffness, offers more reproducibility, and is less operator-dependent. Endoscopic ultrasound-guided shear wave elastography (EUS-SWE) is an emerging technique that overcomes the limitations of transabdominal ultrasound in the evaluation of the pancreas. A growing body of literature has demonstrated its safety and feasibility in the evaluation of pancreatic parenchyma. This article provides a comprehensive review of the current state of the literature on EUS-SWE, including its technical aspects, clinical applications in the evaluation of various pancreatic conditions, technological limitations, and future directions.

Supply Chain Management for Electric Vehicles: Challenges, Opportunities, and the Path to Scalability

Abstract—As electric vehicles (EVs) gain momentum as a solution to reduce greenhouse gas emissions and fossil fuel dependence, their supply chains become increasingly complex, particularly for lithium-ion batteries (LIBs), which are crucial for EV performance, safety, and longevity. This paper reviews existing literature on several key aspects of the EV supply chain, focusing on thermal management systems (TMS), lifecycle environmental assessments, closed- loop supply chains (CLSCs), and the impacts of global disruptions like the COVID-19 pandemic. Research by Murugan et al. (2022) underscores the importance of improving TMS for LIBs, noting that experimental validation remains sparse despite advances in numerical modeling. In addition, Rüdisüli et al. (2022) highlight the environmental benefits and limitations of different mobility technologies, emphasizing the need for advancements in seasonal energy storage systems. Gu et al. (2018) discuss the challenges of establishing profitable CLSCs for EV batteries, where reuse improves sustainability, but recycling faces economic hurdles. Finally, the analysis of Wen et al. (2021) on the COVID-19 pandemic’s impact shows significant disruptions to the Chinese EV market and raises concerns about the global EV supply chain’s resilience. This review identifies gaps in the literature, such as the need for real-world validation of models, exploration of alternative battery chemistries, and improved supply chain governance, all of which are essential for advancing the EV industry toward sustainability. Keywords—Electric Vehicles, Supply Chain Management, Lithium-ion Batteries, Thermal Management Systems, Closed-Loop Supply Chains, Sustainability, Greenhouse Gas Emissions, Lifecycle Assessment, Resource Dependencies, Circular Economy.