Access Points Research Articles

Adapting Books for Students With Extensive Support Needs: Multiple Points of Access

Literacy is a crucial element in a person’s education, and widespread literacy allows societies to thrive. However, individuals with extensive support needs (ESN) often face barriers to attaining literacy. Shared story reading is an evidence-based practice that has demonstrated effectiveness in increasing literacy outcomes for individuals with ESN. A core component of shared story reading is the use of adapted books to create access to the general curriculum. This article presents an inclusive approach to literacy instruction to meet the needs of students with ESN through books adapted for multiple points of access (i.e., physical, cognitive, social, emotional, and cultural). Examples of how to adapt book elements across the multiple access points are provided and the implications for practice are discussed.

Exploring the Dietary Supplement Use During COVID-19: Insights from Community Pharmacists, Pharmacy Staff, Academics, and Industry Experts.

Introduction: The increased use of dietary supplements is a consequence of the coronavirus disease 2019 (COVID-19) pandemic. Although their efficacy has not been consistently proven, they have been perceived as a preventive tool against COVID-19. Less strict regulations and debates on their definitions in a regulatory framework raise concerns about safety issues. Apart from dietary supplement use, information demand has increased, and pharmacists have played a key role as accessible health professionals. This study aimed to reveal the opinions of community pharmacists, pharmacy staff, sector representatives, and academicians regarding the sale and use of dietary supplements during the COVID-19 pandemic. Methods: A qualitative study approach was followed, using grounded theory. Data were collected between September 2021 and July 2022, analyzed using MAXQDA 2020 software, and reported according to the Consolidated Criteria for Reporting Qualitative Research. Results: After refining the open codes, two themes emerged: dietary supplements and the effects of the pandemic. It was found that increased dietary supplement use was rooted in COVID-19 fear, promotional activities, and social media influence. The interaction between consumers and the dietary supplement industry is bidirectional. Conclusions: Pharmacies, as access points for these products, played a crucial role in struggling with the effects of the pandemic. Although dietary supplements are considered safe, their potential for adverse reactions poses a public health risk. Flexible regulations, unregulated alternative advertising channels, and a lack of awareness of the safety of these products pose a challenge to public health. In this regard, all stakeholders should work to minimize the risks from undergraduate education to professional life.

Dog invasions in protected areas: A case study using camera trapping, citizen science and artificial intelligence

Domestic dogs, Canis familiaris, wandering into natural habitats poses a grave threat to wildlife, increasing predation pressure and disease risk and disrupting the ecological balance within ecosystems. This study examines the presence of dogs in a European Protected Area (PA), Doñana National Park (SW Spain), where their access is strictly restricted, and explores how dog presence relates to potential access points. We utilised classifications provided by citizen science and artificial intelligence, subsequently validated by experts, to detect dogs within 5200,000 photos taken by 60 camera traps randomly deployed across the PA from October 2020 to January 2024. We discovered 33 dogs, primarily in groups of 2–5 individuals, recorded across 31 detection events at 22 camera locations. Dogs were detected ranging from 10 to 42 km2 (Minimum Convex Polygon) within the PA. The detection probability of dogs increased by 0.22 log odds per kilometre closer to a village (corresponding to an increase from 0.5 to approximately 0.55) bordering the PA and exceeded 0.9 near it. Our data revealed three types of dogs wandering within the PA: dogs accompanying poachers, free-roaming dogs living in nearby human settlements, and stray dogs, most likely relying on the PA resources. Urgent actions are needed in Doñana as dogs pose severe threats to endangered species like the Iberian lynx Lynx pardinus (six adult female lynx documented killed by dogs). We recommend raising awareness among local authorities of free-roaming dogs, particularly in settlements close to PAs, where their presence should be banned. Regularly monitoring dog presence within PAs is crucial to prevent invasions and their associated impacts. Our findings underscore the importance of using camera traps and integrating artificial intelligence with citizen science to monitor invasive species effectively.

A newly digitized ice-penetrating radar data set acquired over the Greenland ice sheet in 1971–1979

Abstract. We present an ice-penetrating radar data set acquired over the Greenland ice sheet by aircraft during the years 1971, 1972, 1974, 1978, and 1979. The data set comprises over 177 000 km of flight lines and contains a wealth of information on the state of the Greenland ice sheet, including information on ice thickness and englacial properties. During data collection in the 1970s, the data were recorded on optical film rolls, and in this paper, we document the digitization of these film rolls and their associated geographical information. Our data digitization enables interaction with and analysis of the data and facilitates comparison with modern-day radar observations. The complete data set in full resolution is available in the Stanford Digital Repository (https://doi.org/10.25740/wm135gp2721; Karlsson et al., 2023), with the associated technical reports. Part of the data set is available as low-resolution JPG files at the Technical University of Denmark's data repository (https://doi.org/10.6084/m9.figshare.c.7235299.v1; Karlsson et al., 2024), with associated technical reports and digitized geographical information . The Stanford Digital Repository serves as long-term storage, providing archival historic preservation in perpetuity, and is not intended as a primary data access point. The Technical University of Denmark (DTU) data repository serves as a primary entry point for data access, with files organized according to acquisition year and flight line in a simple folder structure. Here, we release the full data sets to enable the larger community to access and interact with the data (Karlsson et al., 2023).

NYC Care: A Large Health Care Access Program for Uninsured New York City Residents.

Millions of people living in the United States are excluded from health insurance due to income or immigration status. These 2 groups are more likely to lack access to health care or a regular source of care. NYC Health + Hospitals is addressing this need with NYC Care, a health care access program. The program is designed to be the single point of access for uninsured care citywide and includes a membership card, a 24-hour customer service line, and direct access to primary care medical homes. Health care is coordinated across NYC Health + Hospitals using integrated electronic referrals and a medical record system. The program uses a single enrollment process across safety net health care resources of NYC Health + Hospitals. A 24-hour call center was established to answer questions, make primary care appointments, and make warm handoffs to enrollment staff. Once eligibility is confirmed and patients are enrolled, they are mailed a membership card, a member handbook, and offered a primary care appointment. A multipronged public awareness campaign including citywide, multilingual marketing and outreach via community-based organizations was essential to build trust. NYC Care had 119 203 members at the end of June 2023. Fifty-eight percent had not seen a primary care doctor in the NYC Health + Hospitals system in the prior 36 months. In total, 76 439 had completed 1 or more primary care visits; 53.1% of enrollees with diabetes had improved hemoglobin A 1c , and 73.4% of enrollees with hypertension had improved blood pressure control after 6 months of enrollment. NYC Care demonstrates that municipalities can improve access to care for the uninsured by simplifying steps to affordable health care services, connecting patients directly to patient-centered medical homes, and improving the patient experience. A comprehensive public awareness campaign is also crucial.

ASAP: IEEE 802.11ax-based seamless access point handover for moving vehicles

The increasing number of connected and automated vehicles has led to a sharp increase in the demand for network access of moving vehicles. Although 5G networks support terminals with high mobility, the traffic load is too heavy to bear if all the vehicles have a large amount of data for transmission. Therefore, IEEE 802.11-based wireless network is a complementary offload solution to provide high-speed network access for vehicles with low cost, easy deployment and high scalability. However, frequent network handover of moving vehicles between multiple roadside access points (APs) results in network performance degradation, which is one of the challenges in vehicular communications. In this paper, we propose a framework (referred to as ASAP) based on the up-to-date IEEE 802.11ax standard to provide moving vehicles with seamless handover between multiple APs. By leveraging the high efficiency (HE) sounding protocol of IEEE 802.11ax, each AP is capable to monitor the current location of moving vehicles in real time. In addition, a mechanism is also proposed for AP uplink/downlink transmissions through collaboration between the APs and the backbone network to achieve seamless handover for moving vehicles. Since ASAP is based on IEEE 802.11ax, the compatible security scheme such as IEEE 802.11i can be applied to ASAP for security enhancement. The proposed solution does not require any modification on the user terminals, making it possible to be implemented in practice. Extensive simulations show that ASAP significantly reduces the network handover delay to microsecond level, and improves network throughput up to 59% compared with the state-of-the-art methods.

Enhancing payload capacity to low earth orbit via a relay solution trajectory with laser-powered vehicles

ABSTRACT Low Earth Orbit (LEO) stands as the primary access point for deep space exploration endeavors, encompassing a variety of space missions such as communication satellites, Earth observation platforms, and scientific research missions. In light of the expanding scope of human activities in space, the cost associated with accessing LEO has emerged as a critical focal point for spacecraft designers. Within the spectrum of proposed solutions, laser-powered vehicle has emerged as a viable avenue toward achieving cost-effective launch options. In order to significantly augment the payload capacity of laser-powered launch vehicles destined for LEO, this study introduced a novel Relay Solution Trajectory Flight Scheme. Central to this scheme was the implementation of a relay mirror system affixed to a satellite, which served to stabilize the incident angle of the laser beam directed toward the laser-powered vehicle, thereby ensuring continuous alignment between the laser beam and the vehicle’s axis throughout the duration of the flight. Following the development of comprehensive structural, propulsive, aerodynamic, and trajectory models tailored for the laser-powered vehicle, an optimization of its flight trajectory was conducted, comparing this innovative strategy with two conventional flight strategies. The outcomes of this analysis showcased a notable enhancement of 269.29% in payload capacity compared to conventional strategies that lacked a relay satellite system, within a designated 400 km LEO flight scenario. This endeavor underscored the transformative potential of advanced relay systems in reshaping payload delivery mechanisms and elevating mission feasibility levels, notwithstanding the intricate coordination challenges posed by intricate satellite systems.

A Proof of Concept Using BLE to Optimize Patients Turnaround Time (PTAT) in Health Care

In 2022, Malaysia's Ministry of Health (MOH) acknowledged that government hospitals continue to face challenges in delivering high-quality patient care. A significant issue is the absence of a Real-Time Patient Location Monitoring and Tracking Solution, which prevents doctors from efficiently tracking patients within medical facilities. This lack of real-time tracking contributes to a decline in service quality, creating an urgent need for optimization in patient monitoring and tracking solutions. Implementing an effective tracking system can significantly enhance patient care quality, improve resource utilization management, and boost staff productivity, ultimately saving costs, time, and resources for hospitals. One critical area for improvement is the Patient Turnaround Time (PTAT), which refers to the duration a patient spends being processed at a hospital before being discharged. PTAT is currently prolonged in many hospitals due to the complex processes involved and patients becoming untraceable while waiting for their turn. To address these challenges, we have developed an Internet of Things (IoT) solution utilizing Bluetooth Low Energy (BLE) technology to optimize PTAT. This solution enables hospital staff to track patient movements and localize them in real-time. By using wearable devices, the system measures the distance from patients to various Access Points (AP) spread throughout the hospital based on the dBm value. This paper presents comprehensive experimentation for the Proof of Concept (POC) and pilot testing of this BLE-based solution. The results demonstrate the accuracy of using the Received Signal Strength Indicator (RSSI) to optimize patient turnaround times in hospitals. By implementing this BLE solution, hospitals can reduce the time patients spend waiting for treatment, thereby improving overall patient satisfaction and hospital efficiency. The ability to monitor patient locations in real-time will ensure that patients receive timely care, reducing the likelihood of them wandering off and becoming untraceable. This technology represents a significant step forward in modernizing patient care and streamlining hospital operations in Malaysia.

Quantifying leaks from Schrader valves in air conditioning systems

Refrigerants are a critical component of air conditioning and heat pump systems, but their high global warming potential has led to increasing concerns about their environmental impact. This research quantifies leakage from Schrader valves commonly used in air conditioning equipment. These valves serve as access points for refrigerant circuits and are often cited by service technicians as a source of leaks, contributing to greenhouse gas emissions. The study conducted laboratory testing of Schrader valves using an innovative experimental apparatus that tracked pressure and temperature of nitrogen gas contained by the valve over a 64-h test. The study found that 8 out of 61 used Schrader valves tested leaked at rates that would result in substantial refrigerant loss (M = 1,280 g/yr, SD = 3,168 g/yr, range = 9.4 to 9,117 g/yr). Gasketed caps were found to prevent refrigerant loss. Combining a Schrader valve with a gasketed cap significantly reduces the risk of leaks, and their use should be encouraged in air conditioning systems. The methodology developed for estimating leak rates from Schrader valves could be applied to other components in refrigeration systems, contributing to more comprehensive efforts to reduce refrigerant emissions.

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Abstract Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

Millimeter wave negative refractive index metamaterial antenna array

In this paper, a novel negative refractive index metamaterial (NIM) is developed and characterized. The proposed metamaterial exhibits negative effective permittivity (εeffe) and negative effective permeability (µeffe) at millimeter wave frequency of 28 GHz. This attractive feature is utilized to enhance the gain of a microstrip patch antenna (MPA). Two thin layers of 5 × 5 subwavelength unit cell array of NIM are placed above a single MPA to enhance the gain of the antenna. Each unit cell has an area of 3.4 × 3.4 mm2. A gain increase of 7.9 dBi has been observed when using the proposed NIM as a superstrate. Furthermore, the NIM array is placed over a 2 × 2 array of MPAs with four ports to demonstrate versatility of the metamaterial. The total size of the 2 × 2 antenna array system with N-MTM is about 61.1 × 34 × 16mm3 (5.71λ × 3.18λ × 1.5λ, where λ is the free-space wavelength at 28 GHz). The measurement result indicate that the maximum gain of the antenna array is 13.5dBi. A gain enhancement of 7.55 dB in E-Plane and 7.25 dB in H-Plane at the resonant frequency of 28 GHz is obtained. The proposed antenna structure is suitable for 5G millimeter wave communications, in particular, for possible implementation in future millimeter wave access points and cellular base stations.

Analyzing Data Streams from Cyber-Physical-Systems: A Case Study

We show that conducting a process-mining-centric analysis concerning cyber-physical systems provides insights into usage behavior. To show that, we perform our analysis on connected-vehicle data. We transform connected-vehicle data into an event log. We analyze the resulting event log using various process-mining techniques. In particular, we apply basic statistical analysis as well as process-discovery and conformance-checking techniques to receive a well-representative process model. We apply various process-enhancement techniques to get deeper insights. Finally, we capture a multi-perspective view using a state-based approach. We show deviations between a de-jure model and our picked process model, leading to better knowledge concerning real user behavior. We observed that the predefined escalation of warning states does not happen. Additionally, we verified system requirements. Furthermore, we show that the reasons for drivers’ behavior are not related to system issues. Applying process-mining techniques to data concerning cyber-physical systems provides valuable insights into their functionality in a real-world setting. By utilizing process-mining techniques, we can extract insights to a human-understandable level and provide a well-studied access point.

On the total energy consumption of scalable cache-aided multi-CPU cell-free massive MIMO systems

In conventional cell-free (CF) systems, the scalability and feasibility are hindered by the fact that a single central processing unit (CPU) manages all access points and the stringent synchronization requirement for coherent transmission (CT) across the entire network. Multi-CPU CF systems emerge as a scalable and feasible alternative, yet they still encounter the typical CF system challenge of high downlink transmit energy consumption from extensive fronthaul and backhaul transmissions. To tackle this issue, we propose a scalable downlink partial coherent transmission (PCT) strategy for multi-CPU CF systems to provide high spectral efficiency (SE), reduce transmission time and thereby lower energy consumption, while keeping decoding complexity manageable. Based on the PCT strategy, we introduce scalable cache-aided multi-CPU CF systems and develop the corresponding total energy consumption (TEC) model. A successive convex approximation algorithm is proposed to obtain the suboptimal cache placement to minimize the TEC. Simulation results indicate that the proposed PCT-based scalable cache-aided multi-CPU CF systems can significantly reduce the TEC and consistently maintain the TEC at a relatively low level across all Zipf parameters by leveraging the high SE of CT and the independent transmission of non-coherent transmission.

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

Creating and Evaluating MARC 21 Bibliographic Records Using ChatGPT

This study investigated the feasibility of using ChatGPT (GPT-4) to create MARC 21 bibliographic records following RDA: Resource Description and Access. This study provided ChatGPT with data from information sources and evaluated its performance in referencing proper records, accurately interpreting information, and correctly applying RDA. The findings demonstrated that although ChatGPT was promising as an assisting tool for human catalogers, it struggled with complex bibliographic patterns and nuanced cataloging rules. Notable errors were observed in various fields, such as primary and additional authorized access points, title statements, edition statements, publication details, and series statements, highlighting areas for improvement.

How to Conduct a Freedom-to-Operate Analysis for a Drug Repurposing Project

This document provides the general methodology and specific resource access points for performing a Freedom-to-Operate (FtO) analysis tailored to the needs of a drug repurposing project. It has been deliberately written to be performed with minimal technical resources: Only web access is required, and no use is made of artificial intelligence tools. It can be performed by the inventors, up to the point where potentially critical findings (if any) are discussed with a patent attorney. When done properly, this method actually improves the relevance of the analysis, since no patent specialist can be expected to be fully familiar with all the scientific aspects of a repurposing project. While patent searches are the dominant motive, there is also discussion of how to recruit suitable suppliers, an issue that is particularly important when the compound is reformulated (as will be the case in most cases).

Moderating model marketplaces: platform governance puzzles for AI intermediaries

ABSTRACT The AI development community is increasingly making use of hosting intermediaries, such as Hugging Face, which provide easy access to user-uploaded models and training data. These model marketplaces lower technical deployment barriers for hundreds of thousands of users, yet can be used in numerous potentially harmful and illegal ways. In this article, we explain the ways in which AI systems, which can both ‘contain’ content and be open-ended tools, present one of the trickiest platform governance challenges seen to date. We provide case studies of several incidents across three illustrative platforms – Hugging Face, GitHub and Civitai – to examine how model marketplaces moderate models. Building on this analysis, we outline important (and yet nevertheless limited) practices that industry has been developing to respond to moderation demands: licensing, access and use restrictions, automated content moderation, and open policy development. While the policy challenge at hand is a considerable one, we conclude with some ideas as to how platforms could better mobilise resources to act as a careful, fair, and proportionate regulatory access point.

Active Reconfigurable Intelligent Surface Enhanced Internet of Medical Things.

The incredible potentiality of reconfigurable intelligent surface (RIS) in addressing power supply and obstacle environment of Internet of Medical Things (IoMT) has been capturing our interest. Considering the nettlesome "double-fading" effect introduced by passive RIS, we investigate an active RIS-enhanced IoMT system in this article, where the wireless power transfer (WPT) from power station (PS) to IoMT devices and the wireless information transfer (WIT) from IoMT devices to the access point (AP) are both implemented with the assistance of active RIS. Aiming to maximize the sum throughput of the considered IoMT system, a joint design of time schedules and reflecting coefficient matrices of the active RIS is proposed. Trapped by the non-convex and obstinate optimization problem, we explore the semi-definite programming (SDP) relaxation and successive convex approximation (SCA) techniques based on alternating optimization (AO) algorithm. Simulation results verify our solution approach to the intractable optimization problem and showcase the boosted spectrum and energy efficiency of the active RIS-enhanced IoMT system.