There is increasing evidence that exposure to weak electromagnetic fields (EMFs) generated by modern telecommunications or household appliances has physiological consequences, including reports of electromagnetic field hypersensitivity (EHS) leading to adverse health effects. Although symptoms can be serious, no underlying mechanism for EHS is known and there is no general cure or effective therapy. Here, we present the case study of a self-reported EHS patient whose symptoms include severe headaches, generalized fatigue, cardiac arrhythmia, attention and memory deficit, and generalized systemic pain within minutes of exposure to telecommunications (Wifi, cellular phones), high tension lines and electronic devices. Tests for cerebral, cardiovascular, and other physiological anomalies proved negative, as did serological tests for inflammation, allergies, infections, auto-immune conditions, and hormonal imbalance. However, further investigation revealed deficits in cellular anti-oxidants and increased radical scavenging enzymes, indicative of systemic oxidative stress. Significantly, there was a large increase in circulating antibodies for oxidized Low-Density Lipoprotein (LDLox), byproducts of oxidative stress accumulating in membranes of vascular cells. Because a known primary effect of EMF exposure is to increase the concentration of cellular oxidants, we propose that pathology in this patient may be causally related to a resulting increase in LDLox synthesis. This in turn could trigger an exaggerated auto-immune response consistent with EHS symptoms. This case report thereby provides a testable mechanistic framework for EHS pathology with therapeutic implications for this debilitating and poorly understood condition.

OBJECTIVE: Sustained clinical and hemodynamic benefit after revascularization for chronic limb-threatening ischemia (CLTI) is needed to resolve symptoms and prevent limb loss. We sought to compare rates of clinical and hemodynamic failure as well as resolution of initial and prevention of recurrent CLTI after endovascular (ENDO) vs bypass (OPEN) revascularization in the Best-Endovascular-versus-best-Surgical-Therapy-in-patients-with-CLTI (BEST-CLI) trial.
METHODS: As planned secondary analyses of the BEST-CLI trial, we examined the rates of (1) clinical failure (a composite of all-cause death, above-ankle amputation, major reintervention, and degradation of WIfI stage); (2) hemodynamic failure (a composite of above-ankle amputation, major and minor reintervention to maintain index limb patency, failure to an initial increase or a subsequent decrease in ankle brachial index of 0.15 or toe brachial index of 0.10, and radiographic evidence of treatment stenosis or occlusion); (3) time to resolution of presenting CLTI symptoms; and (4) incidence of recurrent CLTI. Time-to-event analyses were performed by intention-to-treat assignment in both trial cohorts (cohort 1: suitable single segment great saphenous vein [SSGSV], N = 1434; cohort 2: lacking suitable SSGSV, N = 396), and multivariate stratified Cox regression models were created.
RESULTS: In cohort 1, there was a significant difference in time to clinical failure (log-rank P < .001), hemodynamic failure (log-rank P < .001), and resolution of presenting symptoms (log-rank P = .009) in favor of OPEN. In cohort 2, there was a significantly lower rate of hemodynamic failure (log-rank P = .006) favoring OPEN, and no significant difference in time to clinical failure or resolution of presenting symptoms. Multivariate analysis revealed that assignment to OPEN was associated with a significantly lower risk of clinical and hemodynamic failure in both cohorts and a significantly higher likelihood of resolving initial and preventing recurrent CLTI symptoms in cohort 1, including after adjustment for key baseline patient covariates (end-stage renal disease [ESRD], prior revascularization, smoking, diabetes, age >80 years, WIfI stage, tissue loss, and infrapopliteal disease). Factors independently associated with clinical failure included age >80 years in cohort 1 and ESRD across both cohorts. ESRD was associated with hemodynamic failure in cohort 1. Factors associated with slower resolution of presenting symptoms included diabetes in cohort 1 and WIfI stage in cohort 2.
CONCLUSIONS: Durable clinical and hemodynamic benefit after revascularization for CLTI is important to avoid persistent and recurrent CLTI, reinterventions, and limb loss. When compared with ENDO, initial treatment with OPEN surgical bypass, particularly with available saphenous vein, is associated with improved clinical and hemodynamic outcomes and enhanced resolution of CLTI symptoms.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3-5 m.

OBJECTIVE: The Society for Vascular Surgery (SVS) Wound, Ischemia, and foot Infection (WIfI) classification system aims to risk stratify patients with chronic limb-threatening ischemia (CLTI), predicting both amputation rates and the need for revascularization. However, real-world use of the system and whether it predicts outcomes accurately after open revascularization and peripheral interventions is unclear. Therefore, we sought to determine the adoption of the WIfI classification system within a contemporary statewide collaborative as well as the impact of patient factor, and WIfI risk assessment on short- and long-term outcomes.
METHODS: Using data from a large statewide collaborative, we identified patients with CLTI undergoing open surgical revascularization or peripheral vascular intervention (PVI) between 2016 and 2022. The primary exposure was preoperative clinical WIfI stage. Patients were categorized according to the SVS Lower Extremity Threatened Limb Classification System into clinical WIfI stages 1, 2, 3, or 4. The primary outcomes were 30-day and 1-year amputation and mortality rates. Multivariable logistic regression was performed to estimate the association of WIfI stage on postrevascularization outcomes.
RESULTS: In the cohort of 17,417 patients, 83.4% (n = 14,529) had WIfI stage documented. PVIs were performed on 57.6% of patients, and 42.4% underwent an open surgical revascularization. Of the patients, 49.5% were classified as stage 1, 19.3% stage 2, 12.8% stage 3, and 18.3% of patients met stage 4 criteria. Stage 3 and 4 patients had higher rates of diabetes, congestive heart failure, and renal failure, and were less likely to be current or former smokers. One-half of stage 3 patients underwent open surgical revascularization, whereas stage 1 patients were most likely to have received a PVI (64%). As WIfI stage increased from 1 to 4, 1-year mortality increased from 12% to 21% (P < .001), 30-day amputation rates increased from 5% to 38% (P < .001), and 1-year amputation rates increased from 15% to 55% (P < .001). Finally, patients who did not have WIfI scores classified had significantly higher 30-day and 1-year mortality rates, as well as higher 30-day and 1-year amputation rates.
CONCLUSIONS: The SVS WIfI clinical stage is significantly associated with 1-year amputation rates in patients with CLTI after lower extremity revascularization. Because nearly 55% of stage 4 patients require a major amputation within 1 year of intervention, this finding study supports use of the WIfI classification system in clinical decision-making for patients with CLTI.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals\' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Fetal ECG monitoring is a routine clinical detection method that can reflect the changes of fetal heart in utero in real time. At present, most of the clinical fetal heart rate detection adopts the ultrasonic Doppler method, which is technically difficult and highly specialized in operation and expensive. This study introduces a fetal ECG detection system based on the maternal abdominal electrode method. The weak fetal ECG changes are sensed through the maternal abdominal electrode, and the mixed ECG signal is obtained through the corresponding amplification and filtering circuit. Finally, the obtained signal is passed through WiFi, transmitted to the host computer. The host computer uses the adaptive filtering algorithm to estimate the fetal ECG signal. The system has strong feasibility, low operation expertise, low cost, and is more convenient.

A good night\'s sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict \"sleep\" and \"awake\" periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models-K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.