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Objective: The authors sought to examine the association between the increased use of videoconferencing and image-enhancing software filters during the COVID-19 pandemic and the corresponding rise in interest in cosmetic procedures. We aimed to discern if heightened exposure to one's digital self-image correlates with a greater inclination toward cosmetic interventions. Methods: In this cross-sectional questionnaire survey study, adult participants living in the United States were recruited on Amazon's Mechanical Turk platform. Self-reported demographic data, videoconferencing usage, related behaviors, and attitudes concerning cosmetic procedures were assessed. Questionnaire prompts allowed participants to express in a Likert scale format their levels of agreement with certain statements. Frequencies and proportions of responses to each questionnaire item were collected, categorized, and recoded for comparison and analysis. Chi-squared tests were performed to examine associations between questionnaire responses and demographic characteristics, while significant predictors of videoconferencing and image-enhancing filter options were explored via multivariable logistic regression models. Results: The majority of study participants (n=505, 505/545 = 92.7% completion rate) were 18 to 39 years old (80.1%), female (68.1%), and non-White (58.2%). Income levels varied, and 89.2 percent had completed college or graduate school. Videoconferencing was frequent, with 88.6 percent using it more than three days a week, and 68.1 percent using ";touch-up my appearance"; filters more than half the time. Videoconferencing influenced the desire for cosmetic procedures in 55.9 percent of participants, and 57.8 percent were influenced by filter use. Frequent self-viewing was reported by 67 percent, strongly associated with interest in cosmetic procedures. Significant predictors of filter use included higher income (OR 2.436, p<0.001) and frequent self-viewing behavior (OR 5.034, p<0.001). Notably, mask-wearing was associated with increased self-esteem (68.8%) and reduced desire for cosmetic procedures (67.4%) in frequent self-viewers. Limitations: Recall bias and the demographic composition of the study participant population may limit the generalizability of these self-reported responses. Conclusion: The data reflect the emergence of what may be termed 'videoconferencing dysmorphia,' indicating a significant association between videoconferencing, the application of filters, and the heightened interest in cosmetic procedures among users. This study suggests that digital self-viewing behavior instigates a distinct self-awareness that may drive individuals toward considering cosmetic interventions. These insights are vital for dermatologists in understanding patient motivations, managing expectations, and fostering realistic treatment outcomes. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) fingerprinting is regarded as a promising solution to improve wireless security, especially in applications where resource-limited devices are employed. Unlike steady-state signals, such as preambles or data, the use of short-duration transient signals for RF fingerprinting offers distinct advantages for systems with low latency and low complexity requirements. One of the challenges associated with transient-based methods in practice is achieving high performance while utilizing low-cost receivers. In this study, we demonstrate for the first time that the performance of transient-based RF fingerprinting can be enhanced by designing the filter chain in a software defined radio (SDR) receiver, taking into account the relevant signal characteristics. The performance analysis is conducted using transient-based identification signals captured by the SDR receiver, focusing on the sampling rate and duration of the identification signal. In the experiments, signals collected from twenty IEEE 802.11 transmitters are used. Experimental results indicate that so long as the receiver filter parameters and the duration of the identification signal are properly determined, a high classification performance exceeding 92% can be achieved for transient-based RF fingerprinting, even at sampling rates approaching the Nyquist limit. [ABSTRACT FROM AUTHOR]

SOLNP \(+\) is a derivative-free solver for constrained nonlinear optimization. It starts from SOLve Nonlinear Programming (SOLNP) proposed in 1989 by Ye. The main ideas are to use finite difference to approximate the gradient of the objective function and constraints, and use augmented Lagrangian method and sequential quadratic programming to deal with nonlinear constraints. We incorporate the techniques of implicit filtering, a new restart mechanism, and a modern quadratic programming solver into this new version with an ANSI C implementation. The algorithm exhibits a great advantage in running time and robustness under noise compared with the old version implemented in MATLAB. The numerical experiments show that SOLNP \(+\) is comparable with two widely used solvers, COBYLA and NOMAD. SOLNP \(+\) is available at https://github.com/COPT-Public/SOLNP_plus. [ABSTRACT FROM AUTHOR]

Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ. [ABSTRACT FROM AUTHOR]

This paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB's Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability. [ABSTRACT FROM AUTHOR]

Objective: To assess the relative position of mini‐implants to retain a mandibular overdenture, according to the surgical protocol, technical and anatomical factors. Methods: Mandibular cone‐beam computed tomography (CBCT) scans were analyzed for 73 patients who received four one‐piece titanium‐zirconium mini‐implants. Drilling was performed using a 1.6 mm needle drill and a 2.2 mm Pilot Drill, according to the bone density with a surgical stent. Post‐insertion CBCT images in DICOM format were analyzed using the E‐Vol‐DX software with BAR filters. Divergence angle between implants and between implants and the overdenture path of insertion was measured using CliniView 10.2.6 software. Results: Divergence between implants ranged from 0° to 22.3° (mean = 4.2; SD = 3.7) in the lateral and from 0° to 26.2° (mean = 5.3; SD = 4.1) in the frontal projections (p <.001). Only 1 (0.2%) and 3 (0.7%) of the measurements were higher than 20° in the lateral and frontal views, respectively. The mean angulations between the implant and the path of insertion for the overdenture were 9.3° (SD = 7.5) and 4.0° (SD = 2.9) for the lateral and frontal views, respectively (p <.001). Regression analyses showed a significant association between the divergence of implants and the frontal view projection (p <.001), greater distance between the paired implants (p =.017), the flapped surgical protocol (p =.002), higher final insertion torque (p =.011), and deeper preparation with the needle drill (p <.001). Conclusions: The mini‐implants were placed with low divergence angles and satisfactory parallelism. Factors including shorter distances between the implants, higher density bone, and a flapless surgical approach all contributed positively to improved parallelism of the mini‐implants. [ABSTRACT FROM AUTHOR]

Mobile technology is currently trending toward supporting multiple communication standards on a single device. This means that some reconfigurable techniques must be the foundation of their design. The two essential requirements of channel filters are minimized complexity and reconfigurability. In this research, a novel extension of Frequency Response Masking (FRM) was investigated by employing Time Division Multiplexing (TDM)-based single Multiply and Accumulate (MAC) architecture using the principle of resource sharing to realize multiple sharp filter responses from a single prototype constant group delay low pass filter. This paper uses a single multiply and add units regardless of the quantity of channels and taps. The suggested reconfigurable filter was synthesized on technology based on 0.18-µm CMOS and put into practice. Further trials were carried out on Virtex-II 2v3000ff1152-4 FPGA device. The outcomes revealed that the suggested channel filter, which was synthesized using FPGA, provides 21.36% of the area curtail and 14.88% of power scaling down on average and put into practice using ASIC provides 5.18% of the area reduction and 9.08% of power scaling down on average. [ABSTRACT FROM AUTHOR]

Although heart rate variability (HRV) is a valid method to evaluate the behavior of the autonomic nervous system in individuals with temporomandibular disorder (TMD), the measurement can easily be biased by factors involving the analysis methodology, such as the removal of artifacts. Therefore, the objective of this investigation is to evaluate the impact of using different levels of threshold-based artifact correction to process HRV data in individuals with TMD. This cross-sectional observational study. Adults aged 18 to 55 years old with a diagnosis of myogenic TMD, score ≥ 50 on the Fonseca Anamnestic Index (FAI) and pain ≥ 3 on the Numerical Pain Scale (NPS) participated. The HRV was registered in the supine position (short-term) using a Polar S810i. Kubios software was used for HRV analysis using all filters. One-way ANOVA with Tukey-Kramer post-hoc was used to test the differences in HRV using the different Kubios Software artifact correction filters. The effect size was calculated based on the Cohen d. The very strong filter was statistically different (p < 0.05) compared to the no filter in all overview and time domain variables. In the frequency domain, the variables VLF, LF, HF and Total Power showed statistical differences (p < 0.05) when using the very strong filter. The same occurred with the variables SD1, SD2 and DFA α2 of the non-linear analysis (p < 0.05). The most restrictive filter of the Kubios software (very strong) significantly impacts the quantification of HRV parameters in individuals with TMD. [ABSTRACT FROM AUTHOR]

Software defect prediction datasets often suffer from issues such as class imbalance, noise, and class overlap, making it difficult for classifiers to identify instances of defects. In response, researchers have proposed various techniques to mitigate the impact of these issues on classifier performance. Oversampling is a widely used method to address class imbalance. However, in addition to inherent noise and class overlap in the datasets themselves, oversampling methods can introduce new noise and class overlap while addressing class imbalance. To tackle these challenges, we propose a software defect prediction method called AS-KDENN, which simultaneously improves the effects of class imbalance, noise, and class overlap on classification models. AS-KDENN first performs oversampling using the Adaptive Synthetic Sampling Method (ADASYN), followed by our proposed KDENN method to address noise and class overlap. Unlike traditional methods, KDENN takes into account both the distance and local density information of overlapping samples, allowing for a more reasonable elimination of noise and instances of overlapping. To demonstrate the effectiveness of the AS-KDENN method, we conducted extensive experiments on 19 publicly available software defect prediction datasets. Compared to four commonly used oversampling techniques that also address class overlap or noise, the AS-KDENN method effectively alleviates issues of class imbalance, noise, and class overlap, subsequently improving the performance of the classifier models. [ABSTRACT FROM AUTHOR]

Early fault detection in brushless direct current (BLDC) motors used in electric vehicles is crucial for ensuring the reliability, safety, and performance of the vehicle. Cost-effective fault diagnostics and non-invasive techniques help to enhance industrial practices and promote sustainable and efficient technology. The vibration of a BLDC motor is an important feature for diagnosing various faults. The most common causes of BLDC motor failure are mechanical and electrical faults, which can result in expensive maintenance and downtime. The existing methods for diagnosing BLDC motor faults using invasive or non-invasive methods have many limitations. This work proposes a technique for diagnose the BLDC motor faults non-invasively by projecting high-frequency signal onto the motor and recorded using SIGVIEW software. This technology uses portable ultra-wideband (UWB HB-100) radar, high-frequency noise filtering by software phase-locked loops (SPLL), and rational dilation wavelet transforms (RDWT) for signal analysis to identify BLDC motor faults. The RDWT output is divided into 12 sub-bands, and the energy distribution of each sub-band is calculated using MATLAB-R2023b. As the number of bearing faults increases from 2 to 3, the RDWT output sub-band energy at levels 4 and 5 increases by 17.06%–18.59% and 131.73%–179.10%, respectively, indicating a significant increase in energy due to the faults. By detecting motor faults early on, this technique reduces the likelihood of unexpected defects and provides proactive maintenance. Overall, the proposed approach offers an effective method for identifying defects in BLDC motors. [ABSTRACT FROM AUTHOR]

The characteristics of additively manufactured parts (topological optimisation, freedom of design ...) present a challenge from a metrological point of view. In this respect, non-contact inspection is of great importance for the rapid verification of this type of parts. However, their particular characteristics (stair-effect, high roughness, surface finish) can contribute to measurement errors and deviations when using different optical inspection equipment. In relation to this, 3D scanning point-clouds filtering can improve the results by increasing precision during inspection. In particular, this paper evaluates the influence of applying filters to digitize additive manufactured metal parts, which are scanned using different optical inspection systems commonly used in industry (based on laser triangulation, conoscopic holography and structured light techniques). The main objective is to establish the most suitable inspection procedure depending on the inspection system used. According to the dimensional results achieved, it can be concluded that the filtering does not have significant influence, regardless of the used sensor. However, the geometrical results are strongly influenced by the point-cloud quality; consequently, it is recommended to apply filters for most of the evaluated optical systems. One of the main contributions of this study is the definition of the filtering process methodology prior to non-contact inspection, depending on both the used optical system and the part to be evaluated, in order to define the most suitable filtering parameter to achieve a precise optical inspection. • Effect of sigma filter on optical inspection of additively manufactured metal parts • Point cloud filtering does not influence dimensional results, only geometric ones • Different industrial optical sensors require filtering for accuracy geometric inspection • Applying 3σ/2σ filter allows to achieve geometric results similar to contact values • 1σ filter (more restrictive) distorts values, resulting in form error below reference [ABSTRACT FROM AUTHOR]

The concentration of carbon dioxide (CO2) is an important indicator for coal mine safety. Real-time monitoring of CO2 concentration is of great importance for taking actions in advance to avoid the occurrence of potential accidents. To address the issues of poor portability and high cost associated with existing coal mine CO2 detection equipment, this paper develops a miniaturized CO2 detection system based on non-dispersive infrared (NDIR) technology. This sensor integrates an infrared light source and a dual-channel pyroelectric detector into a reflective gas chamber, thereby achieving an extended optical path and higher system sensitivity within limited space. Meanwhile, the noise interference was greatly mitigated by using hardware and software filtering techniques. Based on principle analysis, the Lambert–Beer law was parametrically corrected, and then, a model relationship between the dual-channel voltage ratio and concentration was established. In addition, temperature compensation for zero and span values was introduced to improve the adaptability of the detection results to temperature changes. Testing results indicate that the developed detection system can realize CO2 measurement in the concentration range of 0 to 50 000 ppm within a temperature range of 0–40 °C, with a maximum detection error of less than 0.12% and a repeatability deviation of less than 1.04%. During a stability test for 12 h, the maximum concentration drift is 0.07%, indicating that the developed system meets the requirements for monitoring CO2 safety in coal mines. [ABSTRACT FROM AUTHOR]

In order to improve the utilization rate of internet data under heterogeneous distribution, increase the diversified usage functions and data transmission rate of the internet, and reduce the running time of the internet, it is necessary to mine internet data under heterogeneous distribution. The author proposes an ontology based optimization method for internet data mining under heterogeneous distribution; This method first preprocesses and selects data features from internet data under heterogeneous distribution, and uses a feature selection decision system to select features from the mining data. Based on this, information entropy is used to filter internet data under heterogeneous distribution. During the filtering process, the theoretical values filtered by information entropy are reduced to obtain the optimal data filtering value, finally, based on the various data information obtained in the preprocessing, the iterative calculation results of the information gain value in the decision tree generation algorithm are used to high-precision mine internet data under heterogeneous distribution; The simulation experimental results demonstrate that the proposed method improves the flexibility of internet data operations under heterogeneous distribution, increases the recyclability of internet data, and makes internet operations under heterogeneous distribution more concise and efficient, providing a strong basis for research and development in this field. [ABSTRACT FROM AUTHOR]

Accurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state‐related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource‐multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on‐board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi‐target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM‐PHD‐LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM‐PHD‐LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system. [ABSTRACT FROM AUTHOR]

Floods are a frequent and challenging disaster, caused by factors such as heavy rainfall and improper waste disposal in drainage systems, leading to significant material and non-material losses. Effective measures are needed to mitigate flood impacts. This study uses bibliometric analysis to explore methods for flood disaster management, employing Publish or Perish software to filter relevant journals from Crossref. The data, including research titles, authors, publication years, and journal sources, is processed using VOSViewer software. The analysis shows that keywords like rainfall, drainage, and flood mitigation are central to the research, highlighting strong interconnections. The diversity of publishers and research types contributes to a global perspective, forming a solid research foundation. The study also tracks the growth of research on flood management in drainage channels from 2016 to 2023 and identifies diverse geographical case study regions. This analysis underscores the continued relevance and potential of this research to support effective global flood mitigation strategies. [ABSTRACT FROM AUTHOR]

In vehicle‐to‐grid (V2G) applications, a voltage source inverter (VSI) directly connects to a residential load or grid for DC/AC conversion and power flow control. In VSI, dead‐time is employed to prevent the breakdown of switching devices, however, the introduction of dead‐time leads to distortions of output voltage and current in both stand‐alone and grid‐tie modes. For the case of a power factor of 1, by analyzing the dead‐time effect of digital‐controlled double‐frequency sinusoidal pulse width modulation, a compensation amount is proposed to introduce in the modulation signals, the value of which is half of the delay register that generates a dead‐time of td. Moreover, considering the phase delay caused by hardware and software filtering in sampling, the grid‐tie mode control strategy based on dead‐time compensation is further proposed, which introduces the grid voltage as a feedforward signal. A 3.5 kW V2G experimental prototype is built and tested to verify the feasibility of the proposed strategies. The simulation and experimental results show that the proposed methods are effective for both modes, which improve the output distortion for different dead‐time without additional hardware circuits. [ABSTRACT FROM AUTHOR]

The study is aimed to examine the subsurface fracture system surrounding the Ikanje Artesian spring as regards the water-bearing zones to determine the source of the spring. The area is in a part of the basement complex of southwestern Nigeria. Using the ABEM WADI Very Low Frequency-Electromagnetic (VLF-EM) equipment, 40 profiles were established, 10 on each geographical coordinate at a 5 m inter-station gap, giving a total of 800 station positions. Four resistivity VES points were deployed for correlation purposes. Excel, Fraser Filter, and Karous-Hjelt Filter software were employed for the analysis. The Fraser Filter and Karous-Hjelt plots revealed underlying water-bearing fractures in all directions with prominence toward the south. The trend of the water-bearing fractures that links all the southern profiles indicates that the spring's source is in the southern area. Inferred 3D conductive maps equally confirmed the highest conductivity in the southern region of the area, indicating wet fractures. The 1D pseudo sections from VES points indicated that the main aquifer linked to Ikanje artesian spring is hosted by a 64.5 m thick fractured basement, overlain by a weathered basement in the SW flank; thus the location could be most suitable for groundwater exploitation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to examine the level of effectiveness of the anti-fraud technologies employed by the South African banking industry for cyberfraud mitigation. Design/methodology/approach: This research employed a qualitative research design involving a purposive sampling method. Primary data was collected from the key organisational staff across the 17 licensed commercial banks in South Africa via the use of structured questionnaires. In particular, these were experts involved in combating fraud and taking managerial decisions regarding the use of anti-fraud technologies for cyberfraud mitigation. Non-parametric statistical analyses were carried out from the responses obtained. Findings: The results obtained indicated that the combination of internal and external anti-fraud technologies such as filtering software, firewalls, encryption, continuous auditing, discovery sampling, virus protection, financial ratios, digital analysis and data mining may have a positive effect on cyberfraud mitigation. These technologies are employed mostly to ensure effective internal control systems capable of minimising cyberfraud. In addition, the anti-fraud technologies employed in the South African banking industry may also be effective in the mitigation of cyberfraud, although significant cases of cyberattacks were reported by the respondents. Practical implications: The study recommends investment in more digital and emerging technologies and the development of human capacities to effectively deploy them in the combat against cybercrime. Originality/value: The novelty of this study lies in the identification of the type of anti-fraud technologies/software employed by the South African banking industry and their level of effectiveness or success rate. [ABSTRACT FROM AUTHOR]

The precision of artificial intelligence (AI)‐generated information has been suggested in the past as a method of nudging consumers' evaluations and intentions, but little is known about whether such effects are also context‐sensitive. Based on four studies, we find a matched condition under which consumers are more likely to make a positive response when precise (imprecise) numbers presented by AI recommenders are used in a utilitarian (hedonic) consumption context (Study 1). Additionally, we show that consumer conceptual fluency also mediates this matching effect on consumer purchase decision‐making (Studies 2). We further show the matching effect is moderated by the recommender type (Study 3) and consumer lay beliefs about the AI and human recommenders (Study 4). This study shows that when consumers' lay belief changes from "AI performs objective tasks well" and "Human performs subjective tasks well" to "AI performs subjective tasks well" and "Human performs objective tasks well," it can change the difference in the matching relationship between human and AI recommenders. [ABSTRACT FROM AUTHOR]

Drum towers are the most prominent cultural markers and the major site for cultural ceremonies in Dong villages. The visibility and audibility of drum towers are important factors influencing the location and construction of buildings in Dong villages. In this paper, the authors map and classify 21 drum tower buildings according to three characteristics: shape of the plan, elevation of the enclosure, and aspect ratio of the longitudinal section. Having used the sound field simulation software Odeon to filter two of the three characteristics that were identical, the authors simulated drum towers with different values of the third characteristic to study the factors influencing their sound field. The correlation between the construction of the drum tower and its characteristics of sound was established, and the characteristics of the sound field as well as their adaptability were assessed by comparing different forms of drum towers. This can provide technical support for the protection and repair of drum towers, their renovation and upgrade, and the construction of new drum towers in the Dong region. [ABSTRACT FROM AUTHOR]

One of the primary geodetic mapping tasks in the post-processing of GNSS data is precise point positioning (PPP). Research institutions and universities have established software packages and online PPP services in prior years. Still, it is problematic to satisfy the high-rate update criterion of PPP due to the quick growth of GNSS constellations. In PPP GNSS data processing, Square Root Information Filter SRIF is not frequently handled. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. The new program has been demonstrated how can effectively enhance initial time and positioning error for multi-GNSS satellites. Processing observation data with the Kalman filter and SRIF was performed using PPP in static mode for the 16 stations, and the influence of errors has been analyzed from the filtering method. The Kalman filter was unable to maintain a stable convergence curve during the PPP filtering procedure, but SRIF was successful in doing so. Based on these findings, SRIF had better numerical stability and was well-suited for settings with PPP demanding precision computing environments. [ABSTRACT FROM AUTHOR]

The increasing network speeds of today's Internet require high-performance, high-throughput network devices. However, the lack of affordable, flexible, and readily available devices poses a challenge for packet classification and filtering. This problem is exacerbated by the increase in volumetric Distributed Denial-of-Service (DDoS) attacks, which require efficient packet processing and filtering. To meet the demands of high-speed networks and configurable network processing devices, this paper investigates a hybrid hardware/software packet filter prototype that combines reconfigurable FPGA technology and high-speed software filtering on commodity hardware. It uses a novel approach that offloads filtering rules to the hardware and employs a Longest Prefix Matching (LPM) algorithm and allowlists/blocklists based on millions of IP prefixes. The hybrid filter demonstrates improvements over software-only filtering, achieving performance gains of nearly 30%, depending on the rulesets, offloading methods, and traffic types. The significance of this research lies in developing a cost-effective alternative to more-expensive or less-effective filters, providing high-speed DDoS packet filtering for IPv4 traffic, as it still dominates over IPv6. Deploying these filters on commodity hardware at the edge of the network can mitigate the impact of DDoS attacks on protected networks, enhancing the security of all devices on the network, including Internet of Things (IoT) devices. [ABSTRACT FROM AUTHOR]

Since the use of light and electronic devices is inevitable, the use of blue light filters (in various light sources, electronic devices or optical devices including intraocular lenses) has been shown to improve sleep quality, especially in later hours of the day and during night time. In this study, we examine the effect of the blue light on sleep and wakefulness rhythms and positive and negative emotions. This randomized clinical trial was conducted with 80 AJA University of Medical Sciences employees who use computers at least 2 h a day. All subjects were employees of the discharge unit of Imam Reza Hospital, which is located next to AJA University. The subjects were divided into two groups of 40 people, blue light filter software intervention and sham treatment. Pittsburgh Sleep Quality Index (PSQI), Positive and Negative Affect Schedule (PANAS), Visual Function Questionnaire (VFQ), Epworth Sleepiness Scale (ESS) and salivary melatonin and cortisol levels were assessed for both groups before and 3 months after the intervention. Data analysis was performed using IBM SPSS statistics for windows, version 21.0 (Armonk, NY: IBM Corporation). P value ≤ 0.05 was considered as statistically significant. The results showed that the Pittsburgh sleep scale after the intervention was significantly lower in the intervention group than in the control group. After the intervention, the VFQ was significantly lower in the intervention group than in the control group (P = 0.018). There was no significant difference in the Epworth Sleepiness Scale (ESS) between the two study groups after the intervention (P = 0.370). There was no significant difference in Positive and Negative Affect Schedule (PANAS) in the two study groups after the intervention (P = 0.140). After the intervention, cortisol levels were significantly higher in the intervention group than in the control group (P = 0.006). Also, the amount of cortisol increased significantly in the intervention group (P = 0.028). The amount of melatonin decreased significantly in the intervention group (P = 0.034). The sleep quality score after the intervention was significantly lower in the intervention group than in the control group. This indicates better sleep quality in the intervention group. The results also show that the level of visual fatigue in the intervention group decreased significantly. However, no significant change was detected regarding positive and negative emotions. After the intervention, cortisol levels were significantly higher in the intervention group than the control group. In addition, cortisol levels increased significantly and melatonin levels decreased significantly in the intervention group during the course of study. [ABSTRACT FROM AUTHOR]

In this work, a capacitive pH sensor consisting of Ta2O5 functional film is designed and fabricated by employing MEMS-based procedures. The Ta2O5 thin film has an amorphous microstructure, and its surface roughness is less than 3.17 nm. A signal processing circuit and a software filtering algorithm are also designed to measure the pH value, thus improving the detection accuracy and anti-interference ability. Good linearity (R2 = 0.99904) and sensitivity (63.12 mV/pH) are recorded for the proposed sensing element in the range of pH 2~12. In addition, the sensor's drift and hysteresis are equal to 5.1 mV and 5.8 mV, respectively. The enhanced sensing performance in combination with the facile miniaturization process, low fabrication cost, and suitability for mass production render the fabricated sensor attractive for applications where pH change measurements in a water environment are required. [ABSTRACT FROM AUTHOR]

As a natural hazard, earthquake has always caused destruction and loss of human life throughout history. Proper planning to prevent or reduce the destructive impact of earthquake hazard is of particular importance. In this study, to prevent and decrease the risk of this phenomenon, faults were detected and earthquake risk zoning was done in Rumeshkan County in Lorestan Province. For this purpose, first, the lineaments in the region were detected by using 2013 satellite images from Landsat 8 OLI sensors (row 37 and pass 166) and applying Directional Filters in ENVI software, as well as Lineament Extraction in Geomatica software. Afterwards, by comparing the lines with the constructed band combinations, the Digital Elevation Model (DEM) and geological map of the study area were investigated, the faults were separated, and their map was prepared in the Geographic Information System (ArcGIS). In this study, by using expert judgment method and AHP-Fuzzy, the factors that affected the risk of earthquakes in Rumeshkan County, including distance from fault, slope, geomorphology, lithology, and distance from epicenters of past earthquakes were weighted and the seismic hazard map of the region was prepared. According to the obtained results, 31.8, 34.3, 10.3, 14.6, and 9.0% of the area were in very low, low, medium, high, and very high hazard classes, respectively. Examination of the earthquake hazard sensitivity map showed that the highest sensitivity to seismic hazard was in the eastern parts of the county and that the central parts had very low and low risks; thus, settlement of population in the latter areas is recommended. [ABSTRACT FROM AUTHOR]

E-commerce platforms often use collaborative filtering (CF) algorithms to recommend products to consumers. What recommendations consumers receive and how they respond to the recommendations largely depend on the design of CF algorithms. However, the extant empirical research on recommender systems has primarily focused on how the presence of recommendations affects product demand, without considering the underlying algorithm design. Leveraging a field experiment on a major e-commerce platform, we examine the differential impact of two widely used CF designs: view-also-view (VAV) and purchase-also-purchase (PAP). We found several striking differences between the impact of these two designs on individual products. First, VAV is about seven times more effective in generating additional product views than PAP but only about twice as effective in generating sales due to a lower conversion rate. Second, VAV is more effective in increasing views for more expensive products, whereas PAP is more effective in increasing the sales of cheaper products. Third, VAV is less effective in increasing the views but more effective in increasing the sales of products with higher purchase incidence rates (PIRs). Finally, when aggregated over all products with the same levels of price or PIRs, VAV dominates PAP in generating views and the difference is more striking for products with higher prices or lower PIRs. Interestingly, PAP is more effective than VAV in increasing the sales of products with low prices or moderate PIRs, though VAV generates more sales than PAP overall. Our findings suggest that platforms may benefit from employing different CF designs for different types of products. [ABSTRACT FROM AUTHOR]

This article analyzes debates about online pornography filters and youth in the Swedish press between 2016 and 2020, focusing on the depiction of boys and young men. Critics of filtering software argued that boys could self-regulate if they were provided better sex education and if parents communicated with them about pornography. In contrast, advocates posited that boys are incapable of developing healthy sexuality because online pornography is a powerful drug that leads to addiction and contributes to a rape culture. As boys were considered unable to get a filter "in their head," proponents argued for disciplinary supervision through software in schools. [ABSTRACT FROM AUTHOR]

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The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user's specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments. [ABSTRACT FROM AUTHOR]

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In order to enhance the filtering of spam on the Internet and improve the experience of Internet users, this paper proposed to convert the email text into vector features using the vector space model, constructed a two-dimensional matrix, and used a convolutional neural network (CNN) to identify spam on the Internet. The CNN was compared with other two classifiers, support vector machine (SVM), and backward-propagation neural network (BPNN), in simulation experiments. The final results showed that the spam recognition algorithm with CNN as the classifier had better recognition performance than the algorithms with SVM and BPNN classifiers and was also more advantageous in terms of recognition cost and time for spam; in addition, the CNN had the best recognition performance when the number of extracted features was 15. [ABSTRACT FROM AUTHOR]

Recommender System (RS) is an information filtering software tool that provides relevant recommendations to users on various products. Finding the preference of users on products is the core component of RS. Most of the available RS datasets are complex enough to represent the user preferences and usually require a lot of processing before their utilization. On the other hand, selecting an appropriate learning algorithm for making relevant recommendations is a complex task. In this paper, we perform various pre-processing steps on a movie dataset to find the key features that help us to identify the actual user preferences. Moreover, we perform a comparative analysis of various randomized and non-randomized learning algorithms by utilizing these features to identify the best learning algorithm. Our study reinforces the superiority of randomized learning algorithms over non-randomized ones on MovieLens dataset. [ABSTRACT FROM AUTHOR]

Fake news have become a social problem which affect people all over the world. Every day there is more false or reality-distorting information that appear, especially on social networks. This study aims to analyze the different tools available by Google against fake news, while presenting the characteristics of media literacy as a methodology to prevent the leak of this kind of information to society. To do that, this study proposes a quantitative methodology. The results and conclusions show the importance of reconciling both factors: internet filters together with media literacy. [ABSTRACT FROM AUTHOR]

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM 2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM 2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi'an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong. [Display omitted] • A pollution-level adaptive air filtration system based on elastic filters was developed. • The adaptive system dynamically adjusted its performance under varied pollution levels. • The filtration performance of the adaptive system remained stable even after 10,000 cycles. • Up to 26.4 % annual energy savings were achieved by implementing the adaptive system. [ABSTRACT FROM AUTHOR]

Fire testing is and will remain of paramount importance in helping industry, regulators and all other fire safety stakeholders to achieve a safer world. For this to happen, test data must be stable and reliable, regardless of the test facility that produced the data, its various operators, the measurement equipment used, and so on. The instruments and sensors used to measure heat release, smoke production and mass loss rate are susceptible to mechanical and/or electrical disturbances and all have their own dynamic behaviour. This leads to a variation in the test results. A scatter that is only due to the measurement. This paper examines the time and frequency domain behaviour of various measurement components and hardware & software filters in use today. It proposes an instrument-independent method of eliminating noise and other interference, describes a method of adjusting the response time of the various input signals to a common value applicable to all test institutes, and proposes a method of accurately synchronising these signals in time. The result is unambiguous qualitative measurement data that can be confidently compared with data from other sample sets or between different laboratories and researchers. It improves the stability and reproducibility of test results leading to a more stable classification of materials. [ABSTRACT FROM AUTHOR]

An increasing number of children are now using smartphones, and the phenomenon is being studied keenly by computer scientists, pediatricians, developmental psychologists and educationists. We reviewed the research literature on what we call child–smartphone interaction. We found that children start actively using smartphones at around the age of two. Two- and three-year-old children can tap the screen at intended places, four-to-six-year-old children can perform single-finger touchscreen gestures and follow instructions provided in non-textual modes, and seven- and eight-year-old children can perform multi-finger touchscreen gestures, follow instructions provided as text, navigate hypertext and use simple augmented reality apps. Suitably designed apps can facilitate child development. For example, video chatting apps can help children communicate with parents, drawing apps and storytelling apps can nurture creativity, vocabulary apps can help children learn new words, and concepts of science and mathematics can also be explained to children using appropriate apps. At the same time, overuse of smartphones has negative consequences such as sleep disorders and may also contribute to obesity in children. Children as young as nine years now access the Internet. Reckless use of the Internet can expose children to inappropriate materials and cyberbullying. Therefore, content filtering software should be installed in smartphones used by children. We recommend objective assessment of children's ability to use smartphones, designing of apps according to the capabilities and interests of children belonging to different age groups, effective use of apps in child rearing and parental oversight on children's use of digital technologies. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the influence of sharpening filters in the detection of root fractures using low-dose cone-beam computed tomography (CBCT). Materials and methods: Eighty-four CBCT volumes acquired at three mA levels of 28 teeth inserted in the dental socket of dry human skull were selected from a previous study. The teeth were divided into four groups according to the presence and absence of root fracture and endodontic filling. Five radiologists evaluated all CBCT volumes for the presence of root fracture with and without the application of "Sharpen 1x" and "Sharpen 2x" filters in OnDemand3D software. Area under the ROC curve (AUC), sensitivity, specificity, and inter- and intra-observer concordance were calculated and compared (α = 0.05). Results: Sharpening filters did not lead to significant differences in AUC, sensitivity, and specificity at the three mA levels tested (p > 0.05), regardless of the presence of endodontic filling (p > 0.05). However, the significant reduction of AUC observed in CBCT volumes at 4 mA without filter (p < 0.05) ceased to exist after the application of filters (p > 0.05). Sensitivity and specificity ranged from low and moderate. Conclusions: The use of sharpening filters can be recommended in CBCT volumes at 4 mA for root fracture detection for leading to the same performance as those at 6.3 and 10 mA. The presence of endodontic filling material did not influence the action of filters in the diagnosis of root fracture. Clinical relevance: Sharpening filters seem to contribute to the diagnosis of root fracture in CBCT volumes acquired with reduced radiation dose. [ABSTRACT FROM AUTHOR]

This paper focuses on how particularities are performed and made to travel through the creation of trust. The Swedish Species Observation System (Artportalen) is one of the largest inscription and calculation centers for citizen data in the world, used extensively by public authorities in Sweden. Observations by members of the public become actionable through environmental governance laws in Sweden. These observations are made through networks of things and humans in which trust is created but unevenly distributed. Important for them to be trusted and to travel are such things as computer software to filter and map observations, red lists, GIS-tools to determine time and place, and validation committees. However, trust is more concentrated in a core set of actors, and there depends on interpersonal relations – though these relations are facilitated by other parts of the epistemic system. [ABSTRACT FROM AUTHOR]

Introduction: Blue light from electronics are linked to several visual problems including blurry vision, eyestrain, dry eye, macular degeneration, and cataracts. Lenses and software that are designed to block out blue light have been recommended for digital reading, but their visual impact remains debatable. This study aims to investigate the effect of blue-blocking ophthalmic lens (BBOL) and built-in blue light filtering software (BIBLFS) on digital reading in the dark on accommodation accuracy. Methods: This cross-sectional study design was approved by the UiTM ethics committee. Fifteen young adults were recruited using convenience sampling. The accommodation response of 1-min direct digital reading (DDR) from an iPad at 40 cm in a dark room was measured using Grand Seiko WAM-5500. The digital reading in the dark was repeated with BBOL and with BIBLFS. There was a 5-min dark adaptation in between each testing condition. The spectral transmittance was analysed using Retinal Index (RI) and Circadian Index (CI). Results: The comparison of accommodation response in three testing conditions (DDR - BBOL - BIBLFS) revealed no significant difference (F = 1.735, p > 0.05). However, our RI and CI analysis revealed that BBOL and BIBLFS displayed different protective effects against spectral emission from the electronic display compared to the standard illuminate test. Conclusion: The effects of BBOL and BIBLFS on accommodation response was negligible in short-term digital reading in the dark. However, the long-term accumulative effects on the accommodation system after prolonged usage requires further investigation. [ABSTRACT FROM AUTHOR]

Issues of trust, privacy and security at the intersection of state intervention and the use of the internet both by 'publics' and by individuals for—from the State's point of view- dispreferred purposes have been of great recent interest to researchers. This has been accompanied by a slowly developing concern for the way in which these issues pan out for people in non-Western cultures. Based on a study of Iranians living in urban centers, we examine the way in which culture, State institutions, technological infrastructure and practices intersect. Iran is a republic with a theocratic constitution and relatively strict regulation of private life. It has one of the highest rates of internet appropriation and social media use in the MENA region, but use is heavily mediated by state interventions, for instance the filtering of sites such as Facebook and Twitter. We show how young Iranians, due to restrictions on their private lives, learn from early age on to deal with illegal access techniques such as proxy servers and virtual private networks (VPNs). These access technologies are often used for private purposes such as contacting and meeting other genders. However, these capabilities become even more important when preparing to leave the country or to articulate their political dissatisfaction, specifically at moments of political unrest. We discuss and develop the concept of 'counter-appropriation' and the 'counter-public' to describe the practices of urban dwellers in circumventing increasingly more sophisticated intervention by the state security apparatus. [ABSTRACT FROM AUTHOR]

Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of −0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE.

Objectives: Cronkhite–Canada syndrome (CCS) is a rare hamartomatous polyposis syndrome with a proposed association with chronic autoimmune inflammation. To date, genetic background of patients with CCS remains less investigated. In this study we aimed to explore the genomic landscape of CCS. Methods: Whole exome sequencing was performed on peripheral blood samples extracted from 18 patients with CCS. Potential function‐impacting germline variants were filtered by R software. Through systematic data analysis, a number of genetic variants were identified. Enrichment analysis was performed using the R package ClusterProfiler. Results: Overall, 3960 low‐frequency (<0.05 or not reported in the Exome Aggregation Consortium East Asian, 1000 Genomes, or ESP6500 database) potentially function‐impacting germline variants were identified, with 18 genes (FDFT1, LOC400863, MUC3A, MUC4, ZNF806, GXYLT1, MUC6, PABPC3, PSPH, ZFPM1, CIC, LOC283710, ARSD, GOLGA6L2, LOC388282, SLC25A5, TMEM247, WDR89) involved over half the patients. Functional enrichment of these genes revealed several biological processes in relation to innate immune responses and glycosylation. Only one likely pathogenic germline variant of an hamartomatous polyposis syndrome‐associated gene, PTCH1, was detected in one patient. Conclusions: CCS has genomic alteration patterns completely distinct from those of traditional hamartomatous polyposis syndrome. The germline mutation landscape indicates potential roles of innate immune responses and glycosylation in the pathogenesis of CCS. [ABSTRACT FROM AUTHOR]

Gaze tracking is basic research in the era of the Internet of Things. This study attempts to improve the performance of gaze tracking in an active infrared source gaze-tracking system. Owing to unavoidable noise interference, the estimated points of regard (PORs) tend to fluctuate within a certain range. To reduce the fluctuation range and obtain more stable results, we introduced a Kalman filter (KF) to filter the gaze parameters. Considering that the effect of filtering is relevant to the motion state of the gaze, we design the measurement noise that varies with the speed of the gaze. In addition, we used a correlation filter-based tracking method to quickly locate the pupil, instead of the detection method. Experiments indicated that the variance of the estimation error decreased by 73.83%, the size of the extracted pupil image decreased by 93.75%, and the extraction speed increased by 1.84 times. We also comprehensively discussed the advantages and disadvantages of the proposed method, which provides a reference for related research. It must be pointed out that the proposed algorithm can also be adopted in any eye camera-based gaze tracker. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands – LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4× and battery life of up to 3.5 years. [ABSTRACT FROM AUTHOR]

The role of miRNAs in connection with the phenomenon of somaclonal variation, which occurs during plant in vitro culture, remains uncertain. This study aims to investigate the possible role of miRNAs in multi-omics regulatory pathways in cucumber somaclonal lines. For this purpose, we performed sRNA sequencing (sRNA-seq) from cucumber fruit samples identified 8, 10 and 44 miRNAs that are differentially expressed between somaclones (S1, S2, S3 lines) and the reference B10 line of Cucumis sativus. For miRNA identification, we use ShortStack software designed to filter miRNAs from sRNAs according to specific program criteria. The identification of predicted in-silico targets revealed 2,886 mRNAs encoded by 644 genes. The functional annotation of miRNA's target genes and gene ontology classification revealed their association with metabolic processes, response to stress, multicellular organism development, biosynthetic process and catalytic activity. We checked with bioinformatic analyses for possible interactions at the level of target proteins, differentially expressed genes (DEGs) and genes affected by genomic polymorphisms. We assume that miRNAs can indirectly influence molecular networks and play a role in many different regulatory pathways, leading to somaclonal variation. This regulation is supposed to occur through the process of the target gene cleavage or translation inhibition, which in turn affects the proteome, as we have shown in the example of molecular networks. This is a new approach combining levels from DNA-seq through mRNA-seq, sRNA-seq and in silico PPI in the area of plants' somaclonal variation. [ABSTRACT FROM AUTHOR]