Miracle-dz/UniFAR-FTUs huggingface dataset:
Room-Scale Location Trace Tracking via Continuous Acoustic Waves

The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim to develop a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained analyses, able to precisely locate moving human traces in the room-scale environment. In particular, a smart speaker is controlled to emit continuous waves at inaudible 20 kHz , with a co-located microphone array to record their Doppler reflections for localization. We first develop solutions to remove potential noises and then propose a novel idea by slicing signals into a set of narrowband signals, each of which is likely to include at most one body segment’s reflection. Different from previous studies, which take original signals themselves as the baseband, our solutions employ the Doppler frequency of a narrowband signal to estimate the velocity first and apply it to get the accurate baseband frequency, which permits a precise phase measurement after I-Q (i.e., in-phase and quadrature) decomposition. A signal model is then developed, able to formulate the phase with body segment’s velocity, range, and angle. We next develop novel solutions to estimate the motion state in each narrowband signal, cluster the motion states for different body segments corresponding to the same person, and locate the moving traces while mitigating multi-path effects. Our system is implemented with commodity devices in room environments for performance evaluation. The experimental results exhibit that our system can conduct effective localization for up to three persons in a room, with the average errors of 7.49 cm for a single person, with 24.06 cm for two persons, with 51.15 cm for three persons.
Numerical Modeling and Characterization of Vertically Aligned Carbon Nanotube Arrays

OF DISSERTATION Since their discoveries, carbon nanotubes have been widely studied, but mostly in the forms of 1D individual carbon nanotube (CNT). From practical application point of view, it is highly desirable to produce carbon nanotubes in large scales. This has resulted in a new class of carbon nanotube material, called the vertically aligned carbon nanotube arrays (VA-CNTs). To date, our ability to design and model this complex material is still limited. The classical molecular mechanics methods used to model individual CNTs are not applicable to the modeling of VA-CNT structures due to the significant computational efforts required. This research is to develop efficient structural mechanics approaches to design, model and characterize the mechanical responses of the VA-CNTs. The structural beam and shell mechanics are generally applicable to the well aligned VACNTs prepared by template synthesis while the structural solid elements are more applicable to much complex, super-long VA-CNTs from template-free synthesis. VACNTs are also highly “tunable” from the structure standpoint. The architectures and geometric parameters of the VA-CNTs have been thoroughly examined, including tube configuration, tube diameter, tube height, nanotube array density, tube distribution pattern, among many other factors. Overall, the structural mechanics approaches are simple and robust methods for design and characterization of these novel carbon nanomaterials.
Identification of Hydraulic Fracture Orientation from Ground Surface Using the Seismic Moment Tensor

Microseismic monitoring from ground surface is applied in the development of hard-torecover reserves, especially in the process of hydraulic fracturing (HF). This paper compares several methods of HF microseismic monitoring from the surface, including diffraction stacking, time reverse modeling, and spectral methods. In (Aki and Richards, 1980) it is shown that signal enhancement from seismic events under correlated noises significantly improves when applying the maximum likelihood method. The maximum likelihood method allows to exclude influence of the correlated noise, and also to estimate the seismic moment tensor from ground surface. Estimation of the seismic moment tensor allows to detect type and orientation of source. Usually, the following source types are identified: “Explosion Point” (EXP), “Tensile Crack” (TC), “Double-Couple” (DC) and “Compensated Linear Vector Dipole” (CLVD). The orientation of the hydraulic fracture can be estimated even when there is no obvious asymmetry of the spatial distribution of the cloud of events. The features of full-wave location technology are presented. The paper also reviews an example of microseismic monitoring of hydraulic fracturing when there is no obvious asymmetry of microseismic activity cloud, but due to the estimation of the seismic moment tensor it becomes possible to identify with confidence the dominant direction of the fracture.
Analysis of Current-Voltage Properties of Al/p-si Schottky Diode with Aluminium Oxide Layer

In our study, how the thin metal oxide (aluminium oxide, Al2O3) thin film placed between the metal and the semiconductor on the diode characteristics will affect was investigated. The Al2O3 thin film was suitable for its grown on p-type silicon substrate by atomic layer deposition (ALD) technique. A diode structure with an oxide interlayer was fabricated. To investigate the electrical parameters of the fabricated Schottky diode, measurements of current-voltage (I-V) were carried out at room temperature and in the 5 V voltage range. Using the I-V measurements, diode parameters such as the barrier height (Φb), the ideality factor (n), and the current density (I0) were evaluated using the Thermionic Emission (TE) method. Using the TE method, the approximate values of Φb, n, and Io parameters were calculated as 0.77 eV, 5.43, and 2.43E-09 A respectively. According to calculations, the developed Schottky diode is a rectifier diode and has been determined to have photodiode properties.
Professional Development Program for High School Counselors on the Engineering Design Process (Evaluation)

Lydia Ross is a clinical assistant professor in the Teachers College. She also serves as the executive director of the Association for Education Finance & Policy. She holds a PhD in Educational Policy and Evaluation from Arizona State University. Her research focuses on equity and access and in higher education, with a focus on STEM. educational challenges, democratization of engineering and and Abstract: High school counselors play a pivotal role in students’ educational pathways to STEM careers. Guidance provided by these school officials can be critical in student selection of elective courses, achievement, and fostering an environment through outreach activities. Each of these factors can influence students’ career interests, college choice, and major selection, which can have a major impact on diversifying who pursues degrees in engineering. This research study details the development of a professional development (PD) program for high school guidance counselors to better understand how such an experience can impact counselor perception of engineering. Counselors participated in a series of engineering design activities to learn more about the engineering process, engineering stereotypes, stereotype threats, implicit biases, and different disciplines within engineering to better inform their students of future career options. Details are provided around the design of the engineering counselor PD as well as insights into how the PD impacted counselor perceptions of engineering. The study has implications toward broadening participation in engineering through school counselor professional development.
Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

Machine- and deep-learning methods are used for industrial applications in prognostics and health management (PHM) for semiconductor processing and equipment anomaly detection to achieve proactive equipment maintenance and prevent process interruptions or equipment downtime. This study proposes a Pruning Quantized Unsupervised Meta-learning DegradingNet Solution (PQUM-DNS) for the fast training and retraining of new equipment or processes with limited data for anomaly detection and the prediction of various equipment and process conditions. This study utilizes real data from a factory chiller host motor, the Paderborn current and vibration open dataset, and the SECOM semiconductor open dataset to conduct experimental simulations, calculate the average value, and obtain the results. Compared to conventional deep autoencoders, PQUM-DNS reduces the average data volume required for rapid training and retraining by about 75% with similar AUC. The average RMSE of the predictive degradation degree is 0.037 for Holt–Winters, and the model size is reduced by about 60% through pruning and quantization which can be realized by edge devices, such as Raspberry Pi. This makes the proposed PQUM-DNS very suitable for intelligent equipment management and maintenance in industrial applications.
Predictable Policing: Predictive Crime Mapping and Geographies of Policing and Race

This article draws on critical geographic engagements with policing and race and geographic information systems (GIS) to investigate the implications that predictive crime mapping has for racialized modes of urban policing. Focusing on the Chicago Police Department (CPD), it analyzes collaborations between geographic information scientists, crime experts, and police who have recently begun integrating temporal data into GIS-based maps to predict when and where future crimes will occur. The article builds the case that predictive crime mapping further entrenches and legitimizes racialized policing as it (1) rearticulates police data sets as scientifically valid and (2) correlates those data with other geocoded information to create new rationalizations for controlling racialized districts through differential policing practices. The article uses a mixed-methods approach that includes analysis of open-ended interviews with computer scientists involved with the CPD's Predictive Analytics Group and city technical documents to explain the recursive relation between GIS-based knowledge production and racialized policing. The article casts into relief the central role that the production of geographic information plays in current modes of racialized policing and how this contributes to the ongoing racial differentiation of urban geographies.
Symptom assessment in cancer patients using the Memorial Symptom Assessment Scale

Objective: Cancer is a chronic disease characterized by the unregulated proliferation and abnormal spread of cells. Symptoms that occur after chemotherapy treatment, which is the most commonly used treatment in cancer, negatively affect morbidity, effectiveness of treatment and quality of life. This study aimed to determine symptoms in cancer patients undergoing chemotherapy using the Memorial Symptom Assessment Scale (MSAS) Material and Methods: A total of 273 cancer patients who applied to Ümraniye Training and Research Hospital emergency department as outpatients and were hospitalized in the internal medicine department were included in the study between 5/1/2014 and 6/4/2014. The study was conducted prospectively. Of the 273 participants, 41.3% were female (N = 112) and 58.7% were male (N = 159). The mean age of the participants was 62.46 ± 12.913 years (range, 32-92 years). Participants were divided into 6 groups based on diagnosis: Lung cancers, gastrointestinal cancers, cancers of the reproductive system, hematologic cancers, cancers of the urinary system and others. Each participant used the Memorial Symptom Assessment Scale (MSAS) containing 32 symptoms to rate whether they had experienced each symptom during the past week. Of these 32 items, 24 provide information on the intensity and frequency of symptoms, while 8 provide information on severity and distress. Results: The most common types of symptoms based on the Memorial Symptom Rating Scale were lack of energy (72.9%), pain (71.1%) and difficulty concentrating (60.8%), while the least common symptoms were feeling “I don’t look like myself” (4.4%), swelling of arms and legs and changes in skin (7.3%) and constipation (8.4%). Conclusion: Cancer patients have a lower quality of life due to the side effects of the treatments they receive or the impact of the cancer itself. The most common symptoms in the patients screened in this study were lack of energy and pain. In light of these data, determining the factors affecting the quality of life in cancer patients may be guiding in determining the symptomatic treatment for these patients and facilitating patient care.
Optimized Design of Scoring Criteria for Large-Scale Innovation Competitions

The reasonable and reliable evaluation score calculation scheme wins the top priority in large-scale innovation-competitions currently. Due to the disparity among individual experts, and the existing standard score calculation model merely based on the experts' own circumstance, it cannot fully reflect the comprehensive level of players, resulting in a certain degree of error in the evaluation results. From the perspective of individual and decision-making of a group, this paper improves existing models and introduces the concept of modified scores. To verify the feasibility of the model, four schemes were designed based on the analysis of data distribution. Consistency and difference factors were applied to compare. Come to a conclusion: scheme adopted the new standard score calculation model ranked the top, indicating that the new calculation model is more reliable. Next, considering that the volatile correction factor affected by data, there is a possibility of some poor actual results with high correction scores. To rectify this case, a power exponent is added to the correction factor and a sign function is adopted to specify the positive or negative of correction scores. To verify the feasibility of the model, four sets of controlled experiments were designed with the introduction of two factors: power exponent and reconsideration bonus, as well as the ranking consistency test based on the condition that the award order of the expert agreement was consistent. In the end, it was found that the scheme that introduced both power exponent and reconsideration bonus points had a sorting consistency rate of 74%, which increased by 30% compared to the original model, indicating the rationality of the modification. The expert evaluation standard score calculation model established in this paper comprehensively considers individual and group decision-making, and provides reasonable correction for the differential scoring between experts. At the same time, this scheme provides a reference basis for further optimizing large-scale innovation competition plans in the future.
High-Performance PEEK/MWCNT Nanocomposites: Combining Enhanced Electrical Conductivity and Nanotube Dispersion

High-performance engineering thermoplastics offer lightweight and excellent mechanical performance in a wide temperature range. Their composites with carbon nanotubes are expected to enhance mechanical performance, while providing thermal and electrical conductivity. These are interesting attributes that may endow additional functionalities to the nanocomposites. The present work investigates the optimal conditions to prepare polyether ether ketone (PEEK)/multi-walled carbon nanotube (MWCNT) nanocomposites, minimizing the MWCNT agglomerate size while maximizing the nanocomposite electrical conductivity. The aim is to achieve PEEK/MWCNT nanocomposites that are suitable for melt-spinning of electrically conductive multifilament’s. Nanocomposites were prepared with compositions ranging from 0.5 to 7 wt. % MWCNT, showing an electrical percolation threshold between 1 and 2 wt. % MWCNT (107–102 S/cm) and a rheological percolation in the same range (1 to 2 wt.% MWCNT), confirming the formation of an MWCNT network in the nanocomposite. Considering the large drop in electrical conductivity typically observed during melt-spinning and the drawing of filaments, the composition PEEK/5 wt. % MWCNT was selected for further investigation. The effect of the melt extrusion parameters, namely screw speed, temperature, and throughput, was studied by evaluating the morphology of MWCNT agglomerates, the nanocomposite rheology, and electrical properties. It was observed that the combination of the higher values of screw speed and temperature profile leads to the smaller number of MWCNT agglomerates with smaller size, albeit at a slightly lower electrical conductivity. Generally, all processing conditions tested yielded nanocomposites with electrical conductivity in the range of 0.50–0.85 S/cm. The nanocomposite processed at higher temperature and screw speed presented the lowest value of elastic modulus, perhaps owing to higher matrix degradation and lower connectivity between the agglomerates. From all the process parameters studied, the screw speed was identified to have the higher impact on nanocomposite properties.
Postoperative Patient Pain Severity and Its Association With Anxiety, Depression, and Sleep Quality

Introduction The experience of pain is a complex phenomenon. A patient in the acute postsurgical pain setting may feel a constant bombardment of nociceptive input from the surgical site; this in turn influences psychological factors that determine the overall emotional experience of pain, which is significant. The aim of our study was to investigate the severity of pain in postsurgical patients three days after surgery using the 100 mm visual analog scale (VAS). Methods This was a cross-sectional assessment of postoperative pain. Participants were patients between 18 and 64 years of age who had undergone a surgical procedure (laparoscopic or open surgery), three days prior to the data collection and who were admitted or discharged postoperatively at the Al Salmaniya Complex, Manama, Bahrain. Participants were asked demographic questions about whether they had laparoscopic or open surgeries and completed self-reporting scales. Patient Health Questionnaire-9 (PHQ-9) was utilized to screen for both the presence and severity of depression; Generalized Anxiety Disorder 7-item (GAD-7) was administered to screen for anxiety; the Insomnia Severity Index (ISI) was used to evaluate insomnia; and the VAS was used to evaluate pain. Results Sixty-seven patients were recruited, with a mean age of 61.53 years (SD = 7.37). Twenty-nine (43%) were females, 38 (57%) were males, 36 (54%) underwent elective surgery, 31 (46%) underwent emergency surgery, 31 (46%) underwent laparoscopic surgery, and 36 (54%) underwent open surgery. The average score on the Brief Pain Inventory Short Form (BPISF) was 8.12 (SD = 1.16), indicating a moderate level of pain. Twenty-six (43%) patients had moderate-severe insomnia, 21 participants (31%) had no insomnia, 17 participants (25%) had subthreshold insomnia, 28 (42%) had moderate depression, five (7%) had moderate-severe depression, and 34 (51%) had severe depression. Eighteen participants (27%) had mild anxiety, 46 (69%) had moderate anxiety, and 3 (4%) had severe anxiety. Six of the participants (9%) reported moderate pain, while 61 participants (91%) reported severe pain.
Exogenous Abscisic Acid Regulates Anthocyanin Biosynthesis and Gene Expression in Blueberry Leaves

Blueberry (Vaccinium corymbosum) leaves have a positive influence on health because of their phenolic contents, including anthocyanins. Phytohormone abscisic acid (ABA) promotes anthocyanin accumulation, but the underlying mechanisms are unclear in blueberry leaves. In this study, we found that exogenous ABA promotes anthocyanin accumulation in blueberry leaves and we explored the global molecular events involved in these physiological changes by treating in vitro-grown blueberry seedlings with ABA and performing transcriptome deep sequencing (RNA-seq). We identified 6390 differentially expressed genes (DEGs), with 2893 DEGs at 6 h and 4789 at 12 h of ABA treatment compared to the control. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to plant hormone signal transduction and phenylpropanoid and flavonoid biosynthesis were significantly enriched at both stages of the ABA treatment. Analysis of DEGs in plant hormone signal transduction pathways revealed that exogenous ABA affected the expression of genes from other plant hormone signaling pathways, especially brassinosteroid, auxin, and gibberellin signaling. To elucidate the mechanism driving anthocyanin biosynthesis in blueberry in response to ABA treatment, we screened anthocyanin biosynthesis structural genes (ASG) from the phenylpropanoid and flavonoid biosynthetic pathways, MYB transcription factor genes from R2R3-MYB subgroups 5, 6, and 7 and ABRE-binding factor (ABF) genes from the ABA signal transduction pathway. Pearson’s correlation coefficient (r) analysis indicated that the ABFs, MYBs, and structural genes form a network to regulate ABA-induced anthocyanin biosynthesis and MYBA1 is likely to play an important role in this regulatory network. These findings lay the foundation for improving anthocyanin biosynthesis in blueberry leaves.
Phytoremediation with Sunflower (Helionthus annus) and Its Capacity for Cadmium Removal in Contaminated Soils

This study assessed the phytoremediation potential of sunflowers for removing cadmium from the soil around a paint manufacturing industry in Eleyele, Ibadan. Background levels of Cd in the topsoil and subsoil were determined. The site was divided into two plots. Organo-mineral fertilizer (OMF) was applied to the first plot, which served as the experimental plot, while the second plot was without OMF and served as the control. Concentrations of cadmium in the plants were determined using an Atomic Absorption Spectrophotometer. Baseline mean Cd contents were 29.23 mg/kg and 33.30 mg/kg for topsoil and subsoil, respectively. Over the planting period, the sunflower plants removed 53.1% and 51.6% of Cd from the topsoil and subsoil in the test plot, while 40.65% and 47.80% were removed from the topsoil and subsoil, respectively, in the control. Cd absorption from the contaminated soils was found to be translocated to all parts of the sunflower. The concentrations of Cd in the sunflower parts were as follows: root system (10.70 mg/kg), shoot (8.17 mg/kg), leaves (6.43 mg/kg), and seeds (2.52 mg/kg) for the test plot. For the control plots, Cd in the root, shoot, leaves, and seeds were 7.60 mg/kg, 7.43 mg/kg, 4.75 mg/kg, and 2.03 mg/kg, respectively. The study confirmed that sunflowers have the potential to remediate Cd from contaminated soil, and this potential was enhanced by the application of OMF.