Miracle-dz/UniFAR-FTUs huggingface dataset:
Exergetic Analysis of a Parabolic Trough Solar Collector Water Heater

Heat exchange mechanisms involved in the conversion of solar energy to heat were determined using a parabolic trough collector. This study's goal is to examine the impact of operational and environmental factors on the energetic, performance of three different Parabolic Trough Collector receivers used to generate hot water. The collectors used uncoated, grey, and black receiver tubes. The parabolic trough concentrator is built of mild steel as the mainframe support with a segmented mirror reflector. Reflectivity is 0.85, rim angle is 90, an aperture area is 2.42 m2, and concentration ratio is 11.7. The parabolic trough concentrator's focal point has galvanized iron receiving tubes. The receiver tubes were fitted individually via the parabolic reflector's focal point. The thermal exergy of each absorber tube was determined while water flowed at 0.003 kg/s. During the investigation, solar radiation, and water temperatures at the absorber tube's input and outflow were all measured. The results show that both the temperature of the heat transfer fluid and the amount of solar radiation have a substantial effect on thermal energetic performance. This concentrator reduces dependency on electric power while minimizing fossil-fuel emissions, reducing pollution.
“Prison TikTok”: Incarcerated Life Shared on Social Media

Different areas of the criminal justice system interact with social media, but not much is known about how those incarcerated may be using it. A thematic analysis of “Prison TikTok” videos was conducted in order to gain a better understanding of what those incarcerated are sharing. The most represented themes in the sample are performance , food , prison views , and pains of imprisonment . Performance and food demonstrate creativity within the sample, and highlight how those incarcerated may be looking for creative outlets. Prison and pains of imprisonment include the realities of
3D bioprintable methacrylated carrageenan/sodium alginate dual network hydrogel for vascular tissue engineering scaffolding

Abstract 3D printing technology has significant advantages in the processing field of vascular tissue engineering. We have developed KCMA/SA hydrogels based on methacrylated carrageenan (KCMA) and sodium alginate (SA), which have low viscosity and shear thinning properties and can be used for 3D printing. The physicochemical double cross-linked network structure leads to good mechanical properties. The composite hydrogels maintain the liquid crystallinity of KC, which causes the HUVECs to align. The KCMA/SA gel filaments printed by the 3D extrusion printing system formed a connected microstructure. These characteristics all indicate the potential of KCMA/SAs in the field of tubular tissue regeneration. Graphical Abstract
Misclassification Bias in Computational Social Science: A Simulation Approach for Assessing the Impact of Classification Errors on Social Indicators Research

A growing body of literature has examined the potential of machine learning algorithms in constructing social indicators based on the automatic classification of digital traces. However, as long as the classification algorithms’ predictions are not completely error-free, the estimate of the relative occurrence of a particular class may be affected by misclassification bias, thereby affecting the value of the calculated social indicator. Although a significant amount of studies have investigated misclassification bias correction techniques, they commonly rely on a set of assumptions that are likely to be violated in practice, which calls into question the effectiveness of these methods. Thus, there is a knowledge gap with respect to the assessment of misclassification bias’s impact on a specific social indicator formula without strict reference to the number of classes. Moreover, given the erroneous nature of automatic classification algorithms, the quality of a predicted indicator can be assessed not only using regression quality metrics, as was done in existing literature, but also using correlation metrics. In this paper, we propose a simulation approach for assessing the impact of misclassification bias on the calculated social indicators in terms of regression and correlation metrics. The proposed approach focuses on indicators calculated based on the distribution of classes and can process any number of classes. The proposed approach allows selecting the most appropriate classification model for a particular social indicator, and vice versa. Moreover, it allows for assessment of the optimistic level of correlation between the indicator calculated based on the results of the classification algorithm and the true underlying indicator.
Design of A New High-Gain Multiband and Wideband Rectangular Patch Antenna for C, X, and Ku Band Applications

In this paper, we propose the design of and provide an analysis of, a rectangular patch antenna and its integration in a 4 element linear array. A T form power divider feeds in parallel the array elements through microstrip lines. The basic antenna has a simple structure, with dimensions of about 16´18´1.6 mm 3 , leading to a good bandwidth. In addition, the patch element characteristics, such as the reflection coefficient and the voltage standing wave ratio (VSWR), are improved by the insertion of a slot in the structure. The main objective of the 4 element array structure is the enhancement of the gain, which is enhanced up to 12.6 dBi. A prototype of the antenna is fabricated and tested with network analyzer. The proposed antenna has 4 resonant frequencies that define 4 bandwidths, defined by a return loss of less than -10 dB, and are: (7.62 GHz, 1250 MHz), (9.20 GHz, 330 MHz), (11.07 GHz, 940 MHz) and (15.02 GHz, 920 MHz), respectively. The simple configuration of this profile permits an easy and low-expense fabrication process, with usability in many applications such as in radar, satellite, and wireless communications.
Recent Strategies for Using Monolithic Materials in Glycoprotein and Glycopeptide Analysis

There is continuous effort towards developing monolithic materials as solid supports for the separation, enrichment, and digestion of glycoproteins. The intention of this review is to discuss and summarize work reported in this area during the period 2015–2021 as a follow-up to our prior review. Reports from the past three decades have already proven the advantages of monolithic materials, such as the ease with which they can be prepared and functionalized, their high permeability and low resistance to mass transfer, and their stability over a wide range of pH. Recent works on glycoprotein analysis introduce different strategies in using monolithic materials specifically in separation, enrichment, and identification of glycoproteins, glycopeptides, and free glycans. A majority of these are focused on boronic acid affinity-based technique and others on lectin affinity and HILIC-based techniques. There are also newly developed ligands that utilize different interactions with glycans, such as encapsulation into β-cyclodextrin vesicles, CH- or OH-π interactions with fullerenes, immunoaffinity with monoclonal antibodies, H-bonding interactions with metallophthalocyanines, coordination interactions with cobalt phthalocyanine tetracarboxylic acid, and hydrophilic interaction with cyclodextrin molecular tubes, zwitterionic iminodiacetic acid, and boric acid. Recent strategies for developing on-line, multidimensional systems use immobilized monolithic enzyme reactors (IMERs) for high-throughput glycoprotein analysis. These works serve as contributions to better understand glycan structure-function relationship, as glycoproteins are now widely accepted disease biomarkers.
DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

Quantum routing plays a key role in the development of the next-generation network system. In particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping among particles (e.g., photons) associated with nodes in the network. From another side of computing, machine learning has achieved numerous breakthrough successes in various application domains, including networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model for quantum networks that employs machine learning architectures to construct the routing path for the maximum number of demands (source–destination pairs) within a time window. Specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to accommodate the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
A Coupled Thermal‐Hydraulic‐Mechanical Approach to Modeling the Impact of Roadbed Frost Loading on Water Main Failure

Subsurface pipe failures in cold regions are generally believed to be exacerbated by differential strain in shallow soils induced by seasonal freeze and thaw cycles. The transient stress–strain fields resulting from soil water phase change may influence the occurrence of local buried pipe breaks including those related to urban water mains. This work proposes that freezing‐induced frost loading results in uneven stress–strain distributions along the buried water mains placing them at risk of bending, breaking, and/or leaking. A coupled thermal‐hydraulic‐mechanical (THM) model was developed to illustrate the interactions among moisture, temperature, and stress–strain fields within variably saturated freezing soils. Several typical cases involving highly frost‐susceptible and lower frost‐susceptible soils underlying roadbeds were examined. Results show that the magnitude of frost‐induced compressive stress and strain changes between different frost‐susceptible soils can vary significantly. Such substantial differences in stress–strain fields would increase the breakage risk of water mains buried within different types of soils. Furthermore, even water mains buried within soils with low frost‐susceptibility are at risk when additional sources of soil water exist and are available to migrate to the freezing front. To reduce the risk of damage to buried pipe‐like infrastructure, such as municipal water mains, from soil freezing phenomena, the selected backfill material should have fairly consistent frost susceptibility or a broad zone of transition should be considered between materials with significantly different frost susceptibility. In addition, buried pipes should be kept as far away from external sources of subsurface water as possible considering the potential for the water source to exacerbate the level of risk to the pipe.
Korean Consumers’ Motivations and Perceived Risks Toward the Purchase of Organic Cotton Apparel

The purpose of this research is to gain insight into collectivist consumers' perception of purchasing organic cotton apparel. An extended TRA model was employed to identify major factors that influence consumers' purchase decisions. Data were collected through a Web-based survey of 200 female South Korean residents older than 18 years of age. The results of SEM showed that perceived benefits, importance of individual expression through dressing well, performance risk, financial risk and subjective norm significantly influenced attitude toward purchasing organic cotton apparel products. In addition, financial risk, attitude, and subjective norm had significant effects on consumers' purchase intention. It was revealed that subjective norm played a critical role in the purchase process: subjective norm was one of the strongest antecedents of attitude among the six variables under examination and exerted a similar influence on purchase intention as attitude. Based on the findings, practical implications for organic cotton marketers and suggestions for future research are proposed.
Determination of optimal shoe dimensions for occasional and regular tennis players

Footwear fit is very important in tennis, directly influencing comfort, security and performance. Within the current literature, there are no in-depth studies regarding the fit of tennis shoes, resulting in a lack of knowledge regarding the effect of player expertise on fit preference. Therefore, the main purpose of this study was to determine optimal inner-shoe dimensions for tennis players. A total of 34 tennis players were recruited and divided into two groups based on frequency of play (occasional and regular). Each subject’s feet were measured, and they were then asked to assess each of the four tennis shoes, which varied in dimension, through the use of a questionnaire. A repeated measure analysis of variance and Pearson’s correlation coefficient were performed to analyse shoe–foot dimensional difference and subjective fit rating. The analysis of variance revealed significant differences on fit rating between shoes. Correlations were found between shoe–foot dimensional differences at length, metatarsal width and metatarsal girth. The optimal shoe length was determined to be nearly 10 mm longer than the foot for both occasional and regular players; the optimal shoe metatarsal width was 8.4 mm less than the foot for regular players; the optimal shoe metatarsal girth was 9.5 mm less than the foot for occasional players and 14.7 mm less than the foot for regular players.
Probabilistic Modeling of Motion Blur for Time-of-Flight Sensors

Synthetically creating motion blur in two-dimensional (2D) images is a well-understood process and has been used in image processing for developing deblurring systems. There are no well-established techniques for synthetically generating arbitrary motion blur within three-dimensional (3D) images, such as depth maps and point clouds since their behavior is not as well understood. As a prerequisite, we have previously developed a method for generating synthetic motion blur in a plane that is parallel to the sensor detector plane. In this work, as a major extension, we generalize our previously developed framework for synthetically generating linear and radial motion blur along planes that are at arbitrary angles with respect to the sensor detector plane. Our framework accurately captures the behavior of the real motion blur that is encountered using a Time-of-Flight (ToF) sensor. This work uses a probabilistic model that predicts the location of invalid pixels that are typically present within depth maps that contain real motion blur. More specifically, the probabilistic model considers different angles of motion paths and the velocity of an object with respect to the image plane of a ToF sensor. Extensive experimental results are shown that demonstrate how our framework can be applied to synthetically create radial, linear, and combined radial-linear motion blur. We quantify the accuracy of the synthetic generation method by comparing the resulting synthetic depth map to the experimentally captured depth map with motion. Our results indicate that our framework achieves an average Boundary F1 (BF) score of 0.7192 for invalid pixels for synthetic radial motion blur, an average BF score of 0.8778 for synthetic linear motion blur, and an average BF score of 0.62 for synthetic combined radial-linear motion blur.
Incorporating “Kansei Engineering” Approach on Traditional Textiles - A Proposed Method for Identifying Multi-Sensorial Experiences on the Kansei Attributes of Traditional Textiles -

When people are asked to described certain textiles, they frequently refer to the expressions of its properties such as attractiveness, uniqueness, shininess, robustness, comfortability, and so on. It shows how senses play important role in it. Human employs their senses when interacting with textiles, most notably visual and tactile/ haptic to absorb its expressive properties. Yet, our sensorial experiences may amplify when interacting with those of traditional textiles, such as batik, as we can entice sensations when seeing its motifs and patterns, smelling its materials, and touching its surfaces. The multi-sensorial importance of seeing, smelling, and touching in the interaction with and experience of textiles suggests that one should address senses in a systematic way when evaluating users’ perception on traditional textiles. To address this issue, the paper proposes the incorporation of Kansei Engineering (KE) approach for identifying multi-sensorial experiences on the expressive properties of traditional textiles, using batik as a case of study. KE approach address person's psychological understanding when observing things in order to analyze and study the inherent relationship between person's perceptual knowledge and objects evaluated. This paper outlines the use of KE approach in correlating sensorial perceptions when experience with traditional textiles and ultimately expose users’ preferences toward them. Background of KE approach on textiles will be explored and its application for the multi-sensorial investigation of traditional textiles will be discussed.
Recent Trends in Neuro marketing – An Exploratory Study

Purpose: The concept of neuro-marketing is explored as an emerging economic approach that originated from human brain research and traditional marketing facts. The core idea of the paper is to understand the concept of Neuromarketing and its influence on customers’ decision-making process. Design: The benefits of various neuromarketing tactics on various market input devices are studied in the paper. From available literature and research studies, the influence of neuromarketing’s various techniques and procedures on verifiable marketing success is been highlighted. Findings: Neuromarketing concept has gain lot of importance in recent years. It has contributed in the various fields of marketing such as framing marketing strategies, selection of brand, consumer behaviour, advertising, ethical concerns and decision-making. The study highlights the importance of neuromarketing principles and concepts for engaging neuroscience in the field of marketing and understanding consumer behaviour which could help in planning new marketing strategies based on neuroscience. Originality: The impact of sensory aspects on a customer’s perception and conscious or subconscious purchasing choice is highlighted in this study. It also addresses the ethical problems that have been raised concerning neuromarketing. In this study, the benefits, limitations, ethical difficulties, and future potential of neuromarketing are discussed. Value: A neuromarketing study will help the companies compete for market leadership, increase customer base and convert them into loyal consumers. It will help to determine what the customer wants, what services he likes, and how to draw the consumer's attention. Marketers can understand customer behaviour, including how it reacts to a company's advertising, brand, and product quality. Neuro-marketing can help a marketer to increase their turnover. Type of Paper: Conceptual Paper.
Reliability Versus Latency in IIoT Visual Applications: A Scalable Task Offloading Framework

In Industrial Internet of Things (IIoT), reliability and latency are two important performance indicators. However, these two performance indicators are contradictory with each other, which are difficult to be enhanced simultaneously. In reality, many IIoT applications are in video or image format with critical requirements of both reliability and latency. In this article, we propose a scalable task offloading scheme for IIoT visual applications considering the unique scalable feature compared with general content. The proposed scheme demonstrates that partial content can be adaptively offloaded to a specific computing node to meet the reliability and latency requirements, meanwhile obtaining an excellent tradeoff between them. For optimization, a utility function is defined to characterize the tradeoff between reliability and latency. Then, a greedy algorithm is introduced to solve the problem of maximizing the utility function, where a near-optimal scheduling policy is adopted to achieve offloading association and properly offload data volume. Simulation results reveal that the proposed scalable task offloading scheme performs better than other benchmark schemes in balancing reliability and latency in IIoT visual applications, especially when the network traffic is moderate and channel conditions are undesirable.
Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems

V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.
3D Printing Technologies in Biosensors Production: Recent Developments

Recent advances in 3D printing technologies and materials have enabled rapid development of innovative sensors for applications in different aspects of human life. Various 3D printing technologies have been adopted to fabricate biosensors or some of their components thanks to the advantages of these methodologies over the traditional ones, such as end-user customization and rapid prototyping. In this review, the works published in the last two years on 3D-printed biosensors are considered and grouped on the basis of the 3D printing technologies applied in different fields of application, highlighting the main analytical parameters. In the first part, 3D methods are discussed, after which the principal achievements and promising aspects obtained with the 3D-printed sensors are reported. An overview of the recent developments on this current topic is provided, as established by the considered works in this multidisciplinary field. Finally, future challenges on the improvement and innovation of the 3D printing technologies utilized for biosensors production are discussed.