Level Up Eco-Friendly Hotel Stay: Guest Power Up and Save Energy with Gamification

Executive Summary Hotel businesses endeavor to achieve successful green practices for long-lasting business success. The key issue is to promote consumers’ willingness to participate in such practices. Despite increased environmental awareness among today’s consumers, a relatively large percentage of consumers hesitate to actively engage with green practices because they are unwilling to sacrifice their comforts and convenience for their paid experience. This report introduces a gamification approach to hotels’ green practices through an investigation of how game components motivate guests’ energy-saving participation. This report finds that the energy-saving game can improve hotel guests’ willingness to save energy and even revisit the hotel. This paper offers fruitful suggestions on using gamification to encourage guests’ energy-saving and revisit behaviors

Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and [email protected] compared with YOLOv5s, among which [email protected] has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects

Design and Implementation on Multi-Function Smart Wheelchair

With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair

Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7

The importance of real-time vehicle detection tracking and counting system based on YOLOv7 is an important tool for monitoring traffic flow on highways. Highway traffic management, planning, and prevention rely heavily on real-time traffic monitoring technologies to avoid frequent traffic snarls, moving violations, and fatal car accidents. These systems rely only on data from timedependent vehicle trajectories used to predict online traffic flow. Three crucial duties include the detection, tracking, and counting of cars on urban roads and highways as well as the calculation of statistical traffic flow statistics (such as determining the real-time vehicles flow and how many different types of vehicles travel). Important phases in these systems include object detection, tracking, categorizing, and counting. The YOLOv7 identification method is presented to address the issues of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban highways, weak perspective perception of small targets, and insufficient feature extraction. This system aims to provide real-time monitoring of vehicles, enabling insights into traffic patterns and facilitating informed decision-making. In this paper, vehicle detecting, tracking, and counting can be calculated on real-time videos based on modified YOLOv7 with high accuracy

Fruit Classification Based on Improved YOLOv7 Algorithm

With the rapid development of technology and advancements, unmanned vending machines have emerged as the primary contactless retail method. The efficient and accurate implementation of automated identification technology for agricultural products in their distribution and sales has become an urgent problem that needs to be addressed. This article presents an improved YOLOv7 (You Only Look Once) algorithm for fruit detection in complex environments. By replacing the 3×3 convolutions in the backbone of YOLOv7 with Deformable ConvNet v2(DCNv2), the recognition accuracy and efficiency of fruit classification in YOLOv7 are significantly enhanced. The results indicate that the overall recognition accuracy of this system for ten types of fruits is 98.3%, showcasing its high precision and stability

SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Mobile Devices

This paper presents SmartMal—a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server’s main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users’ regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices

The Effects of G-CSF on Proliferation of Mouse Myocardial Microvascular Endothelial Cells

This paper explores the effect of granulocyte colony-stimulating factor (G-CSF) on mouse myocardial microvascular endothelial cell (CMECs) proliferation. CMECs were harvested from C57/BL6 mice. CMECs were cultured in medium containing G-CSF (0 ng/mL, 20 ng/mL, 40 ng/mL, 60 ng/mL) for five days. Proliferative activity of CMECs was examined by CCK-8 method. Hypoxia inducible factor-1 (HIF-1) and p53 expression levels was determined from the mRNA obtained by reverse transcription polymerase chain reaction (RT-PCR). Results showed that the purity quotient of the CMECs, which were cultured by the method of modified myocardial tissue explant culture, was higher than 95%. Compared with control untreated cells, the proliferative activity of CMECs and the expression level of HIF-1 mRNA in these cells were enhanced by G-CSF treatment, whereas the expression level of p53 mRNA was markedly reduced. It may be concluded that G-CSF could promote the proliferative activity of CMECs, which might be mediated by upregulation of HIF-1 and downregulation of p53

Snake Cathelicidin from Bungarus fasciatus Is a Potent Peptide Antibiotics

Background: Cathelicidins are a family of antimicrobial peptides acting as multifunctional effector molecules of innate immunity, which are firstly found in mammalians. Recently, several cathelicidins have also been found from chickens and fishes. No cathelicidins from other non-mammalian vertebrates have been reported. Principal Findings: In this work, a cathelicidin-like antimicrobial peptide named cathelicidin-BF has been purified from the snake venoms of Bungarus fasciatus and its cDNA sequence was cloned from the cDNA library, which confirm the presence of cathelicidin in reptiles. As other cathelicidins, the precursor of cathelicidin-BF has cathelin-like domain at the N terminus and carry the mature cathelicidin-BF at the C terminus, but it has an atypical acidic fragment insertion between the cathelin-like domain and the C-terminus. The acidic fragment is similar to acidic domains of amphibian antimicrobial precursors. Phylogenetic analysis revealed that the snake cathelicidin had the nearest evolution relationship with platypus cathelicidin. The secondary structure of cathelicidin-BF investigated by CD and NMR spectroscopy in the presence of the helicogenic solvent TFE is an amphipathic α-helical conformation as many other cathelicidins. The antimicrobial activities of cathelicidin BF against forty strains of microorganisms were tested. Cathelicidin-BF efficiently killed bacteria and some fungal species including clinically isolated drug-resistance microorganisms. It was especially active against Gram-negative bacteria. Furthermore, it could exert antimicrobial activity against some saprophytic fungus. No hemolytic and cytotoxic activity was observed at the dose of up to 400 µg/ml. Cathelicidin-BF could exist stably in the mice plasma for at least 2.5 hours. Conclusion: Discovery of snake cathelicidin with atypical structural and functional characterization offers new insights on the evolution of cathelicidins. Potent, broad spectrum, salt-independent antimicrobial activities make cathelicidin-BF an excellent candidate for clinical or agricultural antibiotics