bluuebunny/crossref_metadata_2025 huggingface dataset:
Privacy Risk Assessment of Smart Home System Based on a STPA–FMEA Method
Authors: Wang Yue , Zhang Rui , Zhang Xiaoyi , Zhang Yalan
Published: May 2023

Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of ‘user-environment-smart home product’. A total of 35 privacy risk scenarios of ‘component-threat-failure-model-incident’ combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems.
Squeeze Film Effect in Surface Micromachined Nano Ultrasonic Sensor for Different Diaphragm Displacement Profiles
Authors: Dastidar Avik Ghosh , Maity Reshmi , Tiwari Ramesh Chandra , Vidojevic Dejan , Kevkic Tijana S. , Nikolic Vojkan , Das Subhajit , Maity Niladri Pratap
Published: May 2023

In the present paper, we have analytically explored the small variations of the local pressure in the trapped air film of both sides of the clamped circular capacitive micromachined ultrasonic transducer (CMUT), which consists of a thin movable membrane of silicon nitride (Si3N4). This time-independent pressure profile has been investigated thoroughly by solving the associated linear Reynold’s equation in the framework of three analytical models, viz. membrane model, plate model, and non-local plate model. The solution involves Bessel functions of the first kind. The Landau–Lifschitz fringing technique has been assimilated to engrave the edge effects in estimation of the capacitance of CMUT, which should be considered in the micrometer or lesser dimension. To divulge the dimension-based efficacy of the considered analytical models, various statistical methods have been employed. Our use of contour plots of absolute quadratic deviation revealed a very satisfactory solution in this direction. Though the analytical expression of the pressure profile is very cumbersome in various models, the analysis of these outputs exhibits that the pressure profile follows the displacement profile in all the cases indicating no viscous damping. A finite element model (FEM) has been used to validate the systematic analyses of displacement profiles for several radii and thicknesses of the CMUT’s diaphragm. The FEM result is further corroborated by published experimental results bearing excellent outcome.
Progressive Learning of a Multimodal Classifier Accounting for Different Modality Combinations
Authors: John Vijay , Kawanishi Yasutomo
Published: May 2023

In classification tasks, such as face recognition and emotion recognition, multimodal information is used for accurate classification. Once a multimodal classification model is trained with a set of modalities, it estimates the class label by using the entire modality set. A trained classifier is typically not formulated to perform classification for various subsets of modalities. Thus, the model would be useful and portable if it could be used for any subset of modalities. We refer to this problem as the multimodal portability problem. Moreover, in the multimodal model, classification accuracy is reduced when one or more modalities are missing. We term this problem the missing modality problem. This article proposes a novel deep learning model, termed KModNet, and a novel learning strategy, termed progressive learning, to simultaneously address missing modality and multimodal portability problems. KModNet, formulated with the transformer, contains multiple branches corresponding to different k-combinations of the modality set S. KModNet is trained using a multi-step progressive learning framework, where the k-th step uses a k-modal model to train different branches up to the k-th combination branch. To address the missing modality problem, the training multimodal data is randomly ablated. The proposed learning framework is formulated and validated using two multimodal classification problems: audio-video-thermal person classification and audio-video emotion classification. The two classification problems are validated using the Speaking Faces, RAVDESS, and SAVEE datasets. The results demonstrate that the progressive learning framework enhances the robustness of multimodal classification, even under the conditions of missing modalities, while being portable to different modality subsets.
Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI
Authors: Pistellato Mara , Bergamasco Filippo , Bigaglia Gianluca , Gasparetto Andrea , Albarelli Andrea , Boschetti Marco , Passerone Roberto
Published: May 2023

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators.
Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation
Authors: Tadisetty Srikanth , Chodavarapu Ranjith , Jin Ruoming , Clements Robert J. , Yu Minzhong
Published: May 2023

With recent advancements in artificial intelligence, fundus diseases can be classified automatically for early diagnosis, and this is an interest of many researchers. The study aims to detect the edges of the optic cup and the optic disc of fundus images taken from glaucoma patients, which has further applications in the analysis of the cup-to-disc ratio (CDR). We apply a modified U-Net model architecture on various fundus datasets and use segmentation metrics to evaluate the model. We apply edge detection and dilation to post-process the segmentation and better visualize the optic cup and optic disc. Our model results are based on ORIGA, RIM-ONE v3, REFUGE, and Drishti-GS datasets. Our results show that our methodology obtains promising segmentation efficiency for CDR analysis.
An Evidence Theory Based Embedding Model for the Management of Smart Water Environments
Authors: Driss Maha , Boulila Wadii , Mezni Haithem , Sellami Mokhtar , Ben Atitallah Safa , Alharbi Nouf
Published: May 2023

Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.
IoT Electrochemical Sensor with Integrated Ni(OH)2–Ni Nanowires for Detecting Formaldehyde in Tap Water
Authors: Trafela Špela , Krishnamurthy Abhilash , Soderžnik Kristina Žagar , Kavčič Urška , Karlovits Igor , Klopčič Beno , Šturm Sašo , Žužek Kristina
Published: May 2023

Simple, low-cost methods for sensing volatile organic compounds that leave no trace and do not have a detrimental effect on the environment are able to protect communities from the impacts of contaminants in water supplies. This paper reports the development of a portable, autonomous, Internet of Things (IoT) electrochemical sensor for detecting formaldehyde in tap water. The sensor is assembled from electronics, i.e., a custom-designed sensor platform and developed HCHO detection system based on Ni(OH)2–Ni nanowires (NWs) and synthetic-paper-based, screen-printed electrodes (pSPEs). The sensor platform, consisting of the IoT technology, a Wi-Fi communication system, and a miniaturized potentiostat can be easily connected to the Ni(OH)2–Ni NWs and pSPEs via a three-terminal electrode. The custom-made sensor, which has a detection capability of 0.8 µM/24 ppb, was tested for an amperometric determination of the HCHO in deionized (DI) and tap-water-based alkaline electrolytes. This promising concept of an electrochemical IoT sensor that is easy to operate, rapid, and affordable (it is considerably cheaper than any lab-grade potentiostat) could lead to the straightforward detection of HCHO in tap water.
YOLOX-Ray: An Efficient Attention-Based Single-Staged Object Detector Tailored for Industrial Inspections
Authors: Raimundo António , Pavia João Pedro , Sebastião Pedro , Postolache Octavian
Published: May 2023

Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.
Evaluation of a Respiration Rate Sensor for Recording Tidal Volume in Calves under Field Conditions
Authors: Dißmann Lena , Reinhold Petra , Smith Hans-Jürgen , Amon Thomas , Sergeeva Alisa , Hoffmann Gundula
Published: May 2023

In the assessment of pulmonary function in health and disease, both respiration rate (RR) and tidal volume (Vt) are fundamental parameters of spontaneous breathing. The aim of this study was to evaluate whether an RR sensor, which was previously developed for cattle, is suitable for additional measurements of Vt in calves. This new method would offer the opportunity to measure Vt continuously in freely moving animals. To measure Vt noninvasively, the application of a Lilly-type pneumotachograph implanted in the impulse oscillometry system (IOS) was used as the gold standard method. For this purpose, we applied both measuring devices in different orders successively, for 2 days on 10 healthy calves. However, the Vt equivalent (RR sensor) could not be converted into a true volume in mL or L. For a reliable recording of the Vt equivalent, a technical revision of the RR sensor excluding artifacts is required. In conclusion, converting the pressure signal of the RR sensor into a flow equivalent, and subsequently into a volume equivalent, by a comprehensive analysis, provides the basis for further improvement of the measuring system.
High-Capacity Spatial Structured Light for Robust and Accurate Reconstruction
Authors: Gu Feifei , Du Hubing , Wang Sicheng , Su Bohuai , Song Zhan
Published: May 2023

Spatial structured light (SL) can achieve three-dimensional measurements with a single shot. As an important branch in the field of dynamic reconstruction, its accuracy, robustness, and density are of vital importance. Currently, there is a wide performance gap of spatial SL between dense reconstruction (but less accurate, e.g., speckle-based SL) and accurate reconstruction (but often sparser, e.g., shape-coded SL). The central problem lies in the coding strategy and the designed coding features. This paper aims to improve the density and quantity of reconstructed point clouds by spatial SL whilst also maintaining a high accuracy. Firstly, a new pseudo-2D pattern generation strategy was developed, which can improve the coding capacity of shape-coded SL greatly. Then, to extract the dense feature points robustly and accurately, an end-to-end corner detection method based on deep learning was developed. Finally, the pseudo-2D pattern was decoded with the aid of the epipolar constraint. Experimental results validated the effectiveness of the proposed system.
Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks
Authors: Luo Xiaoling , Chen Che , Zeng Chunnian , Li Chengtao , Xu Jing , Gong Shimin
Published: May 2023

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.
A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features
Authors: Kibriya Hareem , Amin Rashid , Kim Jinsul , Nawaz Marriam , Gantassi Rahma
Published: May 2023

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient’s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method’s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.
New-Type Shearing Interference Detection System Based on Double-Grating Structure for Suppressing the Skylight Background
Authors: Dong Lei , Wang Bin , Li Hongzhuang , Wu Yuanhao
Published: May 2023

To overcome the influence of the daytime skylight background on long-distance optical detection, a new type of shearing interference detection system was proposed to improve the detection performance of the traditional detection system for finding dark objects such as dim stars during the daytime. This article focuses on the basic principle and mathematical model as well as the simulation and experimental research of the new type of shearing interference detection system. The comparison of the detection performance between this new-type detection system and the traditional system is also carried out in this article. The experimental results show that the detection performance of the new type of shearing interference detection system is significantly better than that of the traditional system, and the image signal-to-noise ratio of this new-type system (about 13.2) is much higher than that of the best result of the traditional detection system (about 5.1).
Environmental Robustness and Resilience of Direct-Write Ultrasonic Transducers Made from P(VDF-TrFE) Piezoelectric Coating
Authors: Han Jin Kyu , Wong Voon-Kean , Lim David Boon Kiang , Christopher Subhodayam Percis Teena , Luo Ping , Yao Kui
Published: May 2023

Conformability, lightweight, consistency and low cost due to batch fabrication in situ on host structures are the attractive advantages of ultrasonic transducers made of piezoelectric polymer coatings for structural health monitoring (SHM). However, knowledge about the environmental impacts of piezoelectric polymer ultrasonic transducers is lacking, limiting their widespread use for SHM in industries. The purpose of this work is to evaluate whether direct-write transducers (DWTs) fabricated from piezoelectric polymer coatings can withstand various natural environmental impacts. The ultrasonic signals of the DWTs and properties of the piezoelectric polymer coatings fabricated in situ on the test coupons were evaluated during and after exposure to various environmental conditions, including high and low temperatures, icing, rain, humidity, and the salt fog test. Our experimental results and analyses showed that it is promising for the DWTs made of piezoelectric P(VDF-TrFE) polymer coating with an appropriate protective layer to pass various operational conditions according to US standards.
A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles
Authors: Ruan Wenbo , Liu Jia , Chen Yuanfang , Islam Sardar M. N. , Alam Muhammad
Published: May 2023

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle’s historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.
Development of a Smart Signalization for Emergency Vehicles
Authors: Siddiqi Muhammad Hameed , Alruwaili Madallah , Tarimer İlhan , Karadağ Buse Cennet , Alhwaiti Yousef , Khan Faheem
Published: May 2023

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos. Due to traffic lights installed in the city, queues of vehicles are formed on the streets for most of the day, and many problems arise because of this. One of the most important problems is that emergency vehicles, such as ambulances, fire engines, police cars, etc., cannot arrive on time despite traffic priorities. Emergency vehicles such as hospitals and police departments need to reach the scene in a very short time. Time loss is a problem that needs to be addressed, especially for emergency vehicles traveling in traffic. In this study, ambulances, fire brigades, police, etc., respond to emergencies. A solution and a related application have been developed so privileged vehicles can reach their target destination as soon as possible. In this study, a route is determined between the current location of an emergency vehicle and its target location in an emergency. Communication between traffic lights is provided with a mobile application developed specifically for the vehicle driver. In this process, the person controlling the lights can turn on the traffic lights during the passage of vehicles. After the vehicles with priority to pass passed, traffic signaling was normalized via the mobile application. This process was repeated until the vehicle reached its destination.
A Secure Secret Key Agreement Scheme among Multiple Twinning Superlattice PUF Holders
Authors: Liu Jing , Xie Jianguo , Zhang Junwei , Liu Biao , Chen Xiaoming , Feng Huamin
Published: May 2023

Modern cryptography attributes the security of a cryptographic system to the security of the key. How to securely distribute the key has always been a bottleneck in key management. This paper proposes a secure group key agreement scheme for multiple parties using a multiple twinning superlattice physical unclonable function (PUF) that can be synchronized. By sharing the challenge and helper data among multiple twinning superlattice PUF holders, the scheme employs a reusable fuzzy extractor to obtain the key locally. Moreover, adopting public-key encryption encrypts public data for establishing the subgroup key, which provides independent communication for the subgroup. At the same time, when the subgroup membership changes, the public key encrypts new public data to update the subgroup key, forming scalable group communication. This paper also presents a cost and formal security analysis, which shows that the proposed scheme can achieve computational security by applying the key obtained by the computationally secure reusable fuzzy extractor to the EAV-secure symmetric-key encryption, which has indistinguishable encryption in the presence of an eavesdropper. Additionally, the scheme is secure against physical attacks, man-in-the-middle attacks, and machine learning modeling attacks.
Strain Virtual Sensing for Structural Health Monitoring under Variable Loads
Authors: Mora Bartomeu , Basurko Jon , Sabahi Iman , Leturiondo Urko , Albizuri Joseba
Published: May 2023

Virtual sensing is the process of using available data from real sensors in combination with a model of the system to obtain estimated data from unmeasured points. In this article, different strain virtual sensing algorithms are tested using real sensor data, under unmeasured different forces applied in different directions. Stochastic algorithms (Kalman filter and augmented Kalman filter) and deterministic algorithms (least-squares strain estimation) are tested with different input sensor configurations. A wind turbine prototype is used to apply the virtual sensing algorithms and evaluate the obtained estimations. An inertial shaker is installed on the top of the prototype, with a rotational base, to generate different external forces in different directions. The results obtained in the performed tests are analyzed to determine the most efficient sensor configurations capable of obtaining accurate estimates. Results show that it is possible to obtain accurate strain estimations at unmeasured points of a structure under an unknown loading condition, using measured strain data from a set of points and a sufficiently accurate FE model as input and applying the augmented Kalman filter or the least-squares strain estimation in combination with modal truncation and expansion techniques.
CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle
Authors: Yang Szu-Yueh , Jan Hsin-Che , Chen Chun-Yu , Wang Ming-Shyan
Published: May 2023

This paper plans to establish a warehouse management system based on an unmanned aerial vehicle (UAV) to scan the QR codes printed on packages. This UAV consists of a positive cross quadcopter drone and a variety of sensors and components, such as flight controllers, single-board computers, optical flow sensors, ultrasonic sensors and cameras, etc. The UAV stabilizes itself by proportional-integral-derivative (PID) control and takes pictures of the package as it reaches ahead of the shelf. Through convolutional neural networks (CNNs), the placement angle of the package can be accurately identified. Some optimization functions are applied to compare system performance. When the angle is 90°, that is, the package is placed normally and correctly, the QR code will be read directly. Otherwise, image processing techniques that include Sobel edge computing, minimum circumscribed rectangle, perspective transformation, and image enhancement is required to assist in reading the QR code. The experimental results showed that the proposed algorithm provided good performance of a recognition rate of 94% for the stochastic gradient descent (SGD) and 95% for Adadelta optimization functions. After that, successful QR code reading was presented.
A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network
Authors: Javeed Madiha , Mudawi Naif Al , Alabduallah Bayan Ibrahimm , Jalal Ahmad , Kim Wooseong
Published: May 2023

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.
Efficient Detection and Tracking of Human Using 3D LiDAR Sensor
Authors: Gómez Juan , Aycard Olivier , Baber Junaid
Published: May 2023

Light Detection and Ranging (LiDAR) technology is now becoming the main tool in many applications such as autonomous driving and human–robot collaboration. Point-cloud-based 3D object detection is becoming popular and widely accepted in the industry and everyday life due to its effectiveness for cameras in challenging environments. In this paper, we present a modular approach to detect, track and classify persons using a 3D LiDAR sensor. It combines multiple principles: a robust implementation for object segmentation, a classifier with local geometric descriptors, and a tracking solution. Moreover, we achieve a real-time solution in a low-performance machine by reducing the number of points to be processed by obtaining and predicting regions of interest via movement detection and motion prediction without any previous knowledge of the environment. Furthermore, our prototype is able to successfully detect and track persons consistently even in challenging cases due to limitations on the sensor field of view or extreme pose changes such as crouching, jumping, and stretching. Lastly, the proposed solution is tested and evaluated in multiple real 3D LiDAR sensor recordings taken in an indoor environment. The results show great potential, with particularly high confidence in positive classifications of the human body as compared to state-of-the-art approaches.
Waves of Structural Deglobalization: A World-Systems Perspective
Authors: Chase-Dunn Christopher , Álvarez Alexis , Liao Yuhao
Published: May 2023

Structural globalization has been both a cycle and an upward trend as periods of greater global integration have been followed by periods of deglobalization on a long-term stair-step toward the greater connectedness of humanity. Since 2008, the world-system may once again be entering another phase of structural deglobalization as the contradictions of capitalist neoliberalism, environmental degradation and uneven development have provoked different kinds of anti-globalization populism, rivalry among contending powers, trade wars and policies and social movements intended to mitigate the effects of anthropogenic climate change. This plateauing and possible downturn in economic connectedness is occurring in the context of U.S. hegemonic decline and the emergence of a more multipolar configuration of economic and political power among states. The combination of greater communications connectivity and greater awareness of North/South inequalities, as well as destabilizing conflicts and climate change, have provoked waves of refugee migrations and political reactions against immigrants. The result has been a period of chaos that is similar in some ways (but different in others) from what occurred during the last half of the 19th century and the first half of the 20th century. This study investigates the question of whether the world-system is indeed once again entering another period of economic deglobalization and compares the current period with what happened in the 19th and 20th centuries to specify the similarities and the differences. We conclude that, based on changes in the level of economic connectedness since 2008, it is still too soon to tell for sure if the world-system is entering another period of deglobalization, but the important similarities between the recent period and earlier periods of deglobalization make it likely that the system is now in another deglobalization or plateau phase.
Exploring a Pathway to Sustainable Organizational Performance of South Korea in the Digital Age: The Effect of Digital Leadership on IT Capabilities and Organizational Learning
Authors: Mollah Md Alamgir , Choi Jae-Hyeok , Hwang Su-Jung , Shin Jin-Kyo
Published: May 2023

In the age of Industry 4.0, the emergence of new technologies is compelling organizations to search for new methods for sustainability. In particular, IT capabilities and organizational learning competencies with digital leadership play crucial roles in managing environmental dynamism, which are profoundly related to sustainable organizational performance in the digital age. This study explored sustainable organizational performance from the perspective of digital leadership (DL) and the role of IT capabilities (IT infrastructure, IT business spanning, IT-proactive stance), as well as organizational learning in sustainable organizational performance. For this research, data from 173 employees from South Korean organizations were collected using an online survey on digital leadership, IT capabilities, organizational learning, and sustainable organizational performance (SOP). Here, an SPSS- and AMOS-based structural equation modeling technique was used to examine the outcomes for analysis. The results confirmed that digital leadership significantly directly affected SOP. Moreover, there was no mediating effect of IT infrastructure and IT business spanning; however, an IT-proactive stance and organizational learning fully mediated the relationship between DL and SOP. This research will aid leadership behavior alongside other knowledge-based studies that empirically tested the role of digital leadership, IT capabilities, organizational learning, and SOP. As digital leadership competencies demand is surging for managing digital challenges alongside the verification of digital leadership behavior and knowledge-based theory, the important role of DL regarding IT capabilities and organizational learning in SOP needs to be prudently considered in the South Korean context.
Green HRM and Servant Leadership: Driving Competitive Advantage and Environmental Performance in Higher Education
Authors: Asfahani Ahmed M.
Published: May 2023

The fast-changing landscape of organizations is driving a move toward environmental performance. Higher education now prioritizes sustainability. This study examines the moderating role of environmentally specific servant leadership (ESSL) from the resource-based view (RBV) and conservation of resources (COR) theories to understand how green human resource management (GHRM) practices affect environmental performance and competitive advantage in Saudi Arabian higher education institutions (HEIs). This study introduces an innovative variable’s structure that has never been implemented in Saudi Arabian higher education. A random sampling method was used to survey 408 faculty and non-faculty members from 58 Saudi higher education institutions. Two direct and one interaction PLS-SEM models tested the framework and associated hypotheses using AMOS and SPSS. Significant and positive relationships are demonstrated among GHRM, environmental performance, and competitive advantage. Furthermore, ESSL had a significant positive effect on the relationship between GHRM and environmental performance, whereas there was a significant negative relationship between GHRM and competitive advantage. Both theoretical and practical implications, as well as various suggestions for future research, are provided.
Multi-Objective Optimization of Daylight Performance and Thermal Comfort of Enclosed-Courtyard Rural Residence in a Cold Climate Zone, China
Authors: Molake Aisikaer , Zhang Rui , Zhou Yihuan
Published: May 2023

This study explored the daylight performance, thermal comfort, and energy-saving level of enclosed-courtyard rural residences with covered roofs in a cold IIA region of China. Using daylight autonomy (DA), annual mean value of PMV (AUMPMV), and total energy consumption of heating, cooling and lighting (Ehcl) in a performance evaluation index, a courtyard roof was optimized in a multi-objective fashion and analyzed for its effect on residential performance. This was achieved through field investigation and mapping, setting the prototype building model in the Rhino platform, and carrying out the iterative optimization design of the roof with a Ladybug tool component. The results show that appropriate roof form can significantly improve daylight performance, thermal comfort, and energy savings. Compared to the open-courtyard rural residence prototype model, the spatial daylight autonomy (sDA) of a flat-skylight-enclosed-courtyard residence decreased by only 0.94%, and AUMPMV improved significantly from −0.774 to 0.87. The Ehcl decreased by 19.7%, and compared with setting a large area of flat skylights on the roof, a reasonable clerestory window roof can achieve better residence performance.
Experiences, Enablers, and Challenges in Service Delivery and Integration of COVID-19 Vaccines: A Rapid Systematic Review
Authors: Nabia Sarah , Wonodi Chizoba Barbara , Vilajeliu Alba , Sussman Sabine , Olson Katharine , Cooke Rianna , Udayakumar Krishna , Twose Claire , Ezeanya Nwamaka , Adefarrell Adewumi Adetola , Lindstrand Ann
Published: May 2023

The COVID-19 vaccination is a crucial public health intervention for controlling the spread and severity of the SARS-CoV2 virus. COVID-19 vaccines have been developed in record time, but their deployment has varied across countries, owing to differences in health system capacity, demand for the vaccine, and purchasing power of countries. The aim of this rapid review is to summarize and synthesize experiences on COVID-19 vaccine service delivery and integration to inform future COVID-19 vaccination programming and contribute to the knowledge base for future pandemic management. A systematic search was conducted in PubMed, Scopus, and Global Index Medicus databases. Twenty-five studies were included in the analysis. Included studies spanned nine countries where COVID-19 vaccines were delivered through mass, mobile, and fixed-post vaccination service delivery models. There was limited evidence of integrating COVID-19 vaccines into routine services for pregnant women, people who inject drugs, and leveraging existing health programs to deliver COVID-19 vaccines to the general population. Common challenges reported were vaccine skepticism, lack of adequate health workers, and linguistic barriers to access. Partnerships with a variety of stakeholders and the involvement of volunteers were vital in overcoming barriers and contributed to the efficient functioning of COVID-19 vaccination programs.
Transcriptomic and Metabolomic Changes Reveal the Immunomodulatory Function of Casein Phosphopeptide-Selenium Chelate in Beagle Dogs
Authors: Wang Wencan , Xu Ling , Cao Yong , Liu Guo , Lin Qianru , Mao Xin
Published: May 2023

Casein phosphopeptide-selenium chelate (CPP-Se) is an organic compound produced by the chelation of casein phosphopeptide with selenium. This compound showed the ability to modulate canine immune response in our previous study; but its effect on the peripheral blood transcriptome and serum metabolome was unknown. This study aims to reveal the potential mechanism behind the immunomodulatory function of CPP-Se. We have identified 341 differentially expressed genes (DEGs) in CPP-Se groups as compared to the control group which comprised 110 up-regulated and 231 down-regulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis found that DEGs were mainly involved in immune-related signaling pathways. Moreover, the immune-related DEGs and hub genes were identified. Similarly, metabolomics identified 53 differentially expressed metabolites (DEMs) in the CPP-Se group, of which 17 were up-regulated and 36 were down-regulated. The pathways mainly enriched by DEMs were primary bile acid biosynthesis, tryptophan metabolism, and other amino acids metabolic pathways. Combined analysis of transcriptomic and metabolomic data showed that the DEGs and DEMs were commonly enriched in fatty acid biosynthesis, pyrimidine metabolism, glutathione metabolism, and glycerolipid metabolic pathways. Taken together, our findings provided a theoretical basis for further understanding of the immunomodulatory function of CPP-Se as well as a scientific reference for the future use of CPP-Se in pet foods as a dietary supplement to modulate the immunity.
Strategi Informasi Pada Kegiatan Perbankan Syariah
Authors: Apriliani Allysa Sekar , Maesaroh Tri , Tunisa Karbaila Felicity Zahro , Hanan Sofia , Santoso Suryo Budi
Published: May 2023

Tujuan yang ingin dicapai dalam artikel  ini untuk menganalisis strategi informasi pada perbankan syariah yang menggunakan nilai-nilai moral dalam Islam untuk memperkuat standar pelayanan melalui optimalisasi penggunaan kecanggihan information tecnology. Inovasi terhadap penggunaan teknologi informasi dapat meningkatkan kinerja bisnis perusahaan dan dapat dijadikan akses pada pelangganya. Sumber utama artikel ini adalah studi literatur. Perencanaan strategis SI menurut metode Ward and Peppard, dimana dilaksanakan tahapan analisis lingkungan bisnis internal, eksternal, serta SI/TI internal dan eksternal. Hasil tersebut mendapat peroleh strategy bisnis SI/TI, dan strategy manajemen SI/TI. Perencanaan strategis sistem informasi ini bertujuan untuk menyediakan rencana yang diperlukan dan berguna bagi perusahaan untuk mencapai tujuan bisnisnya.
Prediksi Tingkat Stress Pada Mahasiswa Universitas Dinamika Bangsa Jambi Dalam Melakukan Perkuliahan Metode Hybrid Menggunakan Algoritma Naive Bayes
Authors: Widyawati Nilu , Khasanah Maliyatul , Muttaqin  , Rasywir Errissya , Feranika Ayu
Published: May 2023

Pandemi covid-19 membuat dunia Pendidikan mengharuskan untuk melakukan metode pembelajaran hybrid. Metode ini menggabungkan perkuliahan tatap muka dan online secara bergantian setiap minggu sehingga banyak membuat mahasiswa menjadi stres. Stres memiliki banyak faktor penyabab. Hal inilah yang membuat peneliti menjadi tertarik untuk melakukan penelitian dengan membuat prediksi tingkat stres pada mahasiswa khususnya mahasiswa Universitas Dinamika Bangsa Jambi dalam melakukan perkuliahan metode hybrid. Penelitian ini akan mengguankan data profil mahasiswa yang dianggap menjadi faktor penyebab terjadinya stres. Penelitian ini menggunakan metode naive bayes. Peneliti memilih metode ini karena pada perhitungan naive bayes data variabelnya tidak hanya berupa angka tetapi dapat juga menggunakan kata. Selain itu, pada algoritma naive bayes hanya membutuhkan jumlah data training yang sedikit untuk menentukan estimasi parameter yang diperlukan dalam proses pengklasifikasian. Penelitian ini dilakukan dengan menghitung probablitias variabel data training dan dilanjutkan dengan membuat klasifikasi data testing. Dari Penelitian ini didapatkan hasil prediksi bahwa data testing termasuk ke dalam Tingkat stres kelas Sedang dengan probabilitas sebesar 0,003840878 dan hasil accuracy sebesar 68% setelah mengalami perbaikan dengan menggunakan teknik laplace correction.
Perancangan Sistem Informasi Stok Barang Berbasis Web Pada PT. Jambi Agung Lestari
Authors: Sanjaya Steven , Jasmir  , Meisak Despita
Published: May 2023

Internet semakin banyak digunakan, misalnya dalam berbisnis. Hal ini akan berdampak pada perkembangan teknologi informasi. Salah satu teknologi informasi yang digunakan perusahaan adalah sistem informasi stok barang berbasis web. Sistem ini akan membantu perusahaan dalam pembuatan catatan stok barang dan laporan, mengurangi kesalahan, dan menghemat waktu. PT. Jambi Agung Lestari adalah perusahaan yang bergerak dibidang pengadaan barang dan jasa. Perusahaan ini menggunakan sistem manual dalam pencatatan stok gudang menggunakan kertas sehingga catatan dapat hilang, basah, robek, membuat pencatatan stok barang dan pembuatan laporan menjadi lama dan kesalahan sering terjadi. Sistem informasi ini dapat memberikan solusi atas permasalahan tersebut. Metode waterfall akan digunakan dalam pembuatan sistem informasi stok barang berbasis web yang terdiri dari requirement definition, software and system design, implementation unit testing, integration and system testing, dan operation and maintenance. Dengan adanya sistem ini, admin gudang dapat dibantu dalam pencatatan stok barang, pembuatan laporan, menghemat waktu, dan meminimalisir kesalahan.