bluuebunny/crossref_metadata_2025 huggingface dataset:
A Monitoring Framework with Integrated Sensing Technologies for Enhanced Food Safety and Traceability
Authors: Radogna Antonio Vincenzo , Latino Maria Elena , Menegoli Marta , Prontera Carmela Tania , Morgante Gabriele , Mongelli Diamantea , Giampetruzzi Lucia , Corallo Angelo , Bondavalli Andrea , Francioso Luca
Published: August 2022

A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO2. The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted.
MineBL: A Battery-Free Localization Scheme with Binocular Camera for Coal Mine
Authors: Qu Song , Bao Zhongxu , Yin Yuqing , Yang Xu
Published: August 2022

Accurate localization in underground coal mining is a challenging technology in coal mine safety production. This paper proposes a low-cost battery-free localization scheme based on depth images, called MineBL. The main idea is to utilize the battery-free low-cost reflective balls as position nodes and realize underground target localization with a series of algorithms. In particular, the paper designs a data enhancement strategy based on small-target reorganization to increase the identification accuracy of tiny position nodes. Moreover, a novel ranging algorithm based on multi-filter cooperative denoising has been proposed, and an optimized weighted centroid location algorithm based on multilateral location errors has been designed to minimize underground localization errors. Many experiments in the indoor laboratories and the underground coal mine laboratories have been conducted, and the experimental results have verified that MineBL has good localization performances, with localization errors less than 30 cm in 95% of cases. Therefore, MineBL has great potential to provide a low-cost and effective solution for precise target localization in complex underground environments.
Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)
Authors: AlSahly Abdullah M. , Hassan Mohammad Mehedi , Saleem Kashif , Alabrah Amerah , Rodrigues Joel J. P. C.
Published: August 2022

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation—in terms of either positioning or tracking—for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.
Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation
Authors: Rodziewicz-Bielewicz Jan , Korzeń Marcin
Published: August 2022

The paper presents a simple, yet robust computer vision system for robot arm tracking with the use of RGB-D cameras. Tracking means to measure in real time the robot state given by three angles and with known restrictions about the robot geometry. The tracking system consists of two parts: image preprocessing and machine learning. In the machine learning part, we compare two approaches: fitting the robot pose to the point cloud and fitting the convolutional neural network model to the sparse 3D depth images. The advantage of the presented approach is direct use of the point cloud transformed to the sparse image in the network input and use of sparse convolutional and pooling layers (sparse CNN). The experiments confirm that the robot tracking is performed in real time and with an accuracy comparable to the accuracy of the depth sensor.
Applying an Integrated System of Cloud Management and Wireless Sensing Network to Green Smart Environments—Green Energy Monitoring on Campus
Authors: Tseng Kuo-Hsiung , Chung Meng-Yun , Chen Li-Hsien , Wei Ming-Yi
Published: August 2022

With increasing urbanization, the application of Internet of things (IoT) technology to city governance has become a trend in architecture, transportation, and healthcare management, making IoT applicable in various domains. This study used IoT to inspect green construction and adopted a front-end sensing system, middle-end wireless transmission, and a back-end multifunctional system structure with cloud management. It integrated civil and electrical engineering to develop environmental monitoring technology and proposed a management information system for the implementation of green engineering. This study collected physical “measurements” of the greening environment on a campus. Ambient temperature and humidity were analyzed to explore the greening and energy-saving benefits of a green roof, a pervious road, and a photovoltaic roof. When the ambient temperature was below 25 °C, the solar panels had an insulation effect on the roof of the building during both 4:00–5:00 and 12:00–13:00, with an optimal insulation effect of 2.45 °C. When the ambient temperature was above 25 °C, the panels had a cooling effect on the roof of the building, whether during 4:00–5:00 or 12:00–13:00, with an optimal cooling effect of 5.77 °C. During the lower temperature period (4:00–5:00), the ecological terrace had an insulation effect on the space beneath, with an effect of approximately 1–3 °C and a mean insulation of 1.95 °C. During the higher temperature period (12:00–13:00), it presented a cooling effect on the space beneath, with an effect of approximately 0.5–9 °C and a mean cooling temperature of 5.16 °C. The cooling effect of the three greening areas on air and ground temperature decreased in the following order: pervious road > photovoltaic roof > ecological terrace.
Predicting Perceived Exhaustion in Rehabilitation Exercises Using Facial Action Units
Authors: Kreis Christopher , Aguirre Andres , Cifuentes Carlos A. , Munera Marcela , Jiménez Mario F. , Schneider Sebastian
Published: August 2022

Physical exercise has become an essential tool for treating various non-communicable diseases (also known as chronic diseases). Due to this, physical exercise allows to counter different symptoms and reduce some risk of death factors without medication. A solution to support people in doing exercises is to use artificial systems that monitor their exercise progress. While one crucial aspect is to monitor the correct physical motions for rehabilitative exercise, another essential element is to give encouraging feedback during workouts. A coaching system can track a user’s exhaustion and give motivating feedback accordingly to boost exercise adherence. For this purpose, this research investigates whether it is possible to predict the subjective exhaustion level based on non-invasive and non-wearable technology. A novel data set was recorded with the facial record as the primary predictor and individual exhaustion levels as the predicted variable. 60 participants (30 male, 30 female) took part in the data recording. 17 facial action units (AU) were extracted as predictor variables for the perceived subjective exhaustion measured using the BORG scale. Using the predictor and the target variables, several regression and classification methods were evaluated aiming to predict exhaustion. The results showed that the decision tree and support vector methods provide reasonable prediction results. The limitation of the results, depending on participants being in the training data set and subjective variables (e.g., participants smiling during the exercises) were further discussed.
Homogeneity Characterization of Textile-Integrated Wearable Sensors based on Impedance Spectroscopy
Authors: Nouri Hanen , Rajendran Dhivakar , Ramalingame Rajarajan , Kanoun Olfa
Published: August 2022

One of the main challenges during the integration of a carbon/polymer-based nanocomposite sensor on textile substrates is the fabrication of a homogeneous surface of the nanocomposite-based thin films, which play a major role in the reproducibility of the sensor. Characterizations are therefore required in every fabrication step to control the quality of the material preparation, deposition, and curing. As a result, microcharacterization methods are more suitable for laboratory investigations, and electrical methods can be easily implemented for in situ characterization within the manufacturing process. In this paper, several textile-based pressure sensors are fabricated at an optimized concentration of 0.3 wt.% of multiwalledcarbon nanotubes (MWCNTs) composite material in PDMS. We propose to use impedance spectroscopy for the characterization of both of the resistive behavior and capacitive behavior of the sensor at several frequencies and under different loads from 50 g to 500 g. The impedance spectra are fitted to a model composed of a resistance in series with a parallel combination of resistance and a constant phase element (CPE). The results show that the printing parameters strongly influence the impedance behavior under different loads. The deviation of the model parameter α of the CPE from the value 1 is strongly dependent on the nonhomogeneity of the sensor. Based on an impedance spectrum measurement followed by parameter extraction, the parameter α can be determined to realize a novel method for homogeneity characterization and in-line quality control of textile-integrated wearable sensors during the manufacturing process.
SAS-SEINet: A SNR-Aware Adaptive Scalable SEI Neural Network Accelerator Using Algorithm–Hardware Co-Design for High-Accuracy and Power-Efficient UAV Surveillance
Authors: Gan Jiayan , Hu Ang , Kang Ziyi , Qu Zhipeng , Yang Zhanxiang , Yang Rui , Wang Yibing , Shao Huaizong , Zhou Jun
Published: August 2022

As a potential air control measure, RF-based surveillance is one of the most commonly used unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter identification (SEI) technology to identify captured RF signal from ground controllers to UAVs. Recently many SEI algorithms based on deep convolution neural network (DCNN) have emerged. However, there is a lack of the implementation of specific hardware. This paper proposes a high-accuracy and power-efficient hardware accelerator using an algorithm–hardware co-design for UAV surveillance. For the algorithm, we propose a scalable SEI neural network with SNR-aware adaptive precision computation. With SNR awareness and precision reconfiguration, it can adaptively switch between DCNN and binary DCNN to cope with low SNR and high SNR tasks, respectively. In addition, a short-time Fourier transform (STFT) reusing DCNN method is proposed to pre-extract feature of UAV signal. For hardware, we designed a SNR sensing engine, denoising engine, and specialized DCNN engine with hybrid-precision convolution and memory access, aiming at SEI acceleration. Finally, we validate the effectiveness of our design on a FPGA, using a public UAV dataset. Compared with a state-of-the-art algorithm, our method can achieve the highest accuracy of 99.3% and an F1 score of 99.3%. Compared with other hardware designs, our accelerator can achieve the highest power efficiency of 40.12 Gops/W and 96.52 Gops/W with INT16 precision and binary precision.
Research on Configuration Constraints of Airborne Bistatic SARs
Authors: Chen Yidi , Chen Renwen , Liu Hao , Guo Jiapeng , Wang Yujie , Zhang Junyi
Published: August 2022

Based on the analysis of the airborne bistatic synthetic aperture radar (SAR) imaging geometric mode, an extended nonlinear chirp scaling algorithm is employed to simulate and verify the imaging effect of the bistatic SARs. A gradient theory-based two-dimensional resolution bistatic SAR model is proposed, and the constraints of the multi-platform flight trajectory parameters meeting the imaging accuracy of the bistatic SAR are analyzed. Finally, through the bistatic SAR imaging simulation of cooperative flight trajectories under various situations, the spatial configuration constraint envelope between the flight vehicles to achieve the optimal resolution is revealed. The results of this paper will provide a theoretical reference for the SAR application in formation flight control.
Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions
Authors: Kuang Jiachen , Tao Tangfei , Wu Qingqiang , Han Chengcheng , Wei Fan , Chen Shengchao , Zhou Wenjie , Yan Cong , Xu Guanghua
Published: August 2022

In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote the generalization ability from the source domain to target domain in a few-shot fault diagnosis. Within this scheme, the DAPRN is composed of a feature extractor, a domain discriminator, and a label predictor. Concretely, the feature extractor is jointly optimized by the minimization of few-shot classification loss and the maximization of domain-discriminative loss. The cosine similarity-based label predictor, which is promoted by the PRS, is exploited to avoid the bias of naïve prototypes in the metric space and recognize the health conditions of machinery in the meta-testing process. The efficacy and advantage of DAPRN are validated by extensive experiments on bearing and gearbox datasets compared with seven popular and well-established few-shot fault diagnosis methods. In practical application, the proposed DAPRN is expected to solve more challenging few-shot fault diagnosis scenarios and facilitate practical fault identification problems in modern manufacturing.
Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in Recreational Men and Women
Authors: Schaffarczyk Marcelle , Rogers Bruce , Reer Rüdiger , Gronwald Thomas
Published: August 2022

Heart rate variability (HRV) is frequently applied in sport-specific settings. The rising use of freely accessible applications for its recording requires validation processes to ensure accurate data. It is the aim of this study to compare the HRV data obtained by the Polar H10 sensor chest strap device and an electrocardiogram (ECG) with the focus on RR intervals and short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA a1) as non-linear metric of HRV analysis. A group of 25 participants performed an exhaustive cycling ramp with measurements of HRV with both recording systems. Average time between heartbeats (RR), heart rate (HR) and DFA a1 were recorded before (PRE), during, and after (POST) the exercise test. High correlations were found for the resting conditions (PRE: r = 0.95, rc = 0.95, ICC3,1 = 0.95, POST: r = 0.86, rc = 0.84, ICC3,1 = 0.85) and for the incremental exercise (r > 0.93, rc > 0.93, ICC3,1 > 0.93). While PRE and POST comparisons revealed no differences, significant bias could be found during the exercise test for all variables (p < 0.001). For RR and HR, bias and limits of agreement (LoA) in the Bland–Altman analysis were minimal (RR: bias of 0.7 to 0.4 ms with LoA of 4.3 to −2.8 ms during low intensity and 1.3 to −0.5 ms during high intensity, HR: bias of −0.1 to −0.2 ms with LoA of 0.3 to −0.5 ms during low intensity and 0.4 to −0.7 ms during high intensity). DFA a1 showed wider bias and LoAs (bias of 0.9 to 8.6% with LoA of 11.6 to −9.9% during low intensity and 58.1 to −40.9% during high intensity). Linear HRV measurements derived from the Polar H10 chest strap device show strong agreement and small bias compared with ECG recordings and can be recommended for practitioners. However, with respect to DFA a1, values in the uncorrelated range and during higher exercise intensities tend to elicit higher bias and wider LoA.
Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications
Authors: Saha Arindam , Dhara Bibhas Chandra , Umer Saiyed , Yurii Kulakov , Alanazi Jazem Mutared , AlZubi Ahmad Ali
Published: August 2022

Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.
The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming
Authors: Wang Shunli , Jiang Honghua , Qiao Yongliang , Jiang Shuzhen , Lin Huaiqin , Sun Qian
Published: August 2022

Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.
A Survey of Digital Television Interactivity Technologies
Authors: da Silva Klehm Volnei , de Souza Braga Rodrigo , de Lucena Vicente Ferreira
Published: August 2022

This paper presents a survey of the worldwide use of Digital Television interactivity (DTVi) standards. First, we recall some concepts of first-generation interactivity middlewares released in the early 2000s, such as the European MHP (based on Java) and the Japanese BML (based on XML). Then, we cover the new standards (emerging after 2010) that introduced the new Integrated Broadcast Broadband (IBB) model, which combines broadcast signals with a broadband interface and leverages synergies to offer high-quality, flexible, interactive and customized services and applications to viewers. Regarding IBB systems, we also cover the main aspects of their architectures and innovations introduced by this kind of technology, such as support to companion devices, e.g., smartphones, tablets, targeted advertisements, and integration with Internet-of-Things (IoT) devices. Finally, we show the current adoption of different IBB systems around the world as well as current challenges regarding IBB technologies.
Improved Fully Convolutional Siamese Networks for Visual Object Tracking Based on Response Behaviour Analysis
Authors: Huang Xianyun , Cao Songxiao , Dong Chenguang , Song Tao , Xu Zhipeng
Published: August 2022

Siamese networks have recently attracted significant attention in the visual tracking community due to their balanced accuracy and speed. However, as a result of the non-update of the appearance model and the changing appearance of the target, the problem of tracking drift is a regular occurrence, particularly in background clutter scenarios. As a means of addressing this problem, this paper proposes an improved fully convolutional Siamese tracker that is based on response behaviour analysis (SiamFC-RBA). Firstly, the response map of the SiamFC is normalised to an 8-bit grey image, and the isohypse contours that represent the candidate target region are generated through thresholding. Secondly, the dynamic behaviour of the contours is analysed in order to check if there are distractors approaching the tracked target. Finally, a peak switching strategy is used as a means of determining the real tracking position of all candidates. Extensive experiments conducted on visual tracking benchmarks, including OTB100, GOT-10k and LaSOT, demonstrated that the proposed tracker outperformed the compared trackers such as DaSiamRPN, SiamRPN, SiamFC, CSK, CFNet and Staple and achieved state-of-the-art performance. In addition, the response behaviour analysis module was embedded into DiMP, with the experimental results showing the performance of the tracker to be improved through the use of the proposed architecture.
Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity
Authors: Ahn Sung-Yoon , Kim Mira , Bae Ji-Eun , Bang Iel-Soo , Lee Sang-Woong
Published: August 2022

Several pathogens that spread through the air are highly contagious, and related infectious diseases are more easily transmitted through airborne transmission under indoor conditions, as observed during the COVID-19 pandemic. Indoor air contaminated by microorganisms, including viruses, bacteria, and fungi, or by derived pathogenic substances, can endanger human health. Thus, identifying and analyzing the potential pathogens residing in the air are crucial to preventing disease and maintaining indoor air quality. Here, we applied deep learning technology to analyze and predict the toxicity of bacteria in indoor air. We trained the ProtBert model on toxic bacterial and virulence factor proteins and applied them to predict the potential toxicity of some bacterial species by analyzing their protein sequences. The results reflect the results of the in vitro analysis of their toxicity in human cells. The in silico-based simulation and the obtained results demonstrated that it is plausible to find possible toxic sequences in unknown protein sequences.
Georg Simmel Goes Virtual: From ‘Philosophy of Landscape’ to the Possibilities of Virtual Reality in Landscape Research
Authors: Kühne Olaf , Edler Dennis
Published: August 2022

With his text “Philosophy of Landscape” (German original: “Philosophie der Landschaft”), the German sociologist and philosopher Georg Simmel laid a foundation for landscape research that is still significant today. In the text, he equates the creation and perception of landscape with the creation of a painting. In doing so, he provided an essential foundation for landscape research with a constructivist orientation. In order to be able to grasp the differentiated nature of landscape analytically and to apply it to Simmel’s understanding of landscape, we resort to the approach of the three landscapes, which was developed from Karl Popper’s theory of the three worlds. The pictorial metaphor of Simmel’s understanding of landscapes, however, had the effect of limiting landscape to the visual, and often to what he described as ‘natural’. It did not address the power-bound nature of landscape. These aspects, however, are of great importance in current discussions about landscape. Aspects of power, multisensuality, and the incorporation of non-natural elements gain additional currency through the creation of augmented and virtual landscapes. This concerns, on the one hand, the creation of these landscapes, on the other hand, their individual internal consciousness, as well as their social construction. These show, not least, the contingency of landscape construction. They offer possibilities for the investigation of landscape stereotypes, and how innovations can be fed into the social construction of landscape to engage other senses beyond the sense of sight. The aim of our paper is to use conceptual critique to reflect on the conceptual development of social and cultural studies in landscape research since Simmel and to present its potential for framing research on AR and VR landscapes.
Contributions of Biotic and Abiotic Factors to the Spatial Heterogeneity of Aboveground Biomass in Subtropical Forests: A Case Study of Guizhou Province
Authors: Zhang Tie , Ding Guijie , Zhang Jiangping , Qi Yujiao
Published: August 2022

The spatial heterogeneity on a regional scale of forest biomass is caused by multiple biotic and abiotic factors. However, the contributions of biotic and abiotic factors to the spatial heterogeneity of forest biomass remain unclear. Based on the data of the National Forest Continuous Inventory (NFCI), digital elevation model (DEM), and meteorological data of Guizhou Province in 2015, we studied the spatial heterogeneity of the aboveground forest biomass in Guizhou province and evaluated the contribution rates of its influencing factors using Moran’s I, semivariogram, distance-based Moran’s eigenvector maps (dbMEMs), and variance partitioning. The results showed that the forest biomass in Guizhou province had strong spatial heterogeneity. Biotic and abiotic factors explained 34.4% and 19.2% of the spatial variation in forest biomass, respectively. Among the biotic factors, the average height of the stand had the greatest influence on forest biomass, while annual precipitation had the greatest influence on forest biomass among abiotic factors. Spatial factors only explained 0.7% of the spatial variation of forest biomass, indicating that the contribution of spatial factors can be explained by some measured abiotic factors. This study provided an effective approach to understand the underlying mechanisms of spatial allocation of forest biomass.
Online Retailer’s Contingent Free-Shipping Decisions under Large-Scale Promotions Considering Delayed Delivery
Authors: Ma Xiaxia , Bian Wenliang , Yang Xiqing , Niu Shengnan , Cai Yongming , Guan Jie , Wang Wenbin
Published: August 2022

Large-scale promotions lead to a huge number of orders, and the quantity of deliveries grows sharply, which puts considerable strain on cities’ logistics and imposes high related shipping costs. To alleviate these consequences, in this paper we provide a new contingent free shipping policy with delayed delivery (DD-CFS) for an online retailer during large-scale promotions and study its CFS threshold decisions, considering consumers’ different sensitivities to delivery time delays.We start by analyzing a consumer’s purchasing decision based on consumer utility theory. Next, we establish a mathematical model to help the online retailer find the optimal CFS threshold to maximize its expected profit. Finally, we analyze the benefit of delayed delivery to the online retailer and conduct a sensitivity analysis to examine the impacts of important parameters on the online retailer’s CFS threshold decisions, profit, and the value of the delayed delivery. We find that the DD-CFS policy can lead to more profits during the large-scale promotions period compared with the traditional CFS policy. As the delayed delivery time and the consumer’s negative attitude towards delayed delivery time increase, the online retailer should reduce the low CFS threshold value. On the other hand, as the shipping fee and the consumer’s negative attitude towards the shipping fee increase, the online retailer should raise the high and low CFS threshold values.
Achieving Effective Thermal Performance of Street Canyons in Various Climatic Zones
Authors: Abdelhafez Mohamed Hssan Hassan , Altaf Fatmaelzhraa , Alshenaifi Mohammad , Hamdy Omar , Ragab Ayman
Published: August 2022

Outdoor thermal comfort is one of the essential characteristics of effective urban planning. The aspect ratio and orientation of the street canyon influence the thermal performance. Regulations standardize construction rules without accounting for regional climatic variations or the effect of these limits on pedestrian thermal comfort. The purpose of this paper is to provide an understanding of the effects of aspect ratios and street canyon orientations on thermal comfort in Alexandria, representing the North Coast Region of Egypt, and Aswan, representing the Southern Egypt Region, using ENVI-met and RayMan software for various aspect ratios and street canyon orientations scenarios. As a result, the aspect ratio (AR = 2.5) gives the best thermal conditions in all the scenarios evaluated in both cities, whereas the aspect ratio (AR = 1) provides the worst. Despite having the highest PET value among the investigated scenarios, the Northeast-Southwest street canyon in Aswan City has the least uncomfortable thermal hours. The North-South street canyon in Alexandria City has the best thermal performance, followed by the Northwest-Southeast street canyon. Finally, it was shown that the aspect ratio and the orientation of the street canyons in the North Coast and Southern Egypt regions can support pedestrian thermal comfort.
Sustainable Organic Agriculture for Developing Agribusiness Sector
Authors: Puvača Nikola , Tufarelli Vincenzo
Published: August 2022

With the expectation that the global population will reach 9 [...]
Fiscal Policy Effects on Private Expenditure for Sustainable Economic Growth: A Panel VAR Study from Selected Developing Countries
Authors: Kaharudin Iszan Hana , Ab-Rahman Mohammad Syuhaimi
Published: August 2022

This study examines the effects of fiscal policy on private expenditure using a panel VAR for the sample of 60 developing countries from 1990 to 2020. The VAR panel model framework was used to focus on the disaggregated government expenditures which included the defense, economic and social expenditure. The main findings showed a positive shock effect of defense expenditure which led to crowding out effect on private expenditure and domestic income. Conversely, economic and social expenditure had the crowding in effect on private consumption and domestic income. To detail out the analysis, the composition of government spending was divided into two, namely productive expenditures and non-productive expenditures. Therefore, the findings indicate that defense expenditure is non-productive since it does not stimulate an increase in private spending and national income while economic expenditure and social expenditure are classified as productive expenditure with the ability to increase economic activity. The policy implication suggests that the government should carefully examine and identify the sectors or composition that have greater potential, capacity and relevance in stimulating sustainable economic growth.
Design and Experimental Evaluation of Composite Magnesium Phosphate Cement-Based Coating with High Cooling Effect
Authors: Quan Xiankai , Guo Wenhua , Duan Binxin , Tian Jun , Wu Xiaowei
Published: August 2022

The application of surface heat reflective coatings is one of the effective measures to solve the temperature disease of concrete structures in sunlit environments. To achieve strong bonding, high durability, and good cooling characteristics, a novel inorganic reflective thermal insulation coating was prepared using magnesium phosphate cement (MPC) as the binder and reflective matrix, and titanium dioxide and glass beads as the reflective thermal insulation reinforcement functional additives. The optimum ratio of the new reflective thermal insulation coating was preferred through laboratory irradiation test, thermal conductivity test, and spectral reflectance test. The results show that MPC itself was a good reflection cooling material, and the surface and internal temperatures of concrete blocks were reduced by 7.6 °C and 6.6 °C, respectively, after using MPC as the cooling coating. When 2% titanium dioxide was added to MPC, the surface and internal temperatures were further reduced by 6.0 °C and 4.9 °C, respectively. On top of this, the surface and internal temperatures of the concrete were reduced by a further 3.9 °C and 2.2 °C when 8% glass beads were added. The bond strength of the MPCTG coating to the concrete matrix reached 2.1 MPa. Finally, the microscopic characteristics and the reflective thermal insulation mechanism of the MPCTG coating were investigated with the aid of SEM, thermo gravimetric analysis, and XRD analysis. The results show that the MPC in the MPCTG coating was well hydrated, and a large number of hydration products encapsulated the unreacted MgO particles, titanium dioxide, and glass beads, forming a dense whole with high reflection and low thermal conductivity, and the coating effectively prevented the entry of radiant heat. At the same time, the MPCTG coating was thermally stable below 70 °C. The magnesium phosphate cement-based reflective thermal insulation coating developed in this study has potential application prospects in concrete structure cooling coatings.
Prediction of China’s Grain Consumption from the Perspective of Sustainable Development—Based on GM(1,1) Model
Authors: Zhang Xiaoyun , Bao Jie , Xu Shiwei , Wang Yu , Wang Shengwei
Published: August 2022

Being the largest producer and consumer of grain in the world, China occupies an extremely important position in the world grain market. The grain security of China is confronted with such problems as shortages of water and soil resources, a fragile ecological environment, and infrastructure constraints. The prediction and analysis of China’s grain consumption is conducive to establishing a resource-saving grain production mode, a sustainable grain supply and demand system, and a national grain security guarantee system at a higher level. In order to judge the future development trend of China’s grain accurately, guide grain production, stabilize grain expectation, and serve the relevant decision making of grain security, the GM(1,1) prediction model of China’s grain consumption has been constructed in this paper. Prediction research has been conducted with the grain consumption structure as the entry point. The model has high prediction accuracy and can be used for medium- and long-term prediction of China’s grain consumption after testing. The prediction results show that China’s grain consumption will continue to increase from 2022 to 2031, which is consistent with the factors of population change, urbanization promotion, consumption structure upgrading, and so on, in the country. Among the different types of consumption, the change in eating consumption will be small, the growth in feeding consumption and squeezing (soybean) consumption will slow down, industrial consumption will increase steadily, and seed consumption will be basically stable.
Energy Efficiency Optimization in Polyisoprene Footwear Production
Authors: Mihelić-Bogdanić Alka , Špelić Ivana
Published: August 2022

The evaluation of energy efficiency improvements in polyisoprene footwear production is shown. By installing air preheater, combustion air natural gas consumption is reduced by 7%. Simultaneously, the boiler outlet flue gases’ temperature is decreased from 204 °C to 66.93 °C, providing a sound basis for both economical savings and energy efficiency improvements, as well as ecological benefits to the environment. The application of condensate heat recovery resulted in flue gases’ volume decreasing by 11.85% and a thermal pollution decrease of 91.34%. Combining air preheating by exhaust flue gases and condensate heat recovery resulted in a decrease in the flue gases’ volume by 17.97%, and in the temperature lowering to 66.93 °C. The energy consumption for a combined system on location φ=45°49′) with a collector field of 12.936 × 103 m2 was investigated. The hybrid system was calculated for four variants: (1) solarized process without flue gases’ heat recovery, (2) solarized processes with heat contend in flue gases using an air preheater, (3) solarized processes with condensate heat recovery, and (4) solarized processes with heat contend in flue gases using air preheater and condensate heat recovery. The highest fuel savings were shown in solarized processes with heat contend in flue gases using air preheater and condensate heat recovery, resulting in savings of up to 78.92%, while the flue gases’ volume decreased from 5390.95 m3FG/h to 932.12 m3FG/h.
A Modified SWAT Model to Simulate Soil Water Content and Soil Temperature in Cold Regions: A Case Study of the South Saskatchewan River Basin in Canada
Authors: Zare Mohammad , Azam Shahid , Sauchyn David
Published: August 2022

Soil water content (SWC) and soil temperature are important hydrologic state variables. Accurate model simulation is critical in hydrologic regimes in cold regions dominated by spring snowmelt. In this study, we developed a combined physically-based soil temperature and energy-balance rain-on-snow (ROS) module for the Soil and Water Assessment Tool (SWAT) model and applied it to the South Saskatchewan River Basin (SSRB). We calibrated the SWAT base (SWAT-B) model and the SWAT modified (SWAT–M) model using daily measured soil temperature and SWC by hydrological response unit (HRU) for the years 2015 to 2020. The results of sensitivity analysis using the SUFI-2 technique in SWAT-CUP indicated that eight parameters have the most significant (p < 0.5) effect on streamflow, soil moisture, and snowmelt. Statistics for the SWAT-B and SWAT-M streamflow models revealed that the new module improved the Nash-Sutcliffe efficiency (NSE) from 0.39 to 0.71 and 0.42 to 0.76 for calibration and validation, respectively. The statistics for SWAT-simulated daily SWC showed that the measured data were a better fit with SWAT-M versus the SWAT-B output. Furthermore, SWAT-B values exceeded SWAT-M output and field measurements, and thus, the range of SWAT-M results was a better fit with observations. SWAT-B tended to underestimate soil temperature in the cold season, while SWAT-M significantly improved soil temperature simulation for winter. This new SWAT module simulated freeze-thaw cycles and captured the influence of snow cover on surface soil ice-water content. Spatial analysis of SWC and soil temperature across the SSRB showed that the SWAT-M model predicted more SWC and lower soil temperature in the western part of SSRB than SWAT-B, with higher soil temperature and lower SWC in the eastern region.
Research on the Value of Land Ecological Service in Yunnan Province Based on the Perspective of Spatial Pattern
Authors: Xu Kai , Yang Zisheng
Published: August 2022

(1) Background: Ecosystem service value assessment is a trending area of sustainable development research and ecological civilization construction. Ecosystems provide supply, regulation, support, and cultural services, and the value assessment of ecosystem services is helpful for people to understand the importance of natural ecosystems to production and life. Based on the insufficiency of the current research on the value of land ecological services, this paper discusses the research direction of the evaluation of the value of ecosystem services in the future, provides a reference for the evaluation of the value of ecosystem services, and promotes multi-scale and multi-directional research experiments on the value of land ecological services. (2) In this paper, the basic information data are obtained through the interpretation of remote sensing images, and the value equivalent factor is corrected according to the average grain yield per unit area and grain price data in 2000, 2010, and 2020 to calculate the land ecological service value. The relevant data obtained by processing the ecological service value are classified into different levels to discuss the changes and trends of 129 county-level administrative units in three periods and analyze the quantitative and spatial variation characteristics of the ecological service value of 129 county-level administrative units. The paper studies the spatial agglomeration state of the total value of ecological services and finds the hotspot areas of the land ecological service value in the study area. (3) During the study period, great changes have taken place in the land use pattern of Yunnan Province, with the area of construction land expanding and the area of wetland decreasing. The ecological service value level of 129 county-level administrative units is relatively stable. The total value of each service is ranked as soil formation and protection > gas regulation > water conservation > biodiversity protection > climate regulation > raw materials > waste treatment > entertainment culture > food production. In the three periods, Shangri-La City has the highest value of ecological services per unit of land and Wuhua District has the lowest. The value of ecological services per unit of land in 129 county-level administrative units continues to rise. The areas with high average values are mainly in western Yunnan, southwestern Yunnan, southern Yunnan, and southeastern Yunnan. (4) The development speed of land ecological service value in Yunnan Province is far less than the development speed of GDP. There is still a lot of room for the development of ecological service value in Yunnan Province. The economic influence of Kunming is radiating to various cities through expressways and high-speed railways. The spatial distribution of land ecological service value in Yunnan Province has a strong correlation, showing a pattern of spatial agglomeration distribution, and the clustered areas are very stable.
Walkability Policies in Developing Countries: What Do People Need and Prefer in Iran?
Authors: Qazimirsaeed Ali , Khosravi Hanie , Rafieian Mojtaba , Mirzahossein Hamid , Forciniti Carmen
Published: August 2022

Examining the willingness to pay (WTP) in the implementation of walkability policies can indicate the needs and preferences of individuals. Consequently, better defined individual preferences lead to successful sustainable transportation plans. While there is an extensive literature discussing WTP in sustainable transportation, they mostly focus on developed countries, and this issue has not been adequately studied for developing countries, especially the Middle East. This study aims to determine the preferences of transportation users in developing countries and their willingness in paying to implement sustainable transportation policies. To this aim, 384 surveys were conducted in Karaj city, Iran, using the contingent valuation method (CVM). The WTP and its contextual criteria (household’s cost, demography, and economic class) and walkability criteria (safety, accessibility, network design, and social) were examined using PLS-SEM with 1000 resamples through the SEMinR package in the R programming language. The results show that 46% of participants were willing to pay extra taxes to implement the walkability policies. Furthermore, the structural model indicates that safety, social, spatial, and accessibility are the most important factors, respectively, and predict WTP in a positive direction. In contrast, gender and cost predict WTP in a negative direction.